- dask.dataframe.from_pandas(data: pandas.core.frame.DataFrame, npartitions: int | None = None, chunksize: int | None = None, sort: bool = True, name: str | None = None) dask.dataframe.core.DataFrame ¶
- dask.dataframe.from_pandas(data: pandas.core.series.Series, npartitions: int | None = None, chunksize: int | None = None, sort: bool = True, name: str | None = None) dask.dataframe.core.Series
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=Falseoption 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.
- 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
- dask.DataFrame or dask.Series
A dask DataFrame/Series partitioned along the index
If something other than a
pandas.Seriesis passed in.
>>> 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'))