I am writing a parquet file from a Spark DataFrame the following way:
df.write.parquet(\"path/myfile.parquet\", mode = \"overwrite\", compression=\"gzip\")
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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 = dataset.read()
df = table.to_pandas()
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 = arrow_dataset.read()
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 = datetime.datetime.now()
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 = datetime.datetime.now()
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)
else:
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.