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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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Doesn't work for Spark Connect, which doesn't allow the use of the KDD API.