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Object dtype for empty describe #27184

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Merged
merged 3 commits into from
Jul 3, 2019
Merged

Object dtype for empty describe #27184

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6c21eab
Object dtype for empty describe
TomAugspurger Jul 2, 2019
79622d3
nan asserts
TomAugspurger Jul 2, 2019
3b811c6
Merge branch 'master' into PR_TOOL_MERGE_PR_27184
jreback Jul 3, 2019
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6 changes: 4 additions & 2 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9644,6 +9644,7 @@ def describe_categorical_1d(data):
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
dtype = None
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]

Expand All @@ -9668,9 +9669,10 @@ def describe_categorical_1d(data):
# to maintain output shape consistency
else:
names += ['top', 'freq']
result += [None, None]
result += [np.nan, np.nan]
dtype = 'object'

return pd.Series(result, index=names, name=data.name)
return pd.Series(result, index=names, name=data.name, dtype=dtype)

def describe_1d(data):
if is_bool_dtype(data):
Expand Down
14 changes: 13 additions & 1 deletion pandas/tests/frame/test_analytics.py
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Expand Up @@ -525,6 +525,17 @@ def test_bool_describe_in_mixed_frame(self):
index=['count', 'unique', 'top', 'freq'])
tm.assert_frame_equal(result, expected)

def test_describe_empty_object(self):
# https://github.com/pandas-dev/pandas/issues/27183
df = pd.DataFrame({"A": [None, None]}, dtype=object)
result = df.describe()
expected = pd.DataFrame({"A": [0, 0, np.nan, np.nan]}, dtype=object,
index=['count', 'unique', 'top', 'freq'])
tm.assert_frame_equal(result, expected)

result = df.iloc[:0].describe()
tm.assert_frame_equal(result, expected)

def test_describe_bool_frame(self):
# GH 13891
df = pd.DataFrame({
Expand Down Expand Up @@ -595,7 +606,8 @@ def test_describe_empty_categorical_column(self):
df = pd.DataFrame({"empty_col": Categorical([])})
result = df.describe()
expected = DataFrame({'empty_col': [0, 0, None, None]},
index=['count', 'unique', 'top', 'freq'])
index=['count', 'unique', 'top', 'freq'],
dtype='object')
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This was added in 0.25.0. Returning object dtype isn't an (additional) API change from 0.24

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should these be np.nan? (and not None); I am not sure we actually check this in the comparator.

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It wasn't clear to me whether I should use None or NaN. Arguments for NaN instead of None

  1. it's our most consistent missing value marker
  2. We often translate None to NaN implicitly

but I don't know what's best.

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right I think we should np.nan; however I don't remember if we actually test this in assert_series_equal; maybe the check_exact arg does this but I don't remember.

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nice catch

In [4]: a = pd.Series([None], dtype=object)

In [5]: b = pd.Series([np.nan], dtype=object)

In [6]: pd.util.testing.assert_series_equal(a, b)

tm.assert_frame_equal(result, expected)

def test_describe_categorical_columns(self):
Expand Down
11 changes: 11 additions & 0 deletions pandas/tests/series/test_analytics.py
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Expand Up @@ -42,6 +42,17 @@ def test_describe(self):
index=['count', 'unique', 'top', 'freq'])
tm.assert_series_equal(result, expected)

def test_describe_empty_object(self):
# https://github.com/pandas-dev/pandas/issues/27183
s = pd.Series([None, None], dtype=object)
result = s.describe()
expected = pd.Series([0, 0, np.nan, np.nan], dtype=object,
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actually you use np.nan here

index=['count', 'unique', 'top', 'freq'])
tm.assert_series_equal(result, expected)

result = s[:0].describe()
tm.assert_series_equal(result, expected)

def test_describe_with_tz(self, tz_naive_fixture):
# GH 21332
tz = tz_naive_fixture
Expand Down