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test_categorical.py
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test_categorical.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime
import pytest
import numpy as np
from numpy import nan
import pandas as pd
from pandas import (Index, MultiIndex, CategoricalIndex,
DataFrame, Categorical, Series, Interval)
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas.util.testing as tm
from .common import MixIn
class TestGroupByCategorical(MixIn, tm.TestCase):
def test_level_groupby_get_group(self):
# GH15155
df = DataFrame(data=np.arange(2, 22, 2),
index=MultiIndex(
levels=[pd.CategoricalIndex(["a", "b"]), range(10)],
labels=[[0] * 5 + [1] * 5, range(10)],
names=["Index1", "Index2"]))
g = df.groupby(level=["Index1"])
# expected should equal test.loc[["a"]]
# GH15166
expected = DataFrame(data=np.arange(2, 12, 2),
index=pd.MultiIndex(levels=[pd.CategoricalIndex(
["a", "b"]), range(5)],
labels=[[0] * 5, range(5)],
names=["Index1", "Index2"]))
result = g.get_group('a')
assert_frame_equal(result, expected)
def test_apply_use_categorical_name(self):
from pandas import qcut
cats = qcut(self.df.C, 4)
def get_stats(group):
return {'min': group.min(),
'max': group.max(),
'count': group.count(),
'mean': group.mean()}
result = self.df.groupby(cats).D.apply(get_stats)
self.assertEqual(result.index.names[0], 'C')
def test_apply_categorical_data(self):
# GH 10138
for ordered in [True, False]:
dense = Categorical(list('abc'), ordered=ordered)
# 'b' is in the categories but not in the list
missing = Categorical(
list('aaa'), categories=['a', 'b'], ordered=ordered)
values = np.arange(len(dense))
df = DataFrame({'missing': missing,
'dense': dense,
'values': values})
grouped = df.groupby(['missing', 'dense'])
# missing category 'b' should still exist in the output index
idx = MultiIndex.from_product(
[Categorical(['a', 'b'], ordered=ordered),
Categorical(['a', 'b', 'c'], ordered=ordered)],
names=['missing', 'dense'])
expected = DataFrame([0, 1, 2, np.nan, np.nan, np.nan],
index=idx,
columns=['values'])
assert_frame_equal(grouped.apply(lambda x: np.mean(x)), expected)
assert_frame_equal(grouped.mean(), expected)
assert_frame_equal(grouped.agg(np.mean), expected)
# but for transform we should still get back the original index
idx = MultiIndex.from_product([['a'], ['a', 'b', 'c']],
names=['missing', 'dense'])
expected = Series(1, index=idx)
assert_series_equal(grouped.apply(lambda x: 1), expected)
def test_groupby_categorical(self):
levels = ['foo', 'bar', 'baz', 'qux']
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
exp_idx = CategoricalIndex(levels, categories=cats.categories,
ordered=True)
expected = expected.reindex(exp_idx)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = np.asarray(cats).take(idx)
ord_data = data.take(idx)
exp_cats = Categorical(ord_labels, ordered=True,
categories=['foo', 'bar', 'baz', 'qux'])
expected = ord_data.groupby(exp_cats, sort=False).describe()
assert_frame_equal(desc_result, expected)
# GH 10460
expc = Categorical.from_codes(np.arange(4).repeat(8),
levels, ordered=True)
exp = CategoricalIndex(expc)
tm.assert_index_equal((desc_result.stack().index
.get_level_values(0)), exp)
exp = Index(['count', 'mean', 'std', 'min', '25%', '50%',
'75%', 'max'] * 4)
tm.assert_index_equal((desc_result.stack().index
.get_level_values(1)), exp)
def test_groupby_datetime_categorical(self):
# GH9049: ensure backward compatibility
levels = pd.date_range('2014-01-01', periods=4)
codes = np.random.randint(0, 4, size=100)
cats = Categorical.from_codes(codes, levels, ordered=True)
data = DataFrame(np.random.randn(100, 4))
result = data.groupby(cats).mean()
expected = data.groupby(np.asarray(cats)).mean()
expected = expected.reindex(levels)
expected.index = CategoricalIndex(expected.index,
categories=expected.index,
ordered=True)
assert_frame_equal(result, expected)
grouped = data.groupby(cats)
desc_result = grouped.describe()
idx = cats.codes.argsort()
ord_labels = cats.take_nd(idx)
ord_data = data.take(idx)
expected = ord_data.groupby(ord_labels).describe()
assert_frame_equal(desc_result, expected)
tm.assert_index_equal(desc_result.index, expected.index)
tm.assert_index_equal(
desc_result.index.get_level_values(0),
expected.index.get_level_values(0))
# GH 10460
expc = Categorical.from_codes(
np.arange(4).repeat(8), levels, ordered=True)
exp = CategoricalIndex(expc)
tm.assert_index_equal((desc_result.stack().index
.get_level_values(0)), exp)
exp = Index(['count', 'mean', 'std', 'min', '25%', '50%',
'75%', 'max'] * 4)
tm.assert_index_equal((desc_result.stack().index
.get_level_values(1)), exp)
def test_groupby_categorical_index(self):
s = np.random.RandomState(12345)
levels = ['foo', 'bar', 'baz', 'qux']
codes = s.randint(0, 4, size=20)
cats = Categorical.from_codes(codes, levels, ordered=True)
df = DataFrame(
np.repeat(
np.arange(20), 4).reshape(-1, 4), columns=list('abcd'))
df['cats'] = cats
# with a cat index
result = df.set_index('cats').groupby(level=0).sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(
Categorical.from_codes(
[0, 1, 2, 3], levels, ordered=True), name='cats')
assert_frame_equal(result, expected)
# with a cat column, should produce a cat index
result = df.groupby('cats').sum()
expected = df[list('abcd')].groupby(cats.codes).sum()
expected.index = CategoricalIndex(
Categorical.from_codes(
[0, 1, 2, 3], levels, ordered=True), name='cats')
assert_frame_equal(result, expected)
def test_groupby_describe_categorical_columns(self):
# GH 11558
cats = pd.CategoricalIndex(['qux', 'foo', 'baz', 'bar'],
categories=['foo', 'bar', 'baz', 'qux'],
ordered=True)
df = DataFrame(np.random.randn(20, 4), columns=cats)
result = df.groupby([1, 2, 3, 4] * 5).describe()
tm.assert_index_equal(result.stack().columns, cats)
tm.assert_categorical_equal(result.stack().columns.values, cats.values)
def test_groupby_unstack_categorical(self):
# GH11558 (example is taken from the original issue)
df = pd.DataFrame({'a': range(10),
'medium': ['A', 'B'] * 5,
'artist': list('XYXXY') * 2})
df['medium'] = df['medium'].astype('category')
gcat = df.groupby(['artist', 'medium'])['a'].count().unstack()
result = gcat.describe()
exp_columns = pd.CategoricalIndex(['A', 'B'], ordered=False,
name='medium')
tm.assert_index_equal(result.columns, exp_columns)
tm.assert_categorical_equal(result.columns.values, exp_columns.values)
result = gcat['A'] + gcat['B']
expected = pd.Series([6, 4], index=pd.Index(['X', 'Y'], name='artist'))
tm.assert_series_equal(result, expected)
def test_groupby_bins_unequal_len(self):
# GH3011
series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
bins = pd.cut(series.dropna().values, 4)
# len(bins) != len(series) here
def f():
series.groupby(bins).mean()
pytest.raises(ValueError, f)
def test_groupby_multi_categorical_as_index(self):
# GH13204
df = DataFrame({'cat': Categorical([1, 2, 2], [1, 2, 3]),
'A': [10, 11, 11],
'B': [101, 102, 103]})
result = df.groupby(['cat', 'A'], as_index=False).sum()
expected = DataFrame({'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10, 11, 10, 11, 10, 11],
'B': [101.0, nan, nan, 205.0, nan, nan]},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# function grouper
f = lambda r: df.loc[r, 'A']
result = df.groupby(['cat', f], as_index=False).sum()
expected = DataFrame({'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10.0, nan, nan, 22.0, nan, nan],
'B': [101.0, nan, nan, 205.0, nan, nan]},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# another not in-axis grouper (conflicting names in index)
s = Series(['a', 'b', 'b'], name='cat')
result = df.groupby(['cat', s], as_index=False).sum()
expected = DataFrame({'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10.0, nan, nan, 22.0, nan, nan],
'B': [101.0, nan, nan, 205.0, nan, nan]},
columns=['cat', 'A', 'B'])
tm.assert_frame_equal(result, expected)
# is original index dropped?
expected = DataFrame({'cat': Categorical([1, 1, 2, 2, 3, 3]),
'A': [10, 11, 10, 11, 10, 11],
'B': [101.0, nan, nan, 205.0, nan, nan]},
columns=['cat', 'A', 'B'])
group_columns = ['cat', 'A']
for name in [None, 'X', 'B', 'cat']:
df.index = Index(list("abc"), name=name)
if name in group_columns and name in df.index.names:
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = df.groupby(group_columns, as_index=False).sum()
else:
result = df.groupby(group_columns, as_index=False).sum()
tm.assert_frame_equal(result, expected, check_index_type=True)
def test_groupby_preserve_categories(self):
# GH-13179
categories = list('abc')
# ordered=True
df = DataFrame({'A': pd.Categorical(list('ba'),
categories=categories,
ordered=True)})
index = pd.CategoricalIndex(categories, categories, ordered=True)
tm.assert_index_equal(df.groupby('A', sort=True).first().index, index)
tm.assert_index_equal(df.groupby('A', sort=False).first().index, index)
# ordered=False
df = DataFrame({'A': pd.Categorical(list('ba'),
categories=categories,
ordered=False)})
sort_index = pd.CategoricalIndex(categories, categories, ordered=False)
nosort_index = pd.CategoricalIndex(list('bac'), list('bac'),
ordered=False)
tm.assert_index_equal(df.groupby('A', sort=True).first().index,
sort_index)
tm.assert_index_equal(df.groupby('A', sort=False).first().index,
nosort_index)
def test_groupby_preserve_categorical_dtype(self):
# GH13743, GH13854
df = DataFrame({'A': [1, 2, 1, 1, 2],
'B': [10, 16, 22, 28, 34],
'C1': Categorical(list("abaab"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("abaab"),
categories=list("bac"),
ordered=True)})
# single grouper
exp_full = DataFrame({'A': [2.0, 1.0, np.nan],
'B': [25.0, 20.0, np.nan],
'C1': Categorical(list("bac"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("bac"),
categories=list("bac"),
ordered=True)})
for col in ['C1', 'C2']:
result1 = df.groupby(by=col, as_index=False).mean()
result2 = df.groupby(by=col, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
# multiple grouper
exp_full = DataFrame({'A': [1, 1, 1, 2, 2, 2],
'B': [np.nan, 20.0, np.nan, 25.0, np.nan,
np.nan],
'C1': Categorical(list("bacbac"),
categories=list("bac"),
ordered=False),
'C2': Categorical(list("bacbac"),
categories=list("bac"),
ordered=True)})
for cols in [['A', 'C1'], ['A', 'C2']]:
result1 = df.groupby(by=cols, as_index=False).mean()
result2 = df.groupby(by=cols, as_index=True).mean().reset_index()
expected = exp_full.reindex(columns=result1.columns)
tm.assert_frame_equal(result1, expected)
tm.assert_frame_equal(result2, expected)
def test_groupby_categorical_no_compress(self):
data = Series(np.random.randn(9))
codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean()
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3])
cats = Categorical.from_codes(codes, [0, 1, 2, 3], ordered=True)
result = data.groupby(cats).mean()
exp = data.groupby(codes).mean().reindex(cats.categories)
exp.index = CategoricalIndex(exp.index, categories=cats.categories,
ordered=cats.ordered)
assert_series_equal(result, exp)
cats = Categorical(["a", "a", "a", "b", "b", "b", "c", "c", "c"],
categories=["a", "b", "c", "d"], ordered=True)
data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
result = data.groupby("b").mean()
result = result["a"].values
exp = np.array([1, 2, 4, np.nan])
tm.assert_numpy_array_equal(result, exp)
def test_groupby_sort_categorical(self):
# dataframe groupby sort was being ignored # GH 8868
df = DataFrame([['(7.5, 10]', 10, 10],
['(7.5, 10]', 8, 20],
['(2.5, 5]', 5, 30],
['(5, 7.5]', 6, 40],
['(2.5, 5]', 4, 50],
['(0, 2.5]', 1, 60],
['(5, 7.5]', 7, 70]], columns=['range', 'foo', 'bar'])
df['range'] = Categorical(df['range'], ordered=True)
index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]',
'(7.5, 10]'], name='range', ordered=True)
result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]],
columns=['foo', 'bar'], index=index)
col = 'range'
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
# when categories is ordered, group is ordered by category's order
assert_frame_equal(result_sort, df.groupby(col, sort=False).first())
df['range'] = Categorical(df['range'], ordered=False)
index = CategoricalIndex(['(0, 2.5]', '(2.5, 5]', '(5, 7.5]',
'(7.5, 10]'], name='range')
result_sort = DataFrame([[1, 60], [5, 30], [6, 40], [10, 10]],
columns=['foo', 'bar'], index=index)
index = CategoricalIndex(['(7.5, 10]', '(2.5, 5]', '(5, 7.5]',
'(0, 2.5]'],
categories=['(7.5, 10]', '(2.5, 5]',
'(5, 7.5]', '(0, 2.5]'],
name='range')
result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
index=index, columns=['foo', 'bar'])
col = 'range'
# this is an unordered categorical, but we allow this ####
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
assert_frame_equal(result_nosort, df.groupby(col, sort=False).first())
def test_groupby_sort_categorical_datetimelike(self):
# GH10505
# use same data as test_groupby_sort_categorical, which category is
# corresponding to datetime.month
df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
datetime(2011, 2, 1), datetime(2011, 5, 1),
datetime(2011, 2, 1), datetime(2011, 1, 1),
datetime(2011, 5, 1)],
'foo': [10, 8, 5, 6, 4, 1, 7],
'bar': [10, 20, 30, 40, 50, 60, 70]},
columns=['dt', 'foo', 'bar'])
# ordered=True
df['dt'] = Categorical(df['dt'], ordered=True)
index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
datetime(2011, 5, 1), datetime(2011, 7, 1)]
result_sort = DataFrame(
[[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
result_sort.index = CategoricalIndex(index, name='dt', ordered=True)
index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
datetime(2011, 5, 1), datetime(2011, 1, 1)]
result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
columns=['foo', 'bar'])
result_nosort.index = CategoricalIndex(index, categories=index,
name='dt', ordered=True)
col = 'dt'
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
# when categories is ordered, group is ordered by category's order
assert_frame_equal(result_sort, df.groupby(col, sort=False).first())
# ordered = False
df['dt'] = Categorical(df['dt'], ordered=False)
index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
datetime(2011, 5, 1), datetime(2011, 7, 1)]
result_sort = DataFrame(
[[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
result_sort.index = CategoricalIndex(index, name='dt')
index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
datetime(2011, 5, 1), datetime(2011, 1, 1)]
result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
columns=['foo', 'bar'])
result_nosort.index = CategoricalIndex(index, categories=index,
name='dt')
col = 'dt'
assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
assert_frame_equal(result_nosort, df.groupby(col, sort=False).first())
def test_groupby_categorical_two_columns(self):
# https://github.com/pandas-dev/pandas/issues/8138
d = {'cat':
pd.Categorical(["a", "b", "a", "b"], categories=["a", "b", "c"],
ordered=True),
'ints': [1, 1, 2, 2],
'val': [10, 20, 30, 40]}
test = pd.DataFrame(d)
# Grouping on a single column
groups_single_key = test.groupby("cat")
res = groups_single_key.agg('mean')
exp_index = pd.CategoricalIndex(["a", "b", "c"], name="cat",
ordered=True)
exp = DataFrame({"ints": [1.5, 1.5, np.nan], "val": [20, 30, np.nan]},
index=exp_index)
tm.assert_frame_equal(res, exp)
# Grouping on two columns
groups_double_key = test.groupby(["cat", "ints"])
res = groups_double_key.agg('mean')
exp = DataFrame({"val": [10, 30, 20, 40, np.nan, np.nan],
"cat": pd.Categorical(["a", "a", "b", "b", "c", "c"],
ordered=True),
"ints": [1, 2, 1, 2, 1, 2]}).set_index(["cat", "ints"
])
tm.assert_frame_equal(res, exp)
# GH 10132
for key in [('a', 1), ('b', 2), ('b', 1), ('a', 2)]:
c, i = key
result = groups_double_key.get_group(key)
expected = test[(test.cat == c) & (test.ints == i)]
assert_frame_equal(result, expected)
d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]}
test = pd.DataFrame(d)
values = pd.cut(test['C1'], [1, 2, 3, 6])
values.name = "cat"
groups_double_key = test.groupby([values, 'C2'])
res = groups_double_key.agg('mean')
nan = np.nan
idx = MultiIndex.from_product(
[Categorical([Interval(1, 2), Interval(2, 3),
Interval(3, 6)], ordered=True),
[1, 2, 3, 4]],
names=["cat", "C2"])
exp = DataFrame({"C1": [nan, nan, nan, nan, 3, 3,
nan, nan, nan, nan, 4, 5],
"C3": [nan, nan, nan, nan, 10, 100,
nan, nan, nan, nan, 200, 34]}, index=idx)
tm.assert_frame_equal(res, exp)