Indexing into Dask DataFrames

Dask DataFrame supports some of pandas’ indexing behavior.

DataFrame.iloc Purely integer-location based indexing for selection by position.
DataFrame.loc Purely label-location based indexer for selection by label.

Label-based Indexing

Just like pandas, Dask DataFrame supports label-based indexing with the .loc accessor for selecting rows or columns, and __getitem__ (square brackets) for selecting just columns.

Note

To select rows, the DataFrame’s divisions must be known (see Internal Design and Dask DataFrame Performance Tips) for more.

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]},
...                   index=['a', 'b', 'c'])
>>> ddf = dd.from_pandas(df, npartitions=2)
>>> ddf
Dask DataFrame Structure:
                   A      B
npartitions=1
a              int64  int64
c                ...    ...
Dask Name: from_pandas, 1 tasks

Selecting columns:

>>> ddf[['B', 'A']]
Dask DataFrame Structure:
                   B      A
npartitions=1
a              int64  int64
c                ...    ...
Dask Name: getitem, 2 tasks

Selecting a single column reduces to a Dask Series:

>>> ddf['A']
Dask Series Structure:
npartitions=1
a    int64
c      ...
Name: A, dtype: int64
Dask Name: getitem, 2 tasks

Slicing rows and (optionally) columns with .loc:

>>> ddf.loc[['b', 'c'], ['A']]
Dask DataFrame Structure:
                   A
npartitions=1
b              int64
c                ...
Dask Name: loc, 2 tasks

Dask DataFrame supports pandas’ partial-string indexing:

>>> ts = dd.demo.make_timeseries()
>>> ts
Dask DataFrame Structure:
                   id    name        x        y
npartitions=11
2000-01-31      int64  object  float64  float64
2000-02-29        ...     ...      ...      ...
...               ...     ...      ...      ...
2000-11-30        ...     ...      ...      ...
2000-12-31        ...     ...      ...      ...
Dask Name: make-timeseries, 11 tasks

>>> ts.loc['2000-02-12']
Dask DataFrame Structure:
                                  id    name        x        y
npartitions=1
2000-02-12 00:00:00.000000000  int64  object  float64  float64
2000-02-12 23:59:59.999999999    ...     ...      ...      ...
Dask Name: loc, 12 tasks

Positional Indexing

Dask DataFrame does not track the length of partitions, making positional indexing with .iloc inefficient for selecting rows. DataFrame.iloc() only supports indexers where the row indexer is slice(None) (which : is a shorthand for.)

>>> ddf.iloc[:, [1, 0]]
Dask DataFrame Structure:
                   B      A
npartitions=1
a              int64  int64
c                ...    ...
Dask Name: iloc, 2 tasks

Trying to select specific rows with iloc will raise an exception:

>>> ddf.iloc[[0, 2], [1]]
Traceback (most recent call last)
  File "<stdin>", line 1, in <module>
ValueError: 'DataFrame.iloc' does not support slicing rows. The indexer must be a 2-tuple whose first item is 'slice(None)'.