Series

Constructor

cudf.Series([data, index, dtype, name, nan_as_null])

One-dimensional GPU array (including time series).

Attributes

Axes

Series.index

Get the labels for the rows.

Series.values

Return a CuPy representation of the DataFrame.

Series.data

The gpu buffer for the data

Series.dtype

dtype of the Series

Series.shape

Get a tuple representing the dimensionality of the Index.

Series.ndim

Get the dimensionality (always 1 for single-columned frames).

Series.nullable

A boolean indicating whether a null-mask is needed

Series.nullmask

The gpu buffer for the null-mask

Series.null_count

Number of null values

Series.size

Return the number of elements in the underlying data.

Series.memory_usage([index, deep])

Return the memory usage of the Series.

Series.has_nulls

Indicator whether Series contains null values.

Series.empty

Indicator whether DataFrame or Series is empty.

Series.name

Get the name of this object.

Series.valid_count

Number of non-null values

Series.values_host

Return a NumPy representation of the data.

Conversion

Series.astype(dtype[, copy, errors])

Cast the Series to the given dtype

Series.copy([deep])

Make a copy of this object's indices and data.

Series.to_list()

Series.__array__([dtype])

Series.as_mask()

Convert booleans to bitmask

Series.scale()

Scale values to [0, 1] in float64

Indexing, iteration

Series.loc

Like @property, but only evaluated upon first invocation.

Series.iloc

Like @property, but only evaluated upon first invocation.

Series.__iter__()

Series.items()

Series.iteritems()

Series.keys()

Return alias for index.

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions

Series.add(other[, level, fill_value, axis])

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

Series.sub(other[, level, fill_value, axis])

Get Subtraction of dataframe or series and other, element-wise (binary operator sub).

Series.subtract(other[, level, fill_value, axis])

Get Subtraction of dataframe or series and other, element-wise (binary operator sub).

Series.mul(other[, level, fill_value, axis])

Get Multiplication of dataframe or series and other, element-wise (binary operator mul).

Series.multiply(other[, level, fill_value, axis])

Get Multiplication of dataframe or series and other, element-wise (binary operator mul).

Series.truediv(other[, level, fill_value, axis])

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

Series.div(other[, level, fill_value, axis])

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

Series.divide(other[, level, fill_value, axis])

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

Series.floordiv(other[, level, fill_value, axis])

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

Series.mod(other[, level, fill_value, axis])

Get Modulo division of dataframe or series and other, element-wise (binary operator mod).

Series.pow(other[, level, fill_value, axis])

Get Exponential power of dataframe series and other, element-wise (binary operator pow).

Series.radd(other[, level, fill_value, axis])

Get Addition of dataframe or series and other, element-wise (binary operator radd).

Series.rsub(other[, level, fill_value, axis])

Get Subtraction of dataframe or series and other, element-wise (binary operator rsub).

Series.rmul(other[, level, fill_value, axis])

Get Multiplication of dataframe or series and other, element-wise (binary operator rmul).

Series.rdiv(other[, level, fill_value, axis])

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

Series.rtruediv(other[, level, fill_value, axis])

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

Series.rfloordiv(other[, level, fill_value, ...])

Get Integer division of dataframe or series and other, element-wise (binary operator rfloordiv).

Series.rmod(other[, level, fill_value, axis])

Get Modulo division of dataframe or series and other, element-wise (binary operator rmod).

Series.rpow(other[, level, fill_value, axis])

Get Exponential power of dataframe or series and other, element-wise (binary operator pow).

Series.round([decimals, how])

Round to a variable number of decimal places.

Series.lt(other[, level, fill_value, axis])

Less than, element-wise (binary operator lt).

Series.gt(other[, level, fill_value, axis])

Greater than, element-wise (binary operator gt).

Series.le(other[, level, fill_value, axis])

Less than or equal, element-wise (binary operator le).

Series.ge(other[, level, fill_value, axis])

Greater than or equal, element-wise (binary operator ge).

Series.ne(other[, level, fill_value, axis])

Not equal to, element-wise (binary operator ne).

Series.eq(other[, level, fill_value, axis])

Equal to, element-wise (binary operator eq).

Series.product([axis, skipna, dtype, level, ...])

Return product of the values in the DataFrame.

Function application, GroupBy & window

Series.apply(func[, convert_dtype, args])

Apply a scalar function to the values of a Series.

Series.applymap(udf[, out_dtype])

Apply an elementwise function to transform the values in the Column.

Series.map(arg[, na_action])

Map values of Series according to input correspondence.

Series.groupby([by, axis, level, as_index, ...])

Group Series using a mapper or by a Series of columns.

Series.rolling(window[, min_periods, ...])

Rolling window calculations.

Series.pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Computations / descriptive stats

Series.abs()

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

Series.all([axis, bool_only, skipna, level])

Return whether all elements are True in DataFrame.

Series.any([axis, bool_only, skipna, level])

Return whether any elements is True in DataFrame.

Series.autocorr([lag])

Compute the lag-N autocorrelation.

Series.ceil()

Rounds each value upward to the smallest integral value not less than the original.

Series.clip([lower, upper, inplace, axis])

Trim values at input threshold(s).

Series.corr(other[, method, min_periods])

Calculates the sample correlation between two Series, excluding missing values.

Series.count([level])

Return number of non-NA/null observations in the Series

Series.cov(other[, min_periods])

Compute covariance with Series, excluding missing values.

Series.cummax([axis, skipna])

Return cumulative maximum of the Series or DataFrame.

Series.cummin([axis, skipna])

Return cumulative minimum of the Series or DataFrame.

Series.cumprod([axis, skipna])

Return cumulative product of the Series or DataFrame.

Series.cumsum([axis, skipna])

Return cumulative sum of the Series or DataFrame.

Series.describe([percentiles, include, ...])

Generate descriptive statistics.

Series.diff([periods])

Calculate the difference between values at positions i and i - N in an array and store the output in a new array.

Series.digitize(bins[, right])

Return the indices of the bins to which each value in series belongs.

Series.factorize([na_sentinel])

Encode the input values as integer labels.

Series.floor()

Rounds each value downward to the largest integral value not greater than the original.

Series.kurt([axis, skipna, level, numeric_only])

Return Fisher's unbiased kurtosis of a sample.

Series.max([axis, skipna, level, numeric_only])

Return the maximum of the values in the DataFrame.

Series.mean([axis, skipna, level, numeric_only])

Return the mean of the values for the requested axis.

Series.median([axis, skipna, level, ...])

Return the median of the values for the requested axis.

Series.min([axis, skipna, level, numeric_only])

Return the minimum of the values in the DataFrame.

Series.mode([dropna])

Return the mode(s) of the dataset.

Series.nlargest([n, keep])

Returns a new Series of the n largest element.

Series.nsmallest([n, keep])

Returns a new Series of the n smallest element.

Series.prod([axis, skipna, dtype, level, ...])

Return product of the values in the DataFrame.

Series.quantile([q, interpolation, exact, ...])

Return values at the given quantile.

Series.rank([axis, method, numeric_only, ...])

Compute numerical data ranks (1 through n) along axis.

Series.skew([axis, skipna, level, numeric_only])

Return unbiased Fisher-Pearson skew of a sample.

Series.std([axis, skipna, level, ddof, ...])

Return sample standard deviation of the DataFrame.

Series.sum([axis, skipna, dtype, level, ...])

Return sum of the values in the DataFrame.

Series.var([axis, skipna, level, ddof, ...])

Return unbiased variance of the DataFrame.

Series.kurtosis([axis, skipna, level, ...])

Return Fisher's unbiased kurtosis of a sample.

Series.unique()

Returns unique values of this Series.

Series.nunique([method, dropna])

Returns the number of unique values of the Series: approximate version, and exact version to be moved to libcudf

Series.is_unique

Return boolean if values in the object are unique.

Series.is_monotonic

Return boolean if values in the object are monotonically increasing.

Series.is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

Series.is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

Series.value_counts([normalize, sort, ...])

Return a Series containing counts of unique values.

Reindexing / selection / label manipulation

Series.add_prefix(prefix)

Prefix labels with string prefix.

Series.add_suffix(suffix)

Suffix labels with string suffix.

Series.drop([labels, axis, index, columns, ...])

Return Series with specified index labels removed.

Series.drop_duplicates([keep, inplace, ...])

Return Series with duplicate values removed.

Series.equals(other, **kwargs)

Test whether two objects contain the same elements.

Series.head([n])

Return the first n rows.

Series.isin(values)

Check whether values are contained in Series.

Series.reindex([index, copy])

Return a Series that conforms to a new index

Series.rename([index, copy])

Alter Series name

Series.reset_index([level, drop, name, inplace])

Reset the index of the Series, or a level of it.

Series.reverse()

Series.sample([n, frac, replace, weights, ...])

Return a random sample of items from an axis of object.

Series.set_index(index)

Returns a new Series with a different index.

Series.set_mask(mask[, null_count])

Series.take(indices[, axis, keep_index])

Return a new object containing the rows specified by positions

Series.tail([n])

Returns the last n rows as a new DataFrame or Series

Series.tile(count)

Repeats the rows from self DataFrame count times to form a new DataFrame.

Series.where(cond[, other, inplace])

Replace values where the condition is False.

Series.mask(cond[, other, inplace])

Replace values where the condition is True.

Missing data handling

Series.dropna([axis, inplace, how])

Return a Series with null values removed.

Series.fillna([value, method, axis, ...])

Fill null values with value or specified method.

Series.isna()

Identify missing values.

Series.isnull()

Identify missing values.

Series.nans_to_nulls()

Convert nans (if any) to nulls

Series.notna()

Identify non-missing values.

Series.notnull()

Identify non-missing values.

Series.replace([to_replace, value])

Replace values given in to_replace with value.

Reshaping, sorting

Series.argsort([axis, kind, order, ...])

Return the integer indices that would sort the Series values.

Series.interleave_columns()

Interleave Series columns of a table into a single column.

Series.sort_values([axis, ascending, ...])

Sort by the values along either axis.

Series.sort_index([axis])

Sort object by labels (along an axis).

Series.explode([ignore_index])

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

Series.scatter_by_map(map_index[, map_size, ...])

Scatter to a list of dataframes.

Series.searchsorted(values[, side, ...])

Find indices where elements should be inserted to maintain order

Series.repeat(repeats[, axis])

Repeats elements consecutively.

Series.transpose()

Return the transpose, which is by definition self.

Combining / comparing / joining / merging / encoding

Series.append(to_append[, ignore_index, ...])

Append values from another Series or array-like object.

Series.update(other)

Modify Series in place using values from passed Series.

Series.label_encoding(cats[, dtype, na_sentinel])

Perform label encoding.

Numerical operations

Series.acos()

Get Trigonometric inverse cosine, element-wise.

Series.asin()

Get Trigonometric inverse sine, element-wise.

Series.atan()

Get Trigonometric inverse tangent, element-wise.

Series.cos()

Get Trigonometric cosine, element-wise.

Series.exp()

Get the exponential of all elements, element-wise.

Series.log()

Get the natural logarithm of all elements, element-wise.

Series.sin()

Get Trigonometric sine, element-wise.

Series.sqrt()

Get the non-negative square-root of all elements, element-wise.

Series.tan()

Get Trigonometric tangent, element-wise.

Accessors

pandas provides dtype-specific methods under various accessors. These are separate namespaces within Series that only apply to specific data types.

Data Type

Accessor

Datetime, Timedelta

dt

String

str

Categorical

cat

List

list

Struct

struct

Datetimelike properties

Series.dt can be used to access the values of the series as datetimelike and return several properties. These can be accessed like Series.dt.<property>.

Datetime properties

day

The day of the datetime.

dayofweek

The day of the week with Monday=0, Sunday=6.

dayofyear

The day of the year, from 1-365 in non-leap years and from 1-366 in leap years.

days_in_month

Get the total number of days in the month that the date falls on.

day_of_year

The day of the year, from 1-365 in non-leap years and from 1-366 in leap years.

hour

The hours of the datetime.

minute

The minutes of the datetime.

month

The month as January=1, December=12.

second

The seconds of the datetime.

weekday

The day of the week with Monday=0, Sunday=6.

year

The year of the datetime.

is_leap_year

Boolean indicator if the date belongs to a leap year.

is_month_start

Booleans indicating if dates are the first day of the month.

is_month_end

Boolean indicator if the date is the last day of the month.

is_quarter_start

Boolean indicator if the date is the first day of a quarter.

is_quarter_end

Boolean indicator if the date is the last day of a quarter.

is_year_start

Boolean indicator if the date is the first day of the year.

is_year_end

Boolean indicator if the date is the last day of the year.

quarter

Integer indicator for which quarter of the year the date belongs in.

Datetime methods

strftime(date_format, *args, **kwargs)

Convert to Series using specified date_format.

isocalendar()

Returns a DataFrame with the year, week, and day calculated according to the ISO 8601 standard.

ceil(freq)

Perform ceil operation on the data to the specified freq.

floor(freq)

Perform floor operation on the data to the specified freq.

round(freq)

Perform round operation on the data to the specified freq.

Timedelta properties

components

Return a Dataframe of the components of the Timedeltas.

days

Number of days.

microseconds

Number of microseconds (>= 0 and less than 1 second).

nanoseconds

Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.

seconds

Number of seconds (>= 0 and less than 1 day).

String handling

Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed like Series.str.<function/property>.

byte_count()

Computes the number of bytes of each string in the Series/Index.

capitalize()

Convert strings in the Series/Index to be capitalized.

cat()

Concatenate strings in the Series/Index with given separator.

center(width[, fillchar])

Filling left and right side of strings in the Series/Index with an additional character.

character_ngrams([n, as_list])

Generate the n-grams from characters in a column of strings.

character_tokenize()

Each string is split into individual characters.

code_points()

Returns an array by filling it with the UTF-8 code point values for each character of each string.

contains(pat[, case, flags, na, regex])

Test if pattern or regex is contained within a string of a Series or Index.

count(pat[, flags])

Count occurrences of pattern in each string of the Series/Index.

detokenize(indices[, separator])

Combines tokens into strings by concatenating them in the order in which they appear in the indices column.

edit_distance(targets)

The targets strings are measured against the strings in this instance using the Levenshtein edit distance algorithm.

edit_distance_matrix()

Computes the edit distance between strings in the series.

endswith(pat)

Test if the end of each string element matches a pattern.

extract(pat[, flags, expand])

Extract capture groups in the regex pat as columns in a DataFrame.

filter_alphanum([repl, keep])

Remove non-alphanumeric characters from strings in this column.

filter_characters(table[, keep, repl])

Remove characters from each string using the character ranges in the given mapping table.

filter_tokens(min_token_length[, ...])

Remove tokens from within each string in the series that are smaller than min_token_length and optionally replace them with the replacement string.

find(sub[, start, end])

Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].

findall(pat[, flags, expand])

Find all occurrences of pattern or regular expression in the Series/Index.

get([i])

Extract element from each component at specified position.

get_json_object(json_path)

Applies a JSONPath string to an input strings column where each row in the column is a valid json string

hex_to_int()

Returns integer value represented by each hex string.

htoi()

Returns integer value represented by each hex string.

index(sub[, start, end])

Return lowest indexes in each strings where the substring is fully contained between [start:end].

insert([start, repl])

Insert the specified string into each string in the specified position.

ip2int()

This converts ip strings to integers

ip_to_int()

This converts ip strings to integers

is_consonant(position)

Return true for strings where the character at position is a consonant.

is_vowel(position)

Return true for strings where the character at position is a vowel -- not a consonant.

isalnum()

Check whether all characters in each string are alphanumeric.

isalpha()

Check whether all characters in each string are alphabetic.

isdecimal()

Check whether all characters in each string are decimal.

isdigit()

Check whether all characters in each string are digits.

isempty()

Check whether each string is an empty string.

isfloat()

Check whether all characters in each string form floating value.

ishex()

Check whether all characters in each string form a hex integer.

isinteger()

Check whether all characters in each string form integer.

isipv4()

Check whether all characters in each string form an IPv4 address.

isspace()

Check whether all characters in each string are whitespace.

islower()

Check whether all characters in each string are lowercase.

isnumeric()

Check whether all characters in each string are numeric.

isupper()

Check whether all characters in each string are uppercase.

istimestamp(format)

Check whether all characters in each string can be converted to a timestamp using the given format.

istitle()

Check whether each string is title formatted.

join([sep, string_na_rep, sep_na_rep])

Join lists contained as elements in the Series/Index with passed delimiter.

len()

Computes the length of each element in the Series/Index.

ljust(width[, fillchar])

Filling right side of strings in the Series/Index with an additional character.

lower()

Converts all characters to lowercase.

lstrip([to_strip])

Remove leading and trailing characters.

match(pat[, case, flags])

Determine if each string matches a regular expression.

ngrams([n, separator])

Generate the n-grams from a set of tokens, each record in series is treated a token.

ngrams_tokenize([n, delimiter, separator])

Generate the n-grams using tokens from each string.

normalize_characters([do_lower])

Normalizes strings characters for tokenizing.

normalize_spaces()

Remove extra whitespace between tokens and trim whitespace from the beginning and the end of each string.

pad(width[, side, fillchar])

Pad strings in the Series/Index up to width.

partition([sep, expand])

Split the string at the first occurrence of sep.

porter_stemmer_measure()

Compute the Porter Stemmer measure for each string.

repeat(repeats)

Duplicate each string in the Series or Index.

replace(pat, repl[, n, case, flags, regex])

Replace occurrences of pattern/regex in the Series/Index with some other string.

replace_tokens(targets, replacements[, ...])

The targets tokens are searched for within each string in the series and replaced with the corresponding replacements if found.

replace_with_backrefs(pat, repl)

Use the repl back-ref template to create a new string with the extracted elements found using the pat expression.

rfind(sub[, start, end])

Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end].

rindex(sub[, start, end])

Return highest indexes in each strings where the substring is fully contained between [start:end].

rjust(width[, fillchar])

Filling left side of strings in the Series/Index with an additional character.

rpartition([sep, expand])

Split the string at the last occurrence of sep.

rsplit([pat, n, expand])

Split strings around given separator/delimiter.

rstrip([to_strip])

Remove leading and trailing characters.

slice([start, stop, step])

Slice substrings from each element in the Series or Index.

slice_from(starts, stops)

Return substring of each string using positions for each string.

slice_replace([start, stop, repl])

Replace the specified section of each string with a new string.

split([pat, n, expand])

Split strings around given separator/delimiter.

rsplit([pat, n, expand])

Split strings around given separator/delimiter.

startswith(pat)

Test if the start of each string element matches a pattern.

strip([to_strip])

Remove leading and trailing characters.

swapcase()

Change each lowercase character to uppercase and vice versa.

title()

Uppercase the first letter of each letter after a space and lowercase the rest.

token_count([delimiter])

Each string is split into tokens using the provided delimiter.

tokenize([delimiter])

Each string is split into tokens using the provided delimiter(s).

translate(table)

Map all characters in the string through the given mapping table.

upper()

Convert each string to uppercase.

url_decode()

Returns a URL-decoded format of each string.

url_encode()

Returns a URL-encoded format of each string.

wrap(width, **kwargs)

Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width.

zfill(width)

Pad strings in the Series/Index by prepending ‘0’ characters.

Categorical accessor

Categorical-dtype specific methods and attributes are available under the Series.cat accessor.

categories

The categories of this categorical.

ordered

Whether the categories have an ordered relationship.

codes

Return Series of codes as well as the index.

reorder_categories(new_categories[, ...])

Reorder categories as specified in new_categories.

add_categories(new_categories[, inplace])

Add new categories.

remove_categories(removals[, inplace])

Remove the specified categories.

set_categories(new_categories[, ordered, ...])

Set the categories to the specified new_categories.

as_ordered([inplace])

Set the Categorical to be ordered.

as_unordered([inplace])

Set the Categorical to be unordered.

List handling

Series.list can be used to access the values of the series as lists and apply list methods to it. These can be accessed like Series.list.<function/property>.

concat([dropna])

For a column with at least one level of nesting, concatenate the lists in each row.

contains(search_key)

Returns boolean values indicating whether the specified scalar is an element of each row.

get(index)

Extract element at the given index from each component

len()

Computes the length of each element in the Series/Index.

sort_values([ascending, inplace, kind, ...])

Sort each list by the values.

take(lists_indices)

Collect list elements based on given indices.

unique()

Returns the unique elements in each list.

Struct handling

Series.struct can be used to access the values of the series as Structs and apply struct methods to it. These can be accessed like Series.struct.<function/property>.

field(key)

Extract children of the specified struct column in the Series

explode()

Return a DataFrame whose columns are the fields of this struct Series.

Serialization / IO / conversion

Series.to_array([fillna])

Series.to_arrow()

Convert to a PyArrow Array.

Series.to_dlpack()

Converts a cuDF object into a DLPack tensor.

Series.to_frame([name])

Convert Series into a DataFrame

Series.to_gpu_array([fillna])

Series.to_hdf(path_or_buf, key, *args, **kwargs)

Write the contained data to an HDF5 file using HDFStore.

Series.to_json([path_or_buf])

Convert the cuDF object to a JSON string.

Series.to_pandas([index, nullable])

Convert to a Pandas Series.

Series.to_string()

Convert to string

Series.from_arrow(array)

Create from PyArrow Array/ChunkedArray.

Series.from_categorical(categorical[, codes])

Creates from a pandas.Categorical

Series.from_masked_array(data, mask[, ...])

Create a Series with null-mask.

Series.from_pandas(s[, nan_as_null])

Convert from a Pandas Series.

Series.hash_values([method])

Compute the hash of values in this column.