Pandas cannot read parquet files created in PySpark

The problem is that Spark partitions the file due to its distributed nature (each executor writes a file inside the directory that receives the filename). This is not something supported by Pandas, which expects a file, not a path.

You can circumvent this issue in different ways:

  • Reading the file with an alternative utility, such as the pyarrow.parquet.ParquetDataset, and then convert that to Pandas (I did not test this code).

      arrow_dataset = pyarrow.parquet.ParquetDataset('path/myfile.parquet')
      arrow_table =
      pandas_df = arrow_table.to_pandas()
  • Another way is to read the separate fragments separately and then concatenate them, as this answer suggest: Read multiple parquet files in a folder and write to single csv file using python

Since this still seems to be an issue even with newer pandas versions, I wrote some functions to circumvent this as part of a larger pyspark helpers library:

import pandas as pd
import datetime

def read_parquet_folder_as_pandas(path, verbosity=1):
  files = [f for f in os.listdir(path) if f.endswith("parquet")]

  if verbosity > 0:
    print("{} parquet files found. Beginning reading...".format(len(files)), end="")
    start =

  df_list = [pd.read_parquet(os.path.join(path, f)) for f in files]
  df = pd.concat(df_list, ignore_index=True)

  if verbosity > 0:
    end =
    print(" Finished. Took {}".format(end-start))
  return df

def read_parquet_as_pandas(path, verbosity=1):
  """Workaround for pandas not being able to read folder-style parquet files.
  if os.path.isdir(path):
    if verbosity>1: print("Parquet file is actually folder.")
    return read_parquet_folder_as_pandas(path, verbosity)
    return pd.read_parquet(path)

This assumes that the relevant files in the parquet "file", which is actually a folder, end with ".parquet". This works for parquet files exported by databricks and might work with others as well (untested, happy about feedback in the comments).

The function read_parquet_as_pandas() can be used if it is not known beforehand whether it is a folder or not.

If the parquet file has been created with spark, (so it's a directory) to import it to pandas use

from pyarrow.parquet import ParquetDataset

dataset = ParquetDataset("file.parquet")
table =
df = table.to_pandas()