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356 lines
12 KiB
356 lines
12 KiB
1 year ago
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import datetime
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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MultiIndex,
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Series,
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)
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import pandas._testing as tm
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class TestMultiLevel:
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def test_reindex_level(self, multiindex_year_month_day_dataframe_random_data):
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# axis=0
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ymd = multiindex_year_month_day_dataframe_random_data
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month_sums = ymd.groupby("month").sum()
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result = month_sums.reindex(ymd.index, level=1)
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expected = ymd.groupby(level="month").transform("sum")
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tm.assert_frame_equal(result, expected)
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# Series
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result = month_sums["A"].reindex(ymd.index, level=1)
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expected = ymd["A"].groupby(level="month").transform("sum")
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tm.assert_series_equal(result, expected, check_names=False)
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# axis=1
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msg = "DataFrame.groupby with axis=1 is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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gb = ymd.T.groupby("month", axis=1)
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month_sums = gb.sum()
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result = month_sums.reindex(columns=ymd.index, level=1)
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expected = ymd.groupby(level="month").transform("sum").T
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tm.assert_frame_equal(result, expected)
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def test_reindex(self, multiindex_dataframe_random_data):
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frame = multiindex_dataframe_random_data
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expected = frame.iloc[[0, 3]]
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reindexed = frame.loc[[("foo", "one"), ("bar", "one")]]
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tm.assert_frame_equal(reindexed, expected)
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def test_reindex_preserve_levels(
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self, multiindex_year_month_day_dataframe_random_data, using_copy_on_write
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):
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ymd = multiindex_year_month_day_dataframe_random_data
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new_index = ymd.index[::10]
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chunk = ymd.reindex(new_index)
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if using_copy_on_write:
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assert chunk.index.is_(new_index)
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else:
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assert chunk.index is new_index
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chunk = ymd.loc[new_index]
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assert chunk.index.equals(new_index)
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ymdT = ymd.T
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chunk = ymdT.reindex(columns=new_index)
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if using_copy_on_write:
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assert chunk.columns.is_(new_index)
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else:
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assert chunk.columns is new_index
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chunk = ymdT.loc[:, new_index]
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assert chunk.columns.equals(new_index)
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def test_groupby_transform(self, multiindex_dataframe_random_data):
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frame = multiindex_dataframe_random_data
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s = frame["A"]
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grouper = s.index.get_level_values(0)
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grouped = s.groupby(grouper, group_keys=False)
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applied = grouped.apply(lambda x: x * 2)
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expected = grouped.transform(lambda x: x * 2)
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result = applied.reindex(expected.index)
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tm.assert_series_equal(result, expected, check_names=False)
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def test_groupby_corner(self):
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midx = MultiIndex(
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levels=[["foo"], ["bar"], ["baz"]],
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codes=[[0], [0], [0]],
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names=["one", "two", "three"],
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)
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df = DataFrame(
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[np.random.default_rng(2).random(4)],
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columns=["a", "b", "c", "d"],
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index=midx,
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)
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# should work
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df.groupby(level="three")
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def test_groupby_level_no_obs(self):
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# #1697
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midx = MultiIndex.from_tuples(
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[
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("f1", "s1"),
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("f1", "s2"),
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("f2", "s1"),
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("f2", "s2"),
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("f3", "s1"),
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("f3", "s2"),
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]
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)
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df = DataFrame([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], columns=midx)
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df1 = df.loc(axis=1)[df.columns.map(lambda u: u[0] in ["f2", "f3"])]
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msg = "DataFrame.groupby with axis=1 is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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grouped = df1.groupby(axis=1, level=0)
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result = grouped.sum()
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assert (result.columns == ["f2", "f3"]).all()
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def test_setitem_with_expansion_multiindex_columns(
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self, multiindex_year_month_day_dataframe_random_data
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):
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ymd = multiindex_year_month_day_dataframe_random_data
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df = ymd[:5].T
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df[2000, 1, 10] = df[2000, 1, 7]
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assert isinstance(df.columns, MultiIndex)
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assert (df[2000, 1, 10] == df[2000, 1, 7]).all()
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def test_alignment(self):
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x = Series(
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data=[1, 2, 3], index=MultiIndex.from_tuples([("A", 1), ("A", 2), ("B", 3)])
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)
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y = Series(
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data=[4, 5, 6], index=MultiIndex.from_tuples([("Z", 1), ("Z", 2), ("B", 3)])
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)
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res = x - y
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exp_index = x.index.union(y.index)
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exp = x.reindex(exp_index) - y.reindex(exp_index)
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tm.assert_series_equal(res, exp)
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# hit non-monotonic code path
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res = x[::-1] - y[::-1]
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exp_index = x.index.union(y.index)
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exp = x.reindex(exp_index) - y.reindex(exp_index)
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tm.assert_series_equal(res, exp)
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def test_groupby_multilevel(self, multiindex_year_month_day_dataframe_random_data):
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ymd = multiindex_year_month_day_dataframe_random_data
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result = ymd.groupby(level=[0, 1]).mean()
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k1 = ymd.index.get_level_values(0)
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k2 = ymd.index.get_level_values(1)
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expected = ymd.groupby([k1, k2]).mean()
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# TODO groupby with level_values drops names
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tm.assert_frame_equal(result, expected, check_names=False)
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assert result.index.names == ymd.index.names[:2]
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result2 = ymd.groupby(level=ymd.index.names[:2]).mean()
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tm.assert_frame_equal(result, result2)
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def test_multilevel_consolidate(self):
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index = MultiIndex.from_tuples(
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[("foo", "one"), ("foo", "two"), ("bar", "one"), ("bar", "two")]
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)
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df = DataFrame(
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np.random.default_rng(2).standard_normal((4, 4)), index=index, columns=index
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)
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df["Totals", ""] = df.sum(1)
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df = df._consolidate()
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def test_level_with_tuples(self):
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index = MultiIndex(
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levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]],
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codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
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)
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series = Series(np.random.default_rng(2).standard_normal(6), index=index)
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frame = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index)
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result = series[("foo", "bar", 0)]
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result2 = series.loc[("foo", "bar", 0)]
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expected = series[:2]
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expected.index = expected.index.droplevel(0)
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tm.assert_series_equal(result, expected)
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tm.assert_series_equal(result2, expected)
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with pytest.raises(KeyError, match=r"^\(\('foo', 'bar', 0\), 2\)$"):
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series[("foo", "bar", 0), 2]
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result = frame.loc[("foo", "bar", 0)]
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result2 = frame.xs(("foo", "bar", 0))
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expected = frame[:2]
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expected.index = expected.index.droplevel(0)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(result2, expected)
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index = MultiIndex(
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levels=[[("foo", "bar"), ("foo", "baz"), ("foo", "qux")], [0, 1]],
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codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
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)
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series = Series(np.random.default_rng(2).standard_normal(6), index=index)
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frame = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index)
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result = series[("foo", "bar")]
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result2 = series.loc[("foo", "bar")]
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expected = series[:2]
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expected.index = expected.index.droplevel(0)
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tm.assert_series_equal(result, expected)
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tm.assert_series_equal(result2, expected)
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result = frame.loc[("foo", "bar")]
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result2 = frame.xs(("foo", "bar"))
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expected = frame[:2]
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expected.index = expected.index.droplevel(0)
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tm.assert_frame_equal(result, expected)
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tm.assert_frame_equal(result2, expected)
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def test_reindex_level_partial_selection(self, multiindex_dataframe_random_data):
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frame = multiindex_dataframe_random_data
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result = frame.reindex(["foo", "qux"], level=0)
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expected = frame.iloc[[0, 1, 2, 7, 8, 9]]
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tm.assert_frame_equal(result, expected)
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result = frame.T.reindex(["foo", "qux"], axis=1, level=0)
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tm.assert_frame_equal(result, expected.T)
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result = frame.loc[["foo", "qux"]]
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tm.assert_frame_equal(result, expected)
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result = frame["A"].loc[["foo", "qux"]]
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tm.assert_series_equal(result, expected["A"])
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result = frame.T.loc[:, ["foo", "qux"]]
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tm.assert_frame_equal(result, expected.T)
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@pytest.mark.parametrize("d", [4, "d"])
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def test_empty_frame_groupby_dtypes_consistency(self, d):
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# GH 20888
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group_keys = ["a", "b", "c"]
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df = DataFrame({"a": [1], "b": [2], "c": [3], "d": [d]})
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g = df[df.a == 2].groupby(group_keys)
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result = g.first().index
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expected = MultiIndex(
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levels=[[1], [2], [3]], codes=[[], [], []], names=["a", "b", "c"]
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)
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tm.assert_index_equal(result, expected)
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def test_duplicate_groupby_issues(self):
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idx_tp = [
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("600809", "20061231"),
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("600809", "20070331"),
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("600809", "20070630"),
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("600809", "20070331"),
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]
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dt = ["demo", "demo", "demo", "demo"]
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idx = MultiIndex.from_tuples(idx_tp, names=["STK_ID", "RPT_Date"])
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s = Series(dt, index=idx)
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result = s.groupby(s.index).first()
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assert len(result) == 3
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def test_subsets_multiindex_dtype(self):
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# GH 20757
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data = [["x", 1]]
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columns = [("a", "b", np.nan), ("a", "c", 0.0)]
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df = DataFrame(data, columns=MultiIndex.from_tuples(columns))
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expected = df.dtypes.a.b
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result = df.a.b.dtypes
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tm.assert_series_equal(result, expected)
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def test_datetime_object_multiindex(self):
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data_dic = {
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(0, datetime.date(2018, 3, 3)): {"A": 1, "B": 10},
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(0, datetime.date(2018, 3, 4)): {"A": 2, "B": 11},
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(1, datetime.date(2018, 3, 3)): {"A": 3, "B": 12},
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(1, datetime.date(2018, 3, 4)): {"A": 4, "B": 13},
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}
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result = DataFrame.from_dict(data_dic, orient="index")
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data = {"A": [1, 2, 3, 4], "B": [10, 11, 12, 13]}
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index = [
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[0, 0, 1, 1],
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[
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datetime.date(2018, 3, 3),
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datetime.date(2018, 3, 4),
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datetime.date(2018, 3, 3),
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datetime.date(2018, 3, 4),
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],
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]
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expected = DataFrame(data=data, index=index)
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tm.assert_frame_equal(result, expected)
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def test_multiindex_with_na(self):
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df = DataFrame(
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[
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["A", np.nan, 1.23, 4.56],
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["A", "G", 1.23, 4.56],
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["A", "D", 9.87, 10.54],
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],
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columns=["pivot_0", "pivot_1", "col_1", "col_2"],
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).set_index(["pivot_0", "pivot_1"])
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df.at[("A", "F"), "col_2"] = 0.0
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expected = DataFrame(
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[
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["A", np.nan, 1.23, 4.56],
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["A", "G", 1.23, 4.56],
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["A", "D", 9.87, 10.54],
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["A", "F", np.nan, 0.0],
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],
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columns=["pivot_0", "pivot_1", "col_1", "col_2"],
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).set_index(["pivot_0", "pivot_1"])
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tm.assert_frame_equal(df, expected)
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class TestSorted:
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"""everything you wanted to test about sorting"""
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def test_sort_non_lexsorted(self):
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# degenerate case where we sort but don't
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# have a satisfying result :<
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# GH 15797
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idx = MultiIndex(
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[["A", "B", "C"], ["c", "b", "a"]], [[0, 1, 2, 0, 1, 2], [0, 2, 1, 1, 0, 2]]
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)
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df = DataFrame({"col": range(len(idx))}, index=idx, dtype="int64")
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assert df.index.is_monotonic_increasing is False
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sorted = df.sort_index()
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|
assert sorted.index.is_monotonic_increasing is True
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{"col": [1, 4, 5, 2]},
|
||
|
|
index=MultiIndex.from_tuples(
|
||
|
|
[("B", "a"), ("B", "c"), ("C", "a"), ("C", "b")]
|
||
|
|
),
|
||
|
|
dtype="int64",
|
||
|
|
)
|
||
|
|
result = sorted.loc[pd.IndexSlice["B":"C", "a":"c"], :]
|
||
|
|
tm.assert_frame_equal(result, expected)
|