Dave Hirschfeld created ARROW-2799:
--------------------------------------
Summary: Table.from_pandas silently truncates data, even when
passed a schema
Key: ARROW-2799
URL: https://issues.apache.org/jira/browse/ARROW-2799
Project: Apache Arrow
Issue Type: Bug
Components: Python
Affects Versions: 0.9.0
Reporter: Dave Hirschfeld
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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