DataFrame

Constructor

DataFrame([data, index, columns, dtype, ...])

A GPU Dataframe object.

Attributes and underlying data

Axes

DataFrame.axes	Return a list representing the axes of the DataFrame.
DataFrame.index	Get the labels for the rows.
DataFrame.columns	Returns a tuple of columns

DataFrame.dtypes	Return the dtypes in this object.
DataFrame.info([verbose, buf, max_cols, ...])	Print a concise summary of a DataFrame.
DataFrame.select_dtypes([include, exclude])	Return a subset of the DataFrame's columns based on the column dtypes.
DataFrame.values	Return a CuPy representation of the DataFrame.
DataFrame.ndim	Dimension of the data.
DataFrame.size	Return the number of elements in the underlying data.
DataFrame.shape	Returns a tuple representing the dimensionality of the DataFrame.
DataFrame.memory_usage([index, deep])	Return the memory usage of an object.
DataFrame.empty	Indicator whether DataFrame or Series is empty.

Conversion

DataFrame.astype(dtype[, copy, errors])	Cast the object to the given dtype.
DataFrame.convert_dtypes([infer_objects, ...])	Convert columns to the best possible nullable dtypes.
DataFrame.copy([deep])	Make a copy of this object's indices and data.

Indexing, iteration

DataFrame.head([n])	Return the first n rows.
DataFrame.at	Alias for DataFrame.loc; provided for compatibility with Pandas.
DataFrame.iat	Alias for DataFrame.iloc; provided for compatibility with Pandas.
DataFrame.loc	Select rows and columns by label or boolean mask.
DataFrame.iloc	Select values by position.
DataFrame.insert(loc, name, value[, nan_as_null])	Add a column to DataFrame at the index specified by loc.
DataFrame.__iter__()
DataFrame.items()	Iterate over column names and series pairs
DataFrame.keys()	Get the columns.
DataFrame.iterrows()	Iteration is unsupported.
DataFrame.itertuples([index, name])	Iteration is unsupported.
DataFrame.pop(item)	Return a column and drop it from the DataFrame.
DataFrame.tail([n])	Returns the last n rows as a new DataFrame or Series
DataFrame.isin(values)	Whether each element in the DataFrame is contained in values.
DataFrame.squeeze([axis])	Squeeze 1 dimensional axis objects into scalars.
DataFrame.where(cond[, other, inplace, ...])	Replace values where the condition is False.
DataFrame.mask(cond[, other, inplace, axis, ...])	Replace values where the condition is True.
DataFrame.query(expr[, local_dict])	Query with a boolean expression using Numba to compile a GPU kernel.

Binary operator functions

DataFrame.add(other[, axis, level, fill_value])	Get Addition of DataFrame or Series and other, element-wise (binary operator add).
DataFrame.sub(other[, axis, level, fill_value])	Get Subtraction of DataFrame or Series and other, element-wise (binary operator sub).
DataFrame.subtract(other[, axis, level, ...])	Get Subtraction of DataFrame or Series and other, element-wise (binary operator sub).
DataFrame.mul(other[, axis, level, fill_value])	Get Multiplication of DataFrame or Series and other, element-wise (binary operator mul).
DataFrame.multiply(other[, axis, level, ...])	Get Multiplication of DataFrame or Series and other, element-wise (binary operator mul).
DataFrame.truediv(other[, axis, level, ...])	Get Floating division of DataFrame or Series and other, element-wise (binary operator truediv).
DataFrame.div(other[, axis, level, fill_value])	Get Floating division of DataFrame or Series and other, element-wise (binary operator truediv).
DataFrame.divide(other[, axis, level, ...])	Get Floating division of DataFrame or Series and other, element-wise (binary operator truediv).
DataFrame.floordiv(other[, axis, level, ...])	Get Integer division of DataFrame or Series and other, element-wise (binary operator floordiv).
DataFrame.mod(other[, axis, level, fill_value])	Get Modulo of DataFrame or Series and other, element-wise (binary operator mod).
DataFrame.pow(other[, axis, level, fill_value])	Get Exponential of DataFrame or Series and other, element-wise (binary operator pow).
DataFrame.dot(other[, reflect])	Get dot product of frame and other, (binary operator dot).
DataFrame.radd(other[, axis, level, fill_value])	Get Addition of DataFrame or Series and other, element-wise (binary operator radd).
DataFrame.rsub(other[, axis, level, fill_value])	Get Subtraction of DataFrame or Series and other, element-wise (binary operator rsub).
DataFrame.rmul(other[, axis, level, fill_value])	Get Multiplication of DataFrame or Series and other, element-wise (binary operator rmul).
DataFrame.rdiv(other[, axis, level, fill_value])	Get Floating division of DataFrame or Series and other, element-wise (binary operator rtruediv).
DataFrame.rtruediv(other[, axis, level, ...])	Get Floating division of DataFrame or Series and other, element-wise (binary operator rtruediv).
DataFrame.rfloordiv(other[, axis, level, ...])	Get Integer division of DataFrame or Series and other, element-wise (binary operator rfloordiv).
DataFrame.rmod(other[, axis, level, fill_value])	Get Modulo of DataFrame or Series and other, element-wise (binary operator rmod).
DataFrame.rpow(other[, axis, level, fill_value])	Get Exponential of DataFrame or Series and other, element-wise (binary operator rpow).
DataFrame.round([decimals, how])	Round to a variable number of decimal places.
DataFrame.lt(other[, axis, level, fill_value])	Get Less than of DataFrame or Series and other, element-wise (binary operator lt).
DataFrame.gt(other[, axis, level, fill_value])	Get Greater than of DataFrame or Series and other, element-wise (binary operator gt).
DataFrame.le(other[, axis, level, fill_value])	Get Less than or equal to of DataFrame or Series and other, element-wise (binary operator le).
DataFrame.ge(other[, axis, level, fill_value])	Get Greater than or equal to of DataFrame or Series and other, element-wise (binary operator ge).
DataFrame.ne(other[, axis, level, fill_value])	Get Not equal to of DataFrame or Series and other, element-wise (binary operator ne).
DataFrame.eq(other[, axis, level, fill_value])	Get Equal to of DataFrame or Series and other, element-wise (binary operator eq).
DataFrame.product([axis, skipna, dtype, ...])	Return product of the values in the DataFrame.

Function application, GroupBy & window

DataFrame.agg(aggs[, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.apply(func[, axis, raw, ...])	Apply a function along an axis of the DataFrame.
DataFrame.applymap(func[, na_action])	Apply a function to a Dataframe elementwise.
DataFrame.apply_chunks(func, incols, outcols)	Transform user-specified chunks using the user-provided function.
DataFrame.apply_rows(func, incols, outcols, ...)	Apply a row-wise user defined function.
DataFrame.groupby([by, axis, level, ...])	Group using a mapper or by a Series of columns.
DataFrame.map(func[, na_action])	Apply a function to a Dataframe elementwise.
DataFrame.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs).
DataFrame.rolling(window[, min_periods, ...])	Rolling window calculations.

Computations / descriptive stats

DataFrame.abs()	Return a Series/DataFrame with absolute numeric value of each element.
DataFrame.all([axis, bool_only, skipna])	Return whether all elements are True in DataFrame.
DataFrame.any([axis, bool_only, skipna])	Return whether any elements is True in DataFrame.
DataFrame.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
DataFrame.corr([method, min_periods])	Compute the correlation matrix of a DataFrame.
DataFrame.count([axis, numeric_only])	Count non-NA cells for each column or row.
DataFrame.cov(**kwargs)	Compute the covariance matrix of a DataFrame.
DataFrame.cummax([axis])	Return cumulative max of the IndexedFrame.
DataFrame.cummin([axis])	Return cumulative min of the IndexedFrame.
DataFrame.cumprod([axis])	Return cumulative product of the IndexedFrame.
DataFrame.cumsum([axis])	Return cumulative sum of the IndexedFrame.
DataFrame.describe([percentiles, include, ...])	Generate descriptive statistics.
DataFrame.diff([periods, axis])	First discrete difference of element.
DataFrame.eval(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
DataFrame.ewm([com, span, halflife, alpha, ...])	Provide exponential weighted (EW) functions.
DataFrame.kurt([axis, skipna, numeric_only])	Return Fisher's unbiased kurtosis of a sample.
DataFrame.kurtosis([axis, skipna, numeric_only])	Return Fisher's unbiased kurtosis of a sample.
DataFrame.max([axis, skipna, numeric_only])	Return the maximum of the values in the DataFrame.
DataFrame.mean([axis, skipna, numeric_only])	Return the mean of the values for the requested axis.
DataFrame.median([axis, skipna, level, ...])	Return the median of the values for the requested axis.
DataFrame.min([axis, skipna, numeric_only])	Return the minimum of the values in the DataFrame.
DataFrame.mode([axis, numeric_only, dropna])	Get the mode(s) of each element along the selected axis.
DataFrame.pct_change([periods, fill_method, ...])	Calculates the percent change between sequential elements in the DataFrame.
DataFrame.prod([axis, skipna, dtype, ...])	Return product of the values in the DataFrame.
DataFrame.product([axis, skipna, dtype, ...])	Return product of the values in the DataFrame.
DataFrame.quantile([q, axis, numeric_only, ...])	Return values at the given quantile.
DataFrame.rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
DataFrame.round([decimals, how])	Round to a variable number of decimal places.
DataFrame.scale()	Scale values to [0, 1] in float64
DataFrame.skew([axis, skipna, numeric_only])	Return unbiased Fisher-Pearson skew of a sample.
DataFrame.sum([axis, skipna, dtype, ...])	Return sum of the values in the DataFrame.
DataFrame.std([axis, skipna, ddof, numeric_only])	Return sample standard deviation of the DataFrame.
DataFrame.var([axis, skipna, ddof, numeric_only])	Return unbiased variance of the DataFrame.
DataFrame.nunique([axis, dropna])	Count number of distinct elements in specified axis.
DataFrame.value_counts([subset, normalize, ...])	Return a Series containing counts of unique rows in the DataFrame.

Reindexing / selection / label manipulation

DataFrame.add_prefix(prefix[, axis])	Prefix labels with string prefix.
DataFrame.add_suffix(suffix[, axis])	Suffix labels with string suffix.
DataFrame.drop([labels, axis, index, ...])	Drop specified labels from rows or columns.
DataFrame.drop_duplicates([subset, keep, ...])	Return DataFrame with duplicate rows removed.
DataFrame.duplicated([subset, keep])	Return boolean Series denoting duplicate rows.
DataFrame.equals(other)	Test whether two objects contain the same elements.
DataFrame.first(offset)	Select initial periods of time series data based on a date offset.
DataFrame.head([n])	Return the first n rows.
DataFrame.last(offset)	Select final periods of time series data based on a date offset.
DataFrame.reindex([labels, index, columns, ...])	Conform DataFrame to new index.
DataFrame.rename([mapper, index, columns, ...])	Alter column and index labels.
DataFrame.reset_index([level, drop, ...])	Reset the index of the DataFrame, or a level of it.
DataFrame.sample([n, frac, replace, ...])	Return a random sample of items from an axis of object.
DataFrame.searchsorted(values[, side, ...])	Find indices where elements should be inserted to maintain order
DataFrame.set_index(keys[, drop, append, ...])	Return a new DataFrame with a new index
DataFrame.repeat(repeats[, axis])	Repeats elements consecutively.
DataFrame.tail([n])	Returns the last n rows as a new DataFrame or Series
DataFrame.take(indices[, axis])	Return a new frame containing the rows specified by indices.
DataFrame.tile(count)	Repeats the rows count times to form a new Frame.
DataFrame.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.

Missing data handling

DataFrame.backfill([value, axis, inplace, limit])	Synonym for Series.fillna() with method='bfill'.
DataFrame.bfill([value, axis, inplace, ...])	Synonym for Series.fillna() with method='bfill'.
DataFrame.dropna([axis, how, thresh, ...])	Drop rows (or columns) containing nulls from a Column.
DataFrame.ffill([value, axis, inplace, ...])	Synonym for Series.fillna() with method='ffill'.
DataFrame.fillna([value, method, axis, ...])	Fill null values with value or specified method.
DataFrame.interpolate([method, axis, limit, ...])	Interpolate data values between some points.
DataFrame.isna()	Identify missing values.
DataFrame.isnull()	Identify missing values.
DataFrame.nans_to_nulls()	Convert nans (if any) to nulls
DataFrame.notna()	Identify non-missing values.
DataFrame.notnull()	Identify non-missing values.
DataFrame.pad([value, axis, inplace, limit])	Synonym for Series.fillna() with method='ffill'.
DataFrame.replace([to_replace, value, ...])	Replace values given in to_replace with value.

Reshaping, sorting, transposing

DataFrame.argsort([by, axis, kind, order, ...])	Return the integer indices that would sort the Series values.
DataFrame.interleave_columns()	Interleave Series columns of a table into a single column.
DataFrame.partition_by_hash(columns, nparts)	Partition the dataframe by the hashed value of data in columns.
DataFrame.pivot(*, columns[, index, values])	Return reshaped DataFrame organized by the given index and column values.
DataFrame.pivot_table([values, index, ...])	Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.scatter_by_map(map_index[, ...])	Scatter to a list of dataframes.
DataFrame.sort_values(by[, axis, ascending, ...])	Sort by the values along either axis.
DataFrame.sort_index([axis, level, ...])	Sort object by labels (along an axis).
DataFrame.nlargest(n, columns[, keep])	Return the first n rows ordered by columns in descending order.
DataFrame.nsmallest(n, columns[, keep])	Return the first n rows ordered by columns in ascending order.
DataFrame.swaplevel([i, j, axis])	Swap level i with level j.
DataFrame.stack([level, dropna, future_stack])	Stack the prescribed level(s) from columns to index
DataFrame.unstack([level, fill_value, sort])	Pivot one or more levels of the (necessarily hierarchical) index labels.
DataFrame.melt(**kwargs)	Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
DataFrame.explode(column[, ignore_index])	Transform each element of a list-like to a row, replicating index values.
DataFrame.to_struct([name])	Return a struct Series composed of the columns of the DataFrame.
DataFrame.T	Transpose index and columns.
DataFrame.transpose()	Transpose index and columns.

Combining / comparing / joining / merging

DataFrame.assign(**kwargs)	Assign columns to DataFrame from keyword arguments.
DataFrame.join(other[, on, how, lsuffix, ...])	Join columns with other DataFrame on index or on a key column.
DataFrame.merge(right[, on, left_on, ...])	Merge GPU DataFrame objects by performing a database-style join operation by columns or indexes.
DataFrame.update(other[, join, overwrite, ...])	Modify a DataFrame in place using non-NA values from another DataFrame.

Time Series-related

DataFrame.shift([periods, freq, axis, ...])	Shift values by periods positions.
DataFrame.resample(rule[, axis, closed, ...])	Convert the frequency of ("resample") the given time series data.

Serialization / IO / conversion

DataFrame.deserialize(header, frames)	Generate an object from a serialized representation.
DataFrame.device_deserialize(header, frames)	Perform device-side deserialization tasks.
DataFrame.device_serialize()	Serialize data and metadata associated with device memory.
DataFrame.from_arrow(table)	Convert from PyArrow Table to DataFrame.
DataFrame.from_dict(data[, orient, dtype, ...])	Construct DataFrame from dict of array-like or dicts.
DataFrame.from_pandas(dataframe[, nan_as_null])	Convert from a Pandas DataFrame.
DataFrame.from_records(data[, index, ...])	Convert structured or record ndarray to DataFrame.
DataFrame.hash_values([method, seed])	Compute the hash of values in this column.
DataFrame.host_deserialize(header, frames)	Perform device-side deserialization tasks.
DataFrame.host_serialize()	Serialize data and metadata associated with host memory.
DataFrame.serialize()	Generate an equivalent serializable representation of an object.
DataFrame.to_arrow([preserve_index])	Convert to a PyArrow Table.
DataFrame.to_dict([orient, into, index])	Convert the DataFrame to a dictionary.
DataFrame.to_dlpack()	Converts a cuDF object into a DLPack tensor.
DataFrame.to_parquet(path[, engine, ...])	Write a DataFrame to the parquet format.
DataFrame.to_csv([path_or_buf, sep, na_rep, ...])	Write a dataframe to csv file format.
DataFrame.to_cupy([dtype, copy, na_value])	Convert the Frame to a CuPy array.
DataFrame.to_hdf(path_or_buf, key, *args, ...)	Write the contained data to an HDF5 file using HDFStore.
DataFrame.to_dict([orient, into, index])	Convert the DataFrame to a dictionary.
DataFrame.to_json([path_or_buf])	Convert the cuDF object to a JSON string.
DataFrame.to_numpy([dtype, copy, na_value])	Convert the Frame to a NumPy array.
DataFrame.to_pandas(*[, nullable, arrow_type])	Convert to a Pandas DataFrame.
DataFrame.to_feather(path, *args, **kwargs)	Write a DataFrame to the feather format.
DataFrame.to_records([index, column_dtypes, ...])	Convert to a numpy recarray
DataFrame.to_string()	Convert to string
DataFrame.values	Return a CuPy representation of the DataFrame.
DataFrame.values_host	Return a NumPy representation of the data.