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import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Series,
date_range,
)
import pandas._testing as tm
class TestDataFrameNonuniqueIndexes:
def test_setattr_columns_vs_construct_with_columns(self):
# assignment
# GH 3687
arr = np.random.default_rng(2).standard_normal((3, 2))
idx = list(range(2))
df = DataFrame(arr, columns=["A", "A"])
df.columns = idx
expected = DataFrame(arr, columns=idx)
tm.assert_frame_equal(df, expected)
def test_setattr_columns_vs_construct_with_columns_datetimeindx(self):
idx = date_range("20130101", periods=4, freq="QE-NOV")
df = DataFrame(
[[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=["a", "a", "a", "a"]
)
df.columns = idx
expected = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=idx)
tm.assert_frame_equal(df, expected)
def test_insert_with_duplicate_columns(self):
# insert
df = DataFrame(
[[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]],
columns=["foo", "bar", "foo", "hello"],
)
df["string"] = "bah"
expected = DataFrame(
[[1, 1, 1, 5, "bah"], [1, 1, 2, 5, "bah"], [2, 1, 3, 5, "bah"]],
columns=["foo", "bar", "foo", "hello", "string"],
)
tm.assert_frame_equal(df, expected)
with pytest.raises(ValueError, match="Length of value"):
df.insert(0, "AnotherColumn", range(len(df.index) - 1))
# insert same dtype
df["foo2"] = 3
expected = DataFrame(
[[1, 1, 1, 5, "bah", 3], [1, 1, 2, 5, "bah", 3], [2, 1, 3, 5, "bah", 3]],
columns=["foo", "bar", "foo", "hello", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# set (non-dup)
df["foo2"] = 4
expected = DataFrame(
[[1, 1, 1, 5, "bah", 4], [1, 1, 2, 5, "bah", 4], [2, 1, 3, 5, "bah", 4]],
columns=["foo", "bar", "foo", "hello", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
df["foo2"] = 3
# delete (non dup)
del df["bar"]
expected = DataFrame(
[[1, 1, 5, "bah", 3], [1, 2, 5, "bah", 3], [2, 3, 5, "bah", 3]],
columns=["foo", "foo", "hello", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# try to delete again (its not consolidated)
del df["hello"]
expected = DataFrame(
[[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
columns=["foo", "foo", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# consolidate
df = df._consolidate()
expected = DataFrame(
[[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]],
columns=["foo", "foo", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# insert
df.insert(2, "new_col", 5.0)
expected = DataFrame(
[[1, 1, 5.0, "bah", 3], [1, 2, 5.0, "bah", 3], [2, 3, 5.0, "bah", 3]],
columns=["foo", "foo", "new_col", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# insert a dup
with pytest.raises(ValueError, match="cannot insert"):
df.insert(2, "new_col", 4.0)
df.insert(2, "new_col", 4.0, allow_duplicates=True)
expected = DataFrame(
[
[1, 1, 4.0, 5.0, "bah", 3],
[1, 2, 4.0, 5.0, "bah", 3],
[2, 3, 4.0, 5.0, "bah", 3],
],
columns=["foo", "foo", "new_col", "new_col", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
# delete (dup)
del df["foo"]
expected = DataFrame(
[[4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3]],
columns=["new_col", "new_col", "string", "foo2"],
)
tm.assert_frame_equal(df, expected)
def test_dup_across_dtypes(self):
# dup across dtypes
df = DataFrame(
[[1, 1, 1.0, 5], [1, 1, 2.0, 5], [2, 1, 3.0, 5]],
columns=["foo", "bar", "foo", "hello"],
)
df["foo2"] = 7.0
expected = DataFrame(
[[1, 1, 1.0, 5, 7.0], [1, 1, 2.0, 5, 7.0], [2, 1, 3.0, 5, 7.0]],
columns=["foo", "bar", "foo", "hello", "foo2"],
)
tm.assert_frame_equal(df, expected)
result = df["foo"]
expected = DataFrame([[1, 1.0], [1, 2.0], [2, 3.0]], columns=["foo", "foo"])
tm.assert_frame_equal(result, expected)
# multiple replacements
df["foo"] = "string"
expected = DataFrame(
[
["string", 1, "string", 5, 7.0],
["string", 1, "string", 5, 7.0],
["string", 1, "string", 5, 7.0],
],
columns=["foo", "bar", "foo", "hello", "foo2"],
)
tm.assert_frame_equal(df, expected)
del df["foo"]
expected = DataFrame(
[[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "hello", "foo2"]
)
tm.assert_frame_equal(df, expected)
def test_column_dups_indexes(self):
# check column dups with index equal and not equal to df's index
df = DataFrame(
np.random.default_rng(2).standard_normal((5, 3)),
index=["a", "b", "c", "d", "e"],
columns=["A", "B", "A"],
)
for index in [df.index, pd.Index(list("edcba"))]:
this_df = df.copy()
expected_ser = Series(index.values, index=this_df.index)
expected_df = DataFrame(
{"A": expected_ser, "B": this_df["B"]},
columns=["A", "B", "A"],
)
this_df["A"] = index
tm.assert_frame_equal(this_df, expected_df)
def test_changing_dtypes_with_duplicate_columns(self):
# multiple assignments that change dtypes
# the location indexer is a slice
# GH 6120
df = DataFrame(
np.random.default_rng(2).standard_normal((5, 2)), columns=["that", "that"]
)
expected = DataFrame(1.0, index=range(5), columns=["that", "that"])
df["that"] = 1.0
tm.assert_frame_equal(df, expected)
df = DataFrame(
np.random.default_rng(2).random((5, 2)), columns=["that", "that"]
)
expected = DataFrame(1, index=range(5), columns=["that", "that"])
df["that"] = 1
tm.assert_frame_equal(df, expected)
def test_dup_columns_comparisons(self):
# equality
df1 = DataFrame([[1, 2], [2, np.nan], [3, 4], [4, 4]], columns=["A", "B"])
df2 = DataFrame([[0, 1], [2, 4], [2, np.nan], [4, 5]], columns=["A", "A"])
# not-comparing like-labelled
msg = (
r"Can only compare identically-labeled \(both index and columns\) "
"DataFrame objects"
)
with pytest.raises(ValueError, match=msg):
df1 == df2
df1r = df1.reindex_like(df2)
result = df1r == df2
expected = DataFrame(
[[False, True], [True, False], [False, False], [True, False]],
columns=["A", "A"],
)
tm.assert_frame_equal(result, expected)
def test_mixed_column_selection(self):
# mixed column selection
# GH 5639
dfbool = DataFrame(
{
"one": Series([True, True, False], index=["a", "b", "c"]),
"two": Series([False, False, True, False], index=["a", "b", "c", "d"]),
"three": Series([False, True, True, True], index=["a", "b", "c", "d"]),
}
)
expected = pd.concat([dfbool["one"], dfbool["three"], dfbool["one"]], axis=1)
result = dfbool[["one", "three", "one"]]
tm.assert_frame_equal(result, expected)
def test_multi_axis_dups(self):
# multi-axis dups
# GH 6121
df = DataFrame(
np.arange(25.0).reshape(5, 5),
index=["a", "b", "c", "d", "e"],
columns=["A", "B", "C", "D", "E"],
)
z = df[["A", "C", "A"]].copy()
expected = z.loc[["a", "c", "a"]]
df = DataFrame(
np.arange(25.0).reshape(5, 5),
index=["a", "b", "c", "d", "e"],
columns=["A", "B", "C", "D", "E"],
)
z = df[["A", "C", "A"]]
result = z.loc[["a", "c", "a"]]
tm.assert_frame_equal(result, expected)
def test_columns_with_dups(self):
# GH 3468 related
# basic
df = DataFrame([[1, 2]], columns=["a", "a"])
df.columns = ["a", "a.1"]
expected = DataFrame([[1, 2]], columns=["a", "a.1"])
tm.assert_frame_equal(df, expected)
df = DataFrame([[1, 2, 3]], columns=["b", "a", "a"])
df.columns = ["b", "a", "a.1"]
expected = DataFrame([[1, 2, 3]], columns=["b", "a", "a.1"])
tm.assert_frame_equal(df, expected)
def test_columns_with_dup_index(self):
# with a dup index
df = DataFrame([[1, 2]], columns=["a", "a"])
df.columns = ["b", "b"]
expected = DataFrame([[1, 2]], columns=["b", "b"])
tm.assert_frame_equal(df, expected)
def test_multi_dtype(self):
# multi-dtype
df = DataFrame(
[[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]],
columns=["a", "a", "b", "b", "d", "c", "c"],
)
df.columns = list("ABCDEFG")
expected = DataFrame(
[[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("ABCDEFG")
)
tm.assert_frame_equal(df, expected)
def test_multi_dtype2(self):
df = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a", "a", "a"])
df.columns = ["a", "a.1", "a.2", "a.3"]
expected = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a.1", "a.2", "a.3"])
tm.assert_frame_equal(df, expected)
def test_dups_across_blocks(self, using_array_manager):
# dups across blocks
df_float = DataFrame(
np.random.default_rng(2).standard_normal((10, 3)), dtype="float64"
)
df_int = DataFrame(
np.random.default_rng(2).standard_normal((10, 3)).astype("int64")
)
df_bool = DataFrame(True, index=df_float.index, columns=df_float.columns)
df_object = DataFrame("foo", index=df_float.index, columns=df_float.columns)
df_dt = DataFrame(
pd.Timestamp("20010101"), index=df_float.index, columns=df_float.columns
)
df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1)
if not using_array_manager:
assert len(df._mgr.blknos) == len(df.columns)
assert len(df._mgr.blklocs) == len(df.columns)
# testing iloc
for i in range(len(df.columns)):
df.iloc[:, i]
def test_dup_columns_across_dtype(self):
# dup columns across dtype GH 2079/2194
vals = [[1, -1, 2.0], [2, -2, 3.0]]
rs = DataFrame(vals, columns=["A", "A", "B"])
xp = DataFrame(vals)
xp.columns = ["A", "A", "B"]
tm.assert_frame_equal(rs, xp)
def test_set_value_by_index(self):
# See gh-12344
warn = None
msg = "will attempt to set the values inplace"
df = DataFrame(np.arange(9).reshape(3, 3).T)
df.columns = list("AAA")
expected = df.iloc[:, 2].copy()
with tm.assert_produces_warning(warn, match=msg):
df.iloc[:, 0] = 3
tm.assert_series_equal(df.iloc[:, 2], expected)
df = DataFrame(np.arange(9).reshape(3, 3).T)
df.columns = [2, float(2), str(2)]
expected = df.iloc[:, 1].copy()
with tm.assert_produces_warning(warn, match=msg):
df.iloc[:, 0] = 3
tm.assert_series_equal(df.iloc[:, 1], expected)
|
