565 lines
17 KiB
Python
565 lines
17 KiB
Python
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from datetime import datetime
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import numpy as np
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import pytest
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from pandas.errors import MergeError
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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date_range,
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period_range,
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)
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import pandas._testing as tm
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from pandas.core.reshape.concat import concat
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@pytest.fixture
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def frame_with_period_index():
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return DataFrame(
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data=np.arange(20).reshape(4, 5),
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columns=list("abcde"),
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index=period_range(start="2000", freq="A", periods=4),
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)
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@pytest.fixture
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def left():
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return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
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@pytest.fixture
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def right():
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return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
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@pytest.fixture
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def left_no_dup():
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return DataFrame(
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{"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
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index=range(4),
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)
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@pytest.fixture
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def right_no_dup():
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return DataFrame(
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{
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"a": ["a", "b", "c", "d", "e"],
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"c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
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},
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index=range(5),
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).set_index("a")
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@pytest.fixture
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def left_w_dups(left_no_dup):
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return concat(
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[left_no_dup, DataFrame({"a": ["a"], "b": ["cow"]}, index=[3])], sort=True
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)
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@pytest.fixture
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def right_w_dups(right_no_dup):
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return concat(
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[right_no_dup, DataFrame({"a": ["e"], "c": ["moo"]}, index=[3])]
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).set_index("a")
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@pytest.mark.parametrize(
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"how, sort, expected",
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[
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("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])),
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("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])),
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(
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"left",
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False,
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DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]),
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),
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(
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"left",
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True,
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DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]),
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),
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(
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"right",
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False,
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DataFrame({"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]),
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),
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(
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"right",
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True,
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DataFrame({"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]),
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),
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(
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"outer",
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False,
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DataFrame(
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{"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
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index=[0, 1, 2, 3],
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),
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),
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(
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"outer",
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True,
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DataFrame(
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{"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
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index=[0, 1, 2, 3],
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),
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),
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],
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)
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def test_join(left, right, how, sort, expected):
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result = left.join(right, how=how, sort=sort, validate="1:1")
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tm.assert_frame_equal(result, expected)
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def test_suffix_on_list_join():
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first = DataFrame({"key": [1, 2, 3, 4, 5]})
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second = DataFrame({"key": [1, 8, 3, 2, 5], "v1": [1, 2, 3, 4, 5]})
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third = DataFrame({"keys": [5, 2, 3, 4, 1], "v2": [1, 2, 3, 4, 5]})
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# check proper errors are raised
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msg = "Suffixes not supported when joining multiple DataFrames"
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with pytest.raises(ValueError, match=msg):
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first.join([second], lsuffix="y")
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with pytest.raises(ValueError, match=msg):
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first.join([second, third], rsuffix="x")
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with pytest.raises(ValueError, match=msg):
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first.join([second, third], lsuffix="y", rsuffix="x")
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with pytest.raises(ValueError, match="Indexes have overlapping values"):
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first.join([second, third])
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# no errors should be raised
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arr_joined = first.join([third])
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norm_joined = first.join(third)
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tm.assert_frame_equal(arr_joined, norm_joined)
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def test_join_invalid_validate(left_no_dup, right_no_dup):
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# GH 46622
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# Check invalid arguments
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msg = "Not a valid argument for validate"
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with pytest.raises(ValueError, match=msg):
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left_no_dup.merge(right_no_dup, on="a", validate="invalid")
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def test_join_on_single_col_dup_on_right(left_no_dup, right_w_dups):
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# GH 46622
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# Dups on right allowed by one_to_many constraint
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left_no_dup.join(
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right_w_dups,
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on="a",
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validate="one_to_many",
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)
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# Dups on right not allowed by one_to_one constraint
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msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
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with pytest.raises(MergeError, match=msg):
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left_no_dup.join(
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right_w_dups,
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on="a",
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validate="one_to_one",
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)
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def test_join_on_single_col_dup_on_left(left_w_dups, right_no_dup):
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# GH 46622
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# Dups on left allowed by many_to_one constraint
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left_w_dups.join(
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right_no_dup,
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on="a",
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validate="many_to_one",
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)
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# Dups on left not allowed by one_to_one constraint
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msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
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with pytest.raises(MergeError, match=msg):
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left_w_dups.join(
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right_no_dup,
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on="a",
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validate="one_to_one",
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)
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def test_join_on_single_col_dup_on_both(left_w_dups, right_w_dups):
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# GH 46622
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# Dups on both allowed by many_to_many constraint
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left_w_dups.join(right_w_dups, on="a", validate="many_to_many")
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# Dups on both not allowed by many_to_one constraint
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msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
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with pytest.raises(MergeError, match=msg):
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left_w_dups.join(
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right_w_dups,
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on="a",
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validate="many_to_one",
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)
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# Dups on both not allowed by one_to_many constraint
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msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
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with pytest.raises(MergeError, match=msg):
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left_w_dups.join(
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right_w_dups,
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on="a",
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validate="one_to_many",
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)
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def test_join_on_multi_col_check_dup():
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# GH 46622
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# Two column join, dups in both, but jointly no dups
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left = DataFrame(
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{
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"a": ["a", "a", "b", "b"],
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"b": [0, 1, 0, 1],
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"c": ["cat", "dog", "weasel", "horse"],
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},
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index=range(4),
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).set_index(["a", "b"])
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right = DataFrame(
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{
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"a": ["a", "a", "b"],
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"b": [0, 1, 0],
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"d": ["meow", "bark", "um... weasel noise?"],
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},
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index=range(3),
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).set_index(["a", "b"])
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expected_multi = DataFrame(
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{
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"a": ["a", "a", "b"],
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"b": [0, 1, 0],
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"c": ["cat", "dog", "weasel"],
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"d": ["meow", "bark", "um... weasel noise?"],
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},
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index=range(3),
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).set_index(["a", "b"])
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# Jointly no dups allowed by one_to_one constraint
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result = left.join(right, how="inner", validate="1:1")
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tm.assert_frame_equal(result, expected_multi)
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def test_join_index(float_frame):
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# left / right
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f = float_frame.loc[float_frame.index[:10], ["A", "B"]]
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f2 = float_frame.loc[float_frame.index[5:], ["C", "D"]].iloc[::-1]
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joined = f.join(f2)
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tm.assert_index_equal(f.index, joined.index)
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expected_columns = Index(["A", "B", "C", "D"])
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tm.assert_index_equal(joined.columns, expected_columns)
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joined = f.join(f2, how="left")
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tm.assert_index_equal(joined.index, f.index)
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tm.assert_index_equal(joined.columns, expected_columns)
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joined = f.join(f2, how="right")
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tm.assert_index_equal(joined.index, f2.index)
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tm.assert_index_equal(joined.columns, expected_columns)
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# inner
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joined = f.join(f2, how="inner")
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tm.assert_index_equal(joined.index, f.index[5:10])
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tm.assert_index_equal(joined.columns, expected_columns)
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# outer
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joined = f.join(f2, how="outer")
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tm.assert_index_equal(joined.index, float_frame.index.sort_values())
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tm.assert_index_equal(joined.columns, expected_columns)
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with pytest.raises(ValueError, match="join method"):
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f.join(f2, how="foo")
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# corner case - overlapping columns
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msg = "columns overlap but no suffix"
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for how in ("outer", "left", "inner"):
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with pytest.raises(ValueError, match=msg):
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float_frame.join(float_frame, how=how)
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def test_join_index_more(float_frame):
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af = float_frame.loc[:, ["A", "B"]]
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bf = float_frame.loc[::2, ["C", "D"]]
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expected = af.copy()
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expected["C"] = float_frame["C"][::2]
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expected["D"] = float_frame["D"][::2]
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result = af.join(bf)
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tm.assert_frame_equal(result, expected)
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result = af.join(bf, how="right")
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tm.assert_frame_equal(result, expected[::2])
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result = bf.join(af, how="right")
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tm.assert_frame_equal(result, expected.loc[:, result.columns])
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def test_join_index_series(float_frame):
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df = float_frame.copy()
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ser = df.pop(float_frame.columns[-1])
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joined = df.join(ser)
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tm.assert_frame_equal(joined, float_frame)
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ser.name = None
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with pytest.raises(ValueError, match="must have a name"):
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df.join(ser)
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def test_join_overlap(float_frame):
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df1 = float_frame.loc[:, ["A", "B", "C"]]
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df2 = float_frame.loc[:, ["B", "C", "D"]]
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joined = df1.join(df2, lsuffix="_df1", rsuffix="_df2")
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df1_suf = df1.loc[:, ["B", "C"]].add_suffix("_df1")
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df2_suf = df2.loc[:, ["B", "C"]].add_suffix("_df2")
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no_overlap = float_frame.loc[:, ["A", "D"]]
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expected = df1_suf.join(df2_suf).join(no_overlap)
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# column order not necessarily sorted
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tm.assert_frame_equal(joined, expected.loc[:, joined.columns])
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def test_join_period_index(frame_with_period_index):
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other = frame_with_period_index.rename(columns=lambda key: f"{key}{key}")
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joined_values = np.concatenate([frame_with_period_index.values] * 2, axis=1)
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joined_cols = frame_with_period_index.columns.append(other.columns)
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joined = frame_with_period_index.join(other)
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expected = DataFrame(
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data=joined_values, columns=joined_cols, index=frame_with_period_index.index
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)
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tm.assert_frame_equal(joined, expected)
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def test_join_left_sequence_non_unique_index():
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# https://github.com/pandas-dev/pandas/issues/19607
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df1 = DataFrame({"a": [0, 10, 20]}, index=[1, 2, 3])
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df2 = DataFrame({"b": [100, 200, 300]}, index=[4, 3, 2])
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df3 = DataFrame({"c": [400, 500, 600]}, index=[2, 2, 4])
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joined = df1.join([df2, df3], how="left")
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expected = DataFrame(
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{
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"a": [0, 10, 10, 20],
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"b": [np.nan, 300, 300, 200],
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"c": [np.nan, 400, 500, np.nan],
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},
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|
|
index=[1, 2, 2, 3],
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(joined, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_join_list_series(float_frame):
|
||
|
|
# GH#46850
|
||
|
|
# Join a DataFrame with a list containing both a Series and a DataFrame
|
||
|
|
left = float_frame.A.to_frame()
|
||
|
|
right = [float_frame.B, float_frame[["C", "D"]]]
|
||
|
|
result = left.join(right)
|
||
|
|
tm.assert_frame_equal(result, float_frame)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("sort_kw", [True, False])
|
||
|
|
def test_suppress_future_warning_with_sort_kw(sort_kw):
|
||
|
|
a = DataFrame({"col1": [1, 2]}, index=["c", "a"])
|
||
|
|
|
||
|
|
b = DataFrame({"col2": [4, 5]}, index=["b", "a"])
|
||
|
|
|
||
|
|
c = DataFrame({"col3": [7, 8]}, index=["a", "b"])
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"col1": {"a": 2.0, "b": float("nan"), "c": 1.0},
|
||
|
|
"col2": {"a": 5.0, "b": 4.0, "c": float("nan")},
|
||
|
|
"col3": {"a": 7.0, "b": 8.0, "c": float("nan")},
|
||
|
|
}
|
||
|
|
)
|
||
|
|
if sort_kw is False:
|
||
|
|
expected = expected.reindex(index=["c", "a", "b"])
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
result = a.join([b, c], how="outer", sort=sort_kw)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class TestDataFrameJoin:
|
||
|
|
def test_join(self, multiindex_dataframe_random_data):
|
||
|
|
frame = multiindex_dataframe_random_data
|
||
|
|
|
||
|
|
a = frame.loc[frame.index[:5], ["A"]]
|
||
|
|
b = frame.loc[frame.index[2:], ["B", "C"]]
|
||
|
|
|
||
|
|
joined = a.join(b, how="outer").reindex(frame.index)
|
||
|
|
expected = frame.copy().values
|
||
|
|
expected[np.isnan(joined.values)] = np.nan
|
||
|
|
expected = DataFrame(expected, index=frame.index, columns=frame.columns)
|
||
|
|
|
||
|
|
assert not np.isnan(joined.values).all()
|
||
|
|
|
||
|
|
tm.assert_frame_equal(joined, expected)
|
||
|
|
|
||
|
|
def test_join_segfault(self):
|
||
|
|
# GH#1532
|
||
|
|
df1 = DataFrame({"a": [1, 1], "b": [1, 2], "x": [1, 2]})
|
||
|
|
df2 = DataFrame({"a": [2, 2], "b": [1, 2], "y": [1, 2]})
|
||
|
|
df1 = df1.set_index(["a", "b"])
|
||
|
|
df2 = df2.set_index(["a", "b"])
|
||
|
|
# it works!
|
||
|
|
for how in ["left", "right", "outer"]:
|
||
|
|
df1.join(df2, how=how)
|
||
|
|
|
||
|
|
def test_join_str_datetime(self):
|
||
|
|
str_dates = ["20120209", "20120222"]
|
||
|
|
dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
|
||
|
|
|
||
|
|
A = DataFrame(str_dates, index=range(2), columns=["aa"])
|
||
|
|
C = DataFrame([[1, 2], [3, 4]], index=str_dates, columns=dt_dates)
|
||
|
|
|
||
|
|
tst = A.join(C, on="aa")
|
||
|
|
|
||
|
|
assert len(tst.columns) == 3
|
||
|
|
|
||
|
|
def test_join_multiindex_leftright(self):
|
||
|
|
# GH 10741
|
||
|
|
df1 = DataFrame(
|
||
|
|
[
|
||
|
|
["a", "x", 0.471780],
|
||
|
|
["a", "y", 0.774908],
|
||
|
|
["a", "z", 0.563634],
|
||
|
|
["b", "x", -0.353756],
|
||
|
|
["b", "y", 0.368062],
|
||
|
|
["b", "z", -1.721840],
|
||
|
|
["c", "x", 1],
|
||
|
|
["c", "y", 2],
|
||
|
|
["c", "z", 3],
|
||
|
|
],
|
||
|
|
columns=["first", "second", "value1"],
|
||
|
|
).set_index(["first", "second"])
|
||
|
|
|
||
|
|
df2 = DataFrame([["a", 10], ["b", 20]], columns=["first", "value2"]).set_index(
|
||
|
|
["first"]
|
||
|
|
)
|
||
|
|
|
||
|
|
exp = DataFrame(
|
||
|
|
[
|
||
|
|
[0.471780, 10],
|
||
|
|
[0.774908, 10],
|
||
|
|
[0.563634, 10],
|
||
|
|
[-0.353756, 20],
|
||
|
|
[0.368062, 20],
|
||
|
|
[-1.721840, 20],
|
||
|
|
[1.000000, np.nan],
|
||
|
|
[2.000000, np.nan],
|
||
|
|
[3.000000, np.nan],
|
||
|
|
],
|
||
|
|
index=df1.index,
|
||
|
|
columns=["value1", "value2"],
|
||
|
|
)
|
||
|
|
|
||
|
|
# these must be the same results (but columns are flipped)
|
||
|
|
tm.assert_frame_equal(df1.join(df2, how="left"), exp)
|
||
|
|
tm.assert_frame_equal(df2.join(df1, how="right"), exp[["value2", "value1"]])
|
||
|
|
|
||
|
|
exp_idx = MultiIndex.from_product(
|
||
|
|
[["a", "b"], ["x", "y", "z"]], names=["first", "second"]
|
||
|
|
)
|
||
|
|
exp = DataFrame(
|
||
|
|
[
|
||
|
|
[0.471780, 10],
|
||
|
|
[0.774908, 10],
|
||
|
|
[0.563634, 10],
|
||
|
|
[-0.353756, 20],
|
||
|
|
[0.368062, 20],
|
||
|
|
[-1.721840, 20],
|
||
|
|
],
|
||
|
|
index=exp_idx,
|
||
|
|
columns=["value1", "value2"],
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(df1.join(df2, how="right"), exp)
|
||
|
|
tm.assert_frame_equal(df2.join(df1, how="left"), exp[["value2", "value1"]])
|
||
|
|
|
||
|
|
def test_join_multiindex_dates(self):
|
||
|
|
# GH 33692
|
||
|
|
date = pd.Timestamp(2000, 1, 1).date()
|
||
|
|
|
||
|
|
df1_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
|
||
|
|
df1 = DataFrame({"col1": [0]}, index=df1_index)
|
||
|
|
df2_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
|
||
|
|
df2 = DataFrame({"col2": [0]}, index=df2_index)
|
||
|
|
df3_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
|
||
|
|
df3 = DataFrame({"col3": [0]}, index=df3_index)
|
||
|
|
|
||
|
|
result = df1.join([df2, df3])
|
||
|
|
|
||
|
|
expected_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
|
||
|
|
expected = DataFrame(
|
||
|
|
{"col1": [0], "col2": [0], "col3": [0]}, index=expected_index
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_merge_join_different_levels(self):
|
||
|
|
# GH#9455
|
||
|
|
|
||
|
|
# first dataframe
|
||
|
|
df1 = DataFrame(columns=["a", "b"], data=[[1, 11], [0, 22]])
|
||
|
|
|
||
|
|
# second dataframe
|
||
|
|
columns = MultiIndex.from_tuples([("a", ""), ("c", "c1")])
|
||
|
|
df2 = DataFrame(columns=columns, data=[[1, 33], [0, 44]])
|
||
|
|
|
||
|
|
# merge
|
||
|
|
columns = ["a", "b", ("c", "c1")]
|
||
|
|
expected = DataFrame(columns=columns, data=[[1, 11, 33], [0, 22, 44]])
|
||
|
|
with tm.assert_produces_warning(FutureWarning):
|
||
|
|
result = pd.merge(df1, df2, on="a")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# join, see discussion in GH#12219
|
||
|
|
columns = ["a", "b", ("a", ""), ("c", "c1")]
|
||
|
|
expected = DataFrame(columns=columns, data=[[1, 11, 0, 44], [0, 22, 1, 33]])
|
||
|
|
msg = "merging between different levels is deprecated"
|
||
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
||
|
|
# stacklevel is chosen to be correct for pd.merge, not DataFrame.join
|
||
|
|
result = df1.join(df2, on="a")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_frame_join_tzaware(self):
|
||
|
|
test1 = DataFrame(
|
||
|
|
np.zeros((6, 3)),
|
||
|
|
index=date_range(
|
||
|
|
"2012-11-15 00:00:00", periods=6, freq="100L", tz="US/Central"
|
||
|
|
),
|
||
|
|
)
|
||
|
|
test2 = DataFrame(
|
||
|
|
np.zeros((3, 3)),
|
||
|
|
index=date_range(
|
||
|
|
"2012-11-15 00:00:00", periods=3, freq="250L", tz="US/Central"
|
||
|
|
),
|
||
|
|
columns=range(3, 6),
|
||
|
|
)
|
||
|
|
|
||
|
|
result = test1.join(test2, how="outer")
|
||
|
|
expected = test1.index.union(test2.index)
|
||
|
|
|
||
|
|
tm.assert_index_equal(result.index, expected)
|
||
|
|
assert result.index.tz.zone == "US/Central"
|