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PySpark faster toPandas using mapPartitions
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import pandas as pd | |
def _map_to_pandas(rdds): | |
""" Needs to be here due to pickling issues """ | |
return [pd.DataFrame(list(rdds))] | |
def toPandas(df, n_partitions=None): | |
""" | |
Returns the contents of `df` as a local `pandas.DataFrame` in a speedy fashion. The DataFrame is | |
repartitioned if `n_partitions` is passed. | |
:param df: pyspark.sql.DataFrame | |
:param n_partitions: int or None | |
:return: pandas.DataFrame | |
""" | |
if n_partitions is not None: df = df.repartition(n_partitions) | |
df_pand = df.rdd.mapPartitions(_map_to_pandas).collect() | |
df_pand = pd.concat(df_pand) | |
df_pand.columns = df.columns | |
return df_pand |
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