912 lines
29 KiB
Python
912 lines
29 KiB
Python
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 DataFrame, Index, MultiIndex, Series, Timestamp
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import pandas._testing as tm
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from pandas.core.reshape.concat import concat
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from pandas.core.reshape.merge import merge
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@pytest.fixture
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def left():
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"""left dataframe (not multi-indexed) for multi-index join tests"""
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# a little relevant example with NAs
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key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
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key2 = ["two", "one", "three", "one", "two", "one", "two", "two", "three", "one"]
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data = np.random.randn(len(key1))
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return DataFrame({"key1": key1, "key2": key2, "data": data})
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@pytest.fixture
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def right():
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"""right dataframe (multi-indexed) for multi-index join tests"""
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index = MultiIndex(
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levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
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codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
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names=["key1", "key2"],
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)
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return DataFrame(
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np.random.randn(10, 3), index=index, columns=["j_one", "j_two", "j_three"]
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)
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@pytest.fixture
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def left_multi():
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return DataFrame(
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{
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"Origin": ["A", "A", "B", "B", "C"],
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"Destination": ["A", "B", "A", "C", "A"],
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"Period": ["AM", "AM", "IP", "AM", "OP"],
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"TripPurp": ["hbw", "nhb", "hbo", "nhb", "hbw"],
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"Trips": [1987, 3647, 2470, 4296, 4444],
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},
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columns=["Origin", "Destination", "Period", "TripPurp", "Trips"],
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).set_index(["Origin", "Destination", "Period", "TripPurp"])
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@pytest.fixture
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def right_multi():
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return DataFrame(
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{
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"Origin": ["A", "A", "B", "B", "C", "C", "E"],
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"Destination": ["A", "B", "A", "B", "A", "B", "F"],
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"Period": ["AM", "AM", "IP", "AM", "OP", "IP", "AM"],
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"LinkType": ["a", "b", "c", "b", "a", "b", "a"],
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"Distance": [100, 80, 90, 80, 75, 35, 55],
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},
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columns=["Origin", "Destination", "Period", "LinkType", "Distance"],
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).set_index(["Origin", "Destination", "Period", "LinkType"])
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@pytest.fixture
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def on_cols_multi():
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return ["Origin", "Destination", "Period"]
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@pytest.fixture
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def idx_cols_multi():
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return ["Origin", "Destination", "Period", "TripPurp", "LinkType"]
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class TestMergeMulti:
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def setup_method(self):
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self.index = MultiIndex(
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levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
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codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
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names=["first", "second"],
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)
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self.to_join = DataFrame(
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np.random.randn(10, 3),
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index=self.index,
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columns=["j_one", "j_two", "j_three"],
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)
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# a little relevant example with NAs
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key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
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key2 = [
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"two",
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"one",
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"three",
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"one",
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"two",
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"one",
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"two",
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"two",
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"three",
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"one",
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]
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data = np.random.randn(len(key1))
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self.data = DataFrame({"key1": key1, "key2": key2, "data": data})
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def test_merge_on_multikey(self, left, right, join_type):
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on_cols = ["key1", "key2"]
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result = left.join(right, on=on_cols, how=join_type).reset_index(drop=True)
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expected = pd.merge(left, right.reset_index(), on=on_cols, how=join_type)
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tm.assert_frame_equal(result, expected)
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result = left.join(right, on=on_cols, how=join_type, sort=True).reset_index(
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drop=True
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)
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expected = pd.merge(
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left, right.reset_index(), on=on_cols, how=join_type, sort=True
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("sort", [False, True])
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def test_left_join_multi_index(self, left, right, sort):
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icols = ["1st", "2nd", "3rd"]
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def bind_cols(df):
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iord = lambda a: 0 if a != a else ord(a)
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f = lambda ts: ts.map(iord) - ord("a")
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return f(df["1st"]) + f(df["3rd"]) * 1e2 + df["2nd"].fillna(0) * 1e4
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def run_asserts(left, right, sort):
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res = left.join(right, on=icols, how="left", sort=sort)
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assert len(left) < len(res) + 1
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assert not res["4th"].isna().any()
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assert not res["5th"].isna().any()
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tm.assert_series_equal(res["4th"], -res["5th"], check_names=False)
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result = bind_cols(res.iloc[:, :-2])
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tm.assert_series_equal(res["4th"], result, check_names=False)
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assert result.name is None
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if sort:
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tm.assert_frame_equal(res, res.sort_values(icols, kind="mergesort"))
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out = merge(left, right.reset_index(), on=icols, sort=sort, how="left")
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res.index = np.arange(len(res))
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tm.assert_frame_equal(out, res)
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lc = list(map(chr, np.arange(ord("a"), ord("z") + 1)))
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left = DataFrame(np.random.choice(lc, (5000, 2)), columns=["1st", "3rd"])
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left.insert(1, "2nd", np.random.randint(0, 1000, len(left)))
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i = np.random.permutation(len(left))
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right = left.iloc[i].copy()
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left["4th"] = bind_cols(left)
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right["5th"] = -bind_cols(right)
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right.set_index(icols, inplace=True)
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run_asserts(left, right, sort)
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# inject some nulls
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left.loc[1::23, "1st"] = np.nan
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left.loc[2::37, "2nd"] = np.nan
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left.loc[3::43, "3rd"] = np.nan
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left["4th"] = bind_cols(left)
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i = np.random.permutation(len(left))
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right = left.iloc[i, :-1]
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right["5th"] = -bind_cols(right)
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right.set_index(icols, inplace=True)
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run_asserts(left, right, sort)
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@pytest.mark.parametrize("sort", [False, True])
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def test_merge_right_vs_left(self, left, right, sort):
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# compare left vs right merge with multikey
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on_cols = ["key1", "key2"]
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merged_left_right = left.merge(
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right, left_on=on_cols, right_index=True, how="left", sort=sort
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)
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merge_right_left = right.merge(
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left, right_on=on_cols, left_index=True, how="right", sort=sort
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)
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# Reorder columns
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merge_right_left = merge_right_left[merged_left_right.columns]
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tm.assert_frame_equal(merged_left_right, merge_right_left)
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def test_merge_multiple_cols_with_mixed_cols_index(self):
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# GH29522
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s = pd.Series(
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range(6),
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MultiIndex.from_product([["A", "B"], [1, 2, 3]], names=["lev1", "lev2"]),
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name="Amount",
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)
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df = DataFrame({"lev1": list("AAABBB"), "lev2": [1, 2, 3, 1, 2, 3], "col": 0})
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result = pd.merge(df, s.reset_index(), on=["lev1", "lev2"])
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expected = DataFrame(
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{
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"lev1": list("AAABBB"),
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"lev2": [1, 2, 3, 1, 2, 3],
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"col": [0] * 6,
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"Amount": range(6),
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}
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)
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tm.assert_frame_equal(result, expected)
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def test_compress_group_combinations(self):
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# ~ 40000000 possible unique groups
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key1 = tm.rands_array(10, 10000)
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key1 = np.tile(key1, 2)
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key2 = key1[::-1]
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df = DataFrame({"key1": key1, "key2": key2, "value1": np.random.randn(20000)})
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df2 = DataFrame(
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{"key1": key1[::2], "key2": key2[::2], "value2": np.random.randn(10000)}
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)
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# just to hit the label compression code path
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merge(df, df2, how="outer")
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def test_left_join_index_preserve_order(self):
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on_cols = ["k1", "k2"]
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left = DataFrame(
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{
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"k1": [0, 1, 2] * 8,
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"k2": ["foo", "bar"] * 12,
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"v": np.array(np.arange(24), dtype=np.int64),
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}
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)
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index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
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right = DataFrame({"v2": [5, 7]}, index=index)
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result = left.join(right, on=on_cols)
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expected = left.copy()
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expected["v2"] = np.nan
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expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
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expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
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tm.assert_frame_equal(result, expected)
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result.sort_values(on_cols, kind="mergesort", inplace=True)
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expected = left.join(right, on=on_cols, sort=True)
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tm.assert_frame_equal(result, expected)
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# test join with multi dtypes blocks
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left = DataFrame(
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{
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"k1": [0, 1, 2] * 8,
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"k2": ["foo", "bar"] * 12,
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"k3": np.array([0, 1, 2] * 8, dtype=np.float32),
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"v": np.array(np.arange(24), dtype=np.int32),
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}
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)
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index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
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right = DataFrame({"v2": [5, 7]}, index=index)
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result = left.join(right, on=on_cols)
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expected = left.copy()
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expected["v2"] = np.nan
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expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
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expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
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tm.assert_frame_equal(result, expected)
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result = result.sort_values(on_cols, kind="mergesort")
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expected = left.join(right, on=on_cols, sort=True)
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tm.assert_frame_equal(result, expected)
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def test_left_join_index_multi_match_multiindex(self):
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left = DataFrame(
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[
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["X", "Y", "C", "a"],
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["W", "Y", "C", "e"],
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["V", "Q", "A", "h"],
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["V", "R", "D", "i"],
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["X", "Y", "D", "b"],
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["X", "Y", "A", "c"],
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["W", "Q", "B", "f"],
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["W", "R", "C", "g"],
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["V", "Y", "C", "j"],
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["X", "Y", "B", "d"],
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],
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columns=["cola", "colb", "colc", "tag"],
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index=[3, 2, 0, 1, 7, 6, 4, 5, 9, 8],
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)
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right = DataFrame(
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[
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["W", "R", "C", 0],
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["W", "Q", "B", 3],
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["W", "Q", "B", 8],
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["X", "Y", "A", 1],
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["X", "Y", "A", 4],
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["X", "Y", "B", 5],
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["X", "Y", "C", 6],
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["X", "Y", "C", 9],
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["X", "Q", "C", -6],
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["X", "R", "C", -9],
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["V", "Y", "C", 7],
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["V", "R", "D", 2],
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["V", "R", "D", -1],
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["V", "Q", "A", -3],
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],
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columns=["col1", "col2", "col3", "val"],
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).set_index(["col1", "col2", "col3"])
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result = left.join(right, on=["cola", "colb", "colc"], how="left")
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expected = DataFrame(
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[
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["X", "Y", "C", "a", 6],
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["X", "Y", "C", "a", 9],
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["W", "Y", "C", "e", np.nan],
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["V", "Q", "A", "h", -3],
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["V", "R", "D", "i", 2],
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["V", "R", "D", "i", -1],
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["X", "Y", "D", "b", np.nan],
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["X", "Y", "A", "c", 1],
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["X", "Y", "A", "c", 4],
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["W", "Q", "B", "f", 3],
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["W", "Q", "B", "f", 8],
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["W", "R", "C", "g", 0],
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["V", "Y", "C", "j", 7],
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["X", "Y", "B", "d", 5],
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],
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columns=["cola", "colb", "colc", "tag", "val"],
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index=[3, 3, 2, 0, 1, 1, 7, 6, 6, 4, 4, 5, 9, 8],
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)
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tm.assert_frame_equal(result, expected)
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result = left.join(right, on=["cola", "colb", "colc"], how="left", sort=True)
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expected = expected.sort_values(["cola", "colb", "colc"], kind="mergesort")
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tm.assert_frame_equal(result, expected)
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def test_left_join_index_multi_match(self):
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left = DataFrame(
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[["c", 0], ["b", 1], ["a", 2], ["b", 3]],
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columns=["tag", "val"],
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index=[2, 0, 1, 3],
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)
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right = DataFrame(
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[
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["a", "v"],
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["c", "w"],
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["c", "x"],
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["d", "y"],
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["a", "z"],
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["c", "r"],
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["e", "q"],
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["c", "s"],
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],
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columns=["tag", "char"],
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).set_index("tag")
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result = left.join(right, on="tag", how="left")
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expected = DataFrame(
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[
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["c", 0, "w"],
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["c", 0, "x"],
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["c", 0, "r"],
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["c", 0, "s"],
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["b", 1, np.nan],
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["a", 2, "v"],
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["a", 2, "z"],
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["b", 3, np.nan],
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],
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columns=["tag", "val", "char"],
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index=[2, 2, 2, 2, 0, 1, 1, 3],
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)
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tm.assert_frame_equal(result, expected)
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result = left.join(right, on="tag", how="left", sort=True)
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expected2 = expected.sort_values("tag", kind="mergesort")
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tm.assert_frame_equal(result, expected2)
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# GH7331 - maintain left frame order in left merge
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result = merge(left, right.reset_index(), how="left", on="tag")
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expected.index = np.arange(len(expected))
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tm.assert_frame_equal(result, expected)
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def test_left_merge_na_buglet(self):
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left = DataFrame(
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{
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"id": list("abcde"),
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"v1": np.random.randn(5),
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"v2": np.random.randn(5),
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"dummy": list("abcde"),
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"v3": np.random.randn(5),
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},
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columns=["id", "v1", "v2", "dummy", "v3"],
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)
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right = DataFrame(
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{
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"id": ["a", "b", np.nan, np.nan, np.nan],
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"sv3": [1.234, 5.678, np.nan, np.nan, np.nan],
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}
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)
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result = merge(left, right, on="id", how="left")
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rdf = right.drop(["id"], axis=1)
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expected = left.join(rdf)
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tm.assert_frame_equal(result, expected)
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def test_merge_na_keys(self):
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data = [
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[1950, "A", 1.5],
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[1950, "B", 1.5],
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[1955, "B", 1.5],
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[1960, "B", np.nan],
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[1970, "B", 4.0],
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[1950, "C", 4.0],
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[1960, "C", np.nan],
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[1965, "C", 3.0],
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[1970, "C", 4.0],
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]
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frame = DataFrame(data, columns=["year", "panel", "data"])
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other_data = [
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[1960, "A", np.nan],
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[1970, "A", np.nan],
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[1955, "A", np.nan],
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[1965, "A", np.nan],
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[1965, "B", np.nan],
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[1955, "C", np.nan],
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]
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other = DataFrame(other_data, columns=["year", "panel", "data"])
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result = frame.merge(other, how="outer")
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expected = frame.fillna(-999).merge(other.fillna(-999), how="outer")
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expected = expected.replace(-999, np.nan)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
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def test_merge_datetime_index(self, klass):
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# see gh-19038
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df = DataFrame(
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[1, 2, 3], ["2016-01-01", "2017-01-01", "2018-01-01"], columns=["a"]
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)
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df.index = pd.to_datetime(df.index)
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on_vector = df.index.year
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if klass is not None:
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on_vector = klass(on_vector)
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expected = DataFrame({"a": [1, 2, 3], "key_1": [2016, 2017, 2018]})
|
|
|
|
result = df.merge(df, on=["a", on_vector], how="inner")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
{"key_0": [2016, 2017, 2018], "a_x": [1, 2, 3], "a_y": [1, 2, 3]}
|
|
)
|
|
|
|
result = df.merge(df, on=[df.index.year], how="inner")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("merge_type", ["left", "right"])
|
|
def test_merge_datetime_multi_index_empty_df(self, merge_type):
|
|
# see gh-36895
|
|
|
|
left = DataFrame(
|
|
data={
|
|
"data": [1.5, 1.5],
|
|
},
|
|
index=MultiIndex.from_tuples(
|
|
[[Timestamp("1950-01-01"), "A"], [Timestamp("1950-01-02"), "B"]],
|
|
names=["date", "panel"],
|
|
),
|
|
)
|
|
|
|
right = DataFrame(
|
|
index=MultiIndex.from_tuples([], names=["date", "panel"]), columns=["state"]
|
|
)
|
|
|
|
expected_index = MultiIndex.from_tuples(
|
|
[[Timestamp("1950-01-01"), "A"], [Timestamp("1950-01-02"), "B"]],
|
|
names=["date", "panel"],
|
|
)
|
|
|
|
if merge_type == "left":
|
|
expected = DataFrame(
|
|
data={
|
|
"data": [1.5, 1.5],
|
|
"state": [None, None],
|
|
},
|
|
index=expected_index,
|
|
)
|
|
results_merge = left.merge(right, how="left", on=["date", "panel"])
|
|
results_join = left.join(right, how="left")
|
|
else:
|
|
expected = DataFrame(
|
|
data={
|
|
"state": [None, None],
|
|
"data": [1.5, 1.5],
|
|
},
|
|
index=expected_index,
|
|
)
|
|
results_merge = right.merge(left, how="right", on=["date", "panel"])
|
|
results_join = right.join(left, how="right")
|
|
|
|
tm.assert_frame_equal(results_merge, expected)
|
|
tm.assert_frame_equal(results_join, expected)
|
|
|
|
def test_join_multi_levels(self):
|
|
|
|
# GH 3662
|
|
# merge multi-levels
|
|
household = DataFrame(
|
|
{
|
|
"household_id": [1, 2, 3],
|
|
"male": [0, 1, 0],
|
|
"wealth": [196087.3, 316478.7, 294750],
|
|
},
|
|
columns=["household_id", "male", "wealth"],
|
|
).set_index("household_id")
|
|
portfolio = DataFrame(
|
|
{
|
|
"household_id": [1, 2, 2, 3, 3, 3, 4],
|
|
"asset_id": [
|
|
"nl0000301109",
|
|
"nl0000289783",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"nl0000289965",
|
|
np.nan,
|
|
],
|
|
"name": [
|
|
"ABN Amro",
|
|
"Robeco",
|
|
"Royal Dutch Shell",
|
|
"Royal Dutch Shell",
|
|
"AAB Eastern Europe Equity Fund",
|
|
"Postbank BioTech Fonds",
|
|
np.nan,
|
|
],
|
|
"share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
|
|
},
|
|
columns=["household_id", "asset_id", "name", "share"],
|
|
).set_index(["household_id", "asset_id"])
|
|
result = household.join(portfolio, how="inner")
|
|
expected = (
|
|
DataFrame(
|
|
{
|
|
"male": [0, 1, 1, 0, 0, 0],
|
|
"wealth": [
|
|
196087.3,
|
|
316478.7,
|
|
316478.7,
|
|
294750.0,
|
|
294750.0,
|
|
294750.0,
|
|
],
|
|
"name": [
|
|
"ABN Amro",
|
|
"Robeco",
|
|
"Royal Dutch Shell",
|
|
"Royal Dutch Shell",
|
|
"AAB Eastern Europe Equity Fund",
|
|
"Postbank BioTech Fonds",
|
|
],
|
|
"share": [1.00, 0.40, 0.60, 0.15, 0.60, 0.25],
|
|
"household_id": [1, 2, 2, 3, 3, 3],
|
|
"asset_id": [
|
|
"nl0000301109",
|
|
"nl0000289783",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"nl0000289965",
|
|
],
|
|
}
|
|
)
|
|
.set_index(["household_id", "asset_id"])
|
|
.reindex(columns=["male", "wealth", "name", "share"])
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# equivalency
|
|
result = merge(
|
|
household.reset_index(),
|
|
portfolio.reset_index(),
|
|
on=["household_id"],
|
|
how="inner",
|
|
).set_index(["household_id", "asset_id"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = household.join(portfolio, how="outer")
|
|
expected = concat(
|
|
[
|
|
expected,
|
|
(
|
|
DataFrame(
|
|
{"share": [1.00]},
|
|
index=MultiIndex.from_tuples(
|
|
[(4, np.nan)], names=["household_id", "asset_id"]
|
|
),
|
|
)
|
|
),
|
|
],
|
|
axis=0,
|
|
sort=True,
|
|
).reindex(columns=expected.columns)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# invalid cases
|
|
household.index.name = "foo"
|
|
|
|
with pytest.raises(
|
|
ValueError, match="cannot join with no overlapping index names"
|
|
):
|
|
household.join(portfolio, how="inner")
|
|
|
|
portfolio2 = portfolio.copy()
|
|
portfolio2.index.set_names(["household_id", "foo"])
|
|
|
|
with pytest.raises(ValueError, match="columns overlap but no suffix specified"):
|
|
portfolio2.join(portfolio, how="inner")
|
|
|
|
def test_join_multi_levels2(self):
|
|
|
|
# some more advanced merges
|
|
# GH6360
|
|
household = DataFrame(
|
|
{
|
|
"household_id": [1, 2, 2, 3, 3, 3, 4],
|
|
"asset_id": [
|
|
"nl0000301109",
|
|
"nl0000301109",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"nl0000289965",
|
|
np.nan,
|
|
],
|
|
"share": [1.0, 0.4, 0.6, 0.15, 0.6, 0.25, 1.0],
|
|
},
|
|
columns=["household_id", "asset_id", "share"],
|
|
).set_index(["household_id", "asset_id"])
|
|
|
|
log_return = DataFrame(
|
|
{
|
|
"asset_id": [
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"lu0197800237",
|
|
],
|
|
"t": [233, 234, 235, 180, 181],
|
|
"log_return": [
|
|
0.09604978,
|
|
-0.06524096,
|
|
0.03532373,
|
|
0.03025441,
|
|
0.036997,
|
|
],
|
|
}
|
|
).set_index(["asset_id", "t"])
|
|
|
|
expected = (
|
|
DataFrame(
|
|
{
|
|
"household_id": [2, 2, 2, 3, 3, 3, 3, 3],
|
|
"asset_id": [
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"lu0197800237",
|
|
],
|
|
"t": [233, 234, 235, 233, 234, 235, 180, 181],
|
|
"share": [0.6, 0.6, 0.6, 0.15, 0.15, 0.15, 0.6, 0.6],
|
|
"log_return": [
|
|
0.09604978,
|
|
-0.06524096,
|
|
0.03532373,
|
|
0.09604978,
|
|
-0.06524096,
|
|
0.03532373,
|
|
0.03025441,
|
|
0.036997,
|
|
],
|
|
}
|
|
)
|
|
.set_index(["household_id", "asset_id", "t"])
|
|
.reindex(columns=["share", "log_return"])
|
|
)
|
|
|
|
# this is the equivalency
|
|
result = merge(
|
|
household.reset_index(),
|
|
log_return.reset_index(),
|
|
on=["asset_id"],
|
|
how="inner",
|
|
).set_index(["household_id", "asset_id", "t"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = (
|
|
DataFrame(
|
|
{
|
|
"household_id": [1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4],
|
|
"asset_id": [
|
|
"nl0000301109",
|
|
"nl0000301109",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"gb00b03mlx29",
|
|
"lu0197800237",
|
|
"lu0197800237",
|
|
"nl0000289965",
|
|
None,
|
|
],
|
|
"t": [
|
|
None,
|
|
None,
|
|
233,
|
|
234,
|
|
235,
|
|
233,
|
|
234,
|
|
235,
|
|
180,
|
|
181,
|
|
None,
|
|
None,
|
|
],
|
|
"share": [
|
|
1.0,
|
|
0.4,
|
|
0.6,
|
|
0.6,
|
|
0.6,
|
|
0.15,
|
|
0.15,
|
|
0.15,
|
|
0.6,
|
|
0.6,
|
|
0.25,
|
|
1.0,
|
|
],
|
|
"log_return": [
|
|
None,
|
|
None,
|
|
0.09604978,
|
|
-0.06524096,
|
|
0.03532373,
|
|
0.09604978,
|
|
-0.06524096,
|
|
0.03532373,
|
|
0.03025441,
|
|
0.036997,
|
|
None,
|
|
None,
|
|
],
|
|
}
|
|
)
|
|
.set_index(["household_id", "asset_id", "t"])
|
|
.reindex(columns=["share", "log_return"])
|
|
)
|
|
|
|
result = merge(
|
|
household.reset_index(),
|
|
log_return.reset_index(),
|
|
on=["asset_id"],
|
|
how="outer",
|
|
).set_index(["household_id", "asset_id", "t"])
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestJoinMultiMulti:
|
|
def test_join_multi_multi(
|
|
self, left_multi, right_multi, join_type, on_cols_multi, idx_cols_multi
|
|
):
|
|
# Multi-index join tests
|
|
expected = (
|
|
pd.merge(
|
|
left_multi.reset_index(),
|
|
right_multi.reset_index(),
|
|
how=join_type,
|
|
on=on_cols_multi,
|
|
)
|
|
.set_index(idx_cols_multi)
|
|
.sort_index()
|
|
)
|
|
|
|
result = left_multi.join(right_multi, how=join_type).sort_index()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_join_multi_empty_frames(
|
|
self, left_multi, right_multi, join_type, on_cols_multi, idx_cols_multi
|
|
):
|
|
|
|
left_multi = left_multi.drop(columns=left_multi.columns)
|
|
right_multi = right_multi.drop(columns=right_multi.columns)
|
|
|
|
expected = (
|
|
pd.merge(
|
|
left_multi.reset_index(),
|
|
right_multi.reset_index(),
|
|
how=join_type,
|
|
on=on_cols_multi,
|
|
)
|
|
.set_index(idx_cols_multi)
|
|
.sort_index()
|
|
)
|
|
|
|
result = left_multi.join(right_multi, how=join_type).sort_index()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("box", [None, np.asarray, Series, Index])
|
|
def test_merge_datetime_index(self, box):
|
|
# see gh-19038
|
|
df = DataFrame(
|
|
[1, 2, 3], ["2016-01-01", "2017-01-01", "2018-01-01"], columns=["a"]
|
|
)
|
|
df.index = pd.to_datetime(df.index)
|
|
on_vector = df.index.year
|
|
|
|
if box is not None:
|
|
on_vector = box(on_vector)
|
|
|
|
expected = DataFrame({"a": [1, 2, 3], "key_1": [2016, 2017, 2018]})
|
|
|
|
result = df.merge(df, on=["a", on_vector], how="inner")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(
|
|
{"key_0": [2016, 2017, 2018], "a_x": [1, 2, 3], "a_y": [1, 2, 3]}
|
|
)
|
|
|
|
result = df.merge(df, on=[df.index.year], how="inner")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_single_common_level(self):
|
|
index_left = MultiIndex.from_tuples(
|
|
[("K0", "X0"), ("K0", "X1"), ("K1", "X2")], names=["key", "X"]
|
|
)
|
|
|
|
left = DataFrame(
|
|
{"A": ["A0", "A1", "A2"], "B": ["B0", "B1", "B2"]}, index=index_left
|
|
)
|
|
|
|
index_right = MultiIndex.from_tuples(
|
|
[("K0", "Y0"), ("K1", "Y1"), ("K2", "Y2"), ("K2", "Y3")], names=["key", "Y"]
|
|
)
|
|
|
|
right = DataFrame(
|
|
{"C": ["C0", "C1", "C2", "C3"], "D": ["D0", "D1", "D2", "D3"]},
|
|
index=index_right,
|
|
)
|
|
|
|
result = left.join(right)
|
|
expected = pd.merge(
|
|
left.reset_index(), right.reset_index(), on=["key"], how="inner"
|
|
).set_index(["key", "X", "Y"])
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_join_multi_wrong_order(self):
|
|
# GH 25760
|
|
# GH 28956
|
|
|
|
midx1 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"])
|
|
midx3 = MultiIndex.from_tuples([(4, 1), (3, 2), (3, 1)], names=["b", "a"])
|
|
|
|
left = DataFrame(index=midx1, data={"x": [10, 20, 30, 40]})
|
|
right = DataFrame(index=midx3, data={"y": ["foo", "bar", "fing"]})
|
|
|
|
result = left.join(right)
|
|
|
|
expected = DataFrame(
|
|
index=midx1,
|
|
data={"x": [10, 20, 30, 40], "y": ["fing", "foo", "bar", np.nan]},
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|