Dask DataFrame API with Logical Query Planning

DataFrame

DataFrame(expr)

DataFrame-like Expr Collection

DataFrame.abs()

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

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

DataFrame.align(other[, join, axis, fill_value])

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

DataFrame.all([axis, skipna, split_every])

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

DataFrame.any([axis, skipna, split_every])

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

DataFrame.apply(function, *args[, meta, axis])

Parallel version of pandas.DataFrame.apply

DataFrame.assign(**pairs)

Assign new columns to a DataFrame.

DataFrame.astype(dtypes)

Cast a pandas object to a specified dtype dtype.

DataFrame.bfill([axis, limit])

Fill NA/NaN values by using the next valid observation to fill the gap.

DataFrame.categorize([columns, index, ...])

Convert columns of the DataFrame to category dtype.

DataFrame.columns

DataFrame.compute([fuse])

Compute this dask collection

DataFrame.copy([deep])

Make a copy of the dataframe

DataFrame.corr([method, min_periods, ...])

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

DataFrame.count([axis, numeric_only, ...])

Count non-NA cells for each column or row.

DataFrame.cov([min_periods, numeric_only, ...])

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

DataFrame.cummax([axis, skipna])

Return cumulative maximum over a DataFrame or Series axis.

DataFrame.cummin([axis, skipna])

Return cumulative minimum over a DataFrame or Series axis.

DataFrame.cumprod([axis, skipna])

Return cumulative product over a DataFrame or Series axis.

DataFrame.cumsum([axis, skipna])

Return cumulative sum over a DataFrame or Series axis.

DataFrame.describe([split_every, ...])

Generate descriptive statistics.

DataFrame.diff([periods, axis])

First discrete difference of element.

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

DataFrame.divide(other[, axis, level, ...])

DataFrame.divisions

Tuple of npartitions + 1 values, in ascending order, marking the lower/upper bounds of each partition's index.

DataFrame.drop([labels, axis, columns, errors])

Drop specified labels from rows or columns.

DataFrame.drop_duplicates([subset, ...])

Return DataFrame with duplicate rows removed.

DataFrame.dropna([how, subset, thresh])

Remove missing values.

DataFrame.dtypes

Return data types

DataFrame.eq(other[, level, axis])

DataFrame.eval(expr, **kwargs)

Evaluate a string describing operations on DataFrame columns.

DataFrame.explode(column)

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

DataFrame.ffill([axis, limit])

Fill NA/NaN values by propagating the last valid observation to next valid.

DataFrame.fillna([value, axis])

Fill NA/NaN values using the specified method.

DataFrame.floordiv(other[, axis, level, ...])

DataFrame.ge(other[, level, axis])

DataFrame.get_partition(n)

Get a dask DataFrame/Series representing the nth partition.

DataFrame.groupby(by[, group_keys, sort, ...])

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

DataFrame.gt(other[, level, axis])

DataFrame.head([n, npartitions, compute])

First n rows of the dataset

DataFrame.idxmax([axis, skipna, ...])

Return index of first occurrence of maximum over requested axis.

DataFrame.idxmin([axis, skipna, ...])

Return index of first occurrence of minimum over requested axis.

DataFrame.iloc

Purely integer-location based indexing for selection by position.

DataFrame.index

Return dask Index instance

DataFrame.info([buf, verbose, memory_usage])

Concise summary of a Dask DataFrame

DataFrame.isin(values)

Whether each element in the DataFrame is contained in values.

DataFrame.isna()

Detect missing values.

DataFrame.isnull()

DataFrame.isnull is an alias for DataFrame.isna.

DataFrame.items()

Iterate over (column name, Series) pairs.

DataFrame.iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

DataFrame.itertuples([index, name])

Iterate over DataFrame rows as namedtuples.

DataFrame.join(other[, on, how, lsuffix, ...])

Join columns of another DataFrame.

DataFrame.known_divisions

Whether the divisions are known.

DataFrame.le(other[, level, axis])

DataFrame.loc

Purely label-location based indexer for selection by label.

DataFrame.lt(other[, level, axis])

DataFrame.map_partitions(func, *args[, ...])

Apply a Python function to each partition

DataFrame.mask(cond[, other])

Replace values where the condition is True.

DataFrame.max([axis, skipna, numeric_only, ...])

Return the maximum of the values over the requested axis.

DataFrame.mean([axis, skipna, numeric_only, ...])

Return the mean of the values over the requested axis.

DataFrame.median([axis, numeric_only])

Return the median of the values over the requested axis.

DataFrame.median_approximate([axis, method, ...])

Return the approximate median of the values over the requested axis.

DataFrame.memory_usage([deep, index])

Return the memory usage of each column in bytes.

DataFrame.memory_usage_per_partition([...])

Return the memory usage of each partition

DataFrame.merge(right[, how, on, left_on, ...])

Merge the DataFrame with another DataFrame

DataFrame.min([axis, skipna, numeric_only, ...])

Return the minimum of the values over the requested axis.

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

DataFrame.mode([dropna, split_every, ...])

Get the mode(s) of each element along the selected axis.

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

DataFrame.ndim

Return dimensionality

DataFrame.ne(other[, level, axis])

DataFrame.nlargest([n, columns, split_every])

Return the first n rows ordered by columns in descending order.

DataFrame.npartitions

Return number of partitions

DataFrame.nsmallest([n, columns, split_every])

Return the first n rows ordered by columns in ascending order.

DataFrame.partitions

Slice dataframe by partitions

DataFrame.persist([fuse])

Persist this dask collection into memory

DataFrame.pivot_table(index, columns, values)

Create a spreadsheet-style pivot table as a DataFrame.

DataFrame.pop(item)

Return item and drop from frame.

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

DataFrame.prod([axis, skipna, numeric_only, ...])

Return the product of the values over the requested axis.

DataFrame.quantile([q, axis, numeric_only, ...])

Approximate row-wise and precise column-wise quantiles of DataFrame

DataFrame.query(expr, **kwargs)

Filter dataframe with complex expression

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

DataFrame.random_split(frac[, random_state, ...])

Pseudorandomly split dataframe into different pieces row-wise

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

DataFrame.rename([index, columns])

Rename columns or index labels.

DataFrame.rename_axis([mapper, index, ...])

Set the name of the axis for the index or columns.

DataFrame.repartition([divisions, ...])

Repartition a collection

DataFrame.replace([to_replace, value, regex])

Replace values given in to_replace with value.

DataFrame.resample(rule[, closed, label])

Resample time-series data.

DataFrame.reset_index([drop])

Reset the index to the default index.

DataFrame.rfloordiv(other[, axis, level, ...])

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

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

DataFrame.round([decimals])

Round a DataFrame to a variable number of decimal places.

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

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

DataFrame.rtruediv(other[, axis, level, ...])

DataFrame.sample([n, frac, replace, ...])

Random sample of items

DataFrame.select_dtypes([include, exclude])

Return a subset of the DataFrame's columns based on the column dtypes.

DataFrame.sem([axis, skipna, ddof, ...])

Return unbiased standard error of the mean over requested axis.

DataFrame.set_index(other[, drop, sorted, ...])

Set the DataFrame index (row labels) using an existing column.

DataFrame.shape

DataFrame.shuffle([on, ignore_index, ...])

Rearrange DataFrame into new partitions

DataFrame.size

Size of the Series or DataFrame as a Delayed object.

DataFrame.sort_values(by[, npartitions, ...])

Sort the dataset by a single column.

DataFrame.squeeze([axis])

Squeeze 1 dimensional axis objects into scalars.

DataFrame.std([axis, skipna, ddof, ...])

Return sample standard deviation over requested axis.

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

DataFrame.sum([axis, skipna, numeric_only, ...])

Return the sum of the values over the requested axis.

DataFrame.tail([n, compute])

Last n rows of the dataset

DataFrame.to_backend([backend])

Move to a new DataFrame backend

DataFrame.to_bag([index, format])

Create a Dask Bag from a Series

DataFrame.to_csv(filename, **kwargs)

See dd.to_csv docstring for more information

DataFrame.to_dask_array([lengths, meta, ...])

Convert a dask DataFrame to a dask array.

DataFrame.to_dask_dataframe([optimize])

Convert to a dask-dataframe collection

DataFrame.to_delayed([optimize_graph])

Convert into a list of dask.delayed objects, one per partition.

DataFrame.to_hdf(path_or_buf, key[, mode, ...])

See dd.to_hdf docstring for more information

DataFrame.to_html([max_rows])

Render a DataFrame as an HTML table.

DataFrame.to_json(filename, *args, **kwargs)

See dd.to_json docstring for more information

DataFrame.to_parquet(path, **kwargs)

DataFrame.to_records([index, lengths])

DataFrame.to_string([max_rows])

Render a DataFrame to a console-friendly tabular output.

DataFrame.to_sql(name, uri[, schema, ...])

DataFrame.to_timestamp([freq, how])

Cast to DatetimeIndex of timestamps, at beginning of period.

DataFrame.truediv(other[, axis, level, ...])

DataFrame.values

Return a dask.array of the values of this dataframe

DataFrame.var([axis, skipna, ddof, ...])

Return unbiased variance over requested axis.

DataFrame.visualize([tasks])

Visualize the expression or task graph

DataFrame.where(cond[, other])

Replace values where the condition is False.

Series

Series(expr)

Series-like Expr Collection

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

Series.align(other[, join, axis, fill_value])

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

Series.all([axis, skipna, split_every])

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

Series.any([axis, skipna, split_every])

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

Series.apply(function, *args[, meta, axis])

Parallel version of pandas.Series.apply

Series.astype(dtypes)

Cast a pandas object to a specified dtype dtype.

Series.autocorr([lag, split_every])

Compute the lag-N autocorrelation.

Series.between(left, right[, inclusive])

Return boolean Series equivalent to left <= series <= right.

Series.bfill([axis, limit])

Fill NA/NaN values by using the next valid observation to fill the gap.

Series.clear_divisions()

Forget division information.

Series.clip([lower, upper, axis])

Trim values at input threshold(s).

Series.compute([fuse])

Compute this dask collection

Series.copy([deep])

Make a copy of the dataframe

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

Compute correlation with other Series, excluding missing values.

Series.count([axis, numeric_only, split_every])

Count non-NA cells for each column or row.

Series.cov(other[, min_periods, split_every])

Compute covariance with Series, excluding missing values.

Series.cummax([axis, skipna])

Return cumulative maximum over a DataFrame or Series axis.

Series.cummin([axis, skipna])

Return cumulative minimum over a DataFrame or Series axis.

Series.cumprod([axis, skipna])

Return cumulative product over a DataFrame or Series axis.

Series.cumsum([axis, skipna])

Return cumulative sum over a DataFrame or Series axis.

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

Generate descriptive statistics.

Series.diff([periods, axis])

First discrete difference of element.

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

Series.drop_duplicates([ignore_index, ...])

Series.dropna()

Return a new Series with missing values removed.

Series.dtype

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

Series.explode()

Transform each element of a list-like to a row.

Series.ffill([axis, limit])

Fill NA/NaN values by propagating the last valid observation to next valid.

Series.fillna([value, axis])

Fill NA/NaN values using the specified method.

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

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

Series.get_partition(n)

Get a dask DataFrame/Series representing the nth partition.

Series.groupby(by, **kwargs)

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

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

Series.head([n, npartitions, compute])

First n rows of the dataset

Series.idxmax([axis, skipna, numeric_only, ...])

Return index of first occurrence of maximum over requested axis.

Series.idxmin([axis, skipna, numeric_only, ...])

Return index of first occurrence of minimum over requested axis.

Series.isin(values)

Whether each element in the DataFrame is contained in values.

Series.isna()

Detect missing values.

Series.isnull()

DataFrame.isnull is an alias for DataFrame.isna.

Series.known_divisions

Whether the divisions are known.

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

Series.loc

Purely label-location based indexer for selection by label.

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

Series.map(arg[, na_action, meta])

Map values of Series according to an input mapping or function.

Series.map_overlap(func, before, after, *args)

Apply a function to each partition, sharing rows with adjacent partitions.

Series.map_partitions(func, *args[, meta, ...])

Apply a Python function to each partition

Series.mask(cond[, other])

Replace values where the condition is True.

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

Return the maximum of the values over the requested axis.

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

Return the mean of the values over the requested axis.

Series.median()

Return the median of the values over the requested axis.

Series.median_approximate([method])

Return the approximate median of the values over the requested axis.

Series.memory_usage([deep, index])

Return the memory usage of the Series.

Series.memory_usage_per_partition([index, deep])

Return the memory usage of each partition

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

Return the minimum of the values over the requested axis.

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

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

Series.nbytes

Number of bytes

Series.ndim

Return dimensionality

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

Series.nlargest([n, split_every])

Return the largest n elements.

Series.notnull()

DataFrame.notnull is an alias for DataFrame.notna.

Series.nsmallest([n, split_every])

Return the smallest n elements.

Series.nunique([dropna, split_every, split_out])

Return number of unique elements in the object.

Series.nunique_approx([split_every])

Approximate number of unique rows.

Series.persist([fuse])

Persist this dask collection into memory

Series.pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

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

Series.prod([axis, skipna, numeric_only, ...])

Return the product of the values over the requested axis.

Series.quantile([q, method])

Approximate quantiles of Series

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

Series.random_split(frac[, random_state, ...])

Pseudorandomly split dataframe into different pieces row-wise

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

Series.repartition([divisions, npartitions, ...])

Repartition a collection

Series.replace([to_replace, value, regex])

Replace values given in to_replace with value.

Series.rename(index[, sorted_index])

Alter Series index labels or name

Series.resample(rule[, closed, label])

Resample time-series data.

Series.reset_index([drop])

Reset the index to the default index.

Series.rolling(window, **kwargs)

Provides rolling transformations.

Series.round([decimals])

Round a DataFrame to a variable number of decimal places.

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

Random sample of items

Series.sem([axis, skipna, ddof, ...])

Return unbiased standard error of the mean over requested axis.

Series.shape

Return a tuple representing the dimensionality of the DataFrame.

Series.shift([periods, freq, axis])

Shift index by desired number of periods with an optional time freq.

Series.size

Size of the Series or DataFrame as a Delayed object.

Series.std([axis, skipna, ddof, ...])

Return sample standard deviation over requested axis.

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

Series.sum([axis, skipna, numeric_only, ...])

Return the sum of the values over the requested axis.

Series.to_backend([backend])

Move to a new DataFrame backend

Series.to_bag([index, format])

Create a Dask Bag from a Series

Series.to_csv(filename, **kwargs)

See dd.to_csv docstring for more information

Series.to_dask_array([lengths, meta, optimize])

Convert a dask DataFrame to a dask array.

Series.to_delayed([optimize_graph])

Convert into a list of dask.delayed objects, one per partition.

Series.to_frame([name])

Convert Series to DataFrame.

Series.to_hdf(path_or_buf, key[, mode, append])

See dd.to_hdf docstring for more information

Series.to_string([max_rows])

Render a string representation of the Series.

Series.to_timestamp([freq, how])

Cast to DatetimeIndex of timestamps, at beginning of period.

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

Series.unique([split_every, split_out, ...])

Return Series of unique values in the object.

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

Return a Series containing counts of unique values.

Series.values

Return a dask.array of the values of this dataframe

Series.var([axis, skipna, ddof, ...])

Return unbiased variance over requested axis.

Series.visualize([tasks])

Visualize the expression or task graph

Series.where(cond[, other])

Replace values where the condition is False.

Index

Index(expr)

Index-like Expr Collection

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

Index.align(other[, join, axis, fill_value])

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

Index.all([axis, skipna, split_every])

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

Index.any([axis, skipna, split_every])

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

Index.apply(function, *args[, meta, axis])

Parallel version of pandas.Series.apply

Index.astype(dtypes)

Cast a pandas object to a specified dtype dtype.

Index.autocorr([lag, split_every])

Compute the lag-N autocorrelation.

Index.between(left, right[, inclusive])

Return boolean Series equivalent to left <= series <= right.

Index.bfill([axis, limit])

Fill NA/NaN values by using the next valid observation to fill the gap.

Index.clear_divisions()

Forget division information.

Index.clip([lower, upper, axis])

Trim values at input threshold(s).

Index.compute([fuse])

Compute this dask collection

Index.copy([deep])

Make a copy of the dataframe

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

Compute correlation with other Series, excluding missing values.

Index.count([split_every])

Count non-NA cells for each column or row.

Index.cov(other[, min_periods, split_every])

Compute covariance with Series, excluding missing values.

Index.cummax([axis, skipna])

Return cumulative maximum over a DataFrame or Series axis.

Index.cummin([axis, skipna])

Return cumulative minimum over a DataFrame or Series axis.

Index.cumprod([axis, skipna])

Return cumulative product over a DataFrame or Series axis.

Index.cumsum([axis, skipna])

Return cumulative sum over a DataFrame or Series axis.

Index.describe([split_every, percentiles, ...])

Generate descriptive statistics.

Index.diff([periods, axis])

First discrete difference of element.

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

Index.drop_duplicates([ignore_index, ...])

Index.dropna()

Return a new Series with missing values removed.

Index.dtype

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

Index.explode()

Transform each element of a list-like to a row.

Index.ffill([axis, limit])

Fill NA/NaN values by propagating the last valid observation to next valid.

Index.fillna([value, axis])

Fill NA/NaN values using the specified method.

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

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

Index.get_partition(n)

Get a dask DataFrame/Series representing the nth partition.

Index.groupby(by, **kwargs)

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

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

Index.head([n, npartitions, compute])

First n rows of the dataset

Index.is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

Index.is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

Index.isin(values)

Whether each element in the DataFrame is contained in values.

Index.isna()

Detect missing values.

Index.isnull()

DataFrame.isnull is an alias for DataFrame.isna.

Index.known_divisions

Whether the divisions are known.

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

Index.loc

Purely label-location based indexer for selection by label.

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

Index.map(arg[, na_action, meta, is_monotonic])

Map values using an input mapping or function.

Index.map_overlap(func, before, after, *args)

Apply a function to each partition, sharing rows with adjacent partitions.

Index.map_partitions(func, *args[, meta, ...])

Apply a Python function to each partition

Index.mask(cond[, other])

Replace values where the condition is True.

Index.max([axis, skipna, numeric_only, ...])

Return the maximum of the values over the requested axis.

Index.median()

Return the median of the values over the requested axis.

Index.median_approximate([method])

Return the approximate median of the values over the requested axis.

Index.memory_usage([deep])

Memory usage of the values.

Index.memory_usage_per_partition([index, deep])

Return the memory usage of each partition

Index.min([axis, skipna, numeric_only, ...])

Return the minimum of the values over the requested axis.

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

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

Index.nbytes

Number of bytes

Index.ndim

Return dimensionality

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

Index.nlargest([n, split_every])

Return the largest n elements.

Index.notnull()

DataFrame.notnull is an alias for DataFrame.notna.

Index.nsmallest([n, split_every])

Return the smallest n elements.

Index.nunique([dropna, split_every, split_out])

Return number of unique elements in the object.

Index.nunique_approx([split_every])

Approximate number of unique rows.

Index.persist([fuse])

Persist this dask collection into memory

Index.pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

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

Index.quantile([q, method])

Approximate quantiles of Series

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

Index.random_split(frac[, random_state, shuffle])

Pseudorandomly split dataframe into different pieces row-wise

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

Index.rename(index[, sorted_index])

Alter Series index labels or name

Index.repartition([divisions, npartitions, ...])

Repartition a collection

Index.replace([to_replace, value, regex])

Replace values given in to_replace with value.

Index.resample(rule[, closed, label])

Resample time-series data.

Index.reset_index([drop])

Reset the index to the default index.

Index.rolling(window, **kwargs)

Provides rolling transformations.

Index.round([decimals])

Round a DataFrame to a variable number of decimal places.

Index.sample([n, frac, replace, random_state])

Random sample of items

Index.sem([axis, skipna, ddof, split_every, ...])

Return unbiased standard error of the mean over requested axis.

Index.shape

Return a tuple representing the dimensionality of the DataFrame.

Index.shift([periods, freq])

Shift index by desired number of periods with an optional time freq.

Index.size

Size of the Series or DataFrame as a Delayed object.

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

Index.to_backend([backend])

Move to a new DataFrame backend

Index.to_bag([index, format])

Create a Dask Bag from a Series

Index.to_csv(filename, **kwargs)

See dd.to_csv docstring for more information

Index.to_dask_array([lengths, meta, optimize])

Convert a dask DataFrame to a dask array.

Index.to_delayed([optimize_graph])

Convert into a list of dask.delayed objects, one per partition.

Index.to_frame([index, name])

Create a DataFrame with a column containing the Index.

Index.to_hdf(path_or_buf, key[, mode, append])

See dd.to_hdf docstring for more information

Index.to_series([index, name])

Create a Series with both index and values equal to the index keys.

Index.to_string([max_rows])

Render a string representation of the Series.

Index.to_timestamp([freq, how])

Cast to DatetimeIndex of timestamps, at beginning of period.

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

Index.unique([split_every, split_out, ...])

Return Series of unique values in the object.

Index.value_counts([sort, ascending, ...])

Return a Series containing counts of unique values.

Index.values

Return a dask.array of the values of this dataframe

Index.visualize([tasks])

Visualize the expression or task graph

Index.where(cond[, other])

Replace values where the condition is False.

Index.to_frame([index, name])

Create a DataFrame with a column containing the Index.

Accessors

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

The accessor implementations are consistent with the current Dask DataFrame implementation.

Groupby Operations

DataFrame Groupby

GroupBy.aggregate([arg, split_every, ...])

Aggregate using one or more specified operations

GroupBy.apply(func, *args[, meta, ...])

Parallel version of pandas GroupBy.apply

GroupBy.bfill([limit, shuffle_method])

Backward fill the values.

GroupBy.count(**kwargs)

Compute count of group, excluding missing values.

GroupBy.cumcount()

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

GroupBy.cumprod([numeric_only])

Cumulative product for each group.

GroupBy.cumsum([numeric_only])

Cumulative sum for each group.

GroupBy.ffill([limit, shuffle_method])

Forward fill the values.

GroupBy.get_group(key)

Construct DataFrame from group with provided name.

GroupBy.max([numeric_only])

Compute max of group values.

GroupBy.mean([numeric_only])

Compute mean of groups, excluding missing values.

GroupBy.min([numeric_only])

Compute min of group values.

GroupBy.size(**kwargs)

Compute group sizes.

GroupBy.std([ddof, split_every, split_out, ...])

Compute standard deviation of groups, excluding missing values.

GroupBy.sum([numeric_only, min_count])

Compute sum of group values.

GroupBy.var([ddof, split_every, split_out, ...])

Compute variance of groups, excluding missing values.

GroupBy.cov([ddof, split_every, split_out, ...])

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

GroupBy.corr([split_every, split_out, ...])

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

GroupBy.first([numeric_only, sort])

Compute the first entry of each column within each group.

GroupBy.last([numeric_only, sort])

Compute the last entry of each column within each group.

GroupBy.idxmin([split_every, split_out, ...])

Return index of first occurrence of minimum over requested axis.

GroupBy.idxmax([split_every, split_out, ...])

Return index of first occurrence of maximum over requested axis.

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

Provides rolling transformations.

GroupBy.transform(func[, meta, shuffle_method])

Parallel version of pandas GroupBy.transform

Series Groupby

SeriesGroupBy.aggregate([arg, split_every, ...])

Aggregate using one or more specified operations

SeriesGroupBy.apply(func, *args[, meta, ...])

Parallel version of pandas GroupBy.apply

SeriesGroupBy.bfill([limit, shuffle_method])

Backward fill the values.

SeriesGroupBy.count(**kwargs)

Compute count of group, excluding missing values.

SeriesGroupBy.cumcount()

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

SeriesGroupBy.cumprod([numeric_only])

Cumulative product for each group.

SeriesGroupBy.cumsum([numeric_only])

Cumulative sum for each group.

SeriesGroupBy.ffill([limit, shuffle_method])

Forward fill the values.

SeriesGroupBy.get_group(key)

Construct DataFrame from group with provided name.

SeriesGroupBy.max([numeric_only])

Compute max of group values.

SeriesGroupBy.mean([numeric_only])

Compute mean of groups, excluding missing values.

SeriesGroupBy.min([numeric_only])

Compute min of group values.

SeriesGroupBy.nunique([split_every, ...])

Examples

SeriesGroupBy.size(**kwargs)

Compute group sizes.

SeriesGroupBy.std([ddof, split_every, ...])

Compute standard deviation of groups, excluding missing values.

SeriesGroupBy.sum([numeric_only, min_count])

Compute sum of group values.

SeriesGroupBy.var([ddof, split_every, ...])

Compute variance of groups, excluding missing values.

SeriesGroupBy.first([numeric_only, sort])

Compute the first entry of each column within each group.

SeriesGroupBy.last([numeric_only, sort])

Compute the last entry of each column within each group.

SeriesGroupBy.idxmin([split_every, ...])

Return index of first occurrence of minimum over requested axis.

SeriesGroupBy.idxmax([split_every, ...])

Return index of first occurrence of maximum over requested axis.

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

Provides rolling transformations.

SeriesGroupBy.transform(func[, meta, ...])

Parallel version of pandas GroupBy.transform

Custom Aggregation

Aggregation(name, chunk, agg[, finalize])

User defined groupby-aggregation.

Rolling Operations

Series.rolling(window, **kwargs)

Provides rolling transformations.

DataFrame.rolling(window, **kwargs)

Provides rolling transformations.

Rolling.apply(func, *args, **kwargs)

Calculate the rolling custom aggregation function.

Rolling.count()

Calculate the rolling count of non NaN observations.

Rolling.kurt()

Calculate the rolling Fisher's definition of kurtosis without bias.

Rolling.max()

Calculate the rolling maximum.

Rolling.mean()

Calculate the rolling mean.

Rolling.median()

Calculate the rolling median.

Rolling.min()

Calculate the rolling minimum.

Rolling.quantile(q)

Calculate the rolling quantile.

Rolling.skew()

Calculate the rolling unbiased skewness.

Rolling.std()

Calculate the rolling standard deviation.

Rolling.sum()

Calculate the rolling sum.

Rolling.var()

Calculate the rolling variance.

Create DataFrames

read_csv(path, *args[, header, usecols, ...])

read_table(path, *args[, header, usecols, ...])

read_fwf(path, *args[, header, usecols, ...])

read_parquet([path, columns, filters, ...])

read_hdf(pattern, key[, start, stop, ...])

read_json(url_path[, orient, lines, ...])

read_orc(path[, engine, columns, index, ...])

read_sql_table(table_name, con, index_col[, ...])

read_sql_query(sql, con, index_col[, ...])

read_sql(sql, con, index_col, **kwargs)

from_array(arr[, chunksize, columns, meta])

from_dask_array(x[, columns, index, meta])

from_delayed(dfs[, meta, divisions, verify_meta])

Create Dask DataFrame from many Dask Delayed objects

from_map(func, *iterables[, args, meta, ...])

Create a dask-expr collection from a custom function map

from_pandas(data[, npartitions, sort, chunksize])

DataFrame.from_dict(data, *[, npartitions, ...])

Construct a Dask DataFrame from a Python Dictionary

Store DataFrames

to_csv(df, filename[, single_file, ...])

to_parquet(df, path[, compression, ...])

to_hdf(df, path, key[, mode, append, ...])

to_records(df)

to_sql(df, name, uri[, schema, if_exists, ...])

to_json(df, url_path[, orient, lines, ...])

Convert DataFrames

DataFrame.to_bag([index, format])

Create a Dask Bag from a Series

DataFrame.to_dask_array([lengths, meta, ...])

Convert a dask DataFrame to a dask array.

DataFrame.to_delayed([optimize_graph])

Convert into a list of dask.delayed objects, one per partition.

Reshape DataFrames

get_dummies(data[, prefix, prefix_sep, ...])

Convert categorical variable into dummy/indicator variables.

pivot_table(df, index, columns, values[, ...])

Concatenate DataFrames

DataFrame.merge(right[, how, on, left_on, ...])

Merge the DataFrame with another DataFrame

dask_expr.concat(dfs[, axis, join, ...])

dask_expr.merge(left, right[, how, on, ...])

dask_expr.merge_asof(left, right[, on, ...])

Resampling

Resampler(obj, rule, **kwargs)

Aggregate using one or more operations

Resampler.agg(func, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Resampler.count()

Compute count of group, excluding missing values.

Resampler.first()

Compute the first entry of each column within each group.

Resampler.last()

Compute the last entry of each column within each group.

Resampler.max()

Compute max value of group.

Resampler.mean()

Compute mean of groups, excluding missing values.

Resampler.median()

Compute median of groups, excluding missing values.

Resampler.min()

Compute min value of group.

Resampler.nunique()

Return number of unique elements in the group.

Resampler.ohlc()

Compute open, high, low and close values of a group, excluding missing values.

Resampler.prod()

Compute prod of group values.

Resampler.quantile()

Return value at the given quantile.

Resampler.sem()

Compute standard error of the mean of groups, excluding missing values.

Resampler.size()

Compute group sizes.

Resampler.std()

Compute standard deviation of groups, excluding missing values.

Resampler.sum()

Compute sum of group values.

Resampler.var()

Compute variance of groups, excluding missing values.

Dask Metadata

make_meta(x[, index, parent_meta])

This method creates meta-data based on the type of x, and parent_meta if supplied.

Other functions

compute(*args[, traverse, optimize_graph, ...])

Compute several dask collections at once.

map_partitions(func, *args[, meta, ...])

Apply Python function on each DataFrame partition.

map_overlap(func, df, before, after, *args)

to_datetime(arg[, meta])

to_numeric(arg[, errors, downcast, meta])

Return type depends on input.

to_timedelta(arg[, unit, errors])