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TST: separate out grouping-type tests #18057

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371 changes: 371 additions & 0 deletions pandas/tests/groupby/test_functional.py
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# -*- coding: utf-8 -*-

""" test function application """
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If I hadn't looked at the filename, I would have been completely confused by this module docstring.


import pytest

from string import ascii_lowercase
from pandas import (date_range, Timestamp,
Index, MultiIndex, DataFrame, Series)
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pandas.compat import product as cart_product

import numpy as np

import pandas.util.testing as tm
import pandas as pd
from .common import MixIn


# describe
# --------------------------------

class TestDescribe(MixIn):

def test_apply_describe_bug(self):
grouped = self.mframe.groupby(level='first')
grouped.describe() # it works!

def test_series_describe_multikey(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.describe()
assert_series_equal(result['mean'], grouped.mean(), check_names=False)
assert_series_equal(result['std'], grouped.std(), check_names=False)
assert_series_equal(result['min'], grouped.min(), check_names=False)

def test_series_describe_single(self):
ts = tm.makeTimeSeries()
grouped = ts.groupby(lambda x: x.month)
result = grouped.apply(lambda x: x.describe())
expected = grouped.describe().stack()
assert_series_equal(result, expected)

def test_series_index_name(self):
grouped = self.df.loc[:, ['C']].groupby(self.df['A'])
result = grouped.agg(lambda x: x.mean())
assert result.index.name == 'A'

def test_frame_describe_multikey(self):
grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.describe()
desc_groups = []
for col in self.tsframe:
group = grouped[col].describe()
group_col = pd.MultiIndex([[col] * len(group.columns),
group.columns],
[[0] * len(group.columns),
range(len(group.columns))])
group = pd.DataFrame(group.values,
columns=group_col,
index=group.index)
desc_groups.append(group)
expected = pd.concat(desc_groups, axis=1)
tm.assert_frame_equal(result, expected)

groupedT = self.tsframe.groupby({'A': 0, 'B': 0,
'C': 1, 'D': 1}, axis=1)
result = groupedT.describe()
expected = self.tsframe.describe().T
expected.index = pd.MultiIndex([[0, 0, 1, 1], expected.index],
[range(4), range(len(expected.index))])
tm.assert_frame_equal(result, expected)

def test_frame_describe_tupleindex(self):

# GH 14848 - regression from 0.19.0 to 0.19.1
df1 = DataFrame({'x': [1, 2, 3, 4, 5] * 3,
'y': [10, 20, 30, 40, 50] * 3,
'z': [100, 200, 300, 400, 500] * 3})
df1['k'] = [(0, 0, 1), (0, 1, 0), (1, 0, 0)] * 5
df2 = df1.rename(columns={'k': 'key'})
pytest.raises(ValueError, lambda: df1.groupby('k').describe())
pytest.raises(ValueError, lambda: df2.groupby('key').describe())

def test_frame_describe_unstacked_format(self):
# GH 4792
prices = {pd.Timestamp('2011-01-06 10:59:05', tz=None): 24990,
pd.Timestamp('2011-01-06 12:43:33', tz=None): 25499,
pd.Timestamp('2011-01-06 12:54:09', tz=None): 25499}
volumes = {pd.Timestamp('2011-01-06 10:59:05', tz=None): 1500000000,
pd.Timestamp('2011-01-06 12:43:33', tz=None): 5000000000,
pd.Timestamp('2011-01-06 12:54:09', tz=None): 100000000}
df = pd.DataFrame({'PRICE': prices,
'VOLUME': volumes})
result = df.groupby('PRICE').VOLUME.describe()
data = [df[df.PRICE == 24990].VOLUME.describe().values.tolist(),
df[df.PRICE == 25499].VOLUME.describe().values.tolist()]
expected = pd.DataFrame(data,
index=pd.Index([24990, 25499], name='PRICE'),
columns=['count', 'mean', 'std', 'min',
'25%', '50%', '75%', 'max'])
tm.assert_frame_equal(result, expected)


# nunique
# --------------------------------

class TestNUnique(MixIn):

def test_series_groupby_nunique(self):

def check_nunique(df, keys, as_index=True):
for sort, dropna in cart_product((False, True), repeat=2):
gr = df.groupby(keys, as_index=as_index, sort=sort)
left = gr['julie'].nunique(dropna=dropna)

gr = df.groupby(keys, as_index=as_index, sort=sort)
right = gr['julie'].apply(Series.nunique, dropna=dropna)
if not as_index:
right = right.reset_index(drop=True)

assert_series_equal(left, right, check_names=False)

days = date_range('2015-08-23', periods=10)

for n, m in cart_product(10 ** np.arange(2, 6), (10, 100, 1000)):
frame = DataFrame({
'jim': np.random.choice(
list(ascii_lowercase), n),
'joe': np.random.choice(days, n),
'julie': np.random.randint(0, m, n)
})

check_nunique(frame, ['jim'])
check_nunique(frame, ['jim', 'joe'])

frame.loc[1::17, 'jim'] = None
frame.loc[3::37, 'joe'] = None
frame.loc[7::19, 'julie'] = None
frame.loc[8::19, 'julie'] = None
frame.loc[9::19, 'julie'] = None

check_nunique(frame, ['jim'])
check_nunique(frame, ['jim', 'joe'])
check_nunique(frame, ['jim'], as_index=False)
check_nunique(frame, ['jim', 'joe'], as_index=False)

def test_nunique(self):
df = DataFrame({
'A': list('abbacc'),
'B': list('abxacc'),
'C': list('abbacx'),
})

expected = DataFrame({'A': [1] * 3, 'B': [1, 2, 1], 'C': [1, 1, 2]})
result = df.groupby('A', as_index=False).nunique()
tm.assert_frame_equal(result, expected)

# as_index
expected.index = list('abc')
expected.index.name = 'A'
result = df.groupby('A').nunique()
tm.assert_frame_equal(result, expected)

# with na
result = df.replace({'x': None}).groupby('A').nunique(dropna=False)
tm.assert_frame_equal(result, expected)

# dropna
expected = DataFrame({'A': [1] * 3, 'B': [1] * 3, 'C': [1] * 3},
index=list('abc'))
expected.index.name = 'A'
result = df.replace({'x': None}).groupby('A').nunique()
tm.assert_frame_equal(result, expected)

def test_nunique_with_object(self):
# GH 11077
data = pd.DataFrame(
[[100, 1, 'Alice'],
[200, 2, 'Bob'],
[300, 3, 'Charlie'],
[-400, 4, 'Dan'],
[500, 5, 'Edith']],
columns=['amount', 'id', 'name']
)

result = data.groupby(['id', 'amount'])['name'].nunique()
index = MultiIndex.from_arrays([data.id, data.amount])
expected = pd.Series([1] * 5, name='name', index=index)
tm.assert_series_equal(result, expected)

def test_nunique_with_empty_series(self):
# GH 12553
data = pd.Series(name='name')
result = data.groupby(level=0).nunique()
expected = pd.Series(name='name', dtype='int64')
tm.assert_series_equal(result, expected)

def test_nunique_with_timegrouper(self):
# GH 13453
test = pd.DataFrame({
'time': [Timestamp('2016-06-28 09:35:35'),
Timestamp('2016-06-28 16:09:30'),
Timestamp('2016-06-28 16:46:28')],
'data': ['1', '2', '3']}).set_index('time')
result = test.groupby(pd.Grouper(freq='h'))['data'].nunique()
expected = test.groupby(
pd.Grouper(freq='h')
)['data'].apply(pd.Series.nunique)
tm.assert_series_equal(result, expected)


# count
# --------------------------------

class TestCount(MixIn):

def test_groupby_timedelta_cython_count(self):
df = DataFrame({'g': list('ab' * 2),
'delt': np.arange(4).astype('timedelta64[ns]')})
expected = Series([
2, 2
], index=pd.Index(['a', 'b'], name='g'), name='delt')
result = df.groupby('g').delt.count()
tm.assert_series_equal(expected, result)

def test_count(self):
n = 1 << 15
dr = date_range('2015-08-30', periods=n // 10, freq='T')

df = DataFrame({
'1st': np.random.choice(
list(ascii_lowercase), n),
'2nd': np.random.randint(0, 5, n),
'3rd': np.random.randn(n).round(3),
'4th': np.random.randint(-10, 10, n),
'5th': np.random.choice(dr, n),
'6th': np.random.randn(n).round(3),
'7th': np.random.randn(n).round(3),
'8th': np.random.choice(dr, n) - np.random.choice(dr, 1),
'9th': np.random.choice(
list(ascii_lowercase), n)
})

for col in df.columns.drop(['1st', '2nd', '4th']):
df.loc[np.random.choice(n, n // 10), col] = np.nan

df['9th'] = df['9th'].astype('category')

for key in '1st', '2nd', ['1st', '2nd']:
left = df.groupby(key).count()
right = df.groupby(key).apply(DataFrame.count).drop(key, axis=1)
assert_frame_equal(left, right)

# GH5610
# count counts non-nulls
df = pd.DataFrame([[1, 2, 'foo'],
[1, np.nan, 'bar'],
[3, np.nan, np.nan]],
columns=['A', 'B', 'C'])

count_as = df.groupby('A').count()
count_not_as = df.groupby('A', as_index=False).count()

expected = DataFrame([[1, 2], [0, 0]], columns=['B', 'C'],
index=[1, 3])
expected.index.name = 'A'
assert_frame_equal(count_not_as, expected.reset_index())
assert_frame_equal(count_as, expected)

count_B = df.groupby('A')['B'].count()
assert_series_equal(count_B, expected['B'])

def test_count_object(self):
df = pd.DataFrame({'a': ['a'] * 3 + ['b'] * 3, 'c': [2] * 3 + [3] * 3})
result = df.groupby('c').a.count()
expected = pd.Series([
3, 3
], index=pd.Index([2, 3], name='c'), name='a')
tm.assert_series_equal(result, expected)

df = pd.DataFrame({'a': ['a', np.nan, np.nan] + ['b'] * 3,
'c': [2] * 3 + [3] * 3})
result = df.groupby('c').a.count()
expected = pd.Series([
1, 3
], index=pd.Index([2, 3], name='c'), name='a')
tm.assert_series_equal(result, expected)

def test_count_cross_type(self): # GH8169
vals = np.hstack((np.random.randint(0, 5, (100, 2)), np.random.randint(
0, 2, (100, 2))))

df = pd.DataFrame(vals, columns=['a', 'b', 'c', 'd'])
df[df == 2] = np.nan
expected = df.groupby(['c', 'd']).count()

for t in ['float32', 'object']:
df['a'] = df['a'].astype(t)
df['b'] = df['b'].astype(t)
result = df.groupby(['c', 'd']).count()
tm.assert_frame_equal(result, expected)

def test_lower_int_prec_count(self):
df = DataFrame({'a': np.array(
[0, 1, 2, 100], np.int8),
'b': np.array(
[1, 2, 3, 6], np.uint32),
'c': np.array(
[4, 5, 6, 8], np.int16),
'grp': list('ab' * 2)})
result = df.groupby('grp').count()
expected = DataFrame({'a': [2, 2],
'b': [2, 2],
'c': [2, 2]}, index=pd.Index(list('ab'),
name='grp'))
tm.assert_frame_equal(result, expected)

def test_count_uses_size_on_exception(self):
class RaisingObjectException(Exception):
pass

class RaisingObject(object):

def __init__(self, msg='I will raise inside Cython'):
super(RaisingObject, self).__init__()
self.msg = msg

def __eq__(self, other):
# gets called in Cython to check that raising calls the method
raise RaisingObjectException(self.msg)

df = DataFrame({'a': [RaisingObject() for _ in range(4)],
'grp': list('ab' * 2)})
result = df.groupby('grp').count()
expected = DataFrame({'a': [2, 2]}, index=pd.Index(
list('ab'), name='grp'))
tm.assert_frame_equal(result, expected)


# size
# --------------------------------

class TestSize(MixIn):

def test_size(self):
grouped = self.df.groupby(['A', 'B'])
result = grouped.size()
for key, group in grouped:
assert result[key] == len(group)

grouped = self.df.groupby('A')
result = grouped.size()
for key, group in grouped:
assert result[key] == len(group)

grouped = self.df.groupby('B')
result = grouped.size()
for key, group in grouped:
assert result[key] == len(group)

df = DataFrame(np.random.choice(20, (1000, 3)), columns=list('abc'))
for sort, key in cart_product((False, True), ('a', 'b', ['a', 'b'])):
left = df.groupby(key, sort=sort).size()
right = df.groupby(key, sort=sort)['c'].apply(lambda a: a.shape[0])
assert_series_equal(left, right, check_names=False)

# GH11699
df = DataFrame([], columns=['A', 'B'])
out = Series([], dtype='int64', index=Index([], name='A'))
assert_series_equal(df.groupby('A').size(), out)
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