API reference#

This page provides a list of all publicly accessible modules, methods, and classes in the dask_cudf namespace.

Creating and storing DataFrames#

Like Dask, Dask-cuDF supports creation of DataFrames from a variety of storage formats. For on-disk data that are not supported directly in Dask-cuDF, we recommend using Dask’s data reading facilities, followed by calling from_dask_dataframe() to obtain a Dask-cuDF object.

dask_cudf.from_cudf(data, npartitions=None, chunksize=None, sort=True, name=None)#

Create a DataFrame from a cudf.DataFrame.

This function is a thin wrapper around dask.dataframe.from_pandas(), accepting the same arguments (described below) excepting that it operates on cuDF rather than pandas objects.

Construct a Dask DataFrame from a Pandas DataFrame

This splits an in-memory Pandas dataframe into several parts and constructs a dask.dataframe from those parts on which Dask.dataframe can operate in parallel. By default, the input dataframe will be sorted by the index to produce cleanly-divided partitions (with known divisions). To preserve the input ordering, make sure the input index is monotonically-increasing. The sort=False option will also avoid reordering, but will not result in known divisions.

Note that, despite parallelism, Dask.dataframe may not always be faster than Pandas. We recommend that you stay with Pandas for as long as possible before switching to Dask.dataframe.

Parameters:

datapandas.DataFrame or pandas.Series: The DataFrame/Series with which to construct a Dask DataFrame/Series
npartitionsint, optional: The number of partitions of the index to create. Note that if there are duplicate values or insufficient elements in data.index, the output may have fewer partitions than requested.
chunksizeint, optional: The desired number of rows per index partition to use. Note that depending on the size and index of the dataframe, actual partition sizes may vary.
sort: bool: Sort the input by index first to obtain cleanly divided partitions (with known divisions). If False, the input will not be sorted, and all divisions will be set to None. Default is True.
name: string, optional: An optional keyname for the dataframe. Defaults to hashing the input

Returns:

dask.DataFrame or dask.Series: A dask DataFrame/Series partitioned along the index

Raises:

TypeError: If something other than a pandas.DataFrame or pandas.Series is passed in.

See also

from_array: Construct a dask.DataFrame from an array that has record dtype
read_csv: Construct a dask.DataFrame from a CSV file

Examples

>>> from dask.dataframe import from_pandas
>>> df = pd.DataFrame(dict(a=list('aabbcc'), b=list(range(6))),
...                   index=pd.date_range(start='20100101', periods=6))
>>> ddf = from_pandas(df, npartitions=3)
>>> ddf.divisions  
(Timestamp('2010-01-01 00:00:00', freq='D'),
 Timestamp('2010-01-03 00:00:00', freq='D'),
 Timestamp('2010-01-05 00:00:00', freq='D'),
 Timestamp('2010-01-06 00:00:00', freq='D'))
>>> ddf = from_pandas(df.a, npartitions=3)  # Works with Series too!
>>> ddf.divisions  
(Timestamp('2010-01-01 00:00:00', freq='D'),
 Timestamp('2010-01-03 00:00:00', freq='D'),
 Timestamp('2010-01-05 00:00:00', freq='D'),
 Timestamp('2010-01-06 00:00:00', freq='D'))

dask_cudf.from_dask_dataframe(df)#

Convert a Dask dask.dataframe.DataFrame to a Dask-cuDF one.

Parameters:

dfdask.dataframe.DataFrame: The Dask dataframe to convert

Returns:

dask_cudf.DataFrameA new Dask collection backed by cuDF objects

dask_cudf.read_csv(path, blocksize='default', **kwargs)#

Read CSV files into a DataFrame.

This API parallelizes the cudf.read_csv() function in the following ways:

It supports loading many files at once using globstrings:

>>> import dask_cudf
>>> df = dask_cudf.read_csv("myfiles.*.csv")

In some cases it can break up large files:

>>> df = dask_cudf.read_csv("largefile.csv", blocksize="256 MiB")

It can read CSV files from external resources (e.g. S3, HTTP, FTP)

>>> df = dask_cudf.read_csv("s3://bucket/myfiles.*.csv")
>>> df = dask_cudf.read_csv("https://www.mycloud.com/sample.csv")

Internally read_csv uses cudf.read_csv() and supports many of the same keyword arguments with the same performance guarantees. See the docstring for cudf.read_csv() for more information on available keyword arguments.

Parameters:

pathstr, path object, or file-like object: Either a path to a file (a str, pathlib.Path, or py._path.local.LocalPath), URL (including http, ftp, and S3 locations), or any object with a read() method (such as builtin open() file handler function or StringIO).
blocksizeint or str, default “256 MiB”: The target task partition size. If None, a single block is used for each file.
**kwargsdict: Passthrough key-word arguments that are sent to cudf.read_csv().

Notes

If any of skipfooter/skiprows/nrows are passed, blocksize will default to None.

Examples

>>> import dask_cudf
>>> ddf = dask_cudf.read_csv("sample.csv", usecols=["a", "b"])
>>> ddf.compute()
   a      b
0  1     hi
1  2  hello
2  3     ai

dask_cudf.read_json(url_path, engine='auto', **kwargs)#

Read JSON data into a DataFrame.

This function wraps dask.dataframe.read_json(), and passes engine=partial(cudf.read_json, engine="auto") by default.

Parameters:

url_pathstr, list of str: Location to read from. If a string, can include a glob character to find a set of file names. Supports protocol specifications such as "s3://".
enginestr or Callable, default “auto”: If str, this value will be used as the engine argument when cudf.read_json() is used to create each partition. If a Callable, this value will be used as the underlying function used to create each partition from JSON data. The default value is “auto”, so that engine=partial(cudf.read_json, engine="auto") will be passed to dask.dataframe.read_json() by default.
**kwargs: Key-word arguments to pass through to dask.dataframe.read_json().

Returns:

DataFrame

See also

dask.dataframe.read_json

Examples

Load single file

>>> from dask_cudf import read_json
>>> read_json('myfile.json')  

Load large line-delimited JSON files using partitions of approx 256MB size

>>> read_json('data/file*.csv', blocksize=2**28)  

Load nested JSON data

>>> read_json('myfile.json')  

dask_cudf.read_orc(path, columns=None, filters=None, storage_options=None, **kwargs)#

Read ORC files into a DataFrame.

Note that this function is mostly borrowed from upstream Dask.

Parameters:

pathstr or list[str]: Location of file(s), which can be a full URL with protocol specifier, and may include glob character if a single string.
columnsNone or list[str]: Columns to load. If None, loads all.
filtersNone or list of tuple or list of lists of tuples: If not None, specifies a filter predicate used to filter out row groups using statistics stored for each row group as Parquet metadata. Row groups that do not match the given filter predicate are not read. The predicate is expressed in disjunctive normal form (DNF) like [[('x', '=', 0), ...], ...]. DNF allows arbitrary boolean logical combinations of single column predicates. The innermost tuples each describe a single column predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple column predicate. Finally, the outermost list combines these filters as a disjunction (OR). Predicates may also be passed as a list of tuples. This form is interpreted as a single conjunction. To express OR in predicates, one must use the (preferred) notation of list of lists of tuples.
storage_optionsNone or dict: Further parameters to pass to the bytes backend.

Returns:

dask_cudf.DataFrame

See also

dask.dataframe.read_orc

dask_cudf.read_parquet(path, columns=None, **kwargs)#

Read parquet files into a DataFrame.

Calls dask.dataframe.read_parquet() with engine=CudfEngine to coordinate the execution of cudf.read_parquet(), and to ultimately create a DataFrame collection.

See the dask.dataframe.read_parquet() documentation for all available options.

See also

cudf.read_parquet
dask.dataframe.read_parquet

Examples

>>> from dask_cudf import read_parquet
>>> df = read_parquet("/path/to/dataset/")  

When dealing with one or more large parquet files having an in-memory footprint >15% device memory, the split_row_groups argument should be used to map Parquet row-groups to DataFrame partitions (instead of files to partitions). For example, the following code will map each row-group to a distinct partition:

>>> df = read_parquet(..., split_row_groups=True)  

To map multiple row-groups to each partition, an integer can be passed to split_row_groups to specify the maximum number of row-groups allowed in each output partition:

>>> df = read_parquet(..., split_row_groups=10)  

dask_cudf.to_orc(df, path, write_index=True, storage_options=None, compression='snappy', compute=True, **kwargs)#

Write a DataFrame to ORC file(s) (one file per partition).

Parameters:

dfDataFrame
pathstr or pathlib.Path: Destination directory for data. Prepend with protocol like s3:// or hdfs:// for remote data.
write_indexboolean, optional: Whether or not to write the index. Defaults to True.
storage_optionsNone or dict: Further parameters to pass to the bytes backend.
compressionstring or dict, optional
computebool, optional: If True (default) then the result is computed immediately. If False then a Delayed object is returned for future computation.

Warning

FIXME: where should the following live?

dask_cudf.concat(dfs, axis=0, join='outer', interleave_partitions=False, ignore_unknown_divisions=False, ignore_order=False, **kwargs)#

Concatenate DataFrames along rows.

When axis=0 (default), concatenate DataFrames row-wise:
- If all divisions are known and ordered, concatenate DataFrames keeping divisions. When divisions are not ordered, specifying interleave_partition=True allows concatenate divisions each by each.
- If any of division is unknown, concatenate DataFrames resetting its division to unknown (None)
When axis=1, concatenate DataFrames column-wise:
- Allowed if all divisions are known.
- If any of division is unknown, it raises ValueError.

Parameters:

dfslist: List of dask.DataFrames to be concatenated
axis{0, 1, ‘index’, ‘columns’}, default 0: The axis to concatenate along
join{‘inner’, ‘outer’}, default ‘outer’: How to handle indexes on other axis
interleave_partitionsbool, default False: Whether to concatenate DataFrames ignoring its order. If True, every divisions are concatenated each by each.
ignore_unknown_divisionsbool, default False: By default a warning is raised if any input has unknown divisions. Set to True to disable this warning.
ignore_orderbool, default False: Whether to ignore order when doing the union of categoricals.

Notes

This differs in from pd.concat in the when concatenating Categoricals with different categories. Pandas currently coerces those to objects before concatenating. Coercing to objects is very expensive for large arrays, so dask preserves the Categoricals by taking the union of the categories.

Examples

If all divisions are known and ordered, divisions are kept.

>>> import dask.dataframe as dd
>>> a                                               
dd.DataFrame<x, divisions=(1, 3, 5)>
>>> b                                               
dd.DataFrame<y, divisions=(6, 8, 10)>
>>> dd.concat([a, b])                               
dd.DataFrame<concat-..., divisions=(1, 3, 6, 8, 10)>

Unable to concatenate if divisions are not ordered.

>>> a                                               
dd.DataFrame<x, divisions=(1, 3, 5)>
>>> b                                               
dd.DataFrame<y, divisions=(2, 3, 6)>
>>> dd.concat([a, b])                               
ValueError: All inputs have known divisions which cannot be concatenated
in order. Specify interleave_partitions=True to ignore order

Specify interleave_partitions=True to ignore the division order.

>>> dd.concat([a, b], interleave_partitions=True)   
dd.DataFrame<concat-..., divisions=(1, 2, 3, 5, 6)>

If any of division is unknown, the result division will be unknown

>>> a                                               
dd.DataFrame<x, divisions=(None, None)>
>>> b                                               
dd.DataFrame<y, divisions=(1, 4, 10)>
>>> dd.concat([a, b])                               
dd.DataFrame<concat-..., divisions=(None, None, None, None)>

By default concatenating with unknown divisions will raise a warning. Set ignore_unknown_divisions=True to disable this:

>>> dd.concat([a, b], ignore_unknown_divisions=True)
dd.DataFrame<concat-..., divisions=(None, None, None, None)>

Different categoricals are unioned

>>> dd.concat([
...     dd.from_pandas(pd.Series(['a', 'b'], dtype='category'), 1),
...     dd.from_pandas(pd.Series(['a', 'c'], dtype='category'), 1),
... ], interleave_partitions=True).dtype
CategoricalDtype(categories=['a', 'b', 'c'], ordered=False)

Create Dask DataFrame from many Dask Delayed objects

Parameters:

dfs: A dask.delayed.Delayed, a distributed.Future, or an iterable of either of these objects, e.g. returned by client.submit. These comprise the individual partitions of the resulting dataframe. If a single object is provided (not an iterable), then the resulting dataframe will have only one partition.
metapd.DataFrame, pd.Series, dict, iterable, tuple, optional: An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.
divisions: Partition boundaries along the index. For tuple, see https://docs.dask.org/en/latest/dataframe-design.html#partitions For string ‘sorted’ will compute the delayed values to find index values. Assumes that the indexes are mutually sorted. If None, then won’t use index information
prefix: Prefix to prepend to the keys.
verify_meta: If True check that the partitions have consistent metadata, defaults to True.

Grouping#

As discussed in the Dask documentation for groupby, groupby, join, and merge, and similar operations that require matching up rows of a DataFrame become significantly more challenging in a parallel setting than they are in serial. Dask-cuDF has the same challenges, however for certain groupby operations, we can take advantage of functionality in cuDF that allows us to compute multiple aggregations at once. There are therefore two interfaces to grouping in Dask-cuDF, the general DataFrame.groupby() which returns a CudfDataFrameGroupBy object, and a specialized groupby_agg(). Generally speaking, you should not need to call groupby_agg() directly, since Dask-cuDF will arrange to call it if possible.

class dask_cudf.groupby.CudfDataFrameGroupBy(*args, sort=None, **kwargs)#

Bases: DataFrameGroupBy

Attributes

index

Methods

`agg`([arg, split_every, split_out, shuffle])	Aggregate using one or more specified operations
`aggregate`(arg[, split_every, split_out, shuffle])	Aggregate using one or more specified operations
`apply`(func, args, *kwargs)	Parallel version of pandas GroupBy.apply
`bfill`([limit])	Backward fill the values.
`corr`([ddof, split_every, split_out, ...])	Compute pairwise correlation of columns, excluding NA/null values.
`count`([split_every, split_out])	Compute count of group, excluding missing values.
`cov`([ddof, split_every, split_out, std, ...])	Compute pairwise covariance of columns, excluding NA/null values.
`cumcount`([axis])	Number each item in each group from 0 to the length of that group - 1.
`cumprod`([axis, numeric_only])	Cumulative product for each group.
`cumsum`([axis, numeric_only])	Cumulative sum for each group.
`ffill`([limit])	Forward fill the values.
`fillna`([value, method, limit, axis])	Fill NA/NaN values using the specified method.
`first`([split_every, split_out])	Compute the first non-null entry of each column.
`get_group`(key)	Construct DataFrame from group with provided name.
`idxmax`([split_every, split_out, shuffle, ...])	Return index of first occurrence of maximum over requested axis.
`idxmin`([split_every, split_out, shuffle, ...])	Return index of first occurrence of minimum over requested axis.
`last`([split_every, split_out])	Compute the last non-null entry of each column.
`max`([split_every, split_out])	Compute max of group values.
`mean`([split_every, split_out])	Compute mean of groups, excluding missing values.
`median`([split_every, split_out, shuffle, ...])	Compute median of groups, excluding missing values.
`min`([split_every, split_out])	Compute min of group values.
`prod`([split_every, split_out, shuffle, ...])	Compute prod of group values.
`rolling`(window[, min_periods, center, ...])	Provides rolling transformations.
`shift`([periods, freq, axis, fill_value, meta])	Parallel version of pandas GroupBy.shift
`size`([split_every, split_out, shuffle])	Compute group sizes.
`std`([split_every, split_out])	Compute standard deviation of groups, excluding missing values.
`sum`([split_every, split_out])	Compute sum of group values.
`transform`(func, args, *kwargs)	Parallel version of pandas GroupBy.transform
`var`([split_every, split_out])	Compute variance of groups, excluding missing values.

collect
compute

agg(arg=None, split_every=None, split_out=1, shuffle=None, **kwargs)#

Aggregate using one or more specified operations

Based on pd.core.groupby.DataFrameGroupBy.agg

Parameters:

argcallable, str, list or dict, optional

Aggregation spec. Accepted combinations are:

callable function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of column names -> function, function name or list of such.
None only if named aggregation syntax is used

split_everyint, optional

Number of intermediate partitions that may be aggregated at once. This defaults to 8. If your intermediate partitions are likely to be small (either due to a small number of groups or a small initial partition size), consider increasing this number for better performance.

split_outint, optional

Number of output partitions. Default is 1.

shufflebool or str, optional

Whether a shuffle-based algorithm should be used. A specific algorithm name may also be specified (e.g. "tasks" or "p2p"). The shuffle-based algorithm is likely to be more efficient than shuffle=False when split_out>1 and the number of unique groups is large (high cardinality). Default is False when split_out = 1. When split_out > 1, it chooses the algorithm set by the shuffle option in the dask config system, or "tasks" if nothing is set.

kwargs: tuple or pd.NamedAgg, optional

Used for named aggregations where the keywords are the output column names and the values are tuples where the first element is the input column name and the second element is the aggregation function. pandas.NamedAgg can also be used as the value. To use the named aggregation syntax, arg must be set to None.

aggregate(arg, split_every=None, split_out=1, shuffle=None)#

Aggregate using one or more specified operations

Based on pd.core.groupby.DataFrameGroupBy.aggregate

Parameters:

argcallable, str, list or dict, optional

Aggregation spec. Accepted combinations are:

callable function
string function name
list of functions and/or function names, e.g. [np.sum, 'mean']
dict of column names -> function, function name or list of such.
None only if named aggregation syntax is used

split_everyint, optional

split_outint, optional

Number of output partitions. Default is 1.

shufflebool or str, optional

kwargs: tuple or pd.NamedAgg, optional

apply(func, *args, **kwargs)#

Parallel version of pandas GroupBy.apply

This mimics the pandas version except for the following:

If the grouper does not align with the index then this causes a full shuffle. The order of rows within each group may not be preserved.
Dask’s GroupBy.apply is not appropriate for aggregations. For custom aggregations, use dask.dataframe.groupby.Aggregation.

Warning

Pandas’ groupby-apply can be used to to apply arbitrary functions, including aggregations that result in one row per group. Dask’s groupby-apply will apply func once on each group, doing a shuffle if needed, such that each group is contained in one partition. When func is a reduction, e.g., you’ll end up with one row per group. To apply a custom aggregation with Dask, use dask.dataframe.groupby.Aggregation.

Parameters:

func: function: Function to apply
args, kwargsScalar, Delayed or object: Arguments and keywords to pass to the function.
metapd.DataFrame, pd.Series, dict, iterable, tuple, optional: An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.

Returns:

appliedSeries or DataFrame depending on columns keyword

bfill(limit=None)#

Backward fill the values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.bfill.

Some inconsistencies with the Dask version may exist.

Parameters:

limitint, optional: Limit of how many values to fill.

Returns:

Series or DataFrame: Object with missing values filled.

See also

Series.bfill: Backward fill the missing values in the dataset.
DataFrame.bfill: Backward fill the missing values in the dataset.
Series.fillna: Fill NaN values of a Series.
DataFrame.fillna: Fill NaN values of a DataFrame.

corr(ddof=1, split_every=None, split_out=1, numeric_only='__no_default__')#

Compute pairwise correlation of columns, excluding NA/null values.

This docstring was copied from pandas.core.frame.DataFrame.corr.

Some inconsistencies with the Dask version may exist.

Groupby correlation: corr(X, Y) = cov(X, Y) / (std_x * std_y)

Parameters:

method{‘pearson’, ‘kendall’, ‘spearman’} or callable (Not supported in Dask)

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional (Not supported in Dask)

Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

numeric_onlybool, default True

Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of numeric_only will be False in a future version of pandas.

Returns:

DataFrame: Correlation matrix.

See also

DataFrame.corrwith: Compute pairwise correlation with another DataFrame or Series.
Series.corr: Compute the correlation between two Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Examples

>>> def histogram_intersection(a, b):  
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],  
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)  
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],  
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)  
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

count(split_every=None, split_out=1)#

Compute count of group, excluding missing values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.count.

Some inconsistencies with the Dask version may exist.

Returns:

Series or DataFrame: Count of values within each group.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

cov(ddof=1, split_every=None, split_out=1, std=False, numeric_only='__no_default__')#

Compute pairwise covariance of columns, excluding NA/null values.

This docstring was copied from pandas.core.frame.DataFrame.cov.

Some inconsistencies with the Dask version may exist.

Groupby covariance is accomplished by

Computing intermediate values for sum, count, and the product of all columns: a b c -> a*a, a*b, b*b, b*c, c*c.
The values are then aggregated and the final covariance value is calculated: cov(X, Y) = X*Y - Xbar * Ybar

When std is True calculate Correlation

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters:

min_periodsint, optional (Not supported in Dask): Minimum number of observations required per pair of columns to have a valid result.
ddofint, default 1: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

New in version 1.1.0.
numeric_onlybool, default True: Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of numeric_only will be False in a future version of pandas.

Returns:

DataFrame: The covariance matrix of the series of the DataFrame.

See also

Series.cov: Compute covariance with another Series.
core.window.ewm.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
core.window.expanding.Expanding.cov: Expanding sample covariance.
core.window.rolling.Rolling.cov: Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices for more details.

Examples

>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],  
...                   columns=['dogs', 'cats'])
>>> df.cov()  
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667

>>> np.random.seed(42)  
>>> df = pd.DataFrame(np.random.randn(1000, 5),  
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()  
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)  
>>> df = pd.DataFrame(np.random.randn(20, 3),  
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan  
>>> df.loc[df.index[5:10], 'b'] = np.nan  
>>> df.cov(min_periods=12)  
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202

cumcount(axis='__no_default__')#

Number each item in each group from 0 to the length of that group - 1.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.cumcount.

Some inconsistencies with the Dask version may exist.

Essentially this is equivalent to

self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))

Parameters:

ascendingbool, default True (Not supported in Dask): If False, number in reverse, from length of group - 1 to 0.

Returns:

Series: Sequence number of each element within each group.

See also

ngroup: Number the groups themselves.

Examples

>>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],  
...                   columns=['A'])
>>> df  
   A
0  a
1  a
2  a
3  b
4  b
5  a
>>> df.groupby('A').cumcount()  
0    0
1    1
2    2
3    0
4    1
5    3
dtype: int64
>>> df.groupby('A').cumcount(ascending=False)  
0    3
1    2
2    1
3    1
4    0
5    0
dtype: int64

cumprod(axis='__no_default__', numeric_only='__no_default__')#

Cumulative product for each group.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.cumprod.

Some inconsistencies with the Dask version may exist.

Returns:

Series or DataFrame

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

cumsum(axis='__no_default__', numeric_only='__no_default__')#

Cumulative sum for each group.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.cumsum.

Some inconsistencies with the Dask version may exist.

Returns:

Series or DataFrame

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

ffill(limit=None)#

Forward fill the values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.ffill.

Some inconsistencies with the Dask version may exist.

Parameters:

limitint, optional: Limit of how many values to fill.

Returns:

Series or DataFrame: Object with missing values filled.

See also

Series.ffill: Returns Series with minimum number of char in object.
DataFrame.ffill: Object with missing values filled or None if inplace=True.
Series.fillna: Fill NaN values of a Series.
DataFrame.fillna: Fill NaN values of a DataFrame.

fillna(value=None, method=None, limit=None, axis='__no_default__')#

Fill NA/NaN values using the specified method.

Parameters:

valuescalar, default None: Value to use to fill holes (e.g. 0).
method{‘bfill’, ‘ffill’, None}, default None: Method to use for filling holes in reindexed Series. ffill: propagate last valid observation forward to next valid. bfill: use next valid observation to fill gap.
axis{0 or ‘index’, 1 or ‘columns’}: Axis along which to fill missing values.
limitint, default None: If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

Returns:

Series or DataFrame: Object with missing values filled

See also

pandas.core.groupby.DataFrameGroupBy.fillna

first(split_every=None, split_out=1)#

Compute the first non-null entry of each column.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.first.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns.
min_countint, default -1 (Not supported in Dask): The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: First non-null of values within each group.

See also

DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.
DataFrame.core.groupby.GroupBy.last: Compute the last non-null entry of each column.
DataFrame.core.groupby.GroupBy.nth: Take the nth row from each group.

Examples

>>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3],  
...                        D=['3/11/2000', '3/12/2000', '3/13/2000']))
>>> df['D'] = pd.to_datetime(df['D'])  
>>> df.groupby("A").first()  
     B  C          D
A
1  5.0  1 2000-03-11
3  6.0  3 2000-03-13
>>> df.groupby("A").first(min_count=2)  
    B    C          D
A
1 NaN  1.0 2000-03-11
3 NaN  NaN        NaT
>>> df.groupby("A").first(numeric_only=True)  
     B  C
A
1  5.0  1
3  6.0  3

get_group(key)#

Construct DataFrame from group with provided name.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.get_group.

Some inconsistencies with the Dask version may exist.

Known inconsistencies:: If the group is not present, Dask will return an empty Series/DataFrame.

Parameters:

nameobject (Not supported in Dask): The name of the group to get as a DataFrame.
objDataFrame, default None (Not supported in Dask): The DataFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used.

Returns:

groupsame type as obj

idxmax(split_every=None, split_out=1, shuffle=None, axis='__no_default__', skipna=True, numeric_only='__no_default__')#

Return index of first occurrence of maximum over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.idxmax.

Some inconsistencies with the Dask version may exist.

NA/null values are excluded.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series: Indexes of maxima along the specified axis.

Raises:

ValueError

If the row/column is empty

See also

Series.idxmax: Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],  
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])

>>> df  
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()  
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")  
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object

idxmin(split_every=None, split_out=1, shuffle=None, axis='__no_default__', skipna=True, numeric_only='__no_default__')#

Return index of first occurrence of minimum over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.idxmin.

Some inconsistencies with the Dask version may exist.

NA/null values are excluded.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series: Indexes of minima along the specified axis.

Raises:

ValueError

If the row/column is empty

See also

Series.idxmin: Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],  
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])

>>> df  
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()  
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")  
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object

last(split_every=None, split_out=1)#

Compute the last non-null entry of each column.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.last.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default -1 (Not supported in Dask): The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Last non-null of values within each group.

See also

DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.
DataFrame.core.groupby.GroupBy.first: Compute the first non-null entry of each column.
DataFrame.core.groupby.GroupBy.nth: Take the nth row from each group.

Examples

>>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3]))  
>>> df.groupby("A").last()  
     B  C
A
1  5.0  2
3  6.0  3

max(split_every=None, split_out=1)#

Compute max of group values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.max.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default -1 (Not supported in Dask): The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Computed max of values within each group.

mean(split_every=None, split_out=1)#

Compute mean of groups, excluding missing values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.mean.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default True

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.

enginestr, default None (Not supported in Dask)

'cython' : Runs the operation through C-extensions from cython.
'numba' : Runs the operation through JIT compiled code from numba.
None : Defaults to 'cython' or globally setting compute.use_numba

New in version 1.4.0.

engine_kwargsdict, default None (Not supported in Dask)

For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}}

New in version 1.4.0.

Returns:

pandas.Series or pandas.DataFrame

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

Examples

>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],  
...                    'B': [np.nan, 2, 3, 4, 5],
...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])

Groupby one column and return the mean of the remaining columns in each group.

>>> df.groupby('A').mean()  
     B         C
A
1  3.0  1.333333
2  4.0  1.500000

Groupby two columns and return the mean of the remaining column.

>>> df.groupby(['A', 'B']).mean()  
         C
A B
1 2.0  2.0
  4.0  1.0
2 3.0  1.0
  5.0  2.0

Groupby one column and return the mean of only particular column in the group.

>>> df.groupby('A')['B'].mean()  
A
1    3.0
2    4.0
Name: B, dtype: float64

median(split_every=None, split_out=1, shuffle=None, numeric_only='__no_default__')#

Compute median of groups, excluding missing values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.median.

Some inconsistencies with the Dask version may exist.

For multiple groupings, the result index will be a MultiIndex

Parameters:

numeric_onlybool, default True: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.

Returns:

Series or DataFrame: Median of values within each group.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

min(split_every=None, split_out=1)#

Compute min of group values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.min.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default False: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default -1 (Not supported in Dask): The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Computed min of values within each group.

prod(split_every=None, split_out=1, shuffle=None, min_count=None, numeric_only='__no_default__')#

Compute prod of group values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.prod.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default True: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Computed prod of values within each group.

rolling(window, min_periods=None, center=False, win_type=None, axis=0)#

Provides rolling transformations.

Note

Since MultiIndexes are not well supported in Dask, this method returns a dataframe with the same index as the original data. The groupby column is not added as the first level of the index like pandas does.

This method works differently from other groupby methods. It does a groupby on each partition (plus some overlap). This means that the output has the same shape and number of partitions as the original.

Parameters:

windowstr, offset: Size of the moving window. This is the number of observations used for calculating the statistic. Data must have a DatetimeIndex
min_periodsint, default None: Minimum number of observations in window required to have a value (otherwise result is NA).
centerboolean, default False: Set the labels at the center of the window.
win_typestring, default None: Provide a window type. The recognized window types are identical to pandas.
axisint, default 0

Returns:

a Rolling object on which to call a method to compute a statistic

Examples

>>> import dask
>>> ddf = dask.datasets.timeseries(freq="1H")
>>> result = ddf.groupby("name").x.rolling('1D').max()

shift(periods=1, freq=None, axis='__no_default__', fill_value=None, meta='__no_default__')#

Parallel version of pandas GroupBy.shift

This mimics the pandas version except for the following:

If the grouper does not align with the index then this causes a full shuffle. The order of rows within each group may not be preserved.

Parameters:

periodsDelayed, Scalar or int, default 1: Number of periods to shift.
freqDelayed, Scalar or str, optional: Frequency string.
axisaxis to shift, default 0: Shift direction.
fill_valueScalar, Delayed or object, optional: The scalar value to use for newly introduced missing values.
metapd.DataFrame, pd.Series, dict, iterable, tuple, optional: An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.

Returns:

shiftedSeries or DataFrame shifted within each group.

Examples

>>> import dask
>>> ddf = dask.datasets.timeseries(freq="1H")
>>> result = ddf.groupby("name").shift(1, meta={"id": int, "x": float, "y": float})

size(split_every=None, split_out=1, shuffle=None)#

Compute group sizes.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.size.

Some inconsistencies with the Dask version may exist.

Returns:

DataFrame or Series: Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

std(split_every=None, split_out=1)#

Compute standard deviation of groups, excluding missing values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.std.

Some inconsistencies with the Dask version may exist.

For multiple groupings, the result index will be a MultiIndex.

Parameters:

ddofint, default 1

Degrees of freedom.

enginestr, default None (Not supported in Dask)

'cython' : Runs the operation through C-extensions from cython.
'numba' : Runs the operation through JIT compiled code from numba.
None : Defaults to 'cython' or globally setting compute.use_numba

New in version 1.4.0.

engine_kwargsdict, default None (Not supported in Dask)

For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}}

New in version 1.4.0.

numeric_onlybool, default True

Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series or DataFrame: Standard deviation of values within each group.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

sum(split_every=None, split_out=1)#

Compute sum of group values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.sum.

Some inconsistencies with the Dask version may exist.

Parameters:

numeric_onlybool, default True: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.

Returns:

Series or DataFrame: Computed sum of values within each group.

transform(func, *args, **kwargs)#

Parallel version of pandas GroupBy.transform

This mimics the pandas version except for the following:

If the grouper does not align with the index then this causes a full shuffle. The order of rows within each group may not be preserved.
Dask’s GroupBy.transform is not appropriate for aggregations. For custom aggregations, use dask.dataframe.groupby.Aggregation.

Warning

Pandas’ groupby-transform can be used to apply arbitrary functions, including aggregations that result in one row per group. Dask’s groupby-transform will apply func once on each group, doing a shuffle if needed, such that each group is contained in one partition. When func is a reduction, e.g., you’ll end up with one row per group. To apply a custom aggregation with Dask, use dask.dataframe.groupby.Aggregation.

Parameters:

func: function: Function to apply
args, kwargsScalar, Delayed or object: Arguments and keywords to pass to the function.
metapd.DataFrame, pd.Series, dict, iterable, tuple, optional: An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.

Returns:

appliedSeries or DataFrame depending on columns keyword

var(split_every=None, split_out=1)#

Compute variance of groups, excluding missing values.

This docstring was copied from pandas.core.groupby.groupby.GroupBy.var.

Some inconsistencies with the Dask version may exist.

For multiple groupings, the result index will be a MultiIndex.

Parameters:

ddofint, default 1

Degrees of freedom.

enginestr, default None (Not supported in Dask)

'cython' : Runs the operation through C-extensions from cython.
'numba' : Runs the operation through JIT compiled code from numba.
None : Defaults to 'cython' or globally setting compute.use_numba

New in version 1.4.0.

engine_kwargsdict, default None (Not supported in Dask)

For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {{'nopython': True, 'nogil': False, 'parallel': False}}

New in version 1.4.0.

numeric_onlybool, default True

Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series or DataFrame: Variance of values within each group.

See also

Series.groupby: Apply a function groupby to a Series.
DataFrame.groupby: Apply a function groupby to each row or column of a DataFrame.

dask_cudf.groupby_agg(ddf, gb_cols, aggs_in, split_every=None, split_out=None, dropna=True, sep='___', sort=False, as_index=True, shuffle=None)#

Optimized groupby aggregation for Dask-CuDF.

Parameters:

ddfDataFrame: DataFrame object to perform grouping on.
gb_colsstr or list[str]: Column names to group by.
aggs_instr, list, or dict: Aggregations to perform.
split_everyint (optional): How to group intermediate aggregates.
dropnabool: Drop grouping key values corresponding to NA values.
as_indexbool: Currently ignored.
sortbool: Sort the group keys, better performance is obtained when not sorting.
shufflestr (optional): Control how shuffling of the DataFrame is performed.
sepstr: Internal usage.

See also

DataFrame.groupby: generic groupby of a DataFrame
dask.dataframe.apply_concat_apply: for more description of the split_every argument.

Notes

This “optimized” approach is more performant than the algorithm in implemented in DataFrame.apply() because it allows the cuDF backend to perform multiple aggregations at once.

This aggregation algorithm only supports the following options

“collect”
“count”
“first”
“last”
“max”
“mean”
“min”
“std”
“sum”
“var”

DataFrames and Series#

The core distributed objects provided by Dask-cuDF are the DataFrame and Series. These inherit respectively from dask.dataframe.DataFrame and dask.dataframe.Series, and so the API is essentially identical. The full API is provided below.

class dask_cudf.DataFrame(dsk, name, meta, divisions)#

Bases: _Frame, DataFrame

A distributed Dask DataFrame where the backing dataframe is a cuDF DataFrame.

Typically you would not construct this object directly, but rather use one of Dask-cuDF’s IO routines.

Most operations on Dask DataFrames are supported, with many of the same caveats.

Attributes

`attrs`	Dictionary of global attributes of this dataset.
`divisions`	Tuple of `npartitions + 1` values, in ascending order, marking the lower/upper bounds of each partition's index.
`dtypes`	Return data types
`iloc`	Purely integer-location based indexing for selection by position.
`index`	Return dask Index instance
`known_divisions`	Whether divisions are already known
`loc`	Purely label-location based indexer for selection by label.
`ndim`	Return dimensionality
`npartitions`	Return number of partitions
`partitions`	Slice dataframe by partitions
`shape`	Return a tuple representing the dimensionality of the DataFrame.
`size`	Size of the Series or DataFrame as a Delayed object.
`values`	Return a dask.array of the values of this dataframe

axes
columns
empty

Methods

`abs`()	Return a Series/DataFrame with absolute numeric value of each element.
`add`(other[, axis, level, fill_value])	Get Addition of DataFrame or Series and other, element-wise (binary operator add).
`add_prefix`(prefix)	Prefix labels with string prefix.
`add_suffix`(suffix)	Suffix labels with string suffix.
`align`(other[, join, axis, fill_value])	Align two objects on their axes with the specified join method.
`all`([axis, skipna, split_every, out])	Return whether all elements are True, potentially over an axis.
`any`([axis, skipna, split_every, out])	Return whether any element is True, potentially over an axis.
`append`(other[, interleave_partitions])	Append rows of other to the end of caller, returning a new object.
`apply`(func[, axis, broadcast, raw, reduce, ...])	Parallel version of pandas.DataFrame.apply
`applymap`(func[, meta])	Apply a function to a Dataframe elementwise.
`assign`(**kwargs)	Assign new columns to a DataFrame.
`astype`(dtype)	Cast a pandas object to a specified dtype `dtype`.
`bfill`([axis, limit])	Synonym for `DataFrame.fillna()` with `method='bfill'`.
`categorize`([columns, index, split_every])	Convert columns of the DataFrame to category dtype.
`clear_divisions`()	Forget division information
`clip`([lower, upper, out])	Trim values at input threshold(s).
`combine`(other, func[, fill_value, overwrite])	Perform column-wise combine with another DataFrame.
`combine_first`(other)	Update null elements with value in the same location in other.
`compute`(**kwargs)	Compute this dask collection
`compute_current_divisions`([col])	Compute the current divisions of the DataFrame.
`copy`([deep])	Make a copy of the dataframe
`corr`([method, min_periods, numeric_only, ...])	Compute pairwise correlation of columns, excluding NA/null values.
`count`([axis, split_every, numeric_only])	Count non-NA cells for each column or row.
`cov`([min_periods, numeric_only, split_every])	Compute pairwise covariance of columns, excluding NA/null values.
`cummax`([axis, skipna, out])	Return cumulative maximum over a DataFrame or Series axis.
`cummin`([axis, skipna, out])	Return cumulative minimum over a DataFrame or Series axis.
`cumprod`([axis, skipna, dtype, out])	Return cumulative product over a DataFrame or Series axis.
`cumsum`([axis, skipna, dtype, out])	Return cumulative sum over a DataFrame or Series axis.
`describe`([split_every, percentiles, ...])	Generate descriptive statistics.
`diff`([periods, axis])	First discrete difference of element.
`div`(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
`divide`(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
`dot`(other[, meta])	Compute the dot product between the Series and the columns of other.
`drop`([labels, axis, columns, errors])	Drop specified labels from rows or columns.
`drop_duplicates`([subset, split_every, ...])	Return DataFrame with duplicate rows removed.
`dropna`([how, subset, thresh])	Remove missing values.
`eq`(other[, axis, level])	Get Equal to of dataframe and other, element-wise (binary operator eq).
`eval`(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
`explode`(column)	Transform each element of a list-like to a row, replicating index values.
`ffill`([axis, limit])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`fillna`([value, method, limit, axis])	Fill NA/NaN values using the specified method.
`first`(offset)	Select initial periods of time series data based on a date offset.
`floordiv`(other[, axis, level, fill_value])	Get Integer division of DataFrame or Series and other, element-wise (binary operator floordiv).
`from_dict`(data, *, npartitions[, orient, ...])	Construct a Dask DataFrame from a Python Dictionary
`ge`(other[, axis, level])	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
`get_partition`(n)	Get a dask DataFrame/Series representing the nth partition.
`groupby`([by])	Group DataFrame using a mapper or by a Series of columns.
`gt`(other[, axis, level])	Get Greater than of dataframe and other, element-wise (binary operator gt).
`head`([n, npartitions, compute])	First n rows of the dataset
`idxmax`([axis, skipna, split_every, numeric_only])	Return index of first occurrence of maximum over requested axis.
`idxmin`([axis, skipna, split_every, numeric_only])	Return index of first occurrence of minimum over requested axis.
`info`([buf, verbose, memory_usage])	Concise summary of a Dask DataFrame.
`isin`(values)	Whether each element in the DataFrame is contained in values.
`isna`()	Detect missing values.
`isnull`()	DataFrame.isnull is an alias for DataFrame.isna.
`items`()	Iterate over (column name, Series) pairs.
`iterrows`()	Iterate over DataFrame rows as (index, Series) pairs.
`itertuples`([index, name])	Iterate over DataFrame rows as namedtuples.
`join`(other[, shuffle])	Join columns of another DataFrame.
`kurtosis`([axis, fisher, bias, nan_policy, ...])	Return unbiased kurtosis over requested axis.
`last`(offset)	Select final periods of time series data based on a date offset.
`le`(other[, axis, level])	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
`lt`(other[, axis, level])	Get Less than of dataframe and other, element-wise (binary operator lt).
`map_overlap`(func, before, after, args, *kwargs)	Apply a function to each partition, sharing rows with adjacent partitions.
`map_partitions`(func, args, *kwargs)	Apply Python function on each DataFrame partition.
`mask`(cond[, other])	Replace values where the condition is True.
`max`([axis, skipna, split_every, out, ...])	Return the maximum of the values over the requested axis.
`mean`([axis, skipna, split_every, dtype, ...])	Return the mean of the values over the requested axis.
`median`([axis, method])	Return the median of the values over the requested axis.
`median_approximate`([axis, method])	Return the approximate median of the values over the requested axis.
`melt`([id_vars, value_vars, var_name, ...])	Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
`memory_usage`([index, deep])	Return the memory usage of each column in bytes.
`memory_usage_per_partition`([index, deep])	Return the memory usage of each partition
`merge`(other[, shuffle])	Merge the DataFrame with another DataFrame
`min`([axis, skipna, split_every, out, ...])	Return the minimum of the values over the requested axis.
`mod`(other[, axis, level, fill_value])	Get Modulo of DataFrame or Series and other, element-wise (binary operator mod).
`mode`([dropna, split_every, numeric_only])	Get the mode(s) of each element along the selected axis.
`mul`(other[, axis, level, fill_value])	Get Multiplication of DataFrame or Series and other, element-wise (binary operator mul).
`ne`(other[, axis, level])	Get Not equal to of dataframe and other, element-wise (binary operator ne).
`nlargest`([n, columns, split_every])	Return the first n rows ordered by columns in descending order.
`notnull`()	DataFrame.notnull is an alias for DataFrame.notna.
`nsmallest`([n, columns, split_every])	Return the first n rows ordered by columns in ascending order.
`nunique`([split_every, dropna, axis])	Count number of distinct elements in specified axis.
`nunique_approx`([split_every])	Approximate number of unique rows.
`persist`(**kwargs)	Persist this dask collection into memory
`pipe`(func, args, *kwargs)	Apply chainable functions that expect Series or DataFrames.
`pivot_table`([index, columns, values, aggfunc])	Create a spreadsheet-style pivot table as a DataFrame.
`pop`(item)	Return item and drop from frame.
`pow`(other[, axis, level, fill_value])	Get Exponential of DataFrame or Series and other, element-wise (binary operator pow).
`prod`([axis, skipna, split_every, dtype, ...])	Return the product of the values over the requested axis.
`product`([axis, skipna, split_every, dtype, ...])	Return the product of the values over the requested axis.
`quantile`([q, axis, numeric_only, method])	Approximate row-wise and precise column-wise quantiles of DataFrame
`query`(expr, **kwargs)	Filter dataframe with complex expression
`radd`(other[, axis, level, fill_value])	Get Addition of DataFrame or Series and other, element-wise (binary operator radd).
`random_split`(frac[, random_state, shuffle])	Pseudorandomly split dataframe into different pieces row-wise
`rdiv`(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
`reduction`(chunk[, aggregate, combine, meta, ...])	Generic row-wise reductions.
`rename`([index, columns])	Alter axes labels.
`repartition`([divisions, npartitions, ...])	Repartition dataframe along new divisions
`replace`([to_replace, value, regex])	Replace values given in to_replace with value.
`resample`(rule[, closed, label])	Resample time-series data.
`reset_index`([drop])	Reset the index to the default index.
`rfloordiv`(other[, axis, level, fill_value])	Get Integer division of DataFrame or Series and other, element-wise (binary operator rfloordiv).
`rmod`(other[, axis, level, fill_value])	Get Modulo of DataFrame or Series and other, element-wise (binary operator rmod).
`rmul`(other[, axis, level, fill_value])	Get Multiplication of DataFrame or Series and other, element-wise (binary operator rmul).
`rolling`(window[, min_periods, center, ...])	Provides rolling transformations.
`round`([decimals])	Round a DataFrame to a variable number of decimal places.
`rpow`(other[, axis, level, fill_value])	Get Exponential of DataFrame or Series and other, element-wise (binary operator rpow).
`rsub`(other[, axis, level, fill_value])	Get Subtraction of DataFrame or Series and other, element-wise (binary operator rsub).
`rtruediv`(other[, axis, level, fill_value])	Get Floating division of DataFrame or Series and other, element-wise (binary operator rtruediv).
`sample`([n, frac, replace, random_state])	Random sample of items
`select_dtypes`([include, exclude])	Return a subset of the DataFrame's columns based on the column dtypes.
`sem`([axis, skipna, ddof, split_every, ...])	Return unbiased standard error of the mean over requested axis.
`set_index`(other[, sorted, divisions, shuffle])	Set the DataFrame index (row labels) using an existing column.
`shift`([periods, freq, axis])	Shift index by desired number of periods with an optional time freq.
`shuffle`(*args[, shuffle])	Wraps dask.dataframe DataFrame.shuffle method
`skew`([axis, bias, nan_policy, out, numeric_only])	Return unbiased skew over requested axis.
`sort_values`(by[, ignore_index, max_branch, ...])	Sort the dataset by a single column.
`squeeze`([axis])	Squeeze 1 dimensional axis objects into scalars.
`std`([axis, skipna, ddof, split_every, ...])	Return sample standard deviation over requested axis.
`sub`(other[, axis, level, fill_value])	Get Subtraction of DataFrame or Series and other, element-wise (binary operator sub).
`sum`([axis, skipna, split_every, dtype, out, ...])	Return the sum of the values over the requested axis.
`tail`([n, compute])	Last n rows of the dataset
`to_backend`([backend])	Move to a new DataFrame backend
`to_bag`([index, format])	Create Dask Bag from a Dask DataFrame
`to_csv`(filename, **kwargs)	Store Dask DataFrame to CSV files
`to_dask_array`([lengths, meta])	Convert a dask DataFrame to a dask array.
`to_dask_dataframe`(**kwargs)	Create a dask.dataframe object from a dask_cudf object
`to_delayed`([optimize_graph])	Convert into a list of `dask.delayed` objects, one per partition.
`to_hdf`(path_or_buf, key[, mode, append])	Store Dask Dataframe to Hierarchical Data Format (HDF) files
`to_html`([max_rows])	Render a DataFrame as an HTML table.
`to_json`(filename, args, *kwargs)	See dd.to_json docstring for more information
`to_orc`(path, **kwargs)	Calls dask_cudf.io.to_orc
`to_parquet`(path, args, *kwargs)	Calls dask.dataframe.io.to_parquet with CudfEngine backend
`to_records`([index, lengths])	Create Dask Array from a Dask Dataframe
`to_sql`(name, uri[, schema, if_exists, ...])	See dd.to_sql docstring for more information
`to_string`([max_rows])	Render a DataFrame to a console-friendly tabular output.
`to_timestamp`([freq, how, axis])	Cast to DatetimeIndex of timestamps, at beginning of period.
`truediv`(other[, axis, level, fill_value])	Get Floating division of DataFrame or Series and other, element-wise (binary operator truediv).
`var`([axis, skipna, ddof, split_every, ...])	Return unbiased variance over requested axis.
`visualize`([filename, format, optimize_graph])	Render the computation of this object's task graph using graphviz.
`where`(cond[, other])	Replace values where the condition is False.

apply_rows
map

abs()#

Return a Series/DataFrame with absolute numeric value of each element.

This docstring was copied from pandas.core.frame.DataFrame.abs.

Some inconsistencies with the Dask version may exist.

This function only applies to elements that are all numeric.

Returns:

abs: Series/DataFrame containing the absolute value of each element.

See also

numpy.absolute: Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is \(\sqrt{ a^2 + b^2 }\).

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])  
>>> s.abs()  
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])  
>>> s.abs()  
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])  
>>> s.abs()  
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow).

>>> df = pd.DataFrame({  
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df  
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]  
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50

add(other, axis='columns', level=None, fill_value=None)#

Get Addition of DataFrame or Series and other, element-wise (binary operator add).

This docstring was copied from cudf.core.series.Series.add.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.add(1)  
        angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.add(b)  
a       2
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.add(b, fill_value=0)  
a       2
b       1
c       1
d       1
e    <NA>
dtype: int64

add_prefix(prefix)#

Prefix labels with string prefix.

This docstring was copied from pandas.core.frame.DataFrame.add_prefix.

Some inconsistencies with the Dask version may exist.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters:

prefixstr: The string to add before each label.

Returns:

Series or DataFrame: New Series or DataFrame with updated labels.

See also

Series.add_suffix: Suffix row labels with string suffix.
DataFrame.add_suffix: Suffix column labels with string suffix.

Examples

>>> s = pd.Series([1, 2, 3, 4])  
>>> s  
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_prefix('item_')  
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})  
>>> df  
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_prefix('col_')  
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6

add_suffix(suffix)#

Suffix labels with string suffix.

This docstring was copied from pandas.core.frame.DataFrame.add_suffix.

Some inconsistencies with the Dask version may exist.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters:

suffixstr: The string to add after each label.

Returns:

Series or DataFrame: New Series or DataFrame with updated labels.

See also

Series.add_prefix: Prefix row labels with string prefix.
DataFrame.add_prefix: Prefix column labels with string prefix.

Examples

>>> s = pd.Series([1, 2, 3, 4])  
>>> s  
0    1
1    2
2    3
3    4
dtype: int64

>>> s.add_suffix('_item')  
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64

>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})  
>>> df  
   A  B
0  1  3
1  2  4
2  3  5
3  4  6

>>> df.add_suffix('_col')  
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6

align(other, join='outer', axis=None, fill_value=None)#

Align two objects on their axes with the specified join method.

This docstring was copied from pandas.core.frame.DataFrame.align.

Some inconsistencies with the Dask version may exist.

Join method is specified for each axis Index.

Parameters:

otherDataFrame or Series

join{‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’

axisallowed axis of the other object, default None

Align on index (0), columns (1), or both (None).

levelint or level name, default None (Not supported in Dask)

Broadcast across a level, matching Index values on the passed MultiIndex level.

copybool, default True (Not supported in Dask)

Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

fill_valuescalar, default np.NaN

Value to use for missing values. Defaults to NaN, but can be any “compatible” value.

method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None (Not supported in Dask)

Method to use for filling holes in reindexed Series:

pad / ffill: propagate last valid observation forward to next valid.
backfill / bfill: use NEXT valid observation to fill gap.

limitint, default None (Not supported in Dask)

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

fill_axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask)

Filling axis, method and limit.

broadcast_axis{0 or ‘index’, 1 or ‘columns’}, default None (Not supported in Dask)

Broadcast values along this axis, if aligning two objects of different dimensions.

Returns:

(left, right)(DataFrame, type of other): Aligned objects.

Examples

>>> df = pd.DataFrame(  
...     [[1, 2, 3, 4], [6, 7, 8, 9]], columns=["D", "B", "E", "A"], index=[1, 2]
... )
>>> other = pd.DataFrame(  
...     [[10, 20, 30, 40], [60, 70, 80, 90], [600, 700, 800, 900]],
...     columns=["A", "B", "C", "D"],
...     index=[2, 3, 4],
... )
>>> df  
   D  B  E  A
1  1  2  3  4
2  6  7  8  9
>>> other  
    A    B    C    D
2   10   20   30   40
3   60   70   80   90
4  600  700  800  900

Align on columns:

>>> left, right = df.align(other, join="outer", axis=1)  
>>> left  
   A  B   C  D  E
1  4  2 NaN  1  3
2  9  7 NaN  6  8
>>> right  
    A    B    C    D   E
2   10   20   30   40 NaN
3   60   70   80   90 NaN
4  600  700  800  900 NaN

We can also align on the index:

>>> left, right = df.align(other, join="outer", axis=0)  
>>> left  
    D    B    E    A
1  1.0  2.0  3.0  4.0
2  6.0  7.0  8.0  9.0
3  NaN  NaN  NaN  NaN
4  NaN  NaN  NaN  NaN
>>> right  
    A      B      C      D
1    NaN    NaN    NaN    NaN
2   10.0   20.0   30.0   40.0
3   60.0   70.0   80.0   90.0
4  600.0  700.0  800.0  900.0

Finally, the default axis=None will align on both index and columns:

>>> left, right = df.align(other, join="outer", axis=None)  
>>> left  
     A    B   C    D    E
1  4.0  2.0 NaN  1.0  3.0
2  9.0  7.0 NaN  6.0  8.0
3  NaN  NaN NaN  NaN  NaN
4  NaN  NaN NaN  NaN  NaN
>>> right  
       A      B      C      D   E
1    NaN    NaN    NaN    NaN NaN
2   10.0   20.0   30.0   40.0 NaN
3   60.0   70.0   80.0   90.0 NaN
4  600.0  700.0  800.0  900.0 NaN

all(axis=None, skipna=True, split_every=False, out=None)#

Return whether all elements are True, potentially over an axis.

This docstring was copied from pandas.core.frame.DataFrame.all.

Some inconsistencies with the Dask version may exist.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters:

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default None (Not supported in Dask)

Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

levelint or level name, default None (Not supported in Dask)

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

See also

Series.all: Return True if all elements are True.
DataFrame.any: Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()  
True
>>> pd.Series([True, False]).all()  
False
>>> pd.Series([], dtype="float64").all()  
True
>>> pd.Series([np.nan]).all()  
True
>>> pd.Series([np.nan]).all(skipna=False)  
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})  
>>> df  
   col1   col2
0  True   True
1  True  False

Default behaviour checks if values in each column all return True.

>>> df.all()  
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if values in each row all return True.

>>> df.all(axis='columns')  
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)  
False

any(axis=None, skipna=True, split_every=False, out=None)#

Return whether any element is True, potentially over an axis.

This docstring was copied from pandas.core.frame.DataFrame.any.

Some inconsistencies with the Dask version may exist.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters:

axis{0 or ‘index’, 1 or ‘columns’, None}, default 0

Indicate which axis or axes should be reduced. For Series this parameter is unused and defaults to 0.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.

bool_onlybool, default None (Not supported in Dask)

Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

skipnabool, default True

Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.

levelint or level name, default None (Not supported in Dask)

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.

**kwargsany, default None

Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: If level is specified, then, DataFrame is returned; otherwise, Series is returned.

See also

numpy.any: Numpy version of this method.
Series.any: Return whether any element is True.
Series.all: Return whether all elements are True.
DataFrame.any: Return whether any element is True over requested axis.
DataFrame.all: Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()  
False
>>> pd.Series([True, False]).any()  
True
>>> pd.Series([], dtype="float64").any()  
False
>>> pd.Series([np.nan]).any()  
False
>>> pd.Series([np.nan]).any(skipna=False)  
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})  
>>> df  
   A  B  C
0  1  0  0
1  2  2  0

>>> df.any()  
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})  
>>> df  
       A  B
0   True  1
1  False  2

>>> df.any(axis='columns')  
0    True
1    True
dtype: bool

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})  
>>> df  
       A  B
0   True  1
1  False  0

>>> df.any(axis='columns')  
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)  
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()  
Series([], dtype: bool)

append(other, interleave_partitions=False)#

Append rows of other to the end of caller, returning a new object.

This docstring was copied from pandas.core.frame.DataFrame.append.

Some inconsistencies with the Dask version may exist.

Deprecated since version 1.4.0: Use concat() instead. For further details see Deprecated DataFrame.append and Series.append

Columns in other that are not in the caller are added as new columns.

Parameters:

otherDataFrame or Series/dict-like object, or list of these: The data to append.
ignore_indexbool, default False (Not supported in Dask): If True, the resulting axis will be labeled 0, 1, …, n - 1.
verify_integritybool, default False (Not supported in Dask): If True, raise ValueError on creating index with duplicates.
sortbool, default False (Not supported in Dask): Sort columns if the columns of self and other are not aligned.

Changed in version 1.0.0: Changed to not sort by default.

Returns:

DataFrame: A new DataFrame consisting of the rows of caller and the rows of other.

See also

concat: General function to concatenate DataFrame or Series objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame’s index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

Examples

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])  
>>> df  
   A  B
x  1  2
y  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])  
>>> df.append(df2)  
   A  B
x  1  2
y  3  4
x  5  6
y  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)  
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])  
>>> for i in range(5):  
...     df = df.append({'A': i}, ignore_index=True)
>>> df  
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],  
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4

apply(func, axis=0, broadcast=None, raw=False, reduce=None, args=(), meta='__no_default__', result_type=None, **kwds)#

Parallel version of pandas.DataFrame.apply

This mimics the pandas version except for the following:

Only axis=1 is supported (and must be specified explicitly).
The user should provide output metadata via the meta keyword.

Parameters:

funcfunction

Function to apply to each column/row

axis{0 or ‘index’, 1 or ‘columns’}, default 0

0 or ‘index’: apply function to each column (NOT SUPPORTED)
1 or ‘columns’: apply function to each row

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

An empty pd.DataFrame or pd.Series that matches the dtypes and column names of the output. This metadata is necessary for many algorithms in dask dataframe to work. For ease of use, some alternative inputs are also available. Instead of a DataFrame, a dict of {name: dtype} or iterable of (name, dtype) can be provided (note that the order of the names should match the order of the columns). Instead of a series, a tuple of (name, dtype) can be used. If not provided, dask will try to infer the metadata. This may lead to unexpected results, so providing meta is recommended. For more information, see dask.dataframe.utils.make_meta.

argstuple

Positional arguments to pass to function in addition to the array/series

Additional keyword arguments will be passed as keywords to the function

Returns:

appliedSeries or DataFrame

See also

dask.DataFrame.map_partitions

Examples

>>> import pandas as pd
>>> import dask.dataframe as dd
>>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5],
...                    'y': [1., 2., 3., 4., 5.]})
>>> ddf = dd.from_pandas(df, npartitions=2)

Apply a function to row-wise passing in extra arguments in args and kwargs:

>>> def myadd(row, a, b=1):
...     return row.sum() + a + b
>>> res = ddf.apply(myadd, axis=1, args=(2,), b=1.5)  

By default, dask tries to infer the output metadata by running your provided function on some fake data. This works well in many cases, but can sometimes be expensive, or even fail. To avoid this, you can manually specify the output metadata with the meta keyword. This can be specified in many forms, for more information see dask.dataframe.utils.make_meta.

Here we specify the output is a Series with name 'x', and dtype float64:

>>> res = ddf.apply(myadd, axis=1, args=(2,), b=1.5, meta=('x', 'f8'))

In the case where the metadata doesn’t change, you can also pass in the object itself directly:

>>> res = ddf.apply(lambda row: row + 1, axis=1, meta=ddf)

applymap(func, meta='__no_default__')#

Apply a function to a Dataframe elementwise.

This docstring was copied from pandas.core.frame.DataFrame.applymap.

Some inconsistencies with the Dask version may exist.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters:

funccallable: Python function, returns a single value from a single value.
na_action{None, ‘ignore’}, default None (Not supported in Dask): If ‘ignore’, propagate NaN values, without passing them to func.

New in version 1.2.
**kwargs: Additional keyword arguments to pass as keywords arguments to func.

New in version 1.3.0.

Returns:

DataFrame: Transformed DataFrame.

See also

DataFrame.apply: Apply a function along input axis of DataFrame.

Examples

>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])  
>>> df  
       0      1
0  1.000  2.120
1  3.356  4.567

>>> df.applymap(lambda x: len(str(x)))  
1
3  4
5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()  
>>> df_copy.iloc[0, 0] = pd.NA  
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')  
     0  1
0  NaN  4
1  5.0  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)  
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it’s better to avoid applymap in that case.

>>> df ** 2  
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

assign(**kwargs)#

Assign new columns to a DataFrame.

This docstring was copied from pandas.core.frame.DataFrame.assign.

Some inconsistencies with the Dask version may exist.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:

**kwargsdict of {str: callable or Series}: The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

Returns:

DataFrame: A new DataFrame with the new columns in addition to all the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},  
...                   index=['Portland', 'Berkeley'])
>>> df  
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)  
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)  
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,  
...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

astype(dtype)#

Cast a pandas object to a specified dtype dtype.

This docstring was copied from pandas.core.frame.DataFrame.astype.

Some inconsistencies with the Dask version may exist.

Parameters:

dtypedata type, or dict of column name -> data type

Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

copybool, default True (Not supported in Dask)

Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

errors{‘raise’, ‘ignore’}, default ‘raise’ (Not supported in Dask)

Control raising of exceptions on invalid data for provided dtype.

raise : allow exceptions to be raised
ignore : suppress exceptions. On error return original object.

Returns:

castedsame type as caller

See also

to_datetime: Convert argument to datetime.
to_timedelta: Convert argument to timedelta.
to_numeric: Convert argument to a numeric type.
numpy.ndarray.astype: Cast a numpy array to a specified type.

Notes

Deprecated since version 1.3.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. Use Series.dt.tz_localize() instead.

Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}  
>>> df = pd.DataFrame(data=d)  
>>> df.dtypes  
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes  
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes  
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')  
>>> ser  
0    1
1    2
dtype: int32
>>> ser.astype('int64')  
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')  
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype  
>>> cat_dtype = CategoricalDtype(  
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)  
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1, 2])  
>>> s2 = s1.astype('int64', copy=False)  
>>> s2[0] = 10  
>>> s1  # note that s1[0] has changed too  
0    10
1     2
dtype: int64

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))  
>>> ser_date  
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]

property attrs#

Dictionary of global attributes of this dataset.

This docstring was copied from pandas.core.frame.DataFrame.attrs.

Some inconsistencies with the Dask version may exist.

Warning

attrs is experimental and may change without warning.

See also

DataFrame.flags: Global flags applying to this object.

bfill(axis=None, limit=None)#

Synonym for DataFrame.fillna() with method='bfill'.

This docstring was copied from pandas.core.frame.DataFrame.bfill.

Some inconsistencies with the Dask version may exist.

Returns:

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

categorize(columns=None, index=None, split_every=None, **kwargs)#

Convert columns of the DataFrame to category dtype.

Parameters:

columnslist, optional: A list of column names to convert to categoricals. By default any column with an object dtype is converted to a categorical, and any unknown categoricals are made known.
indexbool, optional: Whether to categorize the index. By default, object indices are converted to categorical, and unknown categorical indices are made known. Set True to always categorize the index, False to never.
split_everyint, optional: Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used. Default is 16.
kwargs: Keyword arguments are passed on to compute.

clear_divisions()#: Forget division information

clip(lower=None, upper=None, out=None)#

Trim values at input threshold(s).

This docstring was copied from pandas.core.frame.DataFrame.clip.

Some inconsistencies with the Dask version may exist.

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters:

lowerfloat or array-like, default None: Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
upperfloat or array-like, default None: Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
axis{{0 or ‘index’, 1 or ‘columns’, None}}, default None (Not supported in Dask): Align object with lower and upper along the given axis. For Series this parameter is unused and defaults to None.
inplacebool, default False (Not supported in Dask): Whether to perform the operation in place on the data.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with numpy.

Returns:

Series or DataFrame or None: Same type as calling object with the values outside the clip boundaries replaced or None if inplace=True.

See also

Series.clip: Trim values at input threshold in series.
DataFrame.clip: Trim values at input threshold in dataframe.
numpy.clip: Clip (limit) the values in an array.

Examples

>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}  
>>> df = pd.DataFrame(data)  
>>> df  
   col_0  col_1
0      9     -2
1     -3     -7
2      0      6
3     -1      8
4      5     -5

Clips per column using lower and upper thresholds:

>>> df.clip(-4, 6)  
   col_0  col_1
    6     -2
   -3     -4
    0      6
   -1      6
    5     -4

Clips using specific lower and upper thresholds per column element:

>>> t = pd.Series([2, -4, -1, 6, 3])  
>>> t  
0    2
1   -4
2   -1
3    6
4    3
dtype: int64

>>> df.clip(t, t + 4, axis=0)  
   col_0  col_1
    6      2
   -3     -4
    0      3
    6      8
    5      3

Clips using specific lower threshold per column element, with missing values:

>>> t = pd.Series([2, -4, np.NaN, 6, 3])  
>>> t  
0    2.0
1   -4.0
2    NaN
3    6.0
4    3.0
dtype: float64

>>> df.clip(t, axis=0)  
col_0  col_1
    9      2
   -3     -4
    0      6
    6      8
    5      3

combine(other, func, fill_value=None, overwrite=True)#

Perform column-wise combine with another DataFrame.

This docstring was copied from pandas.core.frame.DataFrame.combine.

Some inconsistencies with the Dask version may exist.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters:

otherDataFrame: The DataFrame to merge column-wise.
funcfunction: Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_valuescalar value, default None: The value to fill NaNs with prior to passing any column to the merge func.
overwritebool, default True: If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns:

DataFrame: Combination of the provided DataFrames.

See also

DataFrame.combine_first: Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})  
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})  
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2  
>>> df1.combine(df2, take_smaller)  
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})  
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})  
>>> df1.combine(df2, np.minimum)  
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})  
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})  
>>> df1.combine(df2, take_smaller, fill_value=-5)  
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})  
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})  
>>> df1.combine(df2, take_smaller, fill_value=-5)  
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})  
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])  
>>> df1.combine(df2, take_smaller)  
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0

>>> df1.combine(df2, take_smaller, overwrite=False)  
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])  
>>> df2.combine(df1, take_smaller)  
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN

>>> df2.combine(df1, take_smaller, overwrite=False)  
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0

combine_first(other)#

Update null elements with value in the same location in other.

This docstring was copied from pandas.core.frame.DataFrame.combine_first.

Some inconsistencies with the Dask version may exist.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the ‘first’ dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Parameters:

otherDataFrame: Provided DataFrame to use to fill null values.

Returns:

DataFrame: The result of combining the provided DataFrame with the other object.

See also

DataFrame.combine: Perform series-wise operation on two DataFrames using a given function.

Examples

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})  
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})  
>>> df1.combine_first(df2)  
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})  
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])  
>>> df1.combine_first(df2)  
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0

compute(**kwargs)#

Compute this dask collection

This turns a lazy Dask collection into its in-memory equivalent. For example a Dask array turns into a NumPy array and a Dask dataframe turns into a Pandas dataframe. The entire dataset must fit into memory before calling this operation.

Parameters:

schedulerstring, optional: Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.
optimize_graphbool, optional: If True [default], the graph is optimized before computation. Otherwise the graph is run as is. This can be useful for debugging.
kwargs: Extra keywords to forward to the scheduler function.

See also

dask.compute

compute_current_divisions(col=None)#

Compute the current divisions of the DataFrame.

This method triggers immediate computation. If you find yourself running this command repeatedly for the same dataframe, we recommend storing the result so you don’t have to rerun it.

If the column or index values overlap between partitions, raises ValueError. To prevent this, make sure the data are sorted by the column or index.

Parameters:

colstring, optional: Calculate the divisions for a non-index column by passing in the name of the column. If col is not specified, the index will be used to calculate divisions. In this case, if the divisions are already known, they will be returned immediately without computing.

Examples

>>> import dask
>>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1H").clear_divisions()
>>> divisions = ddf.compute_current_divisions()
>>> print(divisions)  
(Timestamp('2021-01-01 00:00:00'),
 Timestamp('2021-01-02 00:00:00'),
 Timestamp('2021-01-03 00:00:00'),
 Timestamp('2021-01-04 00:00:00'),
 Timestamp('2021-01-05 00:00:00'),
 Timestamp('2021-01-06 00:00:00'),
 Timestamp('2021-01-06 23:00:00'))

>>> ddf.divisions = divisions
>>> ddf.known_divisions
True

>>> ddf = ddf.reset_index().clear_divisions()
>>> divisions = ddf.compute_current_divisions("timestamp")
>>> print(divisions)  
(Timestamp('2021-01-01 00:00:00'),
 Timestamp('2021-01-02 00:00:00'),
 Timestamp('2021-01-03 00:00:00'),
 Timestamp('2021-01-04 00:00:00'),
 Timestamp('2021-01-05 00:00:00'),
 Timestamp('2021-01-06 00:00:00'),
 Timestamp('2021-01-06 23:00:00'))

>>> ddf = ddf.set_index("timestamp", divisions=divisions, sorted=True)

copy(deep=False)#

Make a copy of the dataframe

This is strictly a shallow copy of the underlying computational graph. It does not affect the underlying data

Parameters:

deepboolean, default False: The deep value must be False and it is declared as a parameter just for compatibility with third-party libraries like cuDF

corr(method='pearson', min_periods=None, numeric_only='__no_default__', split_every=False)#

Compute pairwise correlation of columns, excluding NA/null values.

This docstring was copied from pandas.core.frame.DataFrame.corr.

Some inconsistencies with the Dask version may exist.

Parameters:

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method of correlation:

pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional

Minimum number of observations required per pair of columns to have a valid result. Currently only available for Pearson and Spearman correlation.

numeric_onlybool, default True

Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of numeric_only will be False in a future version of pandas.

Returns:

DataFrame: Correlation matrix.

See also

DataFrame.corrwith: Compute pairwise correlation with another DataFrame or Series.
Series.corr: Compute the correlation between two Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Examples

>>> def histogram_intersection(a, b):  
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],  
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)  
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0

>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],  
...                   columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)  
      dogs  cats
dogs   1.0   NaN
cats   NaN   1.0

count(axis=None, split_every=False, numeric_only=False)#

Count non-NA cells for each column or row.

This docstring was copied from pandas.core.frame.DataFrame.count.

Some inconsistencies with the Dask version may exist.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
levelint or str, optional (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame. A str specifies the level name.
numeric_onlybool, default False: Include only float, int or boolean data.

Returns:

Series or DataFrame: For each column/row the number of non-NA/null entries. If level is specified returns a DataFrame.

See also

Series.count: Number of non-NA elements in a Series.
DataFrame.value_counts: Count unique combinations of columns.
DataFrame.shape: Number of DataFrame rows and columns (including NA elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":  
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df  
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()  
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')  
  3
  2
  3
  3
  3
dtype: int64

cov(min_periods=None, numeric_only='__no_default__', split_every=False)#

Compute pairwise covariance of columns, excluding NA/null values.

This docstring was copied from pandas.core.frame.DataFrame.cov.

Some inconsistencies with the Dask version may exist.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters:

min_periodsint, optional: Minimum number of observations required per pair of columns to have a valid result.
ddofint, default 1 (Not supported in Dask): Delta degrees of freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

New in version 1.1.0.
numeric_onlybool, default True: Include only float, int or boolean data.

New in version 1.5.0.

Deprecated since version 1.5.0: The default value of numeric_only will be False in a future version of pandas.

Returns:

DataFrame: The covariance matrix of the series of the DataFrame.

See also

Series.cov: Compute covariance with another Series.
core.window.ewm.ExponentialMovingWindow.cov: Exponential weighted sample covariance.
core.window.expanding.Expanding.cov: Expanding sample covariance.
core.window.rolling.Rolling.cov: Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-ddof.

Examples

>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],  
...                   columns=['dogs', 'cats'])
>>> df.cov()  
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667

>>> np.random.seed(42)  
>>> df = pd.DataFrame(np.random.randn(1000, 5),  
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()  
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)  
>>> df = pd.DataFrame(np.random.randn(20, 3),  
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan  
>>> df.loc[df.index[5:10], 'b'] = np.nan  
>>> df.cov(min_periods=12)  
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202

cummax(axis=None, skipna=True, out=None)#

Return cumulative maximum over a DataFrame or Series axis.

This docstring was copied from pandas.core.frame.DataFrame.cummax.

Some inconsistencies with the Dask version may exist.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: Return cumulative maximum of Series or DataFrame.

See also

core.window.expanding.Expanding.max: Similar functionality but ignores NaN values.
DataFrame.max: Return the maximum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])  
>>> s  
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()  
  2.0
  NaN
  5.0
  5.0
  5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)  
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],  
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df  
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()  
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)  
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0

cummin(axis=None, skipna=True, out=None)#

Return cumulative minimum over a DataFrame or Series axis.

This docstring was copied from pandas.core.frame.DataFrame.cummin.

Some inconsistencies with the Dask version may exist.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: Return cumulative minimum of Series or DataFrame.

See also

core.window.expanding.Expanding.min: Similar functionality but ignores NaN values.
DataFrame.min: Return the minimum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])  
>>> s  
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()  
  2.0
  NaN
  2.0
 -1.0
 -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)  
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],  
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df  
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()  
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)  
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

cumprod(axis=None, skipna=True, dtype=None, out=None)#

Return cumulative product over a DataFrame or Series axis.

This docstring was copied from pandas.core.frame.DataFrame.cumprod.

Some inconsistencies with the Dask version may exist.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: Return cumulative product of Series or DataFrame.

See also

core.window.expanding.Expanding.prod: Similar functionality but ignores NaN values.
DataFrame.prod: Return the product over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])  
>>> s  
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()  
   2.0
   NaN
  10.0
 -10.0
  -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)  
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],  
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df  
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()  
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)  
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0

cumsum(axis=None, skipna=True, dtype=None, out=None)#

Return cumulative sum over a DataFrame or Series axis.

This docstring was copied from pandas.core.frame.DataFrame.cumsum.

Some inconsistencies with the Dask version may exist.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
*args, **kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns:

Series or DataFrame: Return cumulative sum of Series or DataFrame.

See also

core.window.expanding.Expanding.sum: Similar functionality but ignores NaN values.
DataFrame.sum: Return the sum over DataFrame axis.
DataFrame.cummax: Return cumulative maximum over DataFrame axis.
DataFrame.cummin: Return cumulative minimum over DataFrame axis.
DataFrame.cumsum: Return cumulative sum over DataFrame axis.
DataFrame.cumprod: Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])  
>>> s  
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()  
  2.0
  NaN
  7.0
  6.0
  6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)  
  2.0
  NaN
  NaN
  NaN
  NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],  
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df  
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()  
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)  
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0

describe(split_every=False, percentiles=None, percentiles_method='default', include=None, exclude=None, datetime_is_numeric='__no_default__')#

Generate descriptive statistics.

This docstring was copied from pandas.core.frame.DataFrame.describe.

Some inconsistencies with the Dask version may exist.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters:

percentileslist-like of numbers, optional

The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.

include‘all’, list-like of dtypes or None (default), optional

A white list of data types to include in the result. Ignored for Series. Here are the options:

‘all’ : All columns of the input will be included in the output.
A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
None (default) : The result will include all numeric columns.

excludelist-like of dtypes or None (default), optional,

A black list of data types to omit from the result. Ignored for Series. Here are the options:

A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(exclude=['O'])). To exclude pandas categorical columns, use 'category'
None (default) : The result will exclude nothing.

datetime_is_numericbool, default False

Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.

New in version 1.1.0.

Returns:

Series or DataFrame: Summary statistics of the Series or Dataframe provided.

See also

DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])  
>>> s.describe()  
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])  
>>> s.describe()  
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([  
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)  
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),  
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()  
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()  
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])  
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])  
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0

diff(periods=1, axis=0)#

First discrete difference of element.

This docstring was copied from pandas.core.frame.DataFrame.diff.

Some inconsistencies with the Dask version may exist.

Note

Pandas currently uses an object-dtype column to represent boolean data with missing values. This can cause issues for boolean-specific operations, like |. To enable boolean- specific operations, at the cost of metadata that doesn’t match pandas, use .astype(bool) after the shift.

Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row).

Parameters:

periodsint, default 1: Periods to shift for calculating difference, accepts negative values.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Take difference over rows (0) or columns (1).

Returns:

DataFrame: First differences of the Series.

See also

DataFrame.pct_change: Percent change over given number of periods.
DataFrame.shift: Shift index by desired number of periods with an optional time freq.
Series.diff: First discrete difference of object.

Notes

For boolean dtypes, this uses operator.xor() rather than operator.sub(). The result is calculated according to current dtype in DataFrame, however dtype of the result is always float64.

Examples

Difference with previous row

>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],  
...                    'b': [1, 1, 2, 3, 5, 8],
...                    'c': [1, 4, 9, 16, 25, 36]})
>>> df  
   a  b   c
0  1  1   1
1  2  1   4
2  3  2   9
3  4  3  16
4  5  5  25
5  6  8  36

>>> df.diff()  
     a    b     c
NaN  NaN   NaN
1.0  0.0   3.0
1.0  1.0   5.0
1.0  1.0   7.0
1.0  2.0   9.0
1.0  3.0  11.0

Difference with previous column

>>> df.diff(axis=1)  
    a  b   c
NaN  0   0
NaN -1   3
NaN -1   7
NaN -1  13
NaN  0  20
NaN  2  28

Difference with 3rd previous row

>>> df.diff(periods=3)  
     a    b     c
NaN  NaN   NaN
NaN  NaN   NaN
NaN  NaN   NaN
3.0  2.0  15.0
3.0  4.0  21.0
3.0  6.0  27.0

Difference with following row

>>> df.diff(periods=-1)  
     a    b     c
-1.0  0.0  -3.0
-1.0 -1.0  -5.0
-1.0 -1.0  -7.0
-1.0 -2.0  -9.0
-1.0 -3.0 -11.0
NaN  NaN   NaN

Overflow in input dtype

>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)  
>>> df.diff()  
       a
0    NaN
1  255.0

div(other, axis='columns', level=None, fill_value=None)#

Get Floating division of dataframe and other, element-wise (binary operator truediv).

This docstring was copied from pandas.core.frame.DataFrame.div.

Some inconsistencies with the Dask version may exist.

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame: Result of the arithmetic operation.

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],  
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df  
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)  
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)  
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),  
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})  
            angles      degrees
circle           0          720
triangle             0      360
rectangle            0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')  
            angles      degrees
circle               0        0
triangle             6      360
rectangle           12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},  
...                      index=['circle', 'triangle', 'rectangle'])
>>> other  
           angles
circle          0
triangle        3
rectangle       4

>>> df * other  
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)  
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],  
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex  
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)  
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

divide(other, axis='columns', level=None, fill_value=None)#

Get Floating division of dataframe and other, element-wise (binary operator truediv).

This docstring was copied from pandas.core.frame.DataFrame.divide.

Some inconsistencies with the Dask version may exist.

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame: Result of the arithmetic operation.

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],  
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df  
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)  
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)  
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),  
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})  
            angles      degrees
circle           0          720
triangle             0      360
rectangle            0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')  
            angles      degrees
circle               0        0
triangle             6      360
rectangle           12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},  
...                      index=['circle', 'triangle', 'rectangle'])
>>> other  
           angles
circle          0
triangle        3
rectangle       4

>>> df * other  
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)  
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],  
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex  
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)  
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

property divisions#

Tuple of npartitions + 1 values, in ascending order, marking the lower/upper bounds of each partition’s index. Divisions allow Dask to know which partition will contain a given value, significantly speeding up operations like loc, merge, and groupby by not having to search the full dataset.

Example: for divisions = (0, 10, 50, 100), there are three partitions, where the index in each partition contains values [0, 10), [10, 50), and [50, 100], respectively. Dask therefore knows df.loc[45] will be in the second partition.

When every item in divisions is None, the divisions are unknown. Most operations can still be performed, but some will be much slower, and a few may fail.

It is uncommon to set divisions directly. Instead, use set_index, which sorts and splits the data as needed. See https://docs.dask.org/en/latest/dataframe-design.html#partitions.

dot(other, meta='__no_default__')#

Compute the dot product between the Series and the columns of other.

This docstring was copied from pandas.core.series.Series.dot.

Some inconsistencies with the Dask version may exist.

This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array.

It can also be called using self @ other in Python >= 3.5.

Parameters:

otherSeries, DataFrame or array-like: The other object to compute the dot product with its columns.

Returns:

scalar, Series or numpy.ndarray: Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array.

See also

DataFrame.dot: Compute the matrix product with the DataFrame.
Series.mul: Multiplication of series and other, element-wise.

Notes

The Series and other has to share the same index if other is a Series or a DataFrame.

Examples

>>> s = pd.Series([0, 1, 2, 3])  
>>> other = pd.Series([-1, 2, -3, 4])  
>>> s.dot(other)  
8
>>> s @ other  
8
>>> df = pd.DataFrame([[0, 1], [-2, 3], [4, -5], [6, 7]])  
>>> s.dot(df)  
0    24
1    14
dtype: int64
>>> arr = np.array([[0, 1], [-2, 3], [4, -5], [6, 7]])  
>>> s.dot(arr)  
array([24, 14])

drop(labels=None, axis=0, columns=None, errors='raise')#

Drop specified labels from rows or columns.

This docstring was copied from pandas.core.frame.DataFrame.drop.

Some inconsistencies with the Dask version may exist.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.

Parameters:

labelssingle label or list-like: Index or column labels to drop. A tuple will be used as a single label and not treated as a list-like.
axis{0 or ‘index’, 1 or ‘columns’}, default 0: Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
indexsingle label or list-like (Not supported in Dask): Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).
columnssingle label or list-like: Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).
levelint or level name, optional (Not supported in Dask): For MultiIndex, level from which the labels will be removed.
inplacebool, default False (Not supported in Dask): If False, return a copy. Otherwise, do operation inplace and return None.
errors{‘ignore’, ‘raise’}, default ‘raise’: If ‘ignore’, suppress error and only existing labels are dropped.

Returns:

DataFrame or None: DataFrame without the removed index or column labels or None if inplace=True.

Raises:

KeyError: If any of the labels is not found in the selected axis.

See also

DataFrame.loc: Label-location based indexer for selection by label.
DataFrame.dropna: Return DataFrame with labels on given axis omitted where (all or any) data are missing.
DataFrame.drop_duplicates: Return DataFrame with duplicate rows removed, optionally only considering certain columns.
Series.drop: Return Series with specified index labels removed.

Examples

>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),  
...                   columns=['A', 'B', 'C', 'D'])
>>> df  
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)  
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])  
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])  
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],  
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],  
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df  
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2

Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row

>>> df.drop(index=('falcon', 'weight'))  
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        length  0.3     0.2

>>> df.drop(index='cow', columns='small')  
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3

>>> df.drop(index='length', level=1)  
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8

drop_duplicates(subset=None, split_every=None, split_out=1, ignore_index=False, **kwargs)#

Return DataFrame with duplicate rows removed.

This docstring was copied from pandas.core.frame.DataFrame.drop_duplicates.

Some inconsistencies with the Dask version may exist.

Known inconsistencies:: keep=False will raise a NotImplementedError

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters:

subsetcolumn label or sequence of labels, optional: Only consider certain columns for identifying duplicates, by default use all of the columns.
keep{‘first’, ‘last’, False}, default ‘first’ (Not supported in Dask): Determines which duplicates (if any) to keep. - first : Drop duplicates except for the first occurrence. - last : Drop duplicates except for the last occurrence. - False : Drop all duplicates.
inplacebool, default False (Not supported in Dask): Whether to modify the DataFrame rather than creating a new one.
ignore_indexbool, default False: If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 1.0.0.

Returns:

DataFrame or None: DataFrame with duplicates removed or None if inplace=True.

See also

DataFrame.value_counts: Count unique combinations of columns.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({  
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df  
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()  
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])  
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')  
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0

dropna(how='__no_default__', subset=None, thresh='__no_default__')#

Remove missing values.

This docstring was copied from pandas.core.frame.DataFrame.dropna.

Some inconsistencies with the Dask version may exist.

See the User Guide for more on which values are considered missing, and how to work with missing data.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask)

Determine if rows or columns which contain missing values are removed.

0, or ‘index’ : Drop rows which contain missing values.
1, or ‘columns’ : Drop columns which contain missing value.

Changed in version 1.0.0: Pass tuple or list to drop on multiple axes. Only a single axis is allowed.

how{‘any’, ‘all’}, default ‘any’

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

‘any’ : If any NA values are present, drop that row or column.
‘all’ : If all values are NA, drop that row or column.

threshint, optional

Require that many non-NA values. Cannot be combined with how.

subsetcolumn label or sequence of labels, optional

Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

inplacebool, default False (Not supported in Dask)

Whether to modify the DataFrame rather than creating a new one.

Returns:

DataFrame or None: DataFrame with NA entries dropped from it or None if inplace=True.

See also

DataFrame.isna: Indicate missing values.
DataFrame.notna: Indicate existing (non-missing) values.
DataFrame.fillna: Replace missing values.
Series.dropna: Drop missing values.
Index.dropna: Drop missing indices.

Examples

>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],  
...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df  
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()  
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')  
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')  
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)  
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])  
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep the DataFrame with valid entries in the same variable.

>>> df.dropna(inplace=True)  
>>> df  
     name        toy       born
1  Batman  Batmobile 1940-04-25

property dtypes#: Return data types

eq(other, axis='columns', level=None)#

Get Equal to of dataframe and other, element-wise (binary operator eq).

This docstring was copied from pandas.core.frame.DataFrame.eq.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

eval(expr, inplace=None, **kwargs)#

Evaluate a string describing operations on DataFrame columns.

This docstring was copied from pandas.core.frame.DataFrame.eval.

Some inconsistencies with the Dask version may exist.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters:

exprstr: The expression string to evaluate.
inplacebool, default False: If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
**kwargs: See the documentation for eval() for complete details on the keyword arguments accepted by query().

Returns:

ndarray, scalar, pandas object, or None: The result of the evaluation or None if inplace=True.

See also

DataFrame.query: Evaluates a boolean expression to query the columns of a frame.
DataFrame.assign: Can evaluate an expression or function to create new values for a column.
eval: Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

Examples

>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})  
>>> df  
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')  
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')  
   A   B   C
1  10  11
2   8  10
3   6   9
4   4   8
5   2   7
>>> df  
   A   B
1  10
2   8
3   6
4   4
5   2

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)  
>>> df  
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(  
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3

explode(column)#

Transform each element of a list-like to a row, replicating index values.

This docstring was copied from pandas.core.frame.DataFrame.explode.

Some inconsistencies with the Dask version may exist.

New in version 0.25.0.

Parameters:

columnIndexLabel: Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.

New in version 1.3.0: Multi-column explode
ignore_indexbool, default False (Not supported in Dask): If True, the resulting index will be labeled 0, 1, …, n - 1.

New in version 1.1.0.

Returns:

DataFrame: Exploded lists to rows of the subset columns; index will be duplicated for these rows.

Raises:

ValueError

If columns of the frame are not unique.
If specified columns to explode is empty list.
If specified columns to explode have not matching count of elements rowwise in the frame.

See also

DataFrame.unstack: Pivot a level of the (necessarily hierarchical) index labels.
DataFrame.melt: Unpivot a DataFrame from wide format to long format.
Series.explode: Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Reference the user guide for more examples.

Examples

>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],  
...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df  
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')  
     A  B          C
  0  1  [a, b, c]
  1  1  [a, b, c]
  2  1  [a, b, c]
foo  1        NaN
NaN  1         []
  3  1     [d, e]
  4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))  
     A  B    C
  0  1    a
  1  1    b
  2  1    c
foo  1  NaN
NaN  1  NaN
  3  1    d
  4  1    e

ffill(axis=None, limit=None)#

Synonym for DataFrame.fillna() with method='ffill'.

This docstring was copied from pandas.core.frame.DataFrame.ffill.

Some inconsistencies with the Dask version may exist.

Returns:

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

fillna(value=None, method=None, limit=None, axis=None)#

Fill NA/NaN values using the specified method.

This docstring was copied from pandas.core.frame.DataFrame.fillna.

Some inconsistencies with the Dask version may exist.

Parameters:

valuescalar, dict, Series, or DataFrame: Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
method{‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None: Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.
axis{0 or ‘index’, 1 or ‘columns’}: Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.
inplacebool, default False (Not supported in Dask): If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).
limitint, default None: If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
downcastdict, default is None (Not supported in Dask): A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Returns:

DataFrame or None: Object with missing values filled or None if inplace=True.

See also

interpolate: Fill NaN values using interpolation.
reindex: Conform object to new index.
asfreq: Convert TimeSeries to specified frequency.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],  
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df  
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

Replace all NaN elements with 0s.

>>> df.fillna(0)  
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0

We can also propagate non-null values forward or backward.

>>> df.fillna(method="ffill")  
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  3.0  4.0 NaN  1.0
3  3.0  3.0 NaN  4.0

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}  
>>> df.fillna(value=values)  
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)  
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  NaN  1.0
2  NaN  1.0  NaN  3.0
3  NaN  3.0  NaN  4.0

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))  
>>> df.fillna(df2)  
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  NaN
3  0.0  3.0  0.0  4.0

Note that column D is not affected since it is not present in df2.

first(offset)#

Select initial periods of time series data based on a date offset.

This docstring was copied from pandas.core.frame.DataFrame.first.

Some inconsistencies with the Dask version may exist.

When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.

Parameters:

offsetstr, DateOffset or dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘1M’ will display all the rows having their index within the first month.

Returns:

Series or DataFrame: A subset of the caller.

Raises:

TypeError: If the index is not a DatetimeIndex

See also

last: Select final periods of time series based on a date offset.
at_time: Select values at a particular time of the day.
between_time: Select values between particular times of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')  
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)  
>>> ts  
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')  
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

floordiv(other, axis='columns', level=None, fill_value=None)#

Get Integer division of DataFrame or Series and other, element-wise (binary operator floordiv).

This docstring was copied from cudf.core.series.Series.floordiv.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.floordiv(1)  
        angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.floordiv(b)  
a       1
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.floordiv(b, fill_value=0)  
a                      1
b    9223372036854775807
c    9223372036854775807
d                      0
e                   <NA>
dtype: int64

classmethod from_dict(data, *, npartitions, orient='columns', dtype=None, columns=None)#

Construct a Dask DataFrame from a Python Dictionary

See also

dask.dataframe.from_dict

ge(other, axis='columns', level=None)#

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

This docstring was copied from pandas.core.frame.DataFrame.ge.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

get_partition(n)#

Get a dask DataFrame/Series representing the nth partition.

Parameters:

nint: The 0-indexed partition number to select.

Returns:

Dask DataFrame or Series: The same type as the original object.

See also

DataFrame.partitions

Examples

>>> import dask
>>> ddf = dask.datasets.timeseries(start="2021-01-01", end="2021-01-07", freq="1H")
>>> ddf.get_partition(0)  
Dask DataFrame Structure:
                 name     id        x        y
npartitions=1
2021-01-01     object  int64  float64  float64
2021-01-02        ...    ...      ...      ...
Dask Name: get-partition, 2 graph layers

groupby(by=None, **kwargs)#

Group DataFrame using a mapper or by a Series of columns.

This docstring was copied from pandas.core.frame.DataFrame.groupby.

Some inconsistencies with the Dask version may exist.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters:

bymapping, function, label, or list of labels: Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If a list or ndarray of length equal to the selected axis is passed (see the groupby user guide), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask): Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.
levelint, level name, or sequence of such, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.
as_indexbool, default True (Not supported in Dask): For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.
sortbool, default True: Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.
group_keysbool, optional: When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise. This argument has no effect if the result produced is not like-indexed with respect to the input.

Changed in version 1.5.0: Warns that group_keys will no longer be ignored when the result from apply is a like-indexed Series or DataFrame. Specify group_keys explicitly to include the group keys or not.
squeezebool, default False (Not supported in Dask): Reduce the dimensionality of the return type if possible, otherwise return a consistent type.

Deprecated since version 1.1.0.
observedbool, default False: This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
dropnabool, default True: If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

New in version 1.1.0.

Returns:

DataFrameGroupBy: Returns a groupby object that contains information about the groups.

See also

resample: Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide for more detailed usage and examples, including splitting an object into groups, iterating through groups, selecting a group, aggregation, and more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',  
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df  
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()  
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],  
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))  
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},  
...                   index=index)
>>> df  
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()  
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()  
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True.

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]  
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])  

>>> df.groupby(by=["b"]).sum()  
    a   c
b
1.0 2   3
2.0 2   5

>>> df.groupby(by=["b"], dropna=False).sum()  
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]  
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])  

>>> df.groupby(by="a").sum()  
    b     c
a
a   13.0   13.0
b   12.3  123.0

>>> df.groupby(by="a", dropna=False).sum()  
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0

When using .apply(), use group_keys to include or exclude the group keys. The group_keys argument defaults to True (include).

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',  
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df.groupby("Animal", group_keys=True).apply(lambda x: x)  
          Animal  Max Speed
Animal
Falcon 0  Falcon      380.0
       1  Falcon      370.0
Parrot 2  Parrot       24.0
       3  Parrot       26.0

>>> df.groupby("Animal", group_keys=False).apply(lambda x: x)  
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0

gt(other, axis='columns', level=None)#

Get Greater than of dataframe and other, element-wise (binary operator gt).

This docstring was copied from pandas.core.frame.DataFrame.gt.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

head(n=5, npartitions=1, compute=True)#

First n rows of the dataset

Parameters:

nint, optional: The number of rows to return. Default is 5.
npartitionsint, optional: Elements are only taken from the first npartitions, with a default of 1. If there are fewer than n rows in the first npartitions a warning will be raised and any found rows returned. Pass -1 to use all partitions.
computebool, optional: Whether to compute the result, default is True.

idxmax(axis=None, skipna=True, split_every=False, numeric_only='__no_default__')#

Return index of first occurrence of maximum over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.idxmax.

Some inconsistencies with the Dask version may exist.

NA/null values are excluded.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series: Indexes of maxima along the specified axis.

Raises:

ValueError

If the row/column is empty

See also

Series.idxmax: Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],  
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])

>>> df  
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()  
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")  
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object

idxmin(axis=None, skipna=True, split_every=False, numeric_only='__no_default__')#

Return index of first occurrence of minimum over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.idxmin.

Some inconsistencies with the Dask version may exist.

NA/null values are excluded.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipnabool, default True: Exclude NA/null values. If an entire row/column is NA, the result will be NA.
numeric_onlybool, default False: Include only float, int or boolean data.

New in version 1.5.0.

Returns:

Series: Indexes of minima along the specified axis.

Raises:

ValueError

If the row/column is empty

See also

Series.idxmin: Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],  
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])

>>> df  
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()  
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")  
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object

property iloc#

Purely integer-location based indexing for selection by position.

Only indexing the column positions is supported. Trying to select row positions will raise a ValueError.

See Indexing into Dask DataFrames for more.

Examples

>>> df.iloc[:, [2, 0, 1]]  

property index#: Return dask Index instance

info(buf=None, verbose=False, memory_usage=False)#: Concise summary of a Dask DataFrame.

isin(values)#

Whether each element in the DataFrame is contained in values.

This docstring was copied from pandas.core.frame.DataFrame.isin.

Some inconsistencies with the Dask version may exist.

Parameters:

valuesiterable, Series, DataFrame or dict: The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

Returns:

DataFrame: DataFrame of booleans showing whether each element in the DataFrame is contained in values.

See also

DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},  
...                   index=['falcon', 'dog'])
>>> df  
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])  
        num_legs  num_wings
falcon      True       True
dog        False       True

To check if values is not in the DataFrame, use the ~ operator:

>>> ~df.isin([0, 2])  
        num_legs  num_wings
falcon     False      False
dog         True      False

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})  
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.

>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},  
...                      index=['spider', 'falcon'])
>>> df.isin(other)  
        num_legs  num_wings
falcon     False       True
dog        False      False

isna()#

Detect missing values.

This docstring was copied from pandas.core.frame.DataFrame.isna.

Some inconsistencies with the Dask version may exist.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns:

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

See also

DataFrame.isnull: Alias of isna.
DataFrame.notna: Boolean inverse of isna.
DataFrame.dropna: Omit axes labels with missing values.
isna: Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],  
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df  
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()  
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])  
>>> ser  
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()  
0    False
1    False
2     True
dtype: bool

isnull()#

DataFrame.isnull is an alias for DataFrame.isna.

This docstring was copied from pandas.core.frame.DataFrame.isnull.

Some inconsistencies with the Dask version may exist.

Detect missing values.

Returns:

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.

See also

DataFrame.isnull: Alias of isna.
DataFrame.notna: Boolean inverse of isna.
DataFrame.dropna: Omit axes labels with missing values.
isna: Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],  
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df  
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.isna()  
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])  
>>> ser  
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.isna()  
0    False
1    False
2     True
dtype: bool

items()#

Iterate over (column name, Series) pairs.

This docstring was copied from pandas.core.frame.DataFrame.items.

Some inconsistencies with the Dask version may exist.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields:

labelobject: The column names for the DataFrame being iterated over.
contentSeries: The column entries belonging to each label, as a Series.

See also

DataFrame.iterrows: Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.itertuples: Iterate over DataFrame rows as namedtuples of the values.

Examples

>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],  
...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df  
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():  
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64

iterrows()#

Iterate over DataFrame rows as (index, Series) pairs.

This docstring was copied from pandas.core.frame.DataFrame.iterrows.

Some inconsistencies with the Dask version may exist.

Yields:

indexlabel or tuple of label: The index of the row. A tuple for a MultiIndex.
dataSeries: The data of the row as a Series.

See also

DataFrame.itertuples: Iterate over DataFrame rows as namedtuples of the values.
DataFrame.items: Iterate over (column name, Series) pairs.

Notes

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
```
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])  
>>> row = next(df.iterrows())[1]  
>>> row  
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)  
float64
>>> print(df['int'].dtype)  
int64
```
To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

itertuples(index=True, name='Pandas')#

Iterate over DataFrame rows as namedtuples.

This docstring was copied from pandas.core.frame.DataFrame.itertuples.

Some inconsistencies with the Dask version may exist.

Parameters:

indexbool, default True: If True, return the index as the first element of the tuple.
namestr or None, default “Pandas”: The name of the returned namedtuples or None to return regular tuples.

Returns:

iterator: An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

See also

DataFrame.iterrows: Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.items: Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore.

Examples

>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},  
...                   index=['dog', 'hawk'])
>>> df  
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():  
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):  
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):  
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)

join(other, shuffle=None, **kwargs)#

Join columns of another DataFrame.

This docstring was copied from pandas.core.frame.DataFrame.join.

Some inconsistencies with the Dask version may exist.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters:

otherDataFrame, Series, or a list containing any combination of them

Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.

onstr, list of str, or array-like, optional

Column or index level name(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.

how{‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘left’

How to handle the operation of the two objects.

left: use calling frame’s index (or column if on is specified)
right: use other’s index.
outer: form union of calling frame’s index (or column if on is specified) with other’s index, and sort it. lexicographically.
inner: form intersection of calling frame’s index (or column if on is specified) with other’s index, preserving the order of the calling’s one.
cross: creates the cartesian product from both frames, preserves the order of the left keys.

New in version 1.2.0.

lsuffixstr, default ‘’

Suffix to use from left frame’s overlapping columns.

rsuffixstr, default ‘’

Suffix to use from right frame’s overlapping columns.

sortbool, default False (Not supported in Dask)

Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).

validatestr, optional (Not supported in Dask)

If specified, checks if join is of specified type. * “one_to_one” or “1:1”: check if join keys are unique in both left and right datasets. * “one_to_many” or “1:m”: check if join keys are unique in left dataset. * “many_to_one” or “m:1”: check if join keys are unique in right dataset. * “many_to_many” or “m:m”: allowed, but does not result in checks. .. versionadded:: 1.5.0

Returns:

DataFrame: A dataframe containing columns from both the caller and other.

See also

DataFrame.merge: For column(s)-on-column(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Support for specifying index levels as the on parameter was added in version 0.23.0.

Examples

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],  
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df  
  key   A
K0  A0
K1  A1
K2  A2
K3  A3
K4  A4
K5  A5

>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],  
...                       'B': ['B0', 'B1', 'B2']})

>>> other  
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')  
  key_caller   A key_other    B
       K0  A0        K0   B0
       K1  A1        K1   B1
       K2  A2        K2   B2
       K3  A3       NaN  NaN
       K4  A4       NaN  NaN
       K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))  
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in df. This method preserves the original DataFrame’s index in the result.

>>> df.join(other.set_index('key'), on='key')  
  key   A    B
K0  A0   B0
K1  A1   B1
K2  A2   B2
K3  A3  NaN
K4  A4  NaN
K5  A5  NaN

Using non-unique key values shows how they are matched.

>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],  
...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> df  
  key   A
K0  A0
K1  A1
K1  A2
K3  A3
K0  A4
K1  A5

>>> df.join(other.set_index('key'), on='key', validate='m:1')  
  key   A    B
K0  A0   B0
K1  A1   B1
K1  A2   B1
K3  A3  NaN
K0  A4   B0
K1  A5   B1

property known_divisions#: Whether divisions are already known

kurtosis(axis=0, fisher=True, bias=True, nan_policy='propagate', out=None, numeric_only='__no_default__')#

Return unbiased kurtosis over requested axis.

This docstring was copied from pandas.core.frame.DataFrame.kurtosis.

Some inconsistencies with the Dask version may exist.

Note

This implementation follows the dask.array.stats implementation of kurtosis and calculates kurtosis without taking into account a bias term for finite sample size, which corresponds to the default settings of the scipy.stats kurtosis calculation. This differs from pandas.

Further, this method currently does not support filtering out NaN values, which is again a difference to Pandas.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True (Not supported in Dask): Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

last(offset)#

Select final periods of time series data based on a date offset.

This docstring was copied from pandas.core.frame.DataFrame.last.

Some inconsistencies with the Dask version may exist.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters:

offsetstr, DateOffset, dateutil.relativedelta: The offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days.

Returns:

Series or DataFrame: A subset of the caller.

Raises:

TypeError: If the index is not a DatetimeIndex

See also

first: Select initial periods of time series based on a date offset.
at_time: Select values at a particular time of the day.
between_time: Select values between particular times of the day.

Examples

>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')  
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)  
>>> ts  
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')  
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

le(other, axis='columns', level=None)#

Get Less than or equal to of dataframe and other, element-wise (binary operator le).

This docstring was copied from pandas.core.frame.DataFrame.le.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

property loc#

Purely label-location based indexer for selection by label.

>>> df.loc["b"]  
>>> df.loc["b":"d"]  

lt(other, axis='columns', level=None)#

Get Less than of dataframe and other, element-wise (binary operator lt).

This docstring was copied from pandas.core.frame.DataFrame.lt.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

map_overlap(func, before, after, *args, **kwargs)#

Apply a function to each partition, sharing rows with adjacent partitions.

This can be useful for implementing windowing functions such as df.rolling(...).mean() or df.diff().

Parameters:

funcfunction

Function applied to each partition.

beforeint, timedelta or string timedelta

The rows to prepend to partition i from the end of partition i - 1.

afterint, timedelta or string timedelta

The rows to append to partition i from the beginning of partition i + 1.

args, kwargs

Positional and keyword arguments to pass to the function. Positional arguments are computed on a per-partition basis, while keyword arguments are shared across all partitions. The partition itself will be the first positional argument, with all other arguments passed after. Arguments can be Scalar, Delayed, or regular Python objects. DataFrame-like args (both dask and pandas) will be repartitioned to align (if necessary) before applying the function; see align_dataframes to control this behavior.

enforce_metadatabool, default True

Whether to enforce at runtime that the structure of the DataFrame produced by func actually matches the structure of meta. This will rename and reorder columns for each partition, and will raise an error if this doesn’t work, but it won’t raise if dtypes don’t match.

transform_divisionsbool, default True

Whether to apply the function onto the divisions and apply those transformed divisions to the output.

align_dataframesbool, default True

Whether to repartition DataFrame- or Series-like args (both dask and pandas) so their divisions align before applying the function. This requires all inputs to have known divisions. Single-partition inputs will be split into multiple partitions.

If False, all inputs must have either the same number of partitions or a single partition. Single-partition inputs will be broadcast to every partition of multi-partition inputs.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

Notes

Given positive integers before and after, and a function func, map_overlap does the following:

Prepend before rows to each partition i from the end of partition i - 1. The first partition has no rows prepended.
Append after rows to each partition i from the beginning of partition i + 1. The last partition has no rows appended.
Apply func to each partition, passing in any extra args and kwargs if provided.
Trim before rows from the beginning of all but the first partition.
Trim after rows from the end of all but the last partition.

Examples

Given a DataFrame, Series, or Index, such as:

>>> import pandas as pd
>>> import dask.dataframe as dd
>>> df = pd.DataFrame({'x': [1, 2, 4, 7, 11],
...                    'y': [1., 2., 3., 4., 5.]})
>>> ddf = dd.from_pandas(df, npartitions=2)

A rolling sum with a trailing moving window of size 2 can be computed by overlapping 2 rows before each partition, and then mapping calls to df.rolling(2).sum():

>>> ddf.compute()
    x    y
 1  1.0
 2  2.0
 4  3.0
 7  4.0
11  5.0
>>> ddf.map_overlap(lambda df: df.rolling(2).sum(), 2, 0).compute()
      x    y
 NaN  NaN
 3.0  3.0
 6.0  5.0
11.0  7.0
18.0  9.0

The pandas diff method computes a discrete difference shifted by a number of periods (can be positive or negative). This can be implemented by mapping calls to df.diff to each partition after prepending/appending that many rows, depending on sign:

>>> def diff(df, periods=1):
...     before, after = (periods, 0) if periods > 0 else (0, -periods)
...     return df.map_overlap(lambda df, periods=1: df.diff(periods),
...                           periods, 0, periods=periods)
>>> diff(ddf, 1).compute()
     x    y
0  NaN  NaN
1  1.0  1.0
2  2.0  1.0
3  3.0  1.0
4  4.0  1.0

If you have a DatetimeIndex, you can use a pd.Timedelta for time- based windows or any pd.Timedelta convertible string:

>>> ts = pd.Series(range(10), index=pd.date_range('2017', periods=10))
>>> dts = dd.from_pandas(ts, npartitions=2)
>>> dts.map_overlap(lambda df: df.rolling('2D').sum(),
...                 pd.Timedelta('2D'), 0).compute()
2017-01-01     0.0
2017-01-02     1.0
2017-01-03     3.0
2017-01-04     5.0
2017-01-05     7.0
2017-01-06     9.0
2017-01-07    11.0
2017-01-08    13.0
2017-01-09    15.0
2017-01-10    17.0
Freq: D, dtype: float64

map_partitions(func, *args, **kwargs)#

Apply Python function on each DataFrame partition.

Note that the index and divisions are assumed to remain unchanged.

Parameters:

funcfunction

The function applied to each partition. If this function accepts the special partition_info keyword argument, it will receive information on the partition’s relative location within the dataframe.

args, kwargs

enforce_metadatabool, default True

transform_divisionsbool, default True

Whether to apply the function onto the divisions and apply those transformed divisions to the output.

align_dataframesbool, default True

If False, all inputs must have either the same number of partitions or a single partition. Single-partition inputs will be broadcast to every partition of multi-partition inputs.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

Examples

Given a DataFrame, Series, or Index, such as:

>>> import pandas as pd
>>> import dask.dataframe as dd
>>> df = pd.DataFrame({'x': [1, 2, 3, 4, 5],
...                    'y': [1., 2., 3., 4., 5.]})
>>> ddf = dd.from_pandas(df, npartitions=2)

One can use map_partitions to apply a function on each partition. Extra arguments and keywords can optionally be provided, and will be passed to the function after the partition.

Here we apply a function with arguments and keywords to a DataFrame, resulting in a Series:

>>> def myadd(df, a, b=1):
...     return df.x + df.y + a + b
>>> res = ddf.map_partitions(myadd, 1, b=2)
>>> res.dtype
dtype('float64')

Here we apply a function to a Series resulting in a Series:

>>> res = ddf.x.map_partitions(lambda x: len(x)) # ddf.x is a Dask Series Structure
>>> res.dtype
dtype('int64')

Here we specify the output is a Series with no name, and dtype float64:

>>> res = ddf.map_partitions(myadd, 1, b=2, meta=(None, 'f8'))

Here we map a function that takes in a DataFrame, and returns a DataFrame with a new column:

>>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y))
>>> res.dtypes
x      int64
y    float64
z    float64
dtype: object

As before, the output metadata can also be specified manually. This time we pass in a dict, as the output is a DataFrame:

>>> res = ddf.map_partitions(lambda df: df.assign(z=df.x * df.y),
...                          meta={'x': 'i8', 'y': 'f8', 'z': 'f8'})

In the case where the metadata doesn’t change, you can also pass in the object itself directly:

>>> res = ddf.map_partitions(lambda df: df.head(), meta=ddf)

Also note that the index and divisions are assumed to remain unchanged. If the function you’re mapping changes the index/divisions, you’ll need to clear them afterwards:

>>> ddf.map_partitions(func).clear_divisions()  

Your map function gets information about where it is in the dataframe by accepting a special partition_info keyword argument.

>>> def func(partition, partition_info=None):
...     pass

This will receive the following information:

>>> partition_info  
{'number': 1, 'division': 3}

For each argument and keyword arguments that are dask dataframes you will receive the number (n) which represents the nth partition of the dataframe and the division (the first index value in the partition). If divisions are not known (for instance if the index is not sorted) then you will get None as the division.

mask(cond, other=nan)#

Replace values where the condition is True.

This docstring was copied from pandas.core.frame.DataFrame.mask.

Some inconsistencies with the Dask version may exist.

Parameters:

condbool Series/DataFrame, array-like, or callable

Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

otherscalar, Series/DataFrame, or callable

Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it).

inplacebool, default False (Not supported in Dask)

Whether to perform the operation in place on the data.

axisint, default None (Not supported in Dask)

Alignment axis if needed. For Series this parameter is unused and defaults to 0.

levelint, default None (Not supported in Dask)

Alignment level if needed.

errorsstr, {‘raise’, ‘ignore’}, default ‘raise’ (Not supported in Dask)

Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

‘raise’ : allow exceptions to be raised.
‘ignore’ : suppress exceptions. On error return original object.

Deprecated since version 1.5.0: This argument had no effect.

try_castbool, default None (Not supported in Dask)

Try to cast the result back to the input type (if possible).

Deprecated since version 1.3.0: Manually cast back if necessary.

Returns:

Same type as caller or None if inplace=True.

See also

DataFrame.where(): Return an object of same shape as self.

Notes

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with True.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

The dtype of the object takes precedence. The fill value is casted to the object’s dtype, if this can be done losslessly.

Examples

>>> s = pd.Series(range(5))  
>>> s.where(s > 0)  
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0
dtype: float64
>>> s.mask(s > 0)  
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

>>> s = pd.Series(range(5))  
>>> t = pd.Series([True, False])  
>>> s.where(t, 99)  
0     0
1    99
2    99
3    99
4    99
dtype: int64
>>> s.mask(t, 99)  
0    99
1     1
2    99
3    99
4    99
dtype: int64

>>> s.where(s > 1, 10)  
  10
  10
  2
  3
  4
dtype: int64
>>> s.mask(s > 1, 10)  
   0
   1
  10
  10
  10
dtype: int64

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])  
>>> df  
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9
>>> m = df % 3 == 0  
>>> df.where(m, -df)  
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)  
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)  
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

max(axis=0, skipna=True, split_every=False, out=None, numeric_only=None)#

Return the maximum of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.max.

Some inconsistencies with the Dask version may exist.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([  
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)  
>>> s  
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.max()  
8

mean(axis=0, skipna=True, split_every=False, dtype=None, out=None, numeric_only=None)#

Return the mean of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.mean.

Some inconsistencies with the Dask version may exist.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

median(axis=None, method='default')#

Return the median of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.median.

Some inconsistencies with the Dask version may exist.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True (Not supported in Dask): Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None (Not supported in Dask): Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

median_approximate(axis=None, method='default')#

Return the approximate median of the values over the requested axis.

Parameters:

axis{0, 1, “index”, “columns”} (default 0): 0 or "index" for row-wise, 1 or "columns" for column-wise
method{‘default’, ‘tdigest’, ‘dask’}, optional: What method to use. By default will use Dask’s internal custom algorithm ("dask"). If set to "tdigest" will use tdigest for floats and ints and fallback to the "dask" otherwise.

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)#

Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters:

frameDataFrame
id_varstuple, list, or ndarray, optional: Column(s) to use as identifier variables.
value_varstuple, list, or ndarray, optional: Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_namescalar: Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_namescalar, default ‘value’: Name to use for the ‘value’ column.
col_levelint or string, optional: If columns are a MultiIndex then use this level to melt.

Returns:

DataFrame: Unpivoted DataFrame.

See also

pandas.DataFrame.melt

memory_usage(index=True, deep=False)#

Return the memory usage of each column in bytes.

This docstring was copied from pandas.core.frame.DataFrame.memory_usage.

Some inconsistencies with the Dask version may exist.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters:

indexbool, default True: Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
deepbool, default False: If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns:

Series: A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

See also

numpy.ndarray.nbytes: Total bytes consumed by the elements of an ndarray.
Series.memory_usage: Bytes consumed by a Series.
Categorical: Memory-efficient array for string values with many repeated values.
DataFrame.info: Concise summary of a DataFrame.

Notes

See the Frequently Asked Questions for more details.

Examples

>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']  
>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))  
...              for t in dtypes])
>>> df = pd.DataFrame(data)  
>>> df.head()  
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True

>>> df.memory_usage()  
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

>>> df.memory_usage(index=False)  
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)  
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)  
5244

memory_usage_per_partition(index=True, deep=False)#

Return the memory usage of each partition

Parameters:

indexbool, default True: Specifies whether to include the memory usage of the index in returned Series.
deepbool, default False: If True, introspect the data deeply by interrogating object dtypes for system-level memory consumption, and include it in the returned values.

Returns:

Series: A Series whose index is the partition number and whose values are the memory usage of each partition in bytes.

merge(other, shuffle=None, **kwargs)#

Merge the DataFrame with another DataFrame

This will merge the two datasets, either on the indices, a certain column in each dataset or the index in one dataset and the column in another.

Parameters:

right: dask.dataframe.DataFrame

how{‘left’, ‘right’, ‘outer’, ‘inner’}, default: ‘inner’

How to handle the operation of the two objects:

left: use calling frame’s index (or column if on is specified)
right: use other frame’s index
outer: form union of calling frame’s index (or column if on is specified) with other frame’s index, and sort it lexicographically
inner: form intersection of calling frame’s index (or column if on is specified) with other frame’s index, preserving the order of the calling’s one

onlabel or list

Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.

left_onlabel or list, or array-like

Column to join on in the left DataFrame. Other than in pandas arrays and lists are only support if their length is 1.

right_onlabel or list, or array-like

Column to join on in the right DataFrame. Other than in pandas arrays and lists are only support if their length is 1.

left_indexboolean, default False

Use the index from the left DataFrame as the join key.

right_indexboolean, default False

Use the index from the right DataFrame as the join key.

suffixes2-length sequence (tuple, list, …)

Suffix to apply to overlapping column names in the left and right side, respectively

indicatorboolean or string, default False

If True, adds a column to output DataFrame called “_merge” with information on the source of each row. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column is Categorical-type and takes on a value of “left_only” for observations whose merge key only appears in left DataFrame, “right_only” for observations whose merge key only appears in right DataFrame, and “both” if the observation’s merge key is found in both.

npartitions: int or None, optional

The ideal number of output partitions. This is only utilised when performing a hash_join (merging on columns only). If None then npartitions = max(lhs.npartitions, rhs.npartitions). Default is None.

shuffle: {‘disk’, ‘tasks’}, optional

Either 'disk' for single-node operation or 'tasks' for distributed operation. Will be inferred by your current scheduler.

broadcast: boolean or float, optional

Whether to use a broadcast-based join in lieu of a shuffle-based join for supported cases. By default, a simple heuristic will be used to select the underlying algorithm. If a floating-point value is specified, that number will be used as the broadcast_bias within the simple heuristic (a large number makes Dask more likely to choose the broacast_join code path). See broadcast_join for more information.

Notes

There are three ways to join dataframes:

Joining on indices. In this case the divisions are aligned using the function dask.dataframe.multi.align_partitions. Afterwards, each partition is merged with the pandas merge function.
Joining one on index and one on column. In this case the divisions of dataframe merged by index (\(d_i\)) are used to divide the column merged dataframe (\(d_c\)) one using dask.dataframe.multi.rearrange_by_divisions. In this case the merged dataframe (\(d_m\)) has the exact same divisions as (\(d_i\)). This can lead to issues if you merge multiple rows from (\(d_c\)) to one row in (\(d_i\)).
Joining both on columns. In this case a hash join is performed using dask.dataframe.multi.hash_join.

In some cases, you may see a MemoryError if the merge operation requires an internal shuffle, because shuffling places all rows that have the same index in the same partition. To avoid this error, make sure all rows with the same on-column value can fit on a single partition.

min(axis=0, skipna=True, split_every=False, out=None, numeric_only=None)#

Return the minimum of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.min.

Some inconsistencies with the Dask version may exist.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

>>> idx = pd.MultiIndex.from_arrays([  
...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)  
>>> s  
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64

>>> s.min()  
0

mod(other, axis='columns', level=None, fill_value=None)#

Get Modulo of DataFrame or Series and other, element-wise (binary operator mod).

This docstring was copied from cudf.core.series.Series.mod.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.mod(1)  
        angles  degrees
circle          0        0
triangle        0        0
rectangle       0        0

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.mod(b)  
a       0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.mod(b, fill_value=0)  
a             0
b    4294967295
c    4294967295
d             0
e          <NA>
dtype: int64

mode(dropna=True, split_every=False, numeric_only=False)#

Get the mode(s) of each element along the selected axis.

This docstring was copied from pandas.core.frame.DataFrame.mode.

Some inconsistencies with the Dask version may exist.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0 (Not supported in Dask)

The axis to iterate over while searching for the mode:

0 or ‘index’ : get mode of each column
1 or ‘columns’ : get mode of each row.

numeric_onlybool, default False

If True, only apply to numeric columns.

dropnabool, default True

Don’t consider counts of NaN/NaT.

Returns:

DataFrame: The modes of each column or row.

See also

Series.mode: Return the highest frequency value in a Series.
Series.value_counts: Return the counts of values in a Series.

Examples

>>> df = pd.DataFrame([('bird', 2, 2),  
...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df  
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()  
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)  
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)  
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)  
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN

mul(other, axis='columns', level=None, fill_value=None)#

Get Multiplication of DataFrame or Series and other, element-wise (binary operator mul).

This docstring was copied from cudf.core.series.Series.mul.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.multiply(1)  
        angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.multiply(b)  
a       1
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.multiply(b, fill_value=0)  
a       1
b       0
c       0
d       0
e    <NA>
dtype: int64

property ndim#: Return dimensionality

ne(other, axis='columns', level=None)#

Get Not equal to of dataframe and other, element-wise (binary operator ne).

This docstring was copied from pandas.core.frame.DataFrame.ne.

Some inconsistencies with the Dask version may exist.

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}, default ‘columns’: Whether to compare by the index (0 or ‘index’) or columns (1 or ‘columns’).
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.

Returns:

DataFrame of bool: Result of the comparison.

See also

DataFrame.eq: Compare DataFrames for equality elementwise.
DataFrame.ne: Compare DataFrames for inequality elementwise.
DataFrame.le: Compare DataFrames for less than inequality or equality elementwise.
DataFrame.lt: Compare DataFrames for strictly less than inequality elementwise.
DataFrame.ge: Compare DataFrames for greater than inequality or equality elementwise.
DataFrame.gt: Compare DataFrames for strictly greater than inequality elementwise.

Notes

Mismatched indices will be unioned together. NaN values are considered different (i.e. NaN != NaN).

Examples

>>> df = pd.DataFrame({'cost': [250, 150, 100],  
...                    'revenue': [100, 250, 300]},
...                   index=['A', 'B', 'C'])
>>> df  
   cost  revenue
A   250      100
B   150      250
C   100      300

Comparison with a scalar, using either the operator or method:

>>> df == 100  
    cost  revenue
A  False     True
B  False    False
C   True    False

>>> df.eq(100)  
    cost  revenue
A  False     True
B  False    False
C   True    False

When other is a Series, the columns of a DataFrame are aligned with the index of other and broadcast:

>>> df != pd.Series([100, 250], index=["cost", "revenue"])  
    cost  revenue
A   True     True
B   True    False
C  False     True

Use the method to control the broadcast axis:

>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index')  
   cost  revenue
A  True    False
B  True     True
C  True     True
D  True     True

When comparing to an arbitrary sequence, the number of columns must match the number elements in other:

>>> df == [250, 100]  
    cost  revenue
A   True     True
B  False    False
C  False    False

Use the method to control the axis:

>>> df.eq([250, 250, 100], axis='index')  
    cost  revenue
A   True    False
B  False     True
C   True    False

Compare to a DataFrame of different shape.

>>> other = pd.DataFrame({'revenue': [300, 250, 100, 150]},  
...                      index=['A', 'B', 'C', 'D'])
>>> other  
   revenue
A      300
B      250
C      100
D      150

>>> df.gt(other)  
    cost  revenue
A  False    False
B  False    False
C  False     True
D  False    False

Compare to a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'cost': [250, 150, 100, 150, 300, 220],  
...                              'revenue': [100, 250, 300, 200, 175, 225]},
...                             index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'],
...                                    ['A', 'B', 'C', 'A', 'B', 'C']])
>>> df_multindex  
      cost  revenue
Q1 A   250      100
   B   150      250
   C   100      300
Q2 A   150      200
   B   300      175
   C   220      225

>>> df.le(df_multindex, level=1)  
       cost  revenue
Q1 A   True     True
   B   True     True
   C   True     True
Q2 A  False     True
   B   True    False
   C   True    False

nlargest(n=5, columns='__no_default__', split_every=None)#

Return the first n rows ordered by columns in descending order.

This docstring was copied from pandas.core.frame.DataFrame.nlargest.

Some inconsistencies with the Dask version may exist.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters:

nint

Number of rows to return.

columnslabel or list of labels

Column label(s) to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’ (Not supported in Dask)

Where there are duplicate values:

first : prioritize the first occurrence(s)
last : prioritize the last occurrence(s)
all : do not drop any duplicates, even it means selecting more than n items.

Returns:

DataFrame: The first n rows ordered by the given columns in descending order.

See also

DataFrame.nsmallest: Return the first n rows ordered by columns in ascending order.
DataFrame.sort_values: Sort DataFrame by the values.
DataFrame.head: Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,  
...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df  
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column “population”.

>>> df.nlargest(3, 'population')  
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')  
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', all duplicate items are maintained:

>>> df.nlargest(3, 'population', keep='all')  
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])  
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

notnull()#

DataFrame.notnull is an alias for DataFrame.notna.

This docstring was copied from pandas.core.frame.DataFrame.notnull.

Some inconsistencies with the Dask version may exist.

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.

Returns:

DataFrame: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.

See also

DataFrame.notnull: Alias of notna.
DataFrame.isna: Boolean inverse of notna.
DataFrame.dropna: Omit axes labels with missing values.
notna: Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],  
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df  
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker

>>> df.notna()  
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])  
>>> ser  
0    5.0
1    6.0
2    NaN
dtype: float64

>>> ser.notna()  
0     True
1     True
2    False
dtype: bool

property npartitions: int#: Return number of partitions

nsmallest(n=5, columns='__no_default__', split_every=None)#

Return the first n rows ordered by columns in ascending order.

This docstring was copied from pandas.core.frame.DataFrame.nsmallest.

Some inconsistencies with the Dask version may exist.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters:

nint

Number of items to retrieve.

columnslist or str

Column name or names to order by.

keep{‘first’, ‘last’, ‘all’}, default ‘first’ (Not supported in Dask)

Where there are duplicate values:

first : take the first occurrence.
last : take the last occurrence.
all : do not drop any duplicates, even it means selecting more than n items.

Returns:

DataFrame

See also

DataFrame.nlargest: Return the first n rows ordered by columns in descending order.
DataFrame.sort_values: Sort DataFrame by the values.
DataFrame.head: Return the first n rows without re-ordering.

Examples

>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,  
...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df  
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column “population”.

>>> df.nsmallest(3, 'population')  
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')  
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', all duplicate items are maintained:

>>> df.nsmallest(3, 'population', keep='all')  
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column “population” and then “GDP”, we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])  
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR

nunique(split_every=False, dropna=True, axis=0)#

Count number of distinct elements in specified axis.

This docstring was copied from pandas.core.frame.DataFrame.nunique.

Some inconsistencies with the Dask version may exist.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0: The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
dropnabool, default True: Don’t include NaN in the counts.

Returns:

Series

See also

Series.nunique: Method nunique for Series.
DataFrame.count: Count non-NA cells for each column or row.

Examples

>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})  
>>> df.nunique()  
A    3
B    2
dtype: int64

>>> df.nunique(axis=1)  
0    1
1    2
2    2
dtype: int64

nunique_approx(split_every=None)#

Approximate number of unique rows.

This method uses the HyperLogLog algorithm for cardinality estimation to compute the approximate number of unique rows. The approximate error is 0.406%.

Parameters:

split_everyint, optional: Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used. Default is 8.

Returns:

a float representing the approximate number of elements

property partitions#

Slice dataframe by partitions

This allows partitionwise slicing of a Dask Dataframe. You can perform normal Numpy-style slicing, but now rather than slice elements of the array you slice along partitions so, for example, df.partitions[:5] produces a new Dask Dataframe of the first five partitions. Valid indexers are integers, sequences of integers, slices, or boolean masks.

Returns:

A Dask DataFrame

Examples

>>> df.partitions[0]  
>>> df.partitions[:3]  
>>> df.partitions[::10]  

persist(**kwargs)#

Persist this dask collection into memory

This turns a lazy Dask collection into a Dask collection with the same metadata, but now with the results fully computed or actively computing in the background.

The action of function differs significantly depending on the active task scheduler. If the task scheduler supports asynchronous computing, such as is the case of the dask.distributed scheduler, then persist will return immediately and the return value’s task graph will contain Dask Future objects. However if the task scheduler only supports blocking computation then the call to persist will block and the return value’s task graph will contain concrete Python results.

This function is particularly useful when using distributed systems, because the results will be kept in distributed memory, rather than returned to the local process as with compute.

Parameters:

schedulerstring, optional: Which scheduler to use like “threads”, “synchronous” or “processes”. If not provided, the default is to check the global settings first, and then fall back to the collection defaults.
optimize_graphbool, optional: If True [default], the graph is optimized before computation. Otherwise the graph is run as is. This can be useful for debugging.
**kwargs: Extra keywords to forward to the scheduler function.

Returns:

New dask collections backed by in-memory data

See also

dask.persist

pipe(func, *args, **kwargs)#

Apply chainable functions that expect Series or DataFrames.

This docstring was copied from pandas.core.frame.DataFrame.pipe.

Some inconsistencies with the Dask version may exist.

Parameters:

funcfunction: Function to apply to the Series/DataFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
argsiterable, optional: Positional arguments passed into func.
kwargsmapping, optional: A dictionary of keyword arguments passed into func.

Returns:

objectthe return type of func.

See also

DataFrame.apply: Apply a function along input axis of DataFrame.
DataFrame.applymap: Apply a function elementwise on a whole DataFrame.
Series.map: Apply a mapping correspondence on a Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  

You can write

>>> (df.pipe(h)  
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)  
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  

pivot_table(index=None, columns=None, values=None, aggfunc='mean')#

Create a spreadsheet-style pivot table as a DataFrame. Target columns must have category dtype to infer result’s columns. index, columns, values and aggfunc must be all scalar.

Parameters:

valuesscalar: column to aggregate
indexscalar: column to be index
columnsscalar: column to be columns
aggfunc{‘mean’, ‘sum’, ‘count’}, default ‘mean’

Returns:

tableDataFrame

pop(item)#

Return item and drop from frame. Raise KeyError if not found.

This docstring was copied from pandas.core.frame.DataFrame.pop.

Some inconsistencies with the Dask version may exist.

Parameters:

itemlabel: Label of column to be popped.

Returns:

Series

Examples

>>> df = pd.DataFrame([('falcon', 'bird', 389.0),  
...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df  
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

>>> df.pop('class')  
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object

>>> df  
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN

pow(other, axis='columns', level=None, fill_value=None)#

Get Exponential of DataFrame or Series and other, element-wise (binary operator pow).

This docstring was copied from cudf.core.series.Series.pow.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.pow(1)  
        angles  degrees
circle          0      360
triangle        2      180
rectangle       4      360

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.pow(b)  
a       1
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.pow(b, fill_value=0)  
a       1
b       1
c       1
d       0
e    <NA>
dtype: int64

prod(axis=None, skipna=True, split_every=False, dtype=None, out=None, min_count=None, numeric_only=None)#

Return the product of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.prod.

Some inconsistencies with the Dask version may exist.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()  
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)  
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()  
1.0

>>> pd.Series([np.nan]).prod(min_count=1)  
nan

product(axis=None, skipna=True, split_every=False, dtype=None, out=None, min_count=None, numeric_only=None)#

Return the product of the values over the requested axis.

This docstring was copied from pandas.core.frame.DataFrame.prod.

Some inconsistencies with the Dask version may exist.

Parameters:

axis{index (0), columns (1)}: Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
skipnabool, default True: Exclude NA/null values when computing the result.
levelint or level name, default None (Not supported in Dask): If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series.

Deprecated since version 1.3.0: The level keyword is deprecated. Use groupby instead.
numeric_onlybool, default None: Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Deprecated since version 1.5.0: Specifying numeric_only=None is deprecated. The default value will be False in a future version of pandas.
min_countint, default 0: The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA.
**kwargs: Additional keyword arguments to be passed to the function.

Returns:

Series or DataFrame (if level specified)

See also

Series.sum: Return the sum.
Series.min: Return the minimum.
Series.max: Return the maximum.
Series.idxmin: Return the index of the minimum.
Series.idxmax: Return the index of the maximum.
DataFrame.sum: Return the sum over the requested axis.
DataFrame.min: Return the minimum over the requested axis.
DataFrame.max: Return the maximum over the requested axis.
DataFrame.idxmin: Return the index of the minimum over the requested axis.
DataFrame.idxmax: Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()  
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)  
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()  
1.0

>>> pd.Series([np.nan]).prod(min_count=1)  
nan

quantile(q=0.5, axis=0, numeric_only='__no_default__', method='default')#

Approximate row-wise and precise column-wise quantiles of DataFrame

Parameters:

qlist/array of floats, default 0.5 (50%): Iterable of numbers ranging from 0 to 1 for the desired quantiles
axis{0, 1, ‘index’, ‘columns’} (default 0): 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise
method{‘default’, ‘tdigest’, ‘dask’}, optional: What method to use. By default will use dask’s internal custom algorithm ('dask'). If set to 'tdigest' will use tdigest for floats and ints and fallback to the 'dask' otherwise.

query(expr, **kwargs)#

Filter dataframe with complex expression

Blocked version of pd.DataFrame.query

Parameters:

expr: str: The query string to evaluate. You can refer to column names that are not valid Python variable names by surrounding them in backticks. Dask does not fully support referring to variables using the ‘@’ character, use f-strings or the local_dict keyword argument instead.

See also

pandas.DataFrame.query
pandas.eval

Notes

This is like the sequential version except that this will also happen in many threads. This may conflict with numexpr which will use multiple threads itself. We recommend that you set numexpr to use a single thread:

import numexpr
numexpr.set_num_threads(1)

Examples

>>> import pandas as pd
>>> import dask.dataframe as dd
>>> df = pd.DataFrame({'x': [1, 2, 1, 2],
...                    'y': [1, 2, 3, 4],
...                    'z z': [4, 3, 2, 1]})
>>> ddf = dd.from_pandas(df, npartitions=2)

Refer to column names directly:

>>> ddf.query('y > x').compute()
   x  y  z z
2  1  3    2
3  2  4    1

Refer to column name using backticks:

>>> ddf.query('`z z` > x').compute()
   x  y  z z
0  1  1    4
1  2  2    3
2  1  3    2

Refer to variable name using f-strings:

>>> value = 1
>>> ddf.query(f'x == {value}').compute()
   x  y  z z
0  1  1    4
2  1  3    2

Refer to variable name using local_dict:

>>> ddf.query('x == @value', local_dict={"value": value}).compute()
   x  y  z z
0  1  1    4
2  1  3    2

radd(other, axis='columns', level=None, fill_value=None)#

Get Addition of DataFrame or Series and other, element-wise (binary operator radd).

This docstring was copied from cudf.core.series.Series.radd.

Some inconsistencies with the Dask version may exist.

Equivalent to frame + other, but with support to substitute a fill_value for missing data in one of the inputs.

Parameters:

otherscalar, sequence, Series, or DataFrame: Any single or multiple element data structure, or list-like object.
axisint or string: Only 0 is supported for series, 1 or columns supported for dataframe
levelint or name: Broadcast across a level, matching Index values on the passed MultiIndex level. Not yet supported.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame or Series: Result of the arithmetic operation.

Examples

DataFrame

>>> df = cudf.DataFrame(  
...     {'angles': [0, 3, 4], 'degrees': [360, 180, 360]},
...     index=['circle', 'triangle', 'rectangle']
... )

>>> df.radd(1)  
        angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Series

>>> a = cudf.Series([1, 1, 1, None], index=['a', 'b', 'c', 'd'])  
>>> b = cudf.Series([1, None, 1, None], index=['a', 'b', 'd', 'e'])  

>>> a.radd(b)  
a       2
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: int64
>>> a.radd(b, fill_value=0)  
a       2
b       1
c       1
d       1
e    <NA>
dtype: int64

random_split(frac, random_state=None, shuffle=False)#

Pseudorandomly split dataframe into different pieces row-wise

Parameters:

fraclist: List of floats that should sum to one.
random_stateint or np.random.RandomState: If int create a new RandomState with this as the seed. Otherwise draw from the passed RandomState.
shufflebool, default False: If set to True, the dataframe is shuffled (within partition) before the split.

See also

dask.DataFrame.sample

Examples

50/50 split

>>> a, b = df.random_split([0.5, 0.5])  

80/10/10 split, consistent random_state

>>> a, b, c = df.random_split([0.8, 0.1, 0.1], random_state=123)  

rdiv(other, axis='columns', level=None, fill_value=None)#

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

This docstring was copied from pandas.core.frame.DataFrame.rdiv.

Some inconsistencies with the Dask version may exist.

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Parameters:

otherscalar, sequence, Series, dict or DataFrame: Any single or multiple element data structure, or list-like object.
axis{0 or ‘index’, 1 or ‘columns’}: Whether to compare by the index (0 or ‘index’) or columns. (1 or ‘columns’). For Series input, axis to match Series index on.
levelint or label: Broadcast across a level, matching Index values on the passed MultiIndex level.
fill_valuefloat or None, default None: Fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment, with this value before computation. If data in both corresponding DataFrame locations is missing the result will be missing.

Returns:

DataFrame: Result of the arithmetic operation.

See also

DataFrame.add: Add DataFrames.
DataFrame.sub: Subtract DataFrames.
DataFrame.mul: Multiply DataFrames.
DataFrame.div: Divide DataFrames (float division).
DataFrame.truediv: Divide DataFrames (float division).
DataFrame.floordiv: Divide DataFrames (integer division).
DataFrame.mod: Calculate modulo (remainder after division).
DataFrame.pow: Calculate exponential power.

Notes

Mismatched indices will be unioned together.

Examples

>>> df = pd.DataFrame({'angles': [0, 3, 4],  
...                    'degrees': [360, 180, 360]},
...                   index=['circle', 'triangle', 'rectangle'])
>>> df  
           angles  degrees
circle          0      360
triangle        3      180
rectangle       4      360

Add a scalar with operator version which return the same results.

>>> df + 1  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

>>> df.add(1)  
           angles  degrees
circle          1      361
triangle        4      181
rectangle       5      361

Divide by constant with reverse version.

>>> df.div(10)  
           angles  degrees
circle        0.0     36.0
triangle      0.3     18.0
rectangle     0.4     36.0

>>> df.rdiv(10)  
             angles   degrees
circle          inf  0.027778
triangle   3.333333  0.055556
rectangle  2.500000  0.027778

Subtract a list and Series by axis with operator version.

>>> df - [1, 2]  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub([1, 2], axis='columns')  
           angles  degrees
circle         -1      358
triangle        2      178
rectangle       3      358

>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),  
...        axis='index')
           angles  degrees
circle         -1      359
triangle        2      179
rectangle       3      359

Multiply a dictionary by axis.

>>> df.mul({'angles': 0, 'degrees': 2})  
            angles      degrees
circle           0          720
triangle             0      360
rectangle            0      720

>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')  
            angles      degrees
circle               0        0
triangle             6      360
rectangle           12     1080

Multiply a DataFrame of different shape with operator version.

>>> other = pd.DataFrame({'angles': [0, 3, 4]},  
...                      index=['circle', 'triangle', 'rectangle'])
>>> other  
           angles
circle          0
triangle        3
rectangle       4

>>> df * other  
           angles  degrees
circle          0      NaN
triangle        9      NaN
rectangle      16      NaN

>>> df.mul(other, fill_value=0)  
           angles  degrees
circle          0      0.0
triangle        9      0.0
rectangle      16      0.0

Divide by a MultiIndex by level.

>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],  
...                              'degrees': [360, 180, 360, 360, 540, 720]},
...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
...                                    ['circle', 'triangle', 'rectangle',
...                                     'square', 'pentagon', 'hexagon']])
>>> df_multindex  
             angles  degrees
A circle          0      360
  triangle        3      180
  rectangle       4      360
B square          4      360
  pentagon        5      540
  hexagon         6      720

>>> df.div(df_multindex, level=1, fill_value=0)  
             angles  degrees
A circle        NaN      1.0
  triangle      1.0      1.0
  rectangle     1.0      1.0
B square        0.0      0.0
  pentagon      0.0      0.0
  hexagon       0.0      0.0

reduction(chunk, aggregate=None, combine=None, meta='__no_default__', token=None, split_every=None, chunk_kwargs=None, aggregate_kwargs=None, combine_kwargs=None, **kwargs)#

Generic row-wise reductions.

Parameters:

chunkcallable

Function to operate on each partition. Should return a pandas.DataFrame, pandas.Series, or a scalar.

aggregatecallable, optional

Function to operate on the concatenated result of chunk. If not specified, defaults to chunk. Used to do the final aggregation in a tree reduction.

The input to aggregate depends on the output of chunk. If the output of chunk is a:

scalar: Input is a Series, with one row per partition.
Series: Input is a DataFrame, with one row per partition. Columns are the rows in the output series.
DataFrame: Input is a DataFrame, with one row per partition. Columns are the columns in the output dataframes.

Should return a pandas.DataFrame, pandas.Series, or a scalar.

combinecallable, optional

Function to operate on intermediate concatenated results of chunk in a tree-reduction. If not provided, defaults to aggregate. The input/output requirements should match that of aggregate described above.

metapd.DataFrame, pd.Series, dict, iterable, tuple, optional

tokenstr, optional

The name to use for the output keys.

split_everyint, optional

Group partitions into groups of this size while performing a tree-reduction. If set to False, no tree-reduction will be used, and all intermediates will be concatenated and passed to aggregate. Default is 8.

chunk_kwargsdict, optional

Keyword arguments to pass on to chunk only.

aggregate_kwargsdict, optional

Keyword arguments to pass on to aggregate only.

combine_kwargsdict, optional

Keyword arguments to pass on to