如何使用 Pandas 编写分区的 Parquet 文件

Question

I'm trying to write a Pandas dataframe to a partitioned file:

df.to_parquet('output.parquet', engine='pyarrow', partition_cols = ['partone', 'partwo'])

TypeError: __cinit__() got an unexpected keyword argument 'partition_cols'

From the documentation I expected that the partition_cols would be passed as a kwargs to the pyarrow library. How can a partitioned file be written to local disk using pandas?

Answer 1

Pandas DataFrame.to_parquet is a thin wrapper over table = pa.Table.from_pandas(...) and pq.write_table(table, ...) (see pandas.parquet.py#L120 ), and pq.write_table does not support writing partitioned datasets. You should use pq.write_to_dataset instead.

import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq

df = pd.DataFrame(yourData)
table = pa.Table.from_pandas(df)

pq.write_to_dataset(
    table,
    root_path='output.parquet',
    partition_cols=['partone', 'parttwo'],
)

For more info, see pyarrow documentation .

In general, I would always use the PyArrow API directly when reading / writing parquet files, since the Pandas wrapper is rather limited in what it can do.

Answer 2

You need to update to Pandas version 0.24 or above. The functionality of partition_cols is added from that version onwards.

Answer 3

First make sure that you have a reasonably recent version of pandas and pyarrow:

pyenv shell 3.8.2
python -m venv venv
source venv/bin/activate
pip install pandas pyarrow
pip freeze | grep pandas # pandas==1.2.3
pip freeze | grep pyarrow # pyarrow==3.0.0

Then you can use partition_cols to produce the partitioned parquet files:

import pandas as pd

# example dataframe with 3 rows and columns year,month,day,value
df = pd.DataFrame(data={'year':  [2020, 2020, 2021],
                        'month': [1,12,2], 
                        'day':   [1,31,28], 
                        'value': [1000,2000,3000]})

df.to_parquet('./mydf', partition_cols=['year', 'month', 'day'])

This produces:

mydf/year=2020/month=1/day=1/6f0258e6c48a48dbb56cae0494adf659.parquet
mydf/year=2020/month=12/day=31/cf8a45116d8441668c3a397b816cd5f3.parquet
mydf/year=2021/month=2/day=28/7f9ba3f37cb9417a8689290d3f5f9e6e.parquet