FM / base

/usr/local/lib64/python3.6/site-packages/pandas/tests/base/

/usr/local/lib64/python3.6/site-packages/pandas/tests/base

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
common.py	252	0644	edit dl rm
test_constructors.py	5119	0644	edit dl rm
test_conversion.py	14519	0644	edit dl rm
test_drop_duplicates.py	872	0644	edit dl rm
test_factorize.py	1327	0644	edit dl rm
test_fillna.py	1880	0644	edit dl rm
test_misc.py	6195	0644	edit dl rm
test_transpose.py	722	0644	edit dl rm
test_unique.py	4237	0644	edit dl rm
test_value_counts.py	9060	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/base/test_factorize.py (1327B)

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm

@pytest.mark.parametrize("sort", [True, False])
def test_factorize(index_or_series_obj, sort):
    obj = index_or_series_obj
    result_codes, result_uniques = obj.factorize(sort=sort)

constructor = pd.Index
    if isinstance(obj, pd.MultiIndex):
        constructor = pd.MultiIndex.from_tuples
    expected_uniques = constructor(obj.unique())

if sort:
        expected_uniques = expected_uniques.sort_values()

# construct an integer ndarray so that
    # `expected_uniques.take(expected_codes)` is equal to `obj`
    expected_uniques_list = list(expected_uniques)
    expected_codes = [expected_uniques_list.index(val) for val in obj]
    expected_codes = np.asarray(expected_codes, dtype=np.intp)

tm.assert_numpy_array_equal(result_codes, expected_codes)
    tm.assert_index_equal(result_uniques, expected_uniques)

def test_series_factorize_na_sentinel_none():
    # GH35667
    values = np.array([1, 2, 1, np.nan])
    ser = pd.Series(values)
    codes, uniques = ser.factorize(na_sentinel=None)

expected_codes = np.array([0, 1, 0, 2], dtype=np.intp)
    expected_uniques = pd.Index([1.0, 2.0, np.nan])

tm.assert_numpy_array_equal(codes, expected_codes)
    tm.assert_index_equal(uniques, expected_uniques)

import numpy as np import pytest import pandas as pd import pandas._testing as tm @pytest.mark.parametrize("sort", [True, False]) def test_factorize(index_or_series_obj, sort): obj = index_or_series_obj result_codes, result_uniques = obj.factorize(sort=sort) constructor = pd.Index if isinstance(obj, pd.MultiIndex): constructor = pd.MultiIndex.from_tuples expected_uniques = constructor(obj.unique()) if sort: expected_uniques = expected_uniques.sort_values() # construct an integer ndarray so that # `expected_uniques.take(expected_codes)` is equal to `obj` expected_uniques_list = list(expected_uniques) expected_codes = [expected_uniques_list.index(val) for val in obj] expected_codes = np.asarray(expected_codes, dtype=np.intp) tm.assert_numpy_array_equal(result_codes, expected_codes) tm.assert_index_equal(result_uniques, expected_uniques) def test_series_factorize_na_sentinel_none(): # GH35667 values = np.array([1, 2, 1, np.nan]) ser = pd.Series(values) codes, uniques = ser.factorize(na_sentinel=None) expected_codes = np.array([0, 1, 0, 2], dtype=np.intp) expected_uniques = pd.Index([1.0, 2.0, np.nan]) tm.assert_numpy_array_equal(codes, expected_codes) tm.assert_index_equal(uniques, expected_uniques)