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ASF GitHub Bot commented on ARROW-1883: --------------------------------------- cpcloud commented on issue #1386: ARROW-1883: [Python] Fix handling of metadata in to_pandas when not all columns are present URL: https://github.com/apache/arrow/pull/1386#issuecomment-349056473 The travis failure looks unrelated. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org > [Python] BUG: Table.to_pandas metadata checking fails if columns are not > present > -------------------------------------------------------------------------------- > > Key: ARROW-1883 > URL: https://issues.apache.org/jira/browse/ARROW-1883 > Project: Apache Arrow > Issue Type: Bug > Components: Python > Affects Versions: 0.7.1 > Reporter: Joris Van den Bossche > Assignee: Phillip Cloud > Labels: pull-request-available > > Found this bug in the example in the pandas documentation > (http://pandas-docs.github.io/pandas-docs-travis/io.html#parquet), which does: > {code} > df = pd.DataFrame({'a': list('abc'), > 'b': list(range(1, 4)), > 'c': np.arange(3, 6).astype('u1'), > 'd': np.arange(4.0, 7.0, dtype='float64'), > 'e': [True, False, True], > 'f': pd.date_range('20130101', periods=3), > 'g': pd.date_range('20130101', periods=3, > tz='US/Eastern')}) > df.to_parquet('example_pa.parquet', engine='pyarrow') > pd.read_parquet('example_pa.parquet', engine='pyarrow', columns=['a', 'b']) > {code} > and this raises in the last line reading a subset of columns: > {code} > ... > /home/joris/miniconda3/envs/dev/lib/python3.5/site-packages/pyarrow/pandas_compat.py > in _add_any_metadata(table, pandas_metadata) > 357 for i, col_meta in enumerate(pandas_metadata['columns']): > 358 if col_meta['pandas_type'] == 'datetimetz': > --> 359 col = table[i] > 360 converted = col.to_pandas() > 361 tz = col_meta['metadata']['timezone'] > table.pxi in pyarrow.lib.Table.__getitem__() > table.pxi in pyarrow.lib.Table.column() > IndexError: Table column index 6 is out of range > {code} > This is due to checking the `pandas_metadata` for all columns (and in this > case trying to deal with a datetime tz column), while in practice not all > columns are present in this case ('mismatch' between pandas metadata and > actual schema). > A smaller example without parquet: > {code} > In [38]: df = pd.DataFrame({'a': [1, 2, 3], 'b': pd.date_range("2017-01-01", > periods=3, tz='Europe/Brussels')}) > In [39]: table = pyarrow.Table.from_pandas(df) > In [40]: table > Out[40]: > pyarrow.Table > a: int64 > b: timestamp[ns, tz=Europe/Brussels] > __index_level_0__: int64 > metadata > -------- > {b'pandas': b'{"columns": [{"pandas_type": "int64", "metadata": null, > "numpy_t' > b'ype": "int64", "name": "a"}, {"pandas_type": "datetimetz", > "meta' > b'data": {"timezone": "Europe/Brussels"}, "numpy_type": > "datetime6' > b'4[ns, Europe/Brussels]", "name": "b"}, {"pandas_type": "int64", > ' > b'"metadata": null, "numpy_type": "int64", "name": > "__index_level_' > b'0__"}], "index_columns": ["__index_level_0__"], > "pandas_version"' > b': "0.22.0.dev0+277.gd61f411"}'} > In [41]: table.to_pandas() > Out[41]: > a b > 0 1 2017-01-01 00:00:00+01:00 > 1 2 2017-01-02 00:00:00+01:00 > 2 3 2017-01-03 00:00:00+01:00 > In [44]: table_without_tz = table.remove_column(1) > In [45]: table_without_tz > Out[45]: > pyarrow.Table > a: int64 > __index_level_0__: int64 > metadata > -------- > {b'pandas': b'{"columns": [{"pandas_type": "int64", "metadata": null, > "numpy_t' > b'ype": "int64", "name": "a"}, {"pandas_type": "datetimetz", > "meta' > b'data": {"timezone": "Europe/Brussels"}, "numpy_type": > "datetime6' > b'4[ns, Europe/Brussels]", "name": "b"}, {"pandas_type": "int64", > ' > b'"metadata": null, "numpy_type": "int64", "name": > "__index_level_' > b'0__"}], "index_columns": ["__index_level_0__"], > "pandas_version"' > b': "0.22.0.dev0+277.gd61f411"}'} > In [46]: table_without_tz.to_pandas() # <------ wrong output ! > Out[46]: > a > 1970-01-01 01:00:00+01:00 1 > 1970-01-01 01:00:00.000000001+01:00 2 > 1970-01-01 01:00:00.000000002+01:00 3 > In [47]: table_without_tz2 = table_without_tz.remove_column(1) > In [48]: table_without_tz2 > Out[48]: > pyarrow.Table > a: int64 > metadata > -------- > {b'pandas': b'{"columns": [{"pandas_type": "int64", "metadata": null, > "numpy_t' > b'ype": "int64", "name": "a"}, {"pandas_type": "datetimetz", > "meta' > b'data": {"timezone": "Europe/Brussels"}, "numpy_type": > "datetime6' > b'4[ns, Europe/Brussels]", "name": "b"}, {"pandas_type": "int64", > ' > b'"metadata": null, "numpy_type": "int64", "name": > "__index_level_' > b'0__"}], "index_columns": ["__index_level_0__"], > "pandas_version"' > b': "0.22.0.dev0+277.gd61f411"}'} > In [49]: table_without_tz2.to_pandas() # <------ error ! > --------------------------------------------------------------------------- > IndexError Traceback (most recent call last) > <ipython-input-49-c82f33476c6b> in <module>() > ----> 1 table_without_tz2.to_pandas() > table.pxi in pyarrow.lib.Table.to_pandas() > /home/joris/miniconda3/envs/dev/lib/python3.5/site-packages/pyarrow/pandas_compat.py > in table_to_blockmanager(options, table, memory_pool, nthreads) > 289 pandas_metadata = > json.loads(metadata[b'pandas'].decode('utf8')) > 290 index_columns = pandas_metadata['index_columns'] > --> 291 table = _add_any_metadata(table, pandas_metadata) > 292 > 293 block_table = table > /home/joris/miniconda3/envs/dev/lib/python3.5/site-packages/pyarrow/pandas_compat.py > in _add_any_metadata(table, pandas_metadata) > 357 for i, col_meta in enumerate(pandas_metadata['columns']): > 358 if col_meta['pandas_type'] == 'datetimetz': > --> 359 col = table[i] > 360 converted = col.to_pandas() > 361 tz = col_meta['metadata']['timezone'] > table.pxi in pyarrow.lib.Table.__getitem__() > table.pxi in pyarrow.lib.Table.column() > IndexError: Table column index 1 is out of range > {code} > The reason is that `_add_any_metadata` does not check if the column it is > processing (currently only datetime tz columns need such processing) is > actually present in the schema. > Working on a fix, will submit a PR. -- This message was sent by Atlassian JIRA (v6.4.14#64029)