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BUG: Ensure min_itemsize is always a list (#11412) #14728

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BUG: Ensure min_itemsize is always a list (#11412) #14728

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BUG: Ensure min_itemsize is always a list
toobaz Nov 24, 2016
b9bb88f
Tests for previous commit
toobaz Nov 23, 2016
e25cd1f
Whatsnew
toobaz Nov 23, 2016
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.19.2.txt
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Expand Up @@ -59,6 +59,7 @@ Bug Fixes


- Bug ``HDFStore`` writing a ``MultiIndex`` when using ``data_columns=True`` (:issue:`14435`)
- Bug in ``HDFStore.append()`` when writing a ``Series`` and passing a ``min_itemsize`` argument containing a value for ``index`` (:issue:`11412`)
- Bug in ``Series.groupby.nunique()`` raising an ``IndexError`` for an empty ``Series`` (:issue:`12553`)


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4 changes: 2 additions & 2 deletions pandas/io/pytables.py
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Expand Up @@ -3315,7 +3315,7 @@ def validate_data_columns(self, data_columns, min_itemsize):
# evaluate the passed data_columns, True == use all columns
# take only valide axis labels
if data_columns is True:
data_columns = axis_labels
data_columns = list(axis_labels)
elif data_columns is None:
data_columns = []

Expand Down Expand Up @@ -4153,7 +4153,7 @@ def write(self, obj, data_columns=None, **kwargs):
obj = DataFrame({name: obj}, index=obj.index)
obj.columns = [name]
return super(AppendableSeriesTable, self).write(
obj=obj, data_columns=obj.columns, **kwargs)
obj=obj, data_columns=obj.columns.tolist(), **kwargs)

def read(self, columns=None, **kwargs):

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10 changes: 10 additions & 0 deletions pandas/io/tests/test_pytables.py
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Expand Up @@ -1362,6 +1362,16 @@ def check_col(key, name, size):
[[124, 'abcdefqhij'], [346, 'abcdefghijklmnopqrtsuvwxyz']])
self.assertRaises(ValueError, store.append, 'df_new', df_new)

# min_itemsize on Series index (GH 11412)
df = tm.makeMixedDataFrame().set_index('C')
store.append('ss', df['B'], min_itemsize={'index': 4})
tm.assert_series_equal(store.select('ss'), df['B'])

# same as above, with data_columns=True
store.append('ss2', df['B'], data_columns=True,
min_itemsize={'index': 4})
tm.assert_series_equal(store.select('ss2'), df['B'])

# with nans
_maybe_remove(store, 'df')
df = tm.makeTimeDataFrame()
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