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methods.py
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import numpy as np
import pytest
import pandas as pd
import pandas.util.testing as tm
from .base import BaseExtensionTests
class BaseMethodsTests(BaseExtensionTests):
"""Various Series and DataFrame methods."""
@pytest.mark.parametrize('dropna', [True, False])
def test_value_counts(self, all_data, dropna):
all_data = all_data[:10]
if dropna:
other = np.array(all_data[~all_data.isna()])
else:
other = all_data
result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
expected = pd.Series(other).value_counts(
dropna=dropna).sort_index()
self.assert_series_equal(result, expected)
def test_count(self, data_missing):
df = pd.DataFrame({"A": data_missing})
result = df.count(axis='columns')
expected = pd.Series([0, 1])
self.assert_series_equal(result, expected)
def test_apply_simple_series(self, data):
result = pd.Series(data).apply(id)
assert isinstance(result, pd.Series)
def test_argsort(self, data_for_sorting):
result = pd.Series(data_for_sorting).argsort()
expected = pd.Series(np.array([2, 0, 1], dtype=np.int64))
self.assert_series_equal(result, expected)
def test_argsort_missing(self, data_missing_for_sorting):
result = pd.Series(data_missing_for_sorting).argsort()
expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
self.assert_series_equal(result, expected)
@pytest.mark.parametrize('ascending', [True, False])
def test_sort_values(self, data_for_sorting, ascending):
ser = pd.Series(data_for_sorting)
result = ser.sort_values(ascending=ascending)
expected = ser.iloc[[2, 0, 1]]
if not ascending:
expected = expected[::-1]
self.assert_series_equal(result, expected)
@pytest.mark.parametrize('ascending', [True, False])
def test_sort_values_missing(self, data_missing_for_sorting, ascending):
ser = pd.Series(data_missing_for_sorting)
result = ser.sort_values(ascending=ascending)
if ascending:
expected = ser.iloc[[2, 0, 1]]
else:
expected = ser.iloc[[0, 2, 1]]
self.assert_series_equal(result, expected)
@pytest.mark.parametrize('ascending', [True, False])
def test_sort_values_frame(self, data_for_sorting, ascending):
df = pd.DataFrame({"A": [1, 2, 1],
"B": data_for_sorting})
result = df.sort_values(['A', 'B'])
expected = pd.DataFrame({"A": [1, 1, 2],
'B': data_for_sorting.take([2, 0, 1])},
index=[2, 0, 1])
self.assert_frame_equal(result, expected)
@pytest.mark.parametrize('box', [pd.Series, lambda x: x])
@pytest.mark.parametrize('method', [lambda x: x.unique(), pd.unique])
def test_unique(self, data, box, method):
duplicated = box(data._from_sequence([data[0], data[0]]))
result = method(duplicated)
assert len(result) == 1
assert isinstance(result, type(data))
assert result[0] == duplicated[0]
@pytest.mark.parametrize('na_sentinel', [-1, -2])
def test_factorize(self, data_for_grouping, na_sentinel):
labels, uniques = pd.factorize(data_for_grouping,
na_sentinel=na_sentinel)
expected_labels = np.array([0, 0, na_sentinel,
na_sentinel, 1, 1, 0, 2],
dtype=np.intp)
expected_uniques = data_for_grouping.take([0, 4, 7])
tm.assert_numpy_array_equal(labels, expected_labels)
self.assert_extension_array_equal(uniques, expected_uniques)
@pytest.mark.parametrize('na_sentinel', [-1, -2])
def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
l1, u1 = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
l2, u2 = data_for_grouping.factorize(na_sentinel=na_sentinel)
tm.assert_numpy_array_equal(l1, l2)
self.assert_extension_array_equal(u1, u2)
def test_combine_le(self, data_repeated):
# GH 20825
# Test that combine works when doing a <= (le) comparison
orig_data1, orig_data2 = data_repeated(2)
s1 = pd.Series(orig_data1)
s2 = pd.Series(orig_data2)
result = s1.combine(s2, lambda x1, x2: x1 <= x2)
expected = pd.Series([a <= b for (a, b) in
zip(list(orig_data1), list(orig_data2))])
self.assert_series_equal(result, expected)
val = s1.iloc[0]
result = s1.combine(val, lambda x1, x2: x1 <= x2)
expected = pd.Series([a <= val for a in list(orig_data1)])
self.assert_series_equal(result, expected)
def test_combine_add(self, data_repeated):
# GH 20825
orig_data1, orig_data2 = data_repeated(2)
s1 = pd.Series(orig_data1)
s2 = pd.Series(orig_data2)
result = s1.combine(s2, lambda x1, x2: x1 + x2)
with np.errstate(over='ignore'):
expected = pd.Series(
orig_data1._from_sequence([a + b for (a, b) in
zip(list(orig_data1),
list(orig_data2))]))
self.assert_series_equal(result, expected)
val = s1.iloc[0]
result = s1.combine(val, lambda x1, x2: x1 + x2)
expected = pd.Series(
orig_data1._from_sequence([a + val for a in list(orig_data1)]))
self.assert_series_equal(result, expected)
@pytest.mark.parametrize('frame', [True, False])
@pytest.mark.parametrize('periods, indices', [
(-2, [2, 3, 4, -1, -1]),
(0, [0, 1, 2, 3, 4]),
(2, [-1, -1, 0, 1, 2]),
])
def test_container_shift(self, data, frame, periods, indices):
# https://github.com/pandas-dev/pandas/issues/22386
subset = data[:5]
data = pd.Series(subset, name='A')
expected = pd.Series(subset.take(indices, allow_fill=True), name='A')
if frame:
result = data.to_frame(name='A').assign(B=1).shift(periods)
expected = pd.concat([
expected,
pd.Series([1] * 5, name='B').shift(periods)
], axis=1)
compare = self.assert_frame_equal
else:
result = data.shift(periods)
compare = self.assert_series_equal
compare(result, expected)
@pytest.mark.parametrize("as_frame", [True, False])
def test_hash_pandas_object_works(self, data, as_frame):
# https://github.com/pandas-dev/pandas/issues/23066
data = pd.Series(data)
if as_frame:
data = data.to_frame()
a = pd.util.hash_pandas_object(data)
b = pd.util.hash_pandas_object(data)
self.assert_equal(a, b)