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[jira] [Created] (ARROW-3766) pa.Table.from_pandas doesn't use schema ordering


Christian Thiel created ARROW-3766:
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             Summary: pa.Table.from_pandas doesn't use schema ordering
                 Key: ARROW-3766
                 URL: https://issues.apache.org/jira/browse/ARROW-3766
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
            Reporter: Christian Thiel


Pyarrow is sensitive to the order of the columns upon load of partitioned Files.
With the function {{pa.Table.from_pandas(dataframe, schema=my_schema)}} we can apply a schema to a dataframe. I noticed that the returned {{pa.Table}} object does use the ordering of pandas columns rather than the schema columns. Furthermore it is possible to have columns in the schema but not in the DataFrame (and hence in the resulting pa.Table).

This behaviour requires a lot of fiddling with the pandas Frame in the first place if we like to write compatible partitioned files. Hence I argue that for {{pa.Table.from_pandas}}, and any other comparable function, the schema should be the principal source for the Table structure and not the columns and the ordering in the pandas DataFrame. If I specify a schema I simply expect that the resulting Table actually has this schema.

Here is a little example. If you remove the reordering of df2 everything works fine:
{code:python}
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
import os
import numpy as np
import shutil

PATH_PYARROW_MANUAL = '/tmp/pyarrow_manual.pa/'

if os.path.exists(PATH_PYARROW_MANUAL):
    shutil.rmtree(PATH_PYARROW_MANUAL)
os.mkdir(PATH_PYARROW_MANUAL)

arrays = np.array([np.array([0, 1, 2]), np.array([3, 4]), np.nan, np.nan])
strings = np.array([np.nan, np.nan, 'a', 'b'])

df = pd.DataFrame([0, 0, 1, 1], columns=['partition_column'])
df.index.name='DPRD_ID'
df['arrays'] = pd.Series(arrays)
df['strings'] = pd.Series(strings)

my_schema = pa.schema([('DPRD_ID', pa.int64()),
                       ('partition_column', pa.int32()),
                       ('arrays', pa.list_(pa.int32())),
                       ('strings', pa.string()),
                       ('new_column', pa.string())])

df1 = df[df.partition_column==0]
df2 = df[df.partition_column==1][['strings', 'partition_column', 'arrays']]


table1 = pa.Table.from_pandas(df1, schema=my_schema)
table2 = pa.Table.from_pandas(df2, schema=my_schema)

pq.write_table(table1, os.path.join(PATH_PYARROW_MANUAL, '1.pa'))
pq.write_table(table2, os.path.join(PATH_PYARROW_MANUAL, '2.pa'))

pd.read_parquet(PATH_PYARROW_MANUAL)
{code}




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