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Merged
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Apr 20, 2018
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ENH: DataFrame.append preserves columns dtype if possible #19021

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b3e8b93
DataFrame.append preserves columns dtype if possible
Dec 31, 2017
093cd2c
DataFrame.append: add parametrized tests, different dtypes
Jan 4, 2018
4cb8a5b
add ids to parametrization
jreback Jan 14, 2018
ed895a4
add ordered categories
jreback Jan 14, 2018
fd1e7a5
DataFrame.append preserves columns dtype if possible
Dec 31, 2017
13537ae
DataFrame.append simplify tests
Feb 21, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.23.0.txt
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Expand Up @@ -380,6 +380,7 @@ Other Enhancements
- :class:`IntervalIndex` now supports time zone aware ``Interval`` objects (:issue:`18537`, :issue:`18538`)
- :func:`Series` / :func:`DataFrame` tab completion also returns identifiers in the first level of a :func:`MultiIndex`. (:issue:`16326`)
- :func:`read_excel()` has gained the ``nrows`` parameter (:issue:`16645`)
- :meth:`DataFrame.append` can now in more cases preserve the type of the calling dataframe's columns (e.g. if both are ``CategoricalIndex``) (:issue:`18359`)
- :func:``DataFrame.to_json`` and ``Series.to_json`` now accept an ``index`` argument which allows the user to exclude the index from the JSON output (:issue:`17394`)
- ``IntervalIndex.to_tuples()`` has gained the ``na_tuple`` parameter to control whether NA is returned as a tuple of NA, or NA itself (:issue:`18756`)
- ``Categorical.rename_categories``, ``CategoricalIndex.rename_categories`` and :attr:`Series.cat.rename_categories`
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7 changes: 5 additions & 2 deletions pandas/core/frame.py
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Expand Up @@ -6113,8 +6113,11 @@ def append(self, other, ignore_index=False, verify_integrity=False):
# index name will be reset
index = Index([other.name], name=self.index.name)

combined_columns = self.columns.tolist() + self.columns.union(
other.index).difference(self.columns).tolist()
idx_diff = other.index.difference(self.columns)
try:
combined_columns = self.columns.append(idx_diff)
except TypeError:
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so this should not be done here, rather if a TypeError happens, then Index.append handles.

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I don't understand you here, could you expand.

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In [1]: pd.Index([1, 2, 3]).append(pd.Index(list('abc')))
Out[1]: Index([1, 2, 3, 'a', 'b', 'c'], dtype='object')

In [2]: pd.Index([1, 2, 3]).append(pd.CategoricalIndex(list('abc')))
Out[2]: Index([1, 2, 3, 'a', 'b', 'c'], dtype='object')

In [3]: pd.Index([1, 2, 3]).append(pd.MultiIndex.from_product([[1], ['a']]))
Out[3]: Index([1, 2, 3, (1, 'a')], dtype='object')

In [4]: pd.Index([1, 2, 3]).append(pd.IntervalIndex.from_breaks([1, 2, 3]))
Out[4]: Index([1, 2, 3, (1, 2], (2, 3]], dtype='object')

.append`` should never raise

so you are catching something that does not happen.

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@topper-123 topper-123 Jan 2, 2018 •
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The below fails, though:

>>> pd.CategoricalIndex('A B C'.split()).append(pd.Index([1])
TypeError: cannot append a non-category item to a CategoricalIndex

This is what is tested in the if not isinstance(df_columns, (pd.IntervalIndex, pd.MultiIndex)): part of the tests. In addition, the reason why pd.IntervalIndex and pd.MultiIndex are excluded for testing there is because of reindexing issues:

>>> ii = pd.IntervalIndex.from_breaks([0,1,2])
>>> df = pd.DataFrame([[1, 2], [4, 5]], columns=ii)
>>> ser = pd.Series([7], index=['a'])
>>> combined = pd.Index(ii.tolist() + ser.index.tolist())
>>> df.reindex(columns=combined)
TypeError: unorderable types: Interval() > int()

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hmm, the MI case we did on purpose. However you cannot convert to lists and expect things to work. for the TypeError, do .astype(object) on each left and right then .append again. we never should coerce to lists.

combined_columns = self.columns.astype(object).append(idx_diff)
other = other.reindex(combined_columns, copy=False)
other = DataFrame(other.values.reshape((1, len(other))),
index=index,
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100 changes: 97 additions & 3 deletions pandas/tests/reshape/test_concat.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
from warnings import catch_warnings
from itertools import combinations, product

import datetime as dt
import dateutil
import numpy as np
from numpy.random import randn
Expand Down Expand Up @@ -829,12 +831,102 @@ def test_append_preserve_index_name(self):
result = df1.append(df2)
assert result.index.name == 'A'

indexes_can_append = [
pd.RangeIndex(3),
pd.Index([4, 5, 6]),
pd.Index([4.5, 5.5, 6.5]),
pd.Index(list('abc')),
pd.CategoricalIndex('A B C'.split()),
pd.CategoricalIndex('D E F'.split(), ordered=True),
pd.DatetimeIndex([dt.datetime(2013, 1, 3, 0, 0),
dt.datetime(2013, 1, 3, 6, 10),
dt.datetime(2013, 1, 3, 7, 12)]),
]

indexes_cannot_append_with_other = [
pd.IntervalIndex.from_breaks([0, 1, 2, 3]),
pd.MultiIndex.from_arrays(['A B C'.split(), 'D E F'.split()]),
]

all_indexes = indexes_can_append + indexes_cannot_append_with_other

@pytest.mark.parametrize("index",
all_indexes,
ids=lambda x: x.__class__.__name__)
def test_append_same_columns_type(self, index):
# GH18359

# df wider than ser
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
ser_index = index[:2]
ser = pd.Series([7, 8], index=ser_index, name=2)
result = df.append(ser)
expected = pd.DataFrame([[1., 2., 3.], [4, 5, 6], [7, 8, np.nan]],
index=[0, 1, 2],
columns=index)
assert_frame_equal(result, expected)

# ser wider than df
ser_index = index
index = index[:2]
df = pd.DataFrame([[1, 2], [4, 5]], columns=index)
ser = pd.Series([7, 8, 9], index=ser_index, name=2)
result = df.append(ser)
expected = pd.DataFrame([[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
index=[0, 1, 2],
columns=ser_index)
assert_frame_equal(result, expected)

@pytest.mark.parametrize("df_columns, series_index",
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you can just use 2 parameterize here, one for df_columns and one for series_index; you don't need combinations, pytest already does this.

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I don't get this approach to work, AFAIKS, parametrize doesn't do this for you, and I've googled it without results. Can you give an example?

combinations(indexes_can_append, r=2),
ids=lambda x: x.__class__.__name__)
def test_append_different_columns_types(self, df_columns, series_index):
# GH18359
# See also test 'test_append_different_columns_types_raises' below
# for errors raised when appending

df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
ser = pd.Series([7, 8, 9], index=series_index, name=2)

result = df.append(ser)
idx_diff = ser.index.difference(df_columns)
combined_columns = Index(df_columns.tolist()).append(idx_diff)
expected = pd.DataFrame([[1., 2., 3., np.nan, np.nan, np.nan],
[4, 5, 6, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, 7, 8, 9]],
index=[0, 1, 2],
columns=combined_columns)
assert_frame_equal(result, expected)

@pytest.mark.parametrize(
"index_can_append, index_cannot_append_with_other",
product(indexes_can_append, indexes_cannot_append_with_other),
ids=lambda x: x.__class__.__name__)
def test_append_different_columns_types_raises(
self, index_can_append, index_cannot_append_with_other):
# GH18359
# Dataframe.append will raise if IntervalIndex/MultiIndex appends
# or is appended to a different index type
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@topper-123 can you help me understand why raising is the correct thing to do here? i have a branch that fixes a Series construction bug:

    def test_constructor_dict_multiindex_reindex_flat(self):
        # construction involves reindexing with a MultiIndex corner case
        data = {('i', 'i'): 0, ('i', 'j'): 1, ('j', 'i'): 2, "j": np.nan}
        expected = Series(data)

        result = Series(expected[:-1].to_dict(), index=expected.index)
        tm.assert_series_equal(result, expected)

but the fix for that (in MultiIndex.reindex) causes this test to fail

#
# See also test 'test_append_different_columns_types' above for
# appending without raising.

df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=index_can_append)
ser = pd.Series([7, 8, 9], index=index_cannot_append_with_other,
name=2)
with pytest.raises(TypeError):
df.append(ser)

df = pd.DataFrame([[1, 2, 3], [4, 5, 6]],
columns=index_cannot_append_with_other)
ser = pd.Series([7, 8, 9], index=index_can_append, name=2)
with pytest.raises(TypeError):
df.append(ser)

def test_append_dtype_coerce(self):

# GH 4993
# appending with datetime will incorrectly convert datetime64
import datetime as dt
from pandas import NaT

df1 = DataFrame(index=[1, 2], data=[dt.datetime(2013, 1, 1, 0, 0),
dt.datetime(2013, 1, 2, 0, 0)],
Expand All @@ -845,7 +937,9 @@ def test_append_dtype_coerce(self):
dt.datetime(2013, 1, 4, 7, 10)]],
columns=['start_time', 'end_time'])

expected = concat([Series([NaT, NaT, dt.datetime(2013, 1, 3, 6, 10),
expected = concat([Series([pd.NaT,
pd.NaT,
dt.datetime(2013, 1, 3, 6, 10),
dt.datetime(2013, 1, 4, 7, 10)],
name='end_time'),
Series([dt.datetime(2013, 1, 1, 0, 0),
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9 changes: 6 additions & 3 deletions pandas/tests/reshape/test_pivot.py
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Expand Up @@ -1540,12 +1540,14 @@ def test_crosstab_normalize(self):
index=pd.Index([1, 2, 'All'],
name='a',
dtype='object'),
columns=pd.Index([3, 4], name='b'))
columns=pd.Index([3, 4], name='b',
dtype='object'))
col_normal_margins = pd.DataFrame([[0.5, 0, 0.2], [0.5, 1.0, 0.8]],
index=pd.Index([1, 2], name='a',
dtype='object'),
columns=pd.Index([3, 4, 'All'],
name='b'))
name='b',
dtype='object'))

all_normal_margins = pd.DataFrame([[0.2, 0, 0.2],
[0.2, 0.6, 0.8],
Expand All @@ -1554,7 +1556,8 @@ def test_crosstab_normalize(self):
name='a',
dtype='object'),
columns=pd.Index([3, 4, 'All'],
name='b'))
name='b',
dtype='object'))
tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='index',
margins=True), row_normal_margins)
tm.assert_frame_equal(pd.crosstab(df.a, df.b, normalize='columns',
Expand Down