{"action":"created","comment":{"url":"https://api.github.com/repos/pandas-dev/pandas/pulls/comments/198239829","pull_request_review_id":132130951,"id":198239829,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5ODIzOTgyOQ==","diff_hunk":"@@ -0,0 +1,310 @@\n+# -*- coding: utf-8 -*-\n+\n+import re\n+\n+import numpy as np\n+import pandas as pd\n+import pandas.util.testing as tm\n+import pytest\n+from pandas import IntervalIndex, MultiIndex, RangeIndex\n+from pandas.compat import lrange, range\n+from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike\n+\n+\n+def test_labels_dtypes():\n+\n+    # GH 8456\n+    i = MultiIndex.from_tuples([('A', 1), ('A', 2)])\n+    assert i.labels[0].dtype == 'int8'\n+    assert i.labels[1].dtype == 'int8'\n+\n+    i = MultiIndex.from_product([['a'], range(40)])\n+    assert i.labels[1].dtype == 'int8'\n+    i = MultiIndex.from_product([['a'], range(400)])\n+    assert i.labels[1].dtype == 'int16'\n+    i = MultiIndex.from_product([['a'], range(40000)])\n+    assert i.labels[1].dtype == 'int32'\n+\n+    i = pd.MultiIndex.from_product([['a'], range(1000)])\n+    assert (i.labels[0] >= 0).all()\n+    assert (i.labels[1] >= 0).all()\n+\n+\n+def test_values_boxed():\n+    tuples = [(1, pd.Timestamp('2000-01-01')), (2, pd.NaT),\n+              (3, pd.Timestamp('2000-01-03')),\n+              (1, pd.Timestamp('2000-01-04')),\n+              (2, pd.Timestamp('2000-01-02')),\n+              (3, pd.Timestamp('2000-01-03'))]\n+    result = pd.MultiIndex.from_tuples(tuples)\n+    expected = construct_1d_object_array_from_listlike(tuples)\n+    tm.assert_numpy_array_equal(result.values, expected)\n+    # Check that code branches for boxed values produce identical results\n+    tm.assert_numpy_array_equal(result.values[:4], result[:4].values)\n+\n+\n+def test_values_multiindex_datetimeindex():\n+    # Test to ensure we hit the boxing / nobox part of MI.values\n+    ints = np.arange(10 ** 18, 10 ** 18 + 5)\n+    naive = pd.DatetimeIndex(ints)\n+    aware = pd.DatetimeIndex(ints, tz='US/Central')\n+\n+    idx = pd.MultiIndex.from_arrays([naive, aware])\n+    result = idx.values\n+\n+    outer = pd.DatetimeIndex([x[0] for x in result])\n+    tm.assert_index_equal(outer, naive)\n+\n+    inner = pd.DatetimeIndex([x[1] for x in result])\n+    tm.assert_index_equal(inner, aware)\n+\n+    # n_lev > n_lab\n+    result = idx[:2].values\n+\n+    outer = pd.DatetimeIndex([x[0] for x in result])\n+    tm.assert_index_equal(outer, naive[:2])\n+\n+    inner = pd.DatetimeIndex([x[1] for x in result])\n+    tm.assert_index_equal(inner, aware[:2])\n+\n+\n+def test_values_multiindex_periodindex():\n+    # Test to ensure we hit the boxing / nobox part of MI.values\n+    ints = np.arange(2007, 2012)\n+    pidx = pd.PeriodIndex(ints, freq='D')\n+\n+    idx = pd.MultiIndex.from_arrays([ints, pidx])\n+    result = idx.values\n+\n+    outer = pd.Int64Index([x[0] for x in result])\n+    tm.assert_index_equal(outer, pd.Int64Index(ints))\n+\n+    inner = pd.PeriodIndex([x[1] for x in result])\n+    tm.assert_index_equal(inner, pidx)\n+\n+    # n_lev > n_lab\n+    result = idx[:2].values\n+\n+    outer = pd.Int64Index([x[0] for x in result])\n+    tm.assert_index_equal(outer, pd.Int64Index(ints[:2]))\n+\n+    inner = pd.PeriodIndex([x[1] for x in result])\n+    tm.assert_index_equal(inner, pidx[:2])\n+\n+\n+def test_consistency():\n+    # need to construct an overflow\n+    major_axis = lrange(70000)\n+    minor_axis = lrange(10)\n+\n+    major_labels = np.arange(70000)\n+    minor_labels = np.repeat(lrange(10), 7000)\n+\n+    # the fact that is works means it's consistent\n+    index = MultiIndex(levels=[major_axis, minor_axis],\n+                       labels=[major_labels, minor_labels])\n+\n+    # inconsistent\n+    major_labels = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3])\n+    minor_labels = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1])\n+    index = MultiIndex(levels=[major_axis, minor_axis],\n+                       labels=[major_labels, minor_labels])\n+\n+    assert not index.is_unique\n+\n+\n+def test_hash_collisions():\n+    # non-smoke test that we don't get hash collisions\n+\n+    index = MultiIndex.from_product([np.arange(1000), np.arange(1000)],\n+                                    names=['one', 'two'])\n+    result = index.get_indexer(index.values)\n+    tm.assert_numpy_array_equal(result, np.arange(\n+        len(index), dtype='intp'))\n+\n+    for i in [0, 1, len(index) - 2, len(index) - 1]:\n+        result = index.get_loc(index[i])\n+        assert result == i\n+\n+\n+def test_dims():\n+    pass\n+\n+\n+def take_invalid_kwargs():\n+    vals = [['A', 'B'],\n+            [pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-02')]]\n+    idx = pd.MultiIndex.from_product(vals, names=['str', 'dt'])\n+    indices = [1, 2]\n+\n+    msg = r\"take\\(\\) got an unexpected keyword argument 'foo'\"\n+    tm.assert_raises_regex(TypeError, msg, idx.take,\n+                           indices, foo=2)\n+\n+    msg = \"the 'out' parameter is not supported\"\n+    tm.assert_raises_regex(ValueError, msg, idx.take,\n+                           indices, out=indices)\n+\n+    msg = \"the 'mode' parameter is not supported\"\n+    tm.assert_raises_regex(ValueError, msg, idx.take,\n+                           indices, mode='clip')\n+\n+\n+def test_isna_behavior(idx):\n+    # should not segfault GH5123\n+    # NOTE: if MI representation changes, may make sense to allow\n+    # isna(MI)\n+    with pytest.raises(NotImplementedError):\n+        pd.isna(idx)\n+\n+\n+def test_large_multiindex_error():\n+    # GH12527\n+    df_below_1000000 = pd.DataFrame(\n+        1, index=pd.MultiIndex.from_product([[1, 2], range(499999)]),\n+        columns=['dest'])\n+    with pytest.raises(KeyError):\n+        df_below_1000000.loc[(-1, 0), 'dest']\n+    with pytest.raises(KeyError):\n+        df_below_1000000.loc[(3, 0), 'dest']\n+    df_above_1000000 = pd.DataFrame(\n+        1, index=pd.MultiIndex.from_product([[1, 2], range(500001)]),\n+        columns=['dest'])\n+    with pytest.raises(KeyError):\n+        df_above_1000000.loc[(-1, 0), 'dest']\n+    with pytest.raises(KeyError):\n+        df_above_1000000.loc[(3, 0), 'dest']\n+\n+\n+def test_nan_stays_float():\n+","path":"pandas/tests/indexes/multi/test_integrity.py","position":180,"original_position":180,"commit_id":"edfc1ad3e52665ea50f46039bf97185caeab4605","original_commit_id":"edfc1ad3e52665ea50f46039bf97185caeab4605","user":{"login":"jreback","id":953992,"node_id":"MDQ6VXNlcjk1Mzk5Mg==","avatar_url":"https://avatars2.githubusercontent.com/u/953992?v=4","gravatar_id":"","url":"https://api.github.com/users/jreback","html_url":"https://github.com/jreback","followers_url":"https://api.github.com/users/jreback/followers","following_url":"https://api.github.com/users/jreback/following{/other_user}","gists_url":"https://api.github.com/users/jreback/gists{/gist_id}","starred_url":"https://api.github.com/users/jreback/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jreback/subscriptions","organizations_url":"https://api.github.com/users/jreback/orgs","repos_url":"https://api.github.com/users/jreback/repos","events_url":"https://api.github.com/users/jreback/events{/privacy}","received_events_url":"https://api.github.com/users/jreback/received_events","type":"User","site_admin":false},"body":"move to test_missing","created_at":"2018-06-26T17:48:58Z","updated_at":"2018-06-26T17:55:09Z","html_url":"https://github.com/pandas-dev/pandas/pull/21514#discussion_r198239829","pull_request_url":"https://api.github.com/repos/pandas-dev/pandas/pulls/21514","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/pandas-dev/pandas/pulls/comments/198239829"},"html":{"href":"https://github.com/pandas-dev/pandas/pull/21514#discussion_r198239829"},"pull_request":{"href":"https://api.github.com/repos/pandas-dev/pandas/pulls/21514"}}},"pull_request":{"url":"https://api.github.com/repos/pandas-dev/pandas/pulls/21514","id":195388182,"node_id":"MDExOlB1bGxSZXF1ZXN0MTk1Mzg4MTgy","html_url":"https://github.com/pandas-dev/pandas/pull/21514","diff_url":"https://github.com/pandas-dev/pandas/pull/21514.diff","patch_url":"https://github.com/pandas-dev/pandas/pull/21514.patch","issue_url":"https://api.github.com/repos/pandas-dev/pandas/issues/21514","number":21514,"state":"open","locked":false,"title":"split up pandas/tests/indexes/test_multi.py #18644","user":{"login":"elmq0022","id":3450848,"node_id":"MDQ6VXNlcjM0NTA4NDg=","avatar_url":"https://avatars3.githubusercontent.com/u/3450848?v=4","gravatar_id":"","url":"https://api.github.com/users/elmq0022","html_url":"https://github.com/elmq0022","followers_url":"https://api.github.com/users/elmq0022/followers","following_url":"https://api.github.com/users/elmq0022/following{/other_user}","gists_url":"https://api.github.com/users/elmq0022/gists{/gist_id}","starred_url":"https://api.github.com/users/elmq0022/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/elmq0022/subscriptions","organizations_url":"https://api.github.com/users/elmq0022/orgs","repos_url":"https://api.github.com/users/elmq0022/repos","events_url":"https://api.github.com/users/elmq0022/events{/privacy}","received_events_url":"https://api.github.com/users/elmq0022/received_events","type":"User","site_admin":false},"body":"- [ ] closes #18644\r\n- [ ] tests added / passed\r\n- [ ] passes `git diff upstream/master -u -- \"*.py\" | flake8 --diff`\r\n\r\nThe file is split out, the number of tests remains 377 (2 skips on my machine; all other test cases pass), the code uses fixtures in all instances, new fixtures are in a local conftest.py file, and there are no dependencies on classes.  \r\n\r\nI'm happy to receive and address any feedback.  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