forked from pandas-dev/pandas
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test_reshape.py
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test_reshape.py
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# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
from collections import OrderedDict
import numpy as np
from numpy import nan
import pytest
from pandas.compat import u
from pandas.core.dtypes.common import is_integer_dtype
import pandas as pd
from pandas import Categorical, DataFrame, Index, Series, get_dummies
from pandas.core.sparse.api import SparseArray, SparseDtype
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal
class TestGetDummies(object):
@pytest.fixture
def df(self):
return DataFrame({'A': ['a', 'b', 'a'],
'B': ['b', 'b', 'c'],
'C': [1, 2, 3]})
@pytest.fixture(params=['uint8', 'i8', np.float64, bool, None])
def dtype(self, request):
return np.dtype(request.param)
@pytest.fixture(params=['dense', 'sparse'])
def sparse(self, request):
# params are strings to simplify reading test results,
# e.g. TestGetDummies::test_basic[uint8-sparse] instead of [uint8-True]
return request.param == 'sparse'
def effective_dtype(self, dtype):
if dtype is None:
return np.uint8
return dtype
def test_raises_on_dtype_object(self, df):
with pytest.raises(ValueError):
get_dummies(df, dtype='object')
def test_basic(self, sparse, dtype):
s_list = list('abc')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame({'a': [1, 0, 0],
'b': [0, 1, 0],
'c': [0, 0, 1]},
dtype=self.effective_dtype(dtype))
if sparse:
expected = expected.apply(pd.SparseArray, fill_value=0.0)
result = get_dummies(s_list, sparse=sparse, dtype=dtype)
assert_frame_equal(result, expected)
result = get_dummies(s_series, sparse=sparse, dtype=dtype)
assert_frame_equal(result, expected)
expected.index = list('ABC')
result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
assert_frame_equal(result, expected)
def test_basic_types(self, sparse, dtype):
# GH 10531
s_list = list('abc')
s_series = Series(s_list)
s_df = DataFrame({'a': [0, 1, 0, 1, 2],
'b': ['A', 'A', 'B', 'C', 'C'],
'c': [2, 3, 3, 3, 2]})
expected = DataFrame({'a': [1, 0, 0],
'b': [0, 1, 0],
'c': [0, 0, 1]},
dtype=self.effective_dtype(dtype),
columns=list('abc'))
if sparse:
if is_integer_dtype(dtype):
fill_value = 0
elif dtype == bool:
fill_value = False
else:
fill_value = 0.0
expected = expected.apply(SparseArray, fill_value=fill_value)
result = get_dummies(s_list, sparse=sparse, dtype=dtype)
tm.assert_frame_equal(result, expected)
result = get_dummies(s_series, sparse=sparse, dtype=dtype)
tm.assert_frame_equal(result, expected)
result = get_dummies(s_df, columns=s_df.columns,
sparse=sparse, dtype=dtype)
if sparse:
dtype_name = 'Sparse[{}, {}]'.format(
self.effective_dtype(dtype).name,
fill_value
)
else:
dtype_name = self.effective_dtype(dtype).name
expected = Series({dtype_name: 8})
tm.assert_series_equal(result.get_dtype_counts(), expected)
result = get_dummies(s_df, columns=['a'], sparse=sparse, dtype=dtype)
expected_counts = {'int64': 1, 'object': 1}
expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
expected = Series(expected_counts).sort_index()
tm.assert_series_equal(result.get_dtype_counts().sort_index(),
expected)
def test_just_na(self, sparse):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
just_na_series_index = Series(just_na_list, index=['A'])
res_list = get_dummies(just_na_list, sparse=sparse)
res_series = get_dummies(just_na_series, sparse=sparse)
res_series_index = get_dummies(just_na_series_index, sparse=sparse)
assert res_list.empty
assert res_series.empty
assert res_series_index.empty
assert res_list.index.tolist() == [0]
assert res_series.index.tolist() == [0]
assert res_series_index.index.tolist() == ['A']
def test_include_na(self, sparse, dtype):
s = ['a', 'b', np.nan]
res = get_dummies(s, sparse=sparse, dtype=dtype)
exp = DataFrame({'a': [1, 0, 0],
'b': [0, 1, 0]},
dtype=self.effective_dtype(dtype))
if sparse:
exp = exp.apply(pd.SparseArray, fill_value=0.0)
assert_frame_equal(res, exp)
# Sparse dataframes do not allow nan labelled columns, see #GH8822
res_na = get_dummies(s, dummy_na=True, sparse=sparse, dtype=dtype)
exp_na = DataFrame({nan: [0, 0, 1],
'a': [1, 0, 0],
'b': [0, 1, 0]},
dtype=self.effective_dtype(dtype))
exp_na = exp_na.reindex(['a', 'b', nan], axis=1)
# hack (NaN handling in assert_index_equal)
exp_na.columns = res_na.columns
if sparse:
exp_na = exp_na.apply(pd.SparseArray, fill_value=0.0)
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True,
sparse=sparse, dtype=dtype)
exp_just_na = DataFrame(Series(1, index=[0]), columns=[nan],
dtype=self.effective_dtype(dtype))
tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
def test_unicode(self, sparse):
# See GH 6885 - get_dummies chokes on unicode values
import unicodedata
e = 'e'
eacute = unicodedata.lookup('LATIN SMALL LETTER E WITH ACUTE')
s = [e, eacute, eacute]
res = get_dummies(s, prefix='letter', sparse=sparse)
exp = DataFrame({'letter_e': [1, 0, 0],
u('letter_%s') % eacute: [0, 1, 1]},
dtype=np.uint8)
if sparse:
exp = exp.apply(pd.SparseArray, fill_value=0)
assert_frame_equal(res, exp)
def test_dataframe_dummies_all_obj(self, df, sparse):
df = df[['A', 'B']]
result = get_dummies(df, sparse=sparse)
expected = DataFrame({'A_a': [1, 0, 1],
'A_b': [0, 1, 0],
'B_b': [1, 1, 0],
'B_c': [0, 0, 1]},
dtype=np.uint8)
if sparse:
expected = pd.DataFrame({
"A_a": pd.SparseArray([1, 0, 1], dtype='uint8'),
"A_b": pd.SparseArray([0, 1, 0], dtype='uint8'),
"B_b": pd.SparseArray([1, 1, 0], dtype='uint8'),
"B_c": pd.SparseArray([0, 0, 1], dtype='uint8'),
})
assert_frame_equal(result, expected)
def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
result = get_dummies(df, sparse=sparse, dtype=dtype)
if sparse:
arr = SparseArray
typ = SparseDtype(dtype, 0)
else:
arr = np.array
typ = dtype
expected = DataFrame({'C': [1, 2, 3],
'A_a': arr([1, 0, 1], dtype=typ),
'A_b': arr([0, 1, 0], dtype=typ),
'B_b': arr([1, 1, 0], dtype=typ),
'B_c': arr([0, 0, 1], dtype=typ)})
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_list(self, df, sparse):
prefixes = ['from_A', 'from_B']
result = get_dummies(df, prefix=prefixes, sparse=sparse)
expected = DataFrame({'C': [1, 2, 3],
'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'from_B_b': [1, 1, 0],
'from_B_c': [0, 0, 1]},
dtype=np.uint8)
expected[['C']] = df[['C']]
cols = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
expected = expected[['C'] + cols]
typ = pd.SparseArray if sparse else pd.Series
expected[cols] = expected[cols].apply(lambda x: typ(x))
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_str(self, df, sparse):
# not that you should do this...
result = get_dummies(df, prefix='bad', sparse=sparse)
bad_columns = ['bad_a', 'bad_b', 'bad_b', 'bad_c']
expected = DataFrame([[1, 1, 0, 1, 0],
[2, 0, 1, 1, 0],
[3, 1, 0, 0, 1]],
columns=['C'] + bad_columns,
dtype=np.uint8)
expected = expected.astype({"C": np.int64})
if sparse:
# work around astyping & assigning with duplicate columns
# https://github.com/pandas-dev/pandas/issues/14427
expected = pd.concat([
pd.Series([1, 2, 3], name='C'),
pd.Series([1, 0, 1], name='bad_a', dtype='Sparse[uint8]'),
pd.Series([0, 1, 0], name='bad_b', dtype='Sparse[uint8]'),
pd.Series([1, 1, 0], name='bad_b', dtype='Sparse[uint8]'),
pd.Series([0, 0, 1], name='bad_c', dtype='Sparse[uint8]'),
], axis=1)
assert_frame_equal(result, expected)
def test_dataframe_dummies_subset(self, df, sparse):
result = get_dummies(df, prefix=['from_A'], columns=['A'],
sparse=sparse)
expected = DataFrame({'B': ['b', 'b', 'c'],
'C': [1, 2, 3],
'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0]}, dtype=np.uint8)
expected[['C']] = df[['C']]
if sparse:
cols = ['from_A_a', 'from_A_b']
expected[cols] = expected[cols].apply(lambda x: pd.SparseSeries(x))
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_sep(self, df, sparse):
result = get_dummies(df, prefix_sep='..', sparse=sparse)
expected = DataFrame({'C': [1, 2, 3],
'A..a': [1, 0, 1],
'A..b': [0, 1, 0],
'B..b': [1, 1, 0],
'B..c': [0, 0, 1]},
dtype=np.uint8)
expected[['C']] = df[['C']]
expected = expected[['C', 'A..a', 'A..b', 'B..b', 'B..c']]
if sparse:
cols = ['A..a', 'A..b', 'B..b', 'B..c']
expected[cols] = expected[cols].apply(lambda x: pd.SparseSeries(x))
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep=['..', '__'], sparse=sparse)
expected = expected.rename(columns={'B..b': 'B__b', 'B..c': 'B__c'})
assert_frame_equal(result, expected)
result = get_dummies(df, prefix_sep={'A': '..', 'B': '__'},
sparse=sparse)
assert_frame_equal(result, expected)
def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
with pytest.raises(ValueError):
get_dummies(df, prefix=['too few'], sparse=sparse)
def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
with pytest.raises(ValueError):
get_dummies(df, prefix_sep=['bad'], sparse=sparse)
def test_dataframe_dummies_prefix_dict(self, sparse):
prefixes = {'A': 'from_A', 'B': 'from_B'}
df = DataFrame({'C': [1, 2, 3],
'A': ['a', 'b', 'a'],
'B': ['b', 'b', 'c']})
result = get_dummies(df, prefix=prefixes, sparse=sparse)
expected = DataFrame({'C': [1, 2, 3],
'from_A_a': [1, 0, 1],
'from_A_b': [0, 1, 0],
'from_B_b': [1, 1, 0],
'from_B_c': [0, 0, 1]})
columns = ['from_A_a', 'from_A_b', 'from_B_b', 'from_B_c']
expected[columns] = expected[columns].astype(np.uint8)
if sparse:
expected[columns] = expected[columns].apply(
lambda x: pd.SparseSeries(x)
)
assert_frame_equal(result, expected)
def test_dataframe_dummies_with_na(self, df, sparse, dtype):
df.loc[3, :] = [np.nan, np.nan, np.nan]
result = get_dummies(df, dummy_na=True,
sparse=sparse, dtype=dtype).sort_index(axis=1)
if sparse:
arr = SparseArray
typ = SparseDtype(dtype, 0)
else:
arr = np.array
typ = dtype
expected = DataFrame({'C': [1, 2, 3, np.nan],
'A_a': arr([1, 0, 1, 0], dtype=typ),
'A_b': arr([0, 1, 0, 0], dtype=typ),
'A_nan': arr([0, 0, 0, 1], dtype=typ),
'B_b': arr([1, 1, 0, 0], dtype=typ),
'B_c': arr([0, 0, 1, 0], dtype=typ),
'B_nan': arr([0, 0, 0, 1], dtype=typ)
}).sort_index(axis=1)
assert_frame_equal(result, expected)
result = get_dummies(df, dummy_na=False, sparse=sparse, dtype=dtype)
expected = expected[['C', 'A_a', 'A_b', 'B_b', 'B_c']]
assert_frame_equal(result, expected)
def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
df['cat'] = pd.Categorical(['x', 'y', 'y'])
result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
if sparse:
arr = SparseArray
typ = SparseDtype(dtype, 0)
else:
arr = np.array
typ = dtype
expected = DataFrame({'C': [1, 2, 3],
'A_a': arr([1, 0, 1], dtype=typ),
'A_b': arr([0, 1, 0], dtype=typ),
'B_b': arr([1, 1, 0], dtype=typ),
'B_c': arr([0, 0, 1], dtype=typ),
'cat_x': arr([1, 0, 0], dtype=typ),
'cat_y': arr([0, 1, 1], dtype=typ)
}).sort_index(axis=1)
assert_frame_equal(result, expected)
@pytest.mark.parametrize('get_dummies_kwargs,expected', [
({'data': pd.DataFrame(({u'ä': ['a']}))},
pd.DataFrame({u'ä_a': [1]}, dtype=np.uint8)),
({'data': pd.DataFrame({'x': [u'ä']})},
pd.DataFrame({u'x_ä': [1]}, dtype=np.uint8)),
({'data': pd.DataFrame({'x': [u'a']}), 'prefix':u'ä'},
pd.DataFrame({u'ä_a': [1]}, dtype=np.uint8)),
({'data': pd.DataFrame({'x': [u'a']}), 'prefix_sep':u'ä'},
pd.DataFrame({u'xäa': [1]}, dtype=np.uint8))])
def test_dataframe_dummies_unicode(self, get_dummies_kwargs, expected):
# GH22084 pd.get_dummies incorrectly encodes unicode characters
# in dataframe column names
result = get_dummies(**get_dummies_kwargs)
assert_frame_equal(result, expected)
def test_basic_drop_first(self, sparse):
# GH12402 Add a new parameter `drop_first` to avoid collinearity
# Basic case
s_list = list('abc')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame({'b': [0, 1, 0],
'c': [0, 0, 1]},
dtype=np.uint8)
result = get_dummies(s_list, drop_first=True, sparse=sparse)
if sparse:
expected = expected.apply(pd.SparseArray, fill_value=0)
assert_frame_equal(result, expected)
result = get_dummies(s_series, drop_first=True, sparse=sparse)
assert_frame_equal(result, expected)
expected.index = list('ABC')
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
assert_frame_equal(result, expected)
def test_basic_drop_first_one_level(self, sparse):
# Test the case that categorical variable only has one level.
s_list = list('aaa')
s_series = Series(s_list)
s_series_index = Series(s_list, list('ABC'))
expected = DataFrame(index=np.arange(3))
result = get_dummies(s_list, drop_first=True, sparse=sparse)
assert_frame_equal(result, expected)
result = get_dummies(s_series, drop_first=True, sparse=sparse)
assert_frame_equal(result, expected)
expected = DataFrame(index=list('ABC'))
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
assert_frame_equal(result, expected)
def test_basic_drop_first_NA(self, sparse):
# Test NA handling together with drop_first
s_NA = ['a', 'b', np.nan]
res = get_dummies(s_NA, drop_first=True, sparse=sparse)
exp = DataFrame({'b': [0, 1, 0]}, dtype=np.uint8)
if sparse:
exp = exp.apply(pd.SparseArray, fill_value=0)
assert_frame_equal(res, exp)
res_na = get_dummies(s_NA, dummy_na=True, drop_first=True,
sparse=sparse)
exp_na = DataFrame(
{'b': [0, 1, 0],
nan: [0, 0, 1]},
dtype=np.uint8).reindex(['b', nan], axis=1)
if sparse:
exp_na = exp_na.apply(pd.SparseArray, fill_value=0)
assert_frame_equal(res_na, exp_na)
res_just_na = get_dummies([nan], dummy_na=True, drop_first=True,
sparse=sparse)
exp_just_na = DataFrame(index=np.arange(1))
assert_frame_equal(res_just_na, exp_just_na)
def test_dataframe_dummies_drop_first(self, df, sparse):
df = df[['A', 'B']]
result = get_dummies(df, drop_first=True, sparse=sparse)
expected = DataFrame({'A_b': [0, 1, 0],
'B_c': [0, 0, 1]},
dtype=np.uint8)
if sparse:
expected = expected.apply(pd.SparseArray, fill_value=0)
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first_with_categorical(
self, df, sparse, dtype):
df['cat'] = pd.Categorical(['x', 'y', 'y'])
result = get_dummies(df, drop_first=True, sparse=sparse)
expected = DataFrame({'C': [1, 2, 3],
'A_b': [0, 1, 0],
'B_c': [0, 0, 1],
'cat_y': [0, 1, 1]})
cols = ['A_b', 'B_c', 'cat_y']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected[['C', 'A_b', 'B_c', 'cat_y']]
if sparse:
for col in cols:
expected[col] = pd.SparseSeries(expected[col])
assert_frame_equal(result, expected)
def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
df.loc[3, :] = [np.nan, np.nan, np.nan]
result = get_dummies(df, dummy_na=True, drop_first=True,
sparse=sparse).sort_index(axis=1)
expected = DataFrame({'C': [1, 2, 3, np.nan],
'A_b': [0, 1, 0, 0],
'A_nan': [0, 0, 0, 1],
'B_c': [0, 0, 1, 0],
'B_nan': [0, 0, 0, 1]})
cols = ['A_b', 'A_nan', 'B_c', 'B_nan']
expected[cols] = expected[cols].astype(np.uint8)
expected = expected.sort_index(axis=1)
if sparse:
for col in cols:
expected[col] = pd.SparseSeries(expected[col])
assert_frame_equal(result, expected)
result = get_dummies(df, dummy_na=False, drop_first=True,
sparse=sparse)
expected = expected[['C', 'A_b', 'B_c']]
assert_frame_equal(result, expected)
def test_int_int(self):
data = Series([1, 2, 1])
result = pd.get_dummies(data)
expected = DataFrame([[1, 0],
[0, 1],
[1, 0]],
columns=[1, 2],
dtype=np.uint8)
tm.assert_frame_equal(result, expected)
data = Series(pd.Categorical(['a', 'b', 'a']))
result = pd.get_dummies(data)
expected = DataFrame([[1, 0],
[0, 1],
[1, 0]],
columns=pd.Categorical(['a', 'b']),
dtype=np.uint8)
tm.assert_frame_equal(result, expected)
def test_int_df(self, dtype):
data = DataFrame(
{'A': [1, 2, 1],
'B': pd.Categorical(['a', 'b', 'a']),
'C': [1, 2, 1],
'D': [1., 2., 1.]
}
)
columns = ['C', 'D', 'A_1', 'A_2', 'B_a', 'B_b']
expected = DataFrame([
[1, 1., 1, 0, 1, 0],
[2, 2., 0, 1, 0, 1],
[1, 1., 1, 0, 1, 0]
], columns=columns)
expected[columns[2:]] = expected[columns[2:]].astype(dtype)
result = pd.get_dummies(data, columns=['A', 'B'], dtype=dtype)
tm.assert_frame_equal(result, expected)
def test_dataframe_dummies_preserve_categorical_dtype(self, dtype):
# GH13854
for ordered in [False, True]:
cat = pd.Categorical(list("xy"), categories=list("xyz"),
ordered=ordered)
result = get_dummies(cat, dtype=dtype)
data = np.array([[1, 0, 0], [0, 1, 0]],
dtype=self.effective_dtype(dtype))
cols = pd.CategoricalIndex(cat.categories,
categories=cat.categories,
ordered=ordered)
expected = DataFrame(data, columns=cols,
dtype=self.effective_dtype(dtype))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize('sparse', [True, False])
def test_get_dummies_dont_sparsify_all_columns(self, sparse):
# GH18914
df = DataFrame.from_dict(OrderedDict([('GDP', [1, 2]),
('Nation', ['AB', 'CD'])]))
df = get_dummies(df, columns=['Nation'], sparse=sparse)
df2 = df.reindex(columns=['GDP'])
tm.assert_frame_equal(df[['GDP']], df2)
def test_get_dummies_duplicate_columns(self, df):
# GH20839
df.columns = ["A", "A", "A"]
result = get_dummies(df).sort_index(axis=1)
expected = DataFrame([[1, 1, 0, 1, 0],
[2, 0, 1, 1, 0],
[3, 1, 0, 0, 1]],
columns=['A', 'A_a', 'A_b', 'A_b', 'A_c'],
dtype=np.uint8).sort_index(axis=1)
expected = expected.astype({"A": np.int64})
tm.assert_frame_equal(result, expected)
class TestCategoricalReshape(object):
@pytest.mark.filterwarnings("ignore:\\nPanel:FutureWarning")
def test_reshaping_panel_categorical(self):
p = tm.makePanel()
p['str'] = 'foo'
df = p.to_frame()
df['category'] = df['str'].astype('category')
result = df['category'].unstack()
c = Categorical(['foo'] * len(p.major_axis))
expected = DataFrame({'A': c.copy(),
'B': c.copy(),
'C': c.copy(),
'D': c.copy()},
columns=Index(list('ABCD'), name='minor'),
index=p.major_axis.set_names('major'))
tm.assert_frame_equal(result, expected)
class TestMakeAxisDummies(object):
def test_preserve_categorical_dtype(self):
# GH13854
for ordered in [False, True]:
cidx = pd.CategoricalIndex(list("xyz"), ordered=ordered)
midx = pd.MultiIndex(levels=[['a'], cidx],
codes=[[0, 0], [0, 1]])
df = DataFrame([[10, 11]], index=midx)
expected = DataFrame([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]],
index=midx, columns=cidx)
from pandas.core.reshape.reshape import make_axis_dummies
result = make_axis_dummies(df)
tm.assert_frame_equal(result, expected)
result = make_axis_dummies(df, transform=lambda x: x)
tm.assert_frame_equal(result, expected)