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https://issues.apache.org/jira/browse/ARROW-2799?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Krisztian Szucs updated ARROW-2799:
-----------------------------------
Summary: [C++/Python] Add safe option to Table.from_pandas to avoid unsafe
casts (was: [Python] Add safe option to Table.from_pandas to avoid unsafe
casts)
> [C++/Python] Add safe option to Table.from_pandas to avoid unsafe casts
> -----------------------------------------------------------------------
>
> Key: ARROW-2799
> URL: https://issues.apache.org/jira/browse/ARROW-2799
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Affects Versions: 0.9.0
> Reporter: Dave Hirschfeld
> Assignee: Krisztian Szucs
> Priority: Major
> Fix For: 0.11.0
>
>
> Ported over from [https://github.com/apache/arrow/issues/2217]
> ```python
> In [8]: import pandas as pd
> ...: import pyarrow as arw
> In [9]: df = pd.DataFrame({'A': list('abc'), 'B': np.arange(3)})
> ...: df
> Out[9]:
> A B
> 0 a 0
> 1 b 1
> 2 c 2
> In [10]: schema = arw.schema([
> ...: arw.field('A', arw.string()),
> ...: arw.field('B', arw.int32()),
> ...: ])
> In [11]: tbl = arw.Table.from_pandas(df, preserve_index=False, schema=schema)
> ...: tbl
> Out[11]:
> pyarrow.Table
> A: string
> B: int32
> metadata
> --------
> {b'pandas': b'{"index_columns": [], "column_indexes": [], "columns":
> [{"name":'
> b' "A", "field_name": "A", "pandas_type": "unicode",
> "numpy_type":'
> b' "object", "metadata": null}, {"name": "B", "field_name": "B",
> "'
> b'pandas_type": "int32", "numpy_type": "int32", "metadata":
> null}]'
> b', "pandas_version": "0.23.1"}'}
> In [12]: tbl.to_pandas().equals(df)
> Out[12]: True
> ```
> ...so if the `schema` matches the pandas datatypes all is well - we can
> roundtrip the DataFrame.
> Now, say we have some bad data such that column 'B' is now of type float64.
> The datatypes of the DataFrame don't match the explicitly supplied `schema`
> object but rather than raising a `TypeError` the data is silently truncated
> and the roundtrip DataFrame doesn't match our input DataFame without even a
> warning raised!
> ```python
> In [13]: df['B'].iloc[0] = 1.23
> ...: df
> Out[13]:
> A B
> 0 a 1.23
> 1 b 1.00
> 2 c 2.00
> In [14]: # I would expect/want this to raise a TypeError since the schema
> doesn't match the pandas datatypes
> ...: tbl = arw.Table.from_pandas(df, preserve_index=False, schema=schema)
> ...: tbl
> Out[14]:
> pyarrow.Table
> A: string
> B: int32
> metadata
> --------
> {b'pandas': b'{"index_columns": [], "column_indexes": [], "columns":
> [{"name":'
> b' "A", "field_name": "A", "pandas_type": "unicode",
> "numpy_type":'
> b' "object", "metadata": null}, {"name": "B", "field_name": "B",
> "'
> b'pandas_type": "int32", "numpy_type": "float64", "metadata":
> null'
> b'}], "pandas_version": "0.23.1"}'}
> In [15]: tbl.to_pandas() # <-- SILENT TRUNCATION!!!
> Out[15]:
> A B
> 0 a 1
> 1 b 1
> 2 c 2
> ```
> To be clear, I would really like `Table.from_pandas` to raise a `TypeError`
> if the DataFrame types don't match an explicitly supplied schema and would
> hope this current behaviour would be considered a bug.
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