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laywerrobot/lib/python3.6/site-packages/pandas/tests/groupby/test_transform.py

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2020-08-27 21:55:39 +02:00
""" test with the .transform """
import pytest
import numpy as np
import pandas as pd
from pandas.util import testing as tm
from pandas import Series, DataFrame, Timestamp, MultiIndex, concat, date_range
from pandas.core.dtypes.common import (
_ensure_platform_int, is_timedelta64_dtype)
from pandas.compat import StringIO
from pandas._libs import groupby
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pandas.core.groupby.groupby import DataError
from pandas.core.config import option_context
def assert_fp_equal(a, b):
assert (np.abs(a - b) < 1e-12).all()
def test_transform():
data = Series(np.arange(9) // 3, index=np.arange(9))
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
transformed = grouped.transform(lambda x: x * x.sum())
assert transformed[7] == 12
# GH 8046
# make sure that we preserve the input order
df = DataFrame(
np.arange(6, dtype='int64').reshape(
3, 2), columns=["a", "b"], index=[0, 2, 1])
key = [0, 0, 1]
expected = df.sort_index().groupby(key).transform(
lambda x: x - x.mean()).groupby(key).mean()
result = df.groupby(key).transform(lambda x: x - x.mean()).groupby(
key).mean()
assert_frame_equal(result, expected)
def demean(arr):
return arr - arr.mean()
people = DataFrame(np.random.randn(5, 5),
columns=['a', 'b', 'c', 'd', 'e'],
index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
key = ['one', 'two', 'one', 'two', 'one']
result = people.groupby(key).transform(demean).groupby(key).mean()
expected = people.groupby(key).apply(demean).groupby(key).mean()
assert_frame_equal(result, expected)
# GH 8430
df = tm.makeTimeDataFrame()
g = df.groupby(pd.Grouper(freq='M'))
g.transform(lambda x: x - 1)
# GH 9700
df = DataFrame({'a': range(5, 10), 'b': range(5)})
result = df.groupby('a').transform(max)
expected = DataFrame({'b': range(5)})
tm.assert_frame_equal(result, expected)
def test_transform_fast():
df = DataFrame({'id': np.arange(100000) / 3,
'val': np.random.randn(100000)})
grp = df.groupby('id')['val']
values = np.repeat(grp.mean().values,
_ensure_platform_int(grp.count().values))
expected = pd.Series(values, index=df.index, name='val')
result = grp.transform(np.mean)
assert_series_equal(result, expected)
result = grp.transform('mean')
assert_series_equal(result, expected)
# GH 12737
df = pd.DataFrame({'grouping': [0, 1, 1, 3], 'f': [1.1, 2.1, 3.1, 4.5],
'd': pd.date_range('2014-1-1', '2014-1-4'),
'i': [1, 2, 3, 4]},
columns=['grouping', 'f', 'i', 'd'])
result = df.groupby('grouping').transform('first')
dates = [pd.Timestamp('2014-1-1'), pd.Timestamp('2014-1-2'),
pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-4')]
expected = pd.DataFrame({'f': [1.1, 2.1, 2.1, 4.5],
'd': dates,
'i': [1, 2, 2, 4]},
columns=['f', 'i', 'd'])
assert_frame_equal(result, expected)
# selection
result = df.groupby('grouping')[['f', 'i']].transform('first')
expected = expected[['f', 'i']]
assert_frame_equal(result, expected)
# dup columns
df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['g', 'a', 'a'])
result = df.groupby('g').transform('first')
expected = df.drop('g', axis=1)
assert_frame_equal(result, expected)
def test_transform_broadcast(tsframe, ts):
grouped = ts.groupby(lambda x: x.month)
result = grouped.transform(np.mean)
tm.assert_index_equal(result.index, ts.index)
for _, gp in grouped:
assert_fp_equal(result.reindex(gp.index), gp.mean())
grouped = tsframe.groupby(lambda x: x.month)
result = grouped.transform(np.mean)
tm.assert_index_equal(result.index, tsframe.index)
for _, gp in grouped:
agged = gp.mean()
res = result.reindex(gp.index)
for col in tsframe:
assert_fp_equal(res[col], agged[col])
# group columns
grouped = tsframe.groupby({'A': 0, 'B': 0, 'C': 1, 'D': 1},
axis=1)
result = grouped.transform(np.mean)
tm.assert_index_equal(result.index, tsframe.index)
tm.assert_index_equal(result.columns, tsframe.columns)
for _, gp in grouped:
agged = gp.mean(1)
res = result.reindex(columns=gp.columns)
for idx in gp.index:
assert_fp_equal(res.xs(idx), agged[idx])
def test_transform_axis(tsframe):
# make sure that we are setting the axes
# correctly when on axis=0 or 1
# in the presence of a non-monotonic indexer
# GH12713
base = tsframe.iloc[0:5]
r = len(base.index)
c = len(base.columns)
tso = DataFrame(np.random.randn(r, c),
index=base.index,
columns=base.columns,
dtype='float64')
# monotonic
ts = tso
grouped = ts.groupby(lambda x: x.weekday())
result = ts - grouped.transform('mean')
expected = grouped.apply(lambda x: x - x.mean())
assert_frame_equal(result, expected)
ts = ts.T
grouped = ts.groupby(lambda x: x.weekday(), axis=1)
result = ts - grouped.transform('mean')
expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
assert_frame_equal(result, expected)
# non-monotonic
ts = tso.iloc[[1, 0] + list(range(2, len(base)))]
grouped = ts.groupby(lambda x: x.weekday())
result = ts - grouped.transform('mean')
expected = grouped.apply(lambda x: x - x.mean())
assert_frame_equal(result, expected)
ts = ts.T
grouped = ts.groupby(lambda x: x.weekday(), axis=1)
result = ts - grouped.transform('mean')
expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
assert_frame_equal(result, expected)
def test_transform_dtype():
# GH 9807
# Check transform dtype output is preserved
df = DataFrame([[1, 3], [2, 3]])
result = df.groupby(1).transform('mean')
expected = DataFrame([[1.5], [1.5]])
assert_frame_equal(result, expected)
def test_transform_bug():
# GH 5712
# transforming on a datetime column
df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5)))
result = df.groupby('A')['B'].transform(
lambda x: x.rank(ascending=False))
expected = Series(np.arange(5, 0, step=-1), name='B')
assert_series_equal(result, expected)
def test_transform_numeric_to_boolean():
# GH 16875
# inconsistency in transforming boolean values
expected = pd.Series([True, True], name='A')
df = pd.DataFrame({'A': [1.1, 2.2], 'B': [1, 2]})
result = df.groupby('B').A.transform(lambda x: True)
assert_series_equal(result, expected)
df = pd.DataFrame({'A': [1, 2], 'B': [1, 2]})
result = df.groupby('B').A.transform(lambda x: True)
assert_series_equal(result, expected)
def test_transform_datetime_to_timedelta():
# GH 15429
# transforming a datetime to timedelta
df = DataFrame(dict(A=Timestamp('20130101'), B=np.arange(5)))
expected = pd.Series([
Timestamp('20130101') - Timestamp('20130101')] * 5, name='A')
# this does date math without changing result type in transform
base_time = df['A'][0]
result = df.groupby('A')['A'].transform(
lambda x: x.max() - x.min() + base_time) - base_time
assert_series_equal(result, expected)
# this does date math and causes the transform to return timedelta
result = df.groupby('A')['A'].transform(lambda x: x.max() - x.min())
assert_series_equal(result, expected)
def test_transform_datetime_to_numeric():
# GH 10972
# convert dt to float
df = DataFrame({
'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')})
result = df.groupby('a').b.transform(
lambda x: x.dt.dayofweek - x.dt.dayofweek.mean())
expected = Series([-0.5, 0.5], name='b')
assert_series_equal(result, expected)
# convert dt to int
df = DataFrame({
'a': 1, 'b': date_range('2015-01-01', periods=2, freq='D')})
result = df.groupby('a').b.transform(
lambda x: x.dt.dayofweek - x.dt.dayofweek.min())
expected = Series([0, 1], name='b')
assert_series_equal(result, expected)
def test_transform_casting():
# 13046
data = """
idx A ID3 DATETIME
0 B-028 b76cd912ff "2014-10-08 13:43:27"
1 B-054 4a57ed0b02 "2014-10-08 14:26:19"
2 B-076 1a682034f8 "2014-10-08 14:29:01"
3 B-023 b76cd912ff "2014-10-08 18:39:34"
4 B-023 f88g8d7sds "2014-10-08 18:40:18"
5 B-033 b76cd912ff "2014-10-08 18:44:30"
6 B-032 b76cd912ff "2014-10-08 18:46:00"
7 B-037 b76cd912ff "2014-10-08 18:52:15"
8 B-046 db959faf02 "2014-10-08 18:59:59"
9 B-053 b76cd912ff "2014-10-08 19:17:48"
10 B-065 b76cd912ff "2014-10-08 19:21:38"
"""
df = pd.read_csv(StringIO(data), sep=r'\s+',
index_col=[0], parse_dates=['DATETIME'])
result = df.groupby('ID3')['DATETIME'].transform(lambda x: x.diff())
assert is_timedelta64_dtype(result.dtype)
result = df[['ID3', 'DATETIME']].groupby('ID3').transform(
lambda x: x.diff())
assert is_timedelta64_dtype(result.DATETIME.dtype)
def test_transform_multiple(ts):
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
grouped.transform(lambda x: x * 2)
grouped.transform(np.mean)
def test_dispatch_transform(tsframe):
df = tsframe[::5].reindex(tsframe.index)
grouped = df.groupby(lambda x: x.month)
filled = grouped.fillna(method='pad')
fillit = lambda x: x.fillna(method='pad')
expected = df.groupby(lambda x: x.month).transform(fillit)
assert_frame_equal(filled, expected)
def test_transform_select_columns(df):
f = lambda x: x.mean()
result = df.groupby('A')['C', 'D'].transform(f)
selection = df[['C', 'D']]
expected = selection.groupby(df['A']).transform(f)
assert_frame_equal(result, expected)
def test_transform_exclude_nuisance(df):
# this also tests orderings in transform between
# series/frame to make sure it's consistent
expected = {}
grouped = df.groupby('A')
expected['C'] = grouped['C'].transform(np.mean)
expected['D'] = grouped['D'].transform(np.mean)
expected = DataFrame(expected)
result = df.groupby('A').transform(np.mean)
assert_frame_equal(result, expected)
def test_transform_function_aliases(df):
result = df.groupby('A').transform('mean')
expected = df.groupby('A').transform(np.mean)
assert_frame_equal(result, expected)
result = df.groupby('A')['C'].transform('mean')
expected = df.groupby('A')['C'].transform(np.mean)
assert_series_equal(result, expected)
def test_series_fast_transform_date():
# GH 13191
df = pd.DataFrame({'grouping': [np.nan, 1, 1, 3],
'd': pd.date_range('2014-1-1', '2014-1-4')})
result = df.groupby('grouping')['d'].transform('first')
dates = [pd.NaT, pd.Timestamp('2014-1-2'), pd.Timestamp('2014-1-2'),
pd.Timestamp('2014-1-4')]
expected = pd.Series(dates, name='d')
assert_series_equal(result, expected)
def test_transform_length():
# GH 9697
df = pd.DataFrame({'col1': [1, 1, 2, 2], 'col2': [1, 2, 3, np.nan]})
expected = pd.Series([3.0] * 4)
def nsum(x):
return np.nansum(x)
results = [df.groupby('col1').transform(sum)['col2'],
df.groupby('col1')['col2'].transform(sum),
df.groupby('col1').transform(nsum)['col2'],
df.groupby('col1')['col2'].transform(nsum)]
for result in results:
assert_series_equal(result, expected, check_names=False)
def test_transform_coercion():
# 14457
# when we are transforming be sure to not coerce
# via assignment
df = pd.DataFrame(dict(A=['a', 'a'], B=[0, 1]))
g = df.groupby('A')
expected = g.transform(np.mean)
result = g.transform(lambda x: np.mean(x))
assert_frame_equal(result, expected)
def test_groupby_transform_with_int():
# GH 3740, make sure that we might upcast on item-by-item transform
# floats
df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=Series(1, dtype='float64'),
C=Series(
[1, 2, 3, 1, 2, 3], dtype='float64'), D='foo'))
with np.errstate(all='ignore'):
result = df.groupby('A').transform(
lambda x: (x - x.mean()) / x.std())
expected = DataFrame(dict(B=np.nan, C=Series(
[-1, 0, 1, -1, 0, 1], dtype='float64')))
assert_frame_equal(result, expected)
# int case
df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1,
C=[1, 2, 3, 1, 2, 3], D='foo'))
with np.errstate(all='ignore'):
result = df.groupby('A').transform(
lambda x: (x - x.mean()) / x.std())
expected = DataFrame(dict(B=np.nan, C=[-1, 0, 1, -1, 0, 1]))
assert_frame_equal(result, expected)
# int that needs float conversion
s = Series([2, 3, 4, 10, 5, -1])
df = DataFrame(dict(A=[1, 1, 1, 2, 2, 2], B=1, C=s, D='foo'))
with np.errstate(all='ignore'):
result = df.groupby('A').transform(
lambda x: (x - x.mean()) / x.std())
s1 = s.iloc[0:3]
s1 = (s1 - s1.mean()) / s1.std()
s2 = s.iloc[3:6]
s2 = (s2 - s2.mean()) / s2.std()
expected = DataFrame(dict(B=np.nan, C=concat([s1, s2])))
assert_frame_equal(result, expected)
# int downcasting
result = df.groupby('A').transform(lambda x: x * 2 / 2)
expected = DataFrame(dict(B=1, C=[2, 3, 4, 10, 5, -1]))
assert_frame_equal(result, expected)
def test_groupby_transform_with_nan_group():
# GH 9941
df = pd.DataFrame({'a': range(10),
'b': [1, 1, 2, 3, np.nan, 4, 4, 5, 5, 5]})
result = df.groupby(df.b)['a'].transform(max)
expected = pd.Series([1., 1., 2., 3., np.nan, 6., 6., 9., 9., 9.],
name='a')
assert_series_equal(result, expected)
def test_transform_mixed_type():
index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]
])
df = DataFrame({'d': [1., 1., 1., 2., 2., 2.],
'c': np.tile(['a', 'b', 'c'], 2),
'v': np.arange(1., 7.)}, index=index)
def f(group):
group['g'] = group['d'] * 2
return group[:1]
grouped = df.groupby('c')
result = grouped.apply(f)
assert result['d'].dtype == np.float64
# this is by definition a mutating operation!
with option_context('mode.chained_assignment', None):
for key, group in grouped:
res = f(group)
assert_frame_equal(res, result.loc[key])
def test_cython_group_transform_algos():
# GH 4095
dtypes = [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint32,
np.uint64, np.float32, np.float64]
ops = [(groupby.group_cumprod_float64, np.cumproduct, [np.float64]),
(groupby.group_cumsum, np.cumsum, dtypes)]
is_datetimelike = False
for pd_op, np_op, dtypes in ops:
for dtype in dtypes:
data = np.array([[1], [2], [3], [4]], dtype=dtype)
ans = np.zeros_like(data)
labels = np.array([0, 0, 0, 0], dtype=np.int64)
pd_op(ans, data, labels, is_datetimelike)
tm.assert_numpy_array_equal(np_op(data), ans[:, 0],
check_dtype=False)
# with nans
labels = np.array([0, 0, 0, 0, 0], dtype=np.int64)
data = np.array([[1], [2], [3], [np.nan], [4]], dtype='float64')
actual = np.zeros_like(data)
actual.fill(np.nan)
groupby.group_cumprod_float64(actual, data, labels, is_datetimelike)
expected = np.array([1, 2, 6, np.nan, 24], dtype='float64')
tm.assert_numpy_array_equal(actual[:, 0], expected)
actual = np.zeros_like(data)
actual.fill(np.nan)
groupby.group_cumsum(actual, data, labels, is_datetimelike)
expected = np.array([1, 3, 6, np.nan, 10], dtype='float64')
tm.assert_numpy_array_equal(actual[:, 0], expected)
# timedelta
is_datetimelike = True
data = np.array([np.timedelta64(1, 'ns')] * 5, dtype='m8[ns]')[:, None]
actual = np.zeros_like(data, dtype='int64')
groupby.group_cumsum(actual, data.view('int64'), labels,
is_datetimelike)
expected = np.array([np.timedelta64(1, 'ns'), np.timedelta64(
2, 'ns'), np.timedelta64(3, 'ns'), np.timedelta64(4, 'ns'),
np.timedelta64(5, 'ns')])
tm.assert_numpy_array_equal(actual[:, 0].view('m8[ns]'), expected)
@pytest.mark.parametrize(
"op, args, targop",
[('cumprod', (), lambda x: x.cumprod()),
('cumsum', (), lambda x: x.cumsum()),
('shift', (-1, ), lambda x: x.shift(-1)),
('shift', (1, ), lambda x: x.shift())])
def test_cython_transform_series(op, args, targop):
# GH 4095
s = Series(np.random.randn(1000))
s_missing = s.copy()
s_missing.iloc[2:10] = np.nan
labels = np.random.randint(0, 50, size=1000).astype(float)
# series
for data in [s, s_missing]:
# print(data.head())
expected = data.groupby(labels).transform(targop)
tm.assert_series_equal(
expected,
data.groupby(labels).transform(op, *args))
tm.assert_series_equal(expected, getattr(
data.groupby(labels), op)(*args))
@pytest.mark.parametrize("op", ['cumprod', 'cumsum'])
@pytest.mark.parametrize("skipna", [False, True])
@pytest.mark.parametrize('input, exp', [
# When everything is NaN
({'key': ['b'] * 10, 'value': np.nan},
pd.Series([np.nan] * 10, name='value')),
# When there is a single NaN
({'key': ['b'] * 10 + ['a'] * 2,
'value': [3] * 3 + [np.nan] + [3] * 8},
{('cumprod', False): [3.0, 9.0, 27.0] + [np.nan] * 7 + [3.0, 9.0],
('cumprod', True): [3.0, 9.0, 27.0, np.nan, 81., 243., 729.,
2187., 6561., 19683., 3.0, 9.0],
('cumsum', False): [3.0, 6.0, 9.0] + [np.nan] * 7 + [3.0, 6.0],
('cumsum', True): [3.0, 6.0, 9.0, np.nan, 12., 15., 18.,
21., 24., 27., 3.0, 6.0]})])
def test_groupby_cum_skipna(op, skipna, input, exp):
df = pd.DataFrame(input)
result = df.groupby('key')['value'].transform(op, skipna=skipna)
if isinstance(exp, dict):
expected = exp[(op, skipna)]
else:
expected = exp
expected = pd.Series(expected, name='value')
tm.assert_series_equal(expected, result)
@pytest.mark.parametrize(
"op, args, targop",
[('cumprod', (), lambda x: x.cumprod()),
('cumsum', (), lambda x: x.cumsum()),
('shift', (-1, ), lambda x: x.shift(-1)),
('shift', (1, ), lambda x: x.shift())])
def test_cython_transform_frame(op, args, targop):
s = Series(np.random.randn(1000))
s_missing = s.copy()
s_missing.iloc[2:10] = np.nan
labels = np.random.randint(0, 50, size=1000).astype(float)
strings = list('qwertyuiopasdfghjklz')
strings_missing = strings[:]
strings_missing[5] = np.nan
df = DataFrame({'float': s,
'float_missing': s_missing,
'int': [1, 1, 1, 1, 2] * 200,
'datetime': pd.date_range('1990-1-1', periods=1000),
'timedelta': pd.timedelta_range(1, freq='s',
periods=1000),
'string': strings * 50,
'string_missing': strings_missing * 50},
columns=['float', 'float_missing', 'int', 'datetime',
'timedelta', 'string', 'string_missing'])
df['cat'] = df['string'].astype('category')
df2 = df.copy()
df2.index = pd.MultiIndex.from_product([range(100), range(10)])
# DataFrame - Single and MultiIndex,
# group by values, index level, columns
for df in [df, df2]:
for gb_target in [dict(by=labels), dict(level=0), dict(by='string')
]: # dict(by='string_missing')]:
# dict(by=['int','string'])]:
gb = df.groupby(**gb_target)
# whitelisted methods set the selection before applying
# bit a of hack to make sure the cythonized shift
# is equivalent to pre 0.17.1 behavior
if op == 'shift':
gb._set_group_selection()
if op != 'shift' and 'int' not in gb_target:
# numeric apply fastpath promotes dtype so have
# to apply separately and concat
i = gb[['int']].apply(targop)
f = gb[['float', 'float_missing']].apply(targop)
expected = pd.concat([f, i], axis=1)
else:
expected = gb.apply(targop)
expected = expected.sort_index(axis=1)
tm.assert_frame_equal(expected,
gb.transform(op, *args).sort_index(
axis=1))
tm.assert_frame_equal(
expected,
getattr(gb, op)(*args).sort_index(axis=1))
# individual columns
for c in df:
if c not in ['float', 'int', 'float_missing'
] and op != 'shift':
pytest.raises(DataError, gb[c].transform, op)
pytest.raises(DataError, getattr(gb[c], op))
else:
expected = gb[c].apply(targop)
expected.name = c
tm.assert_series_equal(expected,
gb[c].transform(op, *args))
tm.assert_series_equal(expected,
getattr(gb[c], op)(*args))
def test_transform_with_non_scalar_group():
# GH 10165
cols = pd.MultiIndex.from_tuples([
('syn', 'A'), ('mis', 'A'), ('non', 'A'),
('syn', 'C'), ('mis', 'C'), ('non', 'C'),
('syn', 'T'), ('mis', 'T'), ('non', 'T'),
('syn', 'G'), ('mis', 'G'), ('non', 'G')])
df = pd.DataFrame(np.random.randint(1, 10, (4, 12)),
columns=cols,
index=['A', 'C', 'G', 'T'])
tm.assert_raises_regex(ValueError, 'transform must return '
'a scalar value for each '
'group.*',
df.groupby(axis=1, level=1).transform,
lambda z: z.div(z.sum(axis=1), axis=0))
@pytest.mark.parametrize('cols,exp,comp_func', [
('a', pd.Series([1, 1, 1], name='a'), tm.assert_series_equal),
(['a', 'c'], pd.DataFrame({'a': [1, 1, 1], 'c': [1, 1, 1]}),
tm.assert_frame_equal)
])
@pytest.mark.parametrize('agg_func', [
'count', 'rank', 'size'])
def test_transform_numeric_ret(cols, exp, comp_func, agg_func):
if agg_func == 'size' and isinstance(cols, list):
pytest.xfail("'size' transformation not supported with "
"NDFrameGroupy")
# GH 19200
df = pd.DataFrame(
{'a': pd.date_range('2018-01-01', periods=3),
'b': range(3),
'c': range(7, 10)})
result = df.groupby('b')[cols].transform(agg_func)
if agg_func == 'rank':
exp = exp.astype('float')
comp_func(result, exp)
@pytest.mark.parametrize("mix_groupings", [True, False])
@pytest.mark.parametrize("as_series", [True, False])
@pytest.mark.parametrize("val1,val2", [
('foo', 'bar'), (1, 2), (1., 2.)])
@pytest.mark.parametrize("fill_method,limit,exp_vals", [
("ffill", None,
[np.nan, np.nan, 'val1', 'val1', 'val1', 'val2', 'val2', 'val2']),
("ffill", 1,
[np.nan, np.nan, 'val1', 'val1', np.nan, 'val2', 'val2', np.nan]),
("bfill", None,
['val1', 'val1', 'val1', 'val2', 'val2', 'val2', np.nan, np.nan]),
("bfill", 1,
[np.nan, 'val1', 'val1', np.nan, 'val2', 'val2', np.nan, np.nan])
])
def test_group_fill_methods(mix_groupings, as_series, val1, val2,
fill_method, limit, exp_vals):
vals = [np.nan, np.nan, val1, np.nan, np.nan, val2, np.nan, np.nan]
_exp_vals = list(exp_vals)
# Overwrite placeholder values
for index, exp_val in enumerate(_exp_vals):
if exp_val == 'val1':
_exp_vals[index] = val1
elif exp_val == 'val2':
_exp_vals[index] = val2
# Need to modify values and expectations depending on the
# Series / DataFrame that we ultimately want to generate
if mix_groupings: # ['a', 'b', 'a, 'b', ...]
keys = ['a', 'b'] * len(vals)
def interweave(list_obj):
temp = list()
for x in list_obj:
temp.extend([x, x])
return temp
_exp_vals = interweave(_exp_vals)
vals = interweave(vals)
else: # ['a', 'a', 'a', ... 'b', 'b', 'b']
keys = ['a'] * len(vals) + ['b'] * len(vals)
_exp_vals = _exp_vals * 2
vals = vals * 2
df = DataFrame({'key': keys, 'val': vals})
if as_series:
result = getattr(
df.groupby('key')['val'], fill_method)(limit=limit)
exp = Series(_exp_vals, name='val')
assert_series_equal(result, exp)
else:
result = getattr(df.groupby('key'), fill_method)(limit=limit)
exp = DataFrame({'key': keys, 'val': _exp_vals})
assert_frame_equal(result, exp)
@pytest.mark.parametrize("fill_method", ['ffill', 'bfill'])
def test_pad_stable_sorting(fill_method):
# GH 21207
x = [0] * 20
y = [np.nan] * 10 + [1] * 10
if fill_method == 'bfill':
y = y[::-1]
df = pd.DataFrame({'x': x, 'y': y})
expected = df.copy()
result = getattr(df.groupby('x'), fill_method)()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("test_series", [True, False])
@pytest.mark.parametrize("periods,fill_method,limit", [
(1, 'ffill', None), (1, 'ffill', 1),
(1, 'bfill', None), (1, 'bfill', 1),
(-1, 'ffill', None), (-1, 'ffill', 1),
(-1, 'bfill', None), (-1, 'bfill', 1)])
def test_pct_change(test_series, periods, fill_method, limit):
vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan]
exp_vals = Series(vals).pct_change(periods=periods,
fill_method=fill_method,
limit=limit).tolist()
df = DataFrame({'key': ['a'] * len(vals) + ['b'] * len(vals),
'vals': vals * 2})
grp = df.groupby('key')
def get_result(grp_obj):
return grp_obj.pct_change(periods=periods,
fill_method=fill_method,
limit=limit)
if test_series:
exp = pd.Series(exp_vals * 2)
exp.name = 'vals'
grp = grp['vals']
result = get_result(grp)
tm.assert_series_equal(result, exp)
else:
exp = DataFrame({'vals': exp_vals * 2})
result = get_result(grp)
tm.assert_frame_equal(result, exp)
@pytest.mark.parametrize("func", [np.any, np.all])
def test_any_all_np_func(func):
# GH 20653
df = pd.DataFrame([['foo', True],
[np.nan, True],
['foo', True]], columns=['key', 'val'])
exp = pd.Series([True, np.nan, True], name='val')
res = df.groupby('key')['val'].transform(func)
tm.assert_series_equal(res, exp)
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