ai-content-maker/.venv/Lib/site-packages/pandas/tests/frame/methods/test_join.py

565 lines
17 KiB
Python

from datetime import datetime
import numpy as np
import pytest
from pandas.errors import MergeError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
date_range,
period_range,
)
import pandas._testing as tm
from pandas.core.reshape.concat import concat
@pytest.fixture
def frame_with_period_index():
return DataFrame(
data=np.arange(20).reshape(4, 5),
columns=list("abcde"),
index=period_range(start="2000", freq="A", periods=4),
)
@pytest.fixture
def left():
return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
@pytest.fixture
def right():
return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
@pytest.fixture
def left_no_dup():
return DataFrame(
{"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
index=range(4),
)
@pytest.fixture
def right_no_dup():
return DataFrame(
{
"a": ["a", "b", "c", "d", "e"],
"c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
},
index=range(5),
).set_index("a")
@pytest.fixture
def left_w_dups(left_no_dup):
return concat(
[left_no_dup, DataFrame({"a": ["a"], "b": ["cow"]}, index=[3])], sort=True
)
@pytest.fixture
def right_w_dups(right_no_dup):
return concat(
[right_no_dup, DataFrame({"a": ["e"], "c": ["moo"]}, index=[3])]
).set_index("a")
@pytest.mark.parametrize(
"how, sort, expected",
[
("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])),
("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])),
(
"left",
False,
DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]),
),
(
"left",
True,
DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]),
),
(
"right",
False,
DataFrame({"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]),
),
(
"right",
True,
DataFrame({"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]),
),
(
"outer",
False,
DataFrame(
{"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
index=[0, 1, 2, 3],
),
),
(
"outer",
True,
DataFrame(
{"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
index=[0, 1, 2, 3],
),
),
],
)
def test_join(left, right, how, sort, expected):
result = left.join(right, how=how, sort=sort, validate="1:1")
tm.assert_frame_equal(result, expected)
def test_suffix_on_list_join():
first = DataFrame({"key": [1, 2, 3, 4, 5]})
second = DataFrame({"key": [1, 8, 3, 2, 5], "v1": [1, 2, 3, 4, 5]})
third = DataFrame({"keys": [5, 2, 3, 4, 1], "v2": [1, 2, 3, 4, 5]})
# check proper errors are raised
msg = "Suffixes not supported when joining multiple DataFrames"
with pytest.raises(ValueError, match=msg):
first.join([second], lsuffix="y")
with pytest.raises(ValueError, match=msg):
first.join([second, third], rsuffix="x")
with pytest.raises(ValueError, match=msg):
first.join([second, third], lsuffix="y", rsuffix="x")
with pytest.raises(ValueError, match="Indexes have overlapping values"):
first.join([second, third])
# no errors should be raised
arr_joined = first.join([third])
norm_joined = first.join(third)
tm.assert_frame_equal(arr_joined, norm_joined)
def test_join_invalid_validate(left_no_dup, right_no_dup):
# GH 46622
# Check invalid arguments
msg = "Not a valid argument for validate"
with pytest.raises(ValueError, match=msg):
left_no_dup.merge(right_no_dup, on="a", validate="invalid")
def test_join_on_single_col_dup_on_right(left_no_dup, right_w_dups):
# GH 46622
# Dups on right allowed by one_to_many constraint
left_no_dup.join(
right_w_dups,
on="a",
validate="one_to_many",
)
# Dups on right not allowed by one_to_one constraint
msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
with pytest.raises(MergeError, match=msg):
left_no_dup.join(
right_w_dups,
on="a",
validate="one_to_one",
)
def test_join_on_single_col_dup_on_left(left_w_dups, right_no_dup):
# GH 46622
# Dups on left allowed by many_to_one constraint
left_w_dups.join(
right_no_dup,
on="a",
validate="many_to_one",
)
# Dups on left not allowed by one_to_one constraint
msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
with pytest.raises(MergeError, match=msg):
left_w_dups.join(
right_no_dup,
on="a",
validate="one_to_one",
)
def test_join_on_single_col_dup_on_both(left_w_dups, right_w_dups):
# GH 46622
# Dups on both allowed by many_to_many constraint
left_w_dups.join(right_w_dups, on="a", validate="many_to_many")
# Dups on both not allowed by many_to_one constraint
msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
with pytest.raises(MergeError, match=msg):
left_w_dups.join(
right_w_dups,
on="a",
validate="many_to_one",
)
# Dups on both not allowed by one_to_many constraint
msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
with pytest.raises(MergeError, match=msg):
left_w_dups.join(
right_w_dups,
on="a",
validate="one_to_many",
)
def test_join_on_multi_col_check_dup():
# GH 46622
# Two column join, dups in both, but jointly no dups
left = DataFrame(
{
"a": ["a", "a", "b", "b"],
"b": [0, 1, 0, 1],
"c": ["cat", "dog", "weasel", "horse"],
},
index=range(4),
).set_index(["a", "b"])
right = DataFrame(
{
"a": ["a", "a", "b"],
"b": [0, 1, 0],
"d": ["meow", "bark", "um... weasel noise?"],
},
index=range(3),
).set_index(["a", "b"])
expected_multi = DataFrame(
{
"a": ["a", "a", "b"],
"b": [0, 1, 0],
"c": ["cat", "dog", "weasel"],
"d": ["meow", "bark", "um... weasel noise?"],
},
index=range(3),
).set_index(["a", "b"])
# Jointly no dups allowed by one_to_one constraint
result = left.join(right, how="inner", validate="1:1")
tm.assert_frame_equal(result, expected_multi)
def test_join_index(float_frame):
# left / right
f = float_frame.loc[float_frame.index[:10], ["A", "B"]]
f2 = float_frame.loc[float_frame.index[5:], ["C", "D"]].iloc[::-1]
joined = f.join(f2)
tm.assert_index_equal(f.index, joined.index)
expected_columns = Index(["A", "B", "C", "D"])
tm.assert_index_equal(joined.columns, expected_columns)
joined = f.join(f2, how="left")
tm.assert_index_equal(joined.index, f.index)
tm.assert_index_equal(joined.columns, expected_columns)
joined = f.join(f2, how="right")
tm.assert_index_equal(joined.index, f2.index)
tm.assert_index_equal(joined.columns, expected_columns)
# inner
joined = f.join(f2, how="inner")
tm.assert_index_equal(joined.index, f.index[5:10])
tm.assert_index_equal(joined.columns, expected_columns)
# outer
joined = f.join(f2, how="outer")
tm.assert_index_equal(joined.index, float_frame.index.sort_values())
tm.assert_index_equal(joined.columns, expected_columns)
with pytest.raises(ValueError, match="join method"):
f.join(f2, how="foo")
# corner case - overlapping columns
msg = "columns overlap but no suffix"
for how in ("outer", "left", "inner"):
with pytest.raises(ValueError, match=msg):
float_frame.join(float_frame, how=how)
def test_join_index_more(float_frame):
af = float_frame.loc[:, ["A", "B"]]
bf = float_frame.loc[::2, ["C", "D"]]
expected = af.copy()
expected["C"] = float_frame["C"][::2]
expected["D"] = float_frame["D"][::2]
result = af.join(bf)
tm.assert_frame_equal(result, expected)
result = af.join(bf, how="right")
tm.assert_frame_equal(result, expected[::2])
result = bf.join(af, how="right")
tm.assert_frame_equal(result, expected.loc[:, result.columns])
def test_join_index_series(float_frame):
df = float_frame.copy()
ser = df.pop(float_frame.columns[-1])
joined = df.join(ser)
tm.assert_frame_equal(joined, float_frame)
ser.name = None
with pytest.raises(ValueError, match="must have a name"):
df.join(ser)
def test_join_overlap(float_frame):
df1 = float_frame.loc[:, ["A", "B", "C"]]
df2 = float_frame.loc[:, ["B", "C", "D"]]
joined = df1.join(df2, lsuffix="_df1", rsuffix="_df2")
df1_suf = df1.loc[:, ["B", "C"]].add_suffix("_df1")
df2_suf = df2.loc[:, ["B", "C"]].add_suffix("_df2")
no_overlap = float_frame.loc[:, ["A", "D"]]
expected = df1_suf.join(df2_suf).join(no_overlap)
# column order not necessarily sorted
tm.assert_frame_equal(joined, expected.loc[:, joined.columns])
def test_join_period_index(frame_with_period_index):
other = frame_with_period_index.rename(columns=lambda key: f"{key}{key}")
joined_values = np.concatenate([frame_with_period_index.values] * 2, axis=1)
joined_cols = frame_with_period_index.columns.append(other.columns)
joined = frame_with_period_index.join(other)
expected = DataFrame(
data=joined_values, columns=joined_cols, index=frame_with_period_index.index
)
tm.assert_frame_equal(joined, expected)
def test_join_left_sequence_non_unique_index():
# https://github.com/pandas-dev/pandas/issues/19607
df1 = DataFrame({"a": [0, 10, 20]}, index=[1, 2, 3])
df2 = DataFrame({"b": [100, 200, 300]}, index=[4, 3, 2])
df3 = DataFrame({"c": [400, 500, 600]}, index=[2, 2, 4])
joined = df1.join([df2, df3], how="left")
expected = DataFrame(
{
"a": [0, 10, 10, 20],
"b": [np.nan, 300, 300, 200],
"c": [np.nan, 400, 500, np.nan],
},
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"