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TST: Fixturize series/test_apply.py #22769

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merged 7 commits into from
Sep 23, 2018
Merged

TST: Fixturize series/test_apply.py #22769

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e7762ac
TST: Fixturize series/test_analytics.py
Sep 18, 2018
368b756
Fixing PEP8 issues
Sep 18, 2018
3678a62
Fixing PEP8 issues
Sep 18, 2018
795cdc1
TST: Fixturize series/test_validate.py
Sep 18, 2018
52ebfa0
TST: Fixturize series/test_api.py
Sep 19, 2018
dd016cb
TST: Fixturize series/test_apply.py
Sep 19, 2018
8934db9
Revert unrelated test changes
Anjali2019 Sep 23, 2018
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99 changes: 50 additions & 49 deletions pandas/tests/series/test_apply.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,18 +17,18 @@
import pandas.util.testing as tm
from pandas.conftest import _get_cython_table_params

from .common import TestData

class TestSeriesApply():

class TestSeriesApply(TestData):

def test_apply(self):
def test_apply(self, datetime_series):
with np.errstate(all='ignore'):
tm.assert_series_equal(self.ts.apply(np.sqrt), np.sqrt(self.ts))
tm.assert_series_equal(datetime_series.apply(np.sqrt),
np.sqrt(datetime_series))

# element-wise apply
import math
tm.assert_series_equal(self.ts.apply(math.exp), np.exp(self.ts))
tm.assert_series_equal(datetime_series.apply(math.exp),
np.exp(datetime_series))

# empty series
s = Series(dtype=object, name='foo', index=pd.Index([], name='bar'))
Expand Down Expand Up @@ -66,11 +66,11 @@ def test_apply_dont_convert_dtype(self):
result = s.apply(f, convert_dtype=False)
assert result.dtype == object

def test_with_string_args(self):
def test_with_string_args(self, datetime_series):

for arg in ['sum', 'mean', 'min', 'max', 'std']:
result = self.ts.apply(arg)
expected = getattr(self.ts, arg)()
result = datetime_series.apply(arg)
expected = getattr(datetime_series, arg)()
assert result == expected

def test_apply_args(self):
Expand Down Expand Up @@ -165,45 +165,45 @@ def test_apply_dict_depr(self):
tsdf.A.agg({'foo': ['sum', 'mean']})


class TestSeriesAggregate(TestData):
class TestSeriesAggregate():

def test_transform(self):
def test_transform(self, string_series):
# transforming functions

with np.errstate(all='ignore'):

f_sqrt = np.sqrt(self.series)
f_abs = np.abs(self.series)
f_sqrt = np.sqrt(string_series)
f_abs = np.abs(string_series)

# ufunc
result = self.series.transform(np.sqrt)
result = string_series.transform(np.sqrt)
expected = f_sqrt.copy()
assert_series_equal(result, expected)

result = self.series.apply(np.sqrt)
result = string_series.apply(np.sqrt)
assert_series_equal(result, expected)

# list-like
result = self.series.transform([np.sqrt])
result = string_series.transform([np.sqrt])
expected = f_sqrt.to_frame().copy()
expected.columns = ['sqrt']
assert_frame_equal(result, expected)

result = self.series.transform([np.sqrt])
result = string_series.transform([np.sqrt])
assert_frame_equal(result, expected)

result = self.series.transform(['sqrt'])
result = string_series.transform(['sqrt'])
assert_frame_equal(result, expected)

# multiple items in list
# these are in the order as if we are applying both functions per
# series and then concatting
expected = pd.concat([f_sqrt, f_abs], axis=1)
expected.columns = ['sqrt', 'absolute']
result = self.series.apply([np.sqrt, np.abs])
result = string_series.apply([np.sqrt, np.abs])
assert_frame_equal(result, expected)

result = self.series.transform(['sqrt', 'abs'])
result = string_series.transform(['sqrt', 'abs'])
expected.columns = ['sqrt', 'abs']
assert_frame_equal(result, expected)

Expand All @@ -212,28 +212,28 @@ def test_transform(self):
expected.columns = ['foo', 'bar']
expected = expected.unstack().rename('series')

result = self.series.apply({'foo': np.sqrt, 'bar': np.abs})
result = string_series.apply({'foo': np.sqrt, 'bar': np.abs})
assert_series_equal(result.reindex_like(expected), expected)

def test_transform_and_agg_error(self):
def test_transform_and_agg_error(self, string_series):
# we are trying to transform with an aggregator
def f():
self.series.transform(['min', 'max'])
string_series.transform(['min', 'max'])
pytest.raises(ValueError, f)

def f():
with np.errstate(all='ignore'):
self.series.agg(['sqrt', 'max'])
string_series.agg(['sqrt', 'max'])
pytest.raises(ValueError, f)

def f():
with np.errstate(all='ignore'):
self.series.transform(['sqrt', 'max'])
string_series.transform(['sqrt', 'max'])
pytest.raises(ValueError, f)

def f():
with np.errstate(all='ignore'):
self.series.agg({'foo': np.sqrt, 'bar': 'sum'})
string_series.agg({'foo': np.sqrt, 'bar': 'sum'})
pytest.raises(ValueError, f)

def test_demo(self):
Expand Down Expand Up @@ -272,33 +272,34 @@ def test_multiple_aggregators_with_dict_api(self):
'min', 'sum']).unstack().rename('series')
tm.assert_series_equal(result.reindex_like(expected), expected)

def test_agg_apply_evaluate_lambdas_the_same(self):
def test_agg_apply_evaluate_lambdas_the_same(self, string_series):
# test that we are evaluating row-by-row first
# before vectorized evaluation
result = self.series.apply(lambda x: str(x))
expected = self.series.agg(lambda x: str(x))
result = string_series.apply(lambda x: str(x))
expected = string_series.agg(lambda x: str(x))
tm.assert_series_equal(result, expected)

result = self.series.apply(str)
expected = self.series.agg(str)
result = string_series.apply(str)
expected = string_series.agg(str)
tm.assert_series_equal(result, expected)

def test_with_nested_series(self):
def test_with_nested_series(self, datetime_series):
# GH 2316
# .agg with a reducer and a transform, what to do
result = self.ts.apply(lambda x: Series(
result = datetime_series.apply(lambda x: Series(
[x, x ** 2], index=['x', 'x^2']))
expected = DataFrame({'x': self.ts, 'x^2': self.ts ** 2})
expected = DataFrame({'x': datetime_series,
'x^2': datetime_series ** 2})
tm.assert_frame_equal(result, expected)

result = self.ts.agg(lambda x: Series(
result = datetime_series.agg(lambda x: Series(
[x, x ** 2], index=['x', 'x^2']))
tm.assert_frame_equal(result, expected)

def test_replicate_describe(self):
def test_replicate_describe(self, string_series):
# this also tests a result set that is all scalars
expected = self.series.describe()
result = self.series.apply(OrderedDict(
expected = string_series.describe()
result = string_series.apply(OrderedDict(
[('count', 'count'),
('mean', 'mean'),
('std', 'std'),
Expand All @@ -309,13 +310,13 @@ def test_replicate_describe(self):
('max', 'max')]))
assert_series_equal(result, expected)

def test_reduce(self):
def test_reduce(self, string_series):
# reductions with named functions
result = self.series.agg(['sum', 'mean'])
expected = Series([self.series.sum(),
self.series.mean()],
result = string_series.agg(['sum', 'mean'])
expected = Series([string_series.sum(),
string_series.mean()],
['sum', 'mean'],
name=self.series.name)
name=string_series.name)
assert_series_equal(result, expected)

def test_non_callable_aggregates(self):
Expand Down Expand Up @@ -414,9 +415,9 @@ def test_agg_cython_table_raises(self, series, func, expected):
series.agg(func)


class TestSeriesMap(TestData):
class TestSeriesMap():

def test_map(self):
def test_map(self, datetime_series):
index, data = tm.getMixedTypeDict()

source = Series(data['B'], index=data['C'])
Expand All @@ -434,8 +435,8 @@ def test_map(self):
assert v == source[target[k]]

# function
result = self.ts.map(lambda x: x * 2)
tm.assert_series_equal(result, self.ts * 2)
result = datetime_series.map(lambda x: x * 2)
tm.assert_series_equal(result, datetime_series * 2)

# GH 10324
a = Series([1, 2, 3, 4])
Expand Down Expand Up @@ -500,10 +501,10 @@ def test_map_type_inference(self):
s2 = s.map(lambda x: np.where(x == 0, 0, 1))
assert issubclass(s2.dtype.type, np.integer)

def test_map_decimal(self):
def test_map_decimal(self, string_series):
from decimal import Decimal

result = self.series.map(lambda x: Decimal(str(x)))
result = string_series.map(lambda x: Decimal(str(x)))
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)

Expand Down