from datetime import datetime
|
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import numpy as np
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import pytest
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|
import pandas.util._test_decorators as td
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from pandas.core.dtypes.base import _registry as ea_registry
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from pandas.core.dtypes.common import (
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is_categorical_dtype,
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is_interval_dtype,
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is_object_dtype,
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)
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from pandas.core.dtypes.dtypes import (
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CategoricalDtype,
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DatetimeTZDtype,
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IntervalDtype,
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PeriodDtype,
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)
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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DatetimeIndex,
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Index,
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Interval,
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IntervalIndex,
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MultiIndex,
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NaT,
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Period,
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PeriodIndex,
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Series,
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Timestamp,
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cut,
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date_range,
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notna,
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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.arrays import SparseArray
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from pandas.tseries.offsets import BDay
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class TestDataFrameSetItem:
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def test_setitem_str_subclass(self):
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# GH#37366
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class mystring(str):
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pass
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data = ["2020-10-22 01:21:00+00:00"]
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index = DatetimeIndex(data)
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df = DataFrame({"a": [1]}, index=index)
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df["b"] = 2
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df[mystring("c")] = 3
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expected = DataFrame({"a": [1], "b": [2], mystring("c"): [3]}, index=index)
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tm.assert_equal(df, expected)
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@pytest.mark.parametrize(
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"dtype", ["int32", "int64", "uint32", "uint64", "float32", "float64"]
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)
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def test_setitem_dtype(self, dtype, float_frame):
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arr = np.random.randn(len(float_frame))
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float_frame[dtype] = np.array(arr, dtype=dtype)
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assert float_frame[dtype].dtype.name == dtype
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def test_setitem_list_not_dataframe(self, float_frame):
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data = np.random.randn(len(float_frame), 2)
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float_frame[["A", "B"]] = data
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tm.assert_almost_equal(float_frame[["A", "B"]].values, data)
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def test_setitem_error_msmgs(self):
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# GH 7432
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df = DataFrame(
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{"bar": [1, 2, 3], "baz": ["d", "e", "f"]},
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index=Index(["a", "b", "c"], name="foo"),
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)
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ser = Series(
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["g", "h", "i", "j"],
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index=Index(["a", "b", "c", "a"], name="foo"),
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name="fiz",
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)
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msg = "cannot reindex on an axis with duplicate labels"
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with pytest.raises(ValueError, match=msg):
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df["newcol"] = ser
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# GH 4107, more descriptive error message
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df = DataFrame(np.random.randint(0, 2, (4, 4)), columns=["a", "b", "c", "d"])
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msg = "Cannot set a DataFrame with multiple columns to the single column gr"
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with pytest.raises(ValueError, match=msg):
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df["gr"] = df.groupby(["b", "c"]).count()
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def test_setitem_benchmark(self):
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# from the vb_suite/frame_methods/frame_insert_columns
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N = 10
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K = 5
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df = DataFrame(index=range(N))
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new_col = np.random.randn(N)
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for i in range(K):
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df[i] = new_col
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expected = DataFrame(np.repeat(new_col, K).reshape(N, K), index=range(N))
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tm.assert_frame_equal(df, expected)
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def test_setitem_different_dtype(self):
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df = DataFrame(
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np.random.randn(5, 3), index=np.arange(5), columns=["c", "b", "a"]
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)
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df.insert(0, "foo", df["a"])
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df.insert(2, "bar", df["c"])
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# diff dtype
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# new item
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df["x"] = df["a"].astype("float32")
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result = df.dtypes
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expected = Series(
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[np.dtype("float64")] * 5 + [np.dtype("float32")],
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index=["foo", "c", "bar", "b", "a", "x"],
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)
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tm.assert_series_equal(result, expected)
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# replacing current (in different block)
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df["a"] = df["a"].astype("float32")
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result = df.dtypes
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expected = Series(
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[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2,
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index=["foo", "c", "bar", "b", "a", "x"],
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)
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tm.assert_series_equal(result, expected)
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df["y"] = df["a"].astype("int32")
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result = df.dtypes
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expected = Series(
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[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2 + [np.dtype("int32")],
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index=["foo", "c", "bar", "b", "a", "x", "y"],
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)
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tm.assert_series_equal(result, expected)
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def test_setitem_empty_columns(self):
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# GH 13522
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df = DataFrame(index=["A", "B", "C"])
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df["X"] = df.index
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df["X"] = ["x", "y", "z"]
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exp = DataFrame(data={"X": ["x", "y", "z"]}, index=["A", "B", "C"])
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tm.assert_frame_equal(df, exp)
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def test_setitem_dt64_index_empty_columns(self):
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rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
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df = DataFrame(index=np.arange(len(rng)))
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df["A"] = rng
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assert df["A"].dtype == np.dtype("M8[ns]")
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def test_setitem_timestamp_empty_columns(self):
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# GH#19843
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df = DataFrame(index=range(3))
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df["now"] = Timestamp("20130101", tz="UTC")
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expected = DataFrame(
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[[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"]
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)
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tm.assert_frame_equal(df, expected)
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def test_setitem_wrong_length_categorical_dtype_raises(self):
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# GH#29523
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cat = Categorical.from_codes([0, 1, 1, 0, 1, 2], ["a", "b", "c"])
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df = DataFrame(range(10), columns=["bar"])
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msg = (
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rf"Length of values \({len(cat)}\) "
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rf"does not match length of index \({len(df)}\)"
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)
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with pytest.raises(ValueError, match=msg):
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df["foo"] = cat
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def test_setitem_with_sparse_value(self):
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# GH#8131
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df = DataFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]})
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sp_array = SparseArray([0, 0, 1])
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df["new_column"] = sp_array
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expected = Series(sp_array, name="new_column")
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tm.assert_series_equal(df["new_column"], expected)
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def test_setitem_with_unaligned_sparse_value(self):
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df = DataFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]})
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sp_series = Series(SparseArray([0, 0, 1]), index=[2, 1, 0])
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df["new_column"] = sp_series
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expected = Series(SparseArray([1, 0, 0]), name="new_column")
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tm.assert_series_equal(df["new_column"], expected)
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def test_setitem_period_preserves_dtype(self):
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# GH: 26861
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data = [Period("2003-12", "D")]
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result = DataFrame([])
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result["a"] = data
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expected = DataFrame({"a": data})
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tm.assert_frame_equal(result, expected)
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def test_setitem_dict_preserves_dtypes(self):
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# https://github.com/pandas-dev/pandas/issues/34573
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expected = DataFrame(
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{
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"a": Series([0, 1, 2], dtype="int64"),
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"b": Series([1, 2, 3], dtype=float),
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"c": Series([1, 2, 3], dtype=float),
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"d": Series([1, 2, 3], dtype="uint32"),
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}
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)
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df = DataFrame(
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{
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"a": Series([], dtype="int64"),
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"b": Series([], dtype=float),
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"c": Series([], dtype=float),
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"d": Series([], dtype="uint32"),
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}
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)
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for idx, b in enumerate([1, 2, 3]):
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df.loc[df.shape[0]] = {
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"a": int(idx),
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"b": float(b),
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"c": float(b),
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"d": np.uint32(b),
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}
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize(
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"obj,dtype",
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[
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(Period("2020-01"), PeriodDtype("M")),
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(Interval(left=0, right=5), IntervalDtype("int64", "right")),
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(
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Timestamp("2011-01-01", tz="US/Eastern"),
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DatetimeTZDtype(tz="US/Eastern"),
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),
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],
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)
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def test_setitem_extension_types(self, obj, dtype):
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# GH: 34832
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expected = DataFrame({"idx": [1, 2, 3], "obj": Series([obj] * 3, dtype=dtype)})
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df = DataFrame({"idx": [1, 2, 3]})
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df["obj"] = obj
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize(
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"ea_name",
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[
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dtype.name
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for dtype in ea_registry.dtypes
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# property would require instantiation
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if not isinstance(dtype.name, property)
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]
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+ ["datetime64[ns, UTC]", "period[D]"],
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)
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def test_setitem_with_ea_name(self, ea_name):
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# GH 38386
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result = DataFrame([0])
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result[ea_name] = [1]
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expected = DataFrame({0: [0], ea_name: [1]})
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tm.assert_frame_equal(result, expected)
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def test_setitem_dt64_ndarray_with_NaT_and_diff_time_units(self):
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# GH#7492
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data_ns = np.array([1, "nat"], dtype="datetime64[ns]")
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result = Series(data_ns).to_frame()
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result["new"] = data_ns
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expected = DataFrame({0: [1, None], "new": [1, None]}, dtype="datetime64[ns]")
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tm.assert_frame_equal(result, expected)
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# OutOfBoundsDatetime error shouldn't occur; as of 2.0 we preserve "M8[s]"
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data_s = np.array([1, "nat"], dtype="datetime64[s]")
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result["new"] = data_s
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tm.assert_series_equal(result[0], expected[0])
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tm.assert_numpy_array_equal(result["new"].to_numpy(), data_s)
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@pytest.mark.parametrize("unit", ["h", "m", "s", "ms", "D", "M", "Y"])
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def test_frame_setitem_datetime64_col_other_units(self, unit):
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# Check that non-nano dt64 values get cast to dt64 on setitem
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# into a not-yet-existing column
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n = 100
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dtype = np.dtype(f"M8[{unit}]")
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vals = np.arange(n, dtype=np.int64).view(dtype)
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if unit in ["s", "ms"]:
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# supported unit
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ex_vals = vals
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else:
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# we get the nearest supported units, i.e. "s"
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ex_vals = vals.astype("datetime64[s]")
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df = DataFrame({"ints": np.arange(n)}, index=np.arange(n))
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df[unit] = vals
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assert df[unit].dtype == ex_vals.dtype
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assert (df[unit].values == ex_vals).all()
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@pytest.mark.parametrize("unit", ["h", "m", "s", "ms", "D", "M", "Y"])
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def test_frame_setitem_existing_datetime64_col_other_units(self, unit):
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# Check that non-nano dt64 values get cast to dt64 on setitem
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# into an already-existing dt64 column
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n = 100
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dtype = np.dtype(f"M8[{unit}]")
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vals = np.arange(n, dtype=np.int64).view(dtype)
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ex_vals = vals.astype("datetime64[ns]")
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df = DataFrame({"ints": np.arange(n)}, index=np.arange(n))
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df["dates"] = np.arange(n, dtype=np.int64).view("M8[ns]")
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# We overwrite existing dt64 column with new, non-nano dt64 vals
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df["dates"] = vals
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assert (df["dates"].values == ex_vals).all()
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def test_setitem_dt64tz(self, timezone_frame):
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df = timezone_frame
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idx = df["B"].rename("foo")
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# setitem
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df["C"] = idx
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tm.assert_series_equal(df["C"], Series(idx, name="C"))
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df["D"] = "foo"
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df["D"] = idx
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tm.assert_series_equal(df["D"], Series(idx, name="D"))
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del df["D"]
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# assert that A & C are not sharing the same base (e.g. they
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# are copies)
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v1 = df._mgr.arrays[1]
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v2 = df._mgr.arrays[2]
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tm.assert_extension_array_equal(v1, v2)
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v1base = v1._ndarray.base
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v2base = v2._ndarray.base
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assert v1base is None or (id(v1base) != id(v2base))
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# with nan
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df2 = df.copy()
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df2.iloc[1, 1] = NaT
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df2.iloc[1, 2] = NaT
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result = df2["B"]
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tm.assert_series_equal(notna(result), Series([True, False, True], name="B"))
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tm.assert_series_equal(df2.dtypes, df.dtypes)
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def test_setitem_periodindex(self):
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rng = period_range("1/1/2000", periods=5, name="index")
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df = DataFrame(np.random.randn(5, 3), index=rng)
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df["Index"] = rng
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rs = Index(df["Index"])
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tm.assert_index_equal(rs, rng, check_names=False)
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assert rs.name == "Index"
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assert rng.name == "index"
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rs = df.reset_index().set_index("index")
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assert isinstance(rs.index, PeriodIndex)
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tm.assert_index_equal(rs.index, rng)
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def test_setitem_complete_column_with_array(self):
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# GH#37954
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df = DataFrame({"a": ["one", "two", "three"], "b": [1, 2, 3]})
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arr = np.array([[1, 1], [3, 1], [5, 1]])
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df[["c", "d"]] = arr
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expected = DataFrame(
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{
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"a": ["one", "two", "three"],
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"b": [1, 2, 3],
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"c": [1, 3, 5],
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"d": [1, 1, 1],
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}
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)
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expected["c"] = expected["c"].astype(arr.dtype)
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expected["d"] = expected["d"].astype(arr.dtype)
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assert expected["c"].dtype == arr.dtype
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assert expected["d"].dtype == arr.dtype
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tm.assert_frame_equal(df, expected)
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@pytest.mark.parametrize("dtype", ["f8", "i8", "u8"])
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def test_setitem_bool_with_numeric_index(self, dtype):
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# GH#36319
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cols = Index([1, 2, 3], dtype=dtype)
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df = DataFrame(np.random.randn(3, 3), columns=cols)
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df[False] = ["a", "b", "c"]
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expected_cols = Index([1, 2, 3, False], dtype=object)
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if dtype == "f8":
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expected_cols = Index([1.0, 2.0, 3.0, False], dtype=object)
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tm.assert_index_equal(df.columns, expected_cols)
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@pytest.mark.parametrize("indexer", ["B", ["B"]])
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def test_setitem_frame_length_0_str_key(self, indexer):
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# GH#38831
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df = DataFrame(columns=["A", "B"])
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other = DataFrame({"B": [1, 2]})
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df[indexer] = other
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expected = DataFrame({"A": [np.nan] * 2, "B": [1, 2]})
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expected["A"] = expected["A"].astype("object")
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tm.assert_frame_equal(df, expected)
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def test_setitem_frame_duplicate_columns(self):
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# GH#15695
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cols = ["A", "B", "C"] * 2
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df = DataFrame(index=range(3), columns=cols)
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df.loc[0, "A"] = (0, 3)
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df.loc[:, "B"] = (1, 4)
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df["C"] = (2, 5)
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expected = DataFrame(
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[
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[0, 1, 2, 3, 4, 5],
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[np.nan, 1, 2, np.nan, 4, 5],
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[np.nan, 1, 2, np.nan, 4, 5],
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],
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dtype="object",
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)
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# set these with unique columns to be extra-unambiguous
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expected[2] = expected[2].astype(np.int64)
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expected[5] = expected[5].astype(np.int64)
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expected.columns = cols
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tm.assert_frame_equal(df, expected)
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def test_setitem_frame_duplicate_columns_size_mismatch(self):
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# GH#39510
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cols = ["A", "B", "C"] * 2
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df = DataFrame(index=range(3), columns=cols)
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with pytest.raises(ValueError, match="Columns must be same length as key"):
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df[["A"]] = (0, 3, 5)
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df2 = df.iloc[:, :3] # unique columns
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with pytest.raises(ValueError, match="Columns must be same length as key"):
|
df2[["A"]] = (0, 3, 5)
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|
@pytest.mark.parametrize("cols", [["a", "b", "c"], ["a", "a", "a"]])
|
def test_setitem_df_wrong_column_number(self, cols):
|
# GH#38604
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df = DataFrame([[1, 2, 3]], columns=cols)
|
rhs = DataFrame([[10, 11]], columns=["d", "e"])
|
msg = "Columns must be same length as key"
|
with pytest.raises(ValueError, match=msg):
|
df["a"] = rhs
|
|
def test_setitem_listlike_indexer_duplicate_columns(self):
|
# GH#38604
|
df = DataFrame([[1, 2, 3]], columns=["a", "b", "b"])
|
rhs = DataFrame([[10, 11, 12]], columns=["a", "b", "b"])
|
df[["a", "b"]] = rhs
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expected = DataFrame([[10, 11, 12]], columns=["a", "b", "b"])
|
tm.assert_frame_equal(df, expected)
|
|
df[["c", "b"]] = rhs
|
expected = DataFrame([[10, 11, 12, 10]], columns=["a", "b", "b", "c"])
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_listlike_indexer_duplicate_columns_not_equal_length(self):
|
# GH#39403
|
df = DataFrame([[1, 2, 3]], columns=["a", "b", "b"])
|
rhs = DataFrame([[10, 11]], columns=["a", "b"])
|
msg = "Columns must be same length as key"
|
with pytest.raises(ValueError, match=msg):
|
df[["a", "b"]] = rhs
|
|
def test_setitem_intervals(self):
|
df = DataFrame({"A": range(10)})
|
ser = cut(df["A"], 5)
|
assert isinstance(ser.cat.categories, IntervalIndex)
|
|
# B & D end up as Categoricals
|
# the remainder are converted to in-line objects
|
# containing an IntervalIndex.values
|
df["B"] = ser
|
df["C"] = np.array(ser)
|
df["D"] = ser.values
|
df["E"] = np.array(ser.values)
|
df["F"] = ser.astype(object)
|
|
assert is_categorical_dtype(df["B"].dtype)
|
assert is_interval_dtype(df["B"].cat.categories)
|
assert is_categorical_dtype(df["D"].dtype)
|
assert is_interval_dtype(df["D"].cat.categories)
|
|
# These go through the Series constructor and so get inferred back
|
# to IntervalDtype
|
assert is_interval_dtype(df["C"])
|
assert is_interval_dtype(df["E"])
|
|
# But the Series constructor doesn't do inference on Series objects,
|
# so setting df["F"] doesn't get cast back to IntervalDtype
|
assert is_object_dtype(df["F"])
|
|
# they compare equal as Index
|
# when converted to numpy objects
|
c = lambda x: Index(np.array(x))
|
tm.assert_index_equal(c(df.B), c(df.B))
|
tm.assert_index_equal(c(df.B), c(df.C), check_names=False)
|
tm.assert_index_equal(c(df.B), c(df.D), check_names=False)
|
tm.assert_index_equal(c(df.C), c(df.D), check_names=False)
|
|
# B & D are the same Series
|
tm.assert_series_equal(df["B"], df["B"])
|
tm.assert_series_equal(df["B"], df["D"], check_names=False)
|
|
# C & E are the same Series
|
tm.assert_series_equal(df["C"], df["C"])
|
tm.assert_series_equal(df["C"], df["E"], check_names=False)
|
|
def test_setitem_categorical(self):
|
# GH#35369
|
df = DataFrame({"h": Series(list("mn")).astype("category")})
|
df.h = df.h.cat.reorder_categories(["n", "m"])
|
expected = DataFrame(
|
{"h": Categorical(["m", "n"]).reorder_categories(["n", "m"])}
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_with_empty_listlike(self):
|
# GH#17101
|
index = Index([], name="idx")
|
result = DataFrame(columns=["A"], index=index)
|
result["A"] = []
|
expected = DataFrame(columns=["A"], index=index)
|
tm.assert_index_equal(result.index, expected.index)
|
|
@pytest.mark.parametrize(
|
"cols, values, expected",
|
[
|
(["C", "D", "D", "a"], [1, 2, 3, 4], 4), # with duplicates
|
(["D", "C", "D", "a"], [1, 2, 3, 4], 4), # mixed order
|
(["C", "B", "B", "a"], [1, 2, 3, 4], 4), # other duplicate cols
|
(["C", "B", "a"], [1, 2, 3], 3), # no duplicates
|
(["B", "C", "a"], [3, 2, 1], 1), # alphabetical order
|
(["C", "a", "B"], [3, 2, 1], 2), # in the middle
|
],
|
)
|
def test_setitem_same_column(self, cols, values, expected):
|
# GH#23239
|
df = DataFrame([values], columns=cols)
|
df["a"] = df["a"]
|
result = df["a"].values[0]
|
assert result == expected
|
|
def test_setitem_multi_index(self):
|
# GH#7655, test that assigning to a sub-frame of a frame
|
# with multi-index columns aligns both rows and columns
|
it = ["jim", "joe", "jolie"], ["first", "last"], ["left", "center", "right"]
|
|
cols = MultiIndex.from_product(it)
|
index = date_range("20141006", periods=20)
|
vals = np.random.randint(1, 1000, (len(index), len(cols)))
|
df = DataFrame(vals, columns=cols, index=index)
|
|
i, j = df.index.values.copy(), it[-1][:]
|
|
np.random.shuffle(i)
|
df["jim"] = df["jolie"].loc[i, ::-1]
|
tm.assert_frame_equal(df["jim"], df["jolie"])
|
|
np.random.shuffle(j)
|
df[("joe", "first")] = df[("jolie", "last")].loc[i, j]
|
tm.assert_frame_equal(df[("joe", "first")], df[("jolie", "last")])
|
|
np.random.shuffle(j)
|
df[("joe", "last")] = df[("jolie", "first")].loc[i, j]
|
tm.assert_frame_equal(df[("joe", "last")], df[("jolie", "first")])
|
|
@pytest.mark.parametrize(
|
"columns,box,expected",
|
[
|
(
|
["A", "B", "C", "D"],
|
7,
|
DataFrame(
|
[[7, 7, 7, 7], [7, 7, 7, 7], [7, 7, 7, 7]],
|
columns=["A", "B", "C", "D"],
|
),
|
),
|
(
|
["C", "D"],
|
[7, 8],
|
DataFrame(
|
[[1, 2, 7, 8], [3, 4, 7, 8], [5, 6, 7, 8]],
|
columns=["A", "B", "C", "D"],
|
),
|
),
|
(
|
["A", "B", "C"],
|
np.array([7, 8, 9], dtype=np.int64),
|
DataFrame([[7, 8, 9], [7, 8, 9], [7, 8, 9]], columns=["A", "B", "C"]),
|
),
|
(
|
["B", "C", "D"],
|
[[7, 8, 9], [10, 11, 12], [13, 14, 15]],
|
DataFrame(
|
[[1, 7, 8, 9], [3, 10, 11, 12], [5, 13, 14, 15]],
|
columns=["A", "B", "C", "D"],
|
),
|
),
|
(
|
["C", "A", "D"],
|
np.array([[7, 8, 9], [10, 11, 12], [13, 14, 15]], dtype=np.int64),
|
DataFrame(
|
[[8, 2, 7, 9], [11, 4, 10, 12], [14, 6, 13, 15]],
|
columns=["A", "B", "C", "D"],
|
),
|
),
|
(
|
["A", "C"],
|
DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]),
|
DataFrame(
|
[[7, 2, 8], [9, 4, 10], [11, 6, 12]], columns=["A", "B", "C"]
|
),
|
),
|
],
|
)
|
def test_setitem_list_missing_columns(self, columns, box, expected):
|
# GH#29334
|
df = DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "B"])
|
df[columns] = box
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_list_of_tuples(self, float_frame):
|
tuples = list(zip(float_frame["A"], float_frame["B"]))
|
float_frame["tuples"] = tuples
|
|
result = float_frame["tuples"]
|
expected = Series(tuples, index=float_frame.index, name="tuples")
|
tm.assert_series_equal(result, expected)
|
|
def test_setitem_iloc_generator(self):
|
# GH#39614
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
indexer = (x for x in [1, 2])
|
df.iloc[indexer] = 1
|
expected = DataFrame({"a": [1, 1, 1], "b": [4, 1, 1]})
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_iloc_two_dimensional_generator(self):
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
|
indexer = (x for x in [1, 2])
|
df.iloc[indexer, 1] = 1
|
expected = DataFrame({"a": [1, 2, 3], "b": [4, 1, 1]})
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_dtypes_bytes_type_to_object(self):
|
# GH 20734
|
index = Series(name="id", dtype="S24")
|
df = DataFrame(index=index)
|
df["a"] = Series(name="a", index=index, dtype=np.uint32)
|
df["b"] = Series(name="b", index=index, dtype="S64")
|
df["c"] = Series(name="c", index=index, dtype="S64")
|
df["d"] = Series(name="d", index=index, dtype=np.uint8)
|
result = df.dtypes
|
expected = Series([np.uint32, object, object, np.uint8], index=list("abcd"))
|
tm.assert_series_equal(result, expected)
|
|
def test_boolean_mask_nullable_int64(self):
|
# GH 28928
|
result = DataFrame({"a": [3, 4], "b": [5, 6]}).astype(
|
{"a": "int64", "b": "Int64"}
|
)
|
mask = Series(False, index=result.index)
|
result.loc[mask, "a"] = result["a"]
|
result.loc[mask, "b"] = result["b"]
|
expected = DataFrame({"a": [3, 4], "b": [5, 6]}).astype(
|
{"a": "int64", "b": "Int64"}
|
)
|
tm.assert_frame_equal(result, expected)
|
|
def test_setitem_ea_dtype_rhs_series(self):
|
# GH#47425
|
df = DataFrame({"a": [1, 2]})
|
df["a"] = Series([1, 2], dtype="Int64")
|
expected = DataFrame({"a": [1, 2]}, dtype="Int64")
|
tm.assert_frame_equal(df, expected)
|
|
# TODO(ArrayManager) set column with 2d column array, see #44788
|
@td.skip_array_manager_not_yet_implemented
|
def test_setitem_npmatrix_2d(self):
|
# GH#42376
|
# for use-case df["x"] = sparse.random(10, 10).mean(axis=1)
|
expected = DataFrame(
|
{"np-array": np.ones(10), "np-matrix": np.ones(10)}, index=np.arange(10)
|
)
|
|
a = np.ones((10, 1))
|
df = DataFrame(index=np.arange(10))
|
df["np-array"] = a
|
|
# Instantiation of `np.matrix` gives PendingDeprecationWarning
|
with tm.assert_produces_warning(PendingDeprecationWarning):
|
df["np-matrix"] = np.matrix(a)
|
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("vals", [{}, {"d": "a"}])
|
def test_setitem_aligning_dict_with_index(self, vals):
|
# GH#47216
|
df = DataFrame({"a": [1, 2], "b": [3, 4], **vals})
|
df.loc[:, "a"] = {1: 100, 0: 200}
|
df.loc[:, "c"] = {0: 5, 1: 6}
|
df.loc[:, "e"] = {1: 5}
|
expected = DataFrame(
|
{"a": [200, 100], "b": [3, 4], **vals, "c": [5, 6], "e": [np.nan, 5]}
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_rhs_dataframe(self):
|
# GH#47578
|
df = DataFrame({"a": [1, 2]})
|
df["a"] = DataFrame({"a": [10, 11]}, index=[1, 2])
|
expected = DataFrame({"a": [np.nan, 10]})
|
tm.assert_frame_equal(df, expected)
|
|
df = DataFrame({"a": [1, 2]})
|
df.isetitem(0, DataFrame({"a": [10, 11]}, index=[1, 2]))
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_frame_overwrite_with_ea_dtype(self, any_numeric_ea_dtype):
|
# GH#46896
|
df = DataFrame(columns=["a", "b"], data=[[1, 2], [3, 4]])
|
df["a"] = DataFrame({"a": [10, 11]}, dtype=any_numeric_ea_dtype)
|
expected = DataFrame(
|
{
|
"a": Series([10, 11], dtype=any_numeric_ea_dtype),
|
"b": [2, 4],
|
}
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_frame_midx_columns(self):
|
# GH#49121
|
df = DataFrame({("a", "b"): [10]})
|
expected = df.copy()
|
col_name = ("a", "b")
|
df[col_name] = df[[col_name]]
|
tm.assert_frame_equal(df, expected)
|
|
|
class TestSetitemTZAwareValues:
|
@pytest.fixture
|
def idx(self):
|
naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B")
|
idx = naive.tz_localize("US/Pacific")
|
return idx
|
|
@pytest.fixture
|
def expected(self, idx):
|
expected = Series(np.array(idx.tolist(), dtype="object"), name="B")
|
assert expected.dtype == idx.dtype
|
return expected
|
|
def test_setitem_dt64series(self, idx, expected):
|
# convert to utc
|
df = DataFrame(np.random.randn(2, 1), columns=["A"])
|
df["B"] = idx
|
df["B"] = idx.to_series(index=[0, 1]).dt.tz_convert(None)
|
|
result = df["B"]
|
comp = Series(idx.tz_convert("UTC").tz_localize(None), name="B")
|
tm.assert_series_equal(result, comp)
|
|
def test_setitem_datetimeindex(self, idx, expected):
|
# setting a DataFrame column with a tzaware DTI retains the dtype
|
df = DataFrame(np.random.randn(2, 1), columns=["A"])
|
|
# assign to frame
|
df["B"] = idx
|
result = df["B"]
|
tm.assert_series_equal(result, expected)
|
|
def test_setitem_object_array_of_tzaware_datetimes(self, idx, expected):
|
# setting a DataFrame column with a tzaware DTI retains the dtype
|
df = DataFrame(np.random.randn(2, 1), columns=["A"])
|
|
# object array of datetimes with a tz
|
df["B"] = idx.to_pydatetime()
|
result = df["B"]
|
tm.assert_series_equal(result, expected)
|
|
|
class TestDataFrameSetItemWithExpansion:
|
def test_setitem_listlike_views(self, using_copy_on_write):
|
# GH#38148
|
df = DataFrame({"a": [1, 2, 3], "b": [4, 4, 6]})
|
|
# get one column as a view of df
|
ser = df["a"]
|
|
# add columns with list-like indexer
|
df[["c", "d"]] = np.array([[0.1, 0.2], [0.3, 0.4], [0.4, 0.5]])
|
|
# edit in place the first column to check view semantics
|
df.iloc[0, 0] = 100
|
|
if using_copy_on_write:
|
expected = Series([1, 2, 3], name="a")
|
else:
|
expected = Series([100, 2, 3], name="a")
|
tm.assert_series_equal(ser, expected)
|
|
def test_setitem_string_column_numpy_dtype_raising(self):
|
# GH#39010
|
df = DataFrame([[1, 2], [3, 4]])
|
df["0 - Name"] = [5, 6]
|
expected = DataFrame([[1, 2, 5], [3, 4, 6]], columns=[0, 1, "0 - Name"])
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_empty_df_duplicate_columns(self, using_copy_on_write):
|
# GH#38521
|
df = DataFrame(columns=["a", "b", "b"], dtype="float64")
|
df.loc[:, "a"] = list(range(2))
|
expected = DataFrame(
|
[[0, np.nan, np.nan], [1, np.nan, np.nan]], columns=["a", "b", "b"]
|
)
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_with_expansion_categorical_dtype(self):
|
# assignment
|
df = DataFrame(
|
{"value": np.array(np.random.randint(0, 10000, 100), dtype="int32")}
|
)
|
labels = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
|
|
df = df.sort_values(by=["value"], ascending=True)
|
ser = cut(df.value, range(0, 10500, 500), right=False, labels=labels)
|
cat = ser.values
|
|
# setting with a Categorical
|
df["D"] = cat
|
str(df)
|
|
result = df.dtypes
|
expected = Series(
|
[np.dtype("int32"), CategoricalDtype(categories=labels, ordered=False)],
|
index=["value", "D"],
|
)
|
tm.assert_series_equal(result, expected)
|
|
# setting with a Series
|
df["E"] = ser
|
str(df)
|
|
result = df.dtypes
|
expected = Series(
|
[
|
np.dtype("int32"),
|
CategoricalDtype(categories=labels, ordered=False),
|
CategoricalDtype(categories=labels, ordered=False),
|
],
|
index=["value", "D", "E"],
|
)
|
tm.assert_series_equal(result, expected)
|
|
result1 = df["D"]
|
result2 = df["E"]
|
tm.assert_categorical_equal(result1._mgr.array, cat)
|
|
# sorting
|
ser.name = "E"
|
tm.assert_series_equal(result2.sort_index(), ser.sort_index())
|
|
def test_setitem_scalars_no_index(self):
|
# GH#16823 / GH#17894
|
df = DataFrame()
|
df["foo"] = 1
|
expected = DataFrame(columns=["foo"]).astype(np.int64)
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_newcol_tuple_key(self, float_frame):
|
assert (
|
"A",
|
"B",
|
) not in float_frame.columns
|
float_frame["A", "B"] = float_frame["A"]
|
assert ("A", "B") in float_frame.columns
|
|
result = float_frame["A", "B"]
|
expected = float_frame["A"]
|
tm.assert_series_equal(result, expected, check_names=False)
|
|
def test_frame_setitem_newcol_timestamp(self):
|
# GH#2155
|
columns = date_range(start="1/1/2012", end="2/1/2012", freq=BDay())
|
data = DataFrame(columns=columns, index=range(10))
|
t = datetime(2012, 11, 1)
|
ts = Timestamp(t)
|
data[ts] = np.nan # works, mostly a smoke-test
|
assert np.isnan(data[ts]).all()
|
|
def test_frame_setitem_rangeindex_into_new_col(self):
|
# GH#47128
|
df = DataFrame({"a": ["a", "b"]})
|
df["b"] = df.index
|
df.loc[[False, True], "b"] = 100
|
result = df.loc[[1], :]
|
expected = DataFrame({"a": ["b"], "b": [100]}, index=[1])
|
tm.assert_frame_equal(result, expected)
|
|
def test_setitem_frame_keep_ea_dtype(self, any_numeric_ea_dtype):
|
# GH#46896
|
df = DataFrame(columns=["a", "b"], data=[[1, 2], [3, 4]])
|
df["c"] = DataFrame({"a": [10, 11]}, dtype=any_numeric_ea_dtype)
|
expected = DataFrame(
|
{
|
"a": [1, 3],
|
"b": [2, 4],
|
"c": Series([10, 11], dtype=any_numeric_ea_dtype),
|
}
|
)
|
tm.assert_frame_equal(df, expected)
|
|
|
class TestDataFrameSetItemSlicing:
|
def test_setitem_slice_position(self):
|
# GH#31469
|
df = DataFrame(np.zeros((100, 1)))
|
df[-4:] = 1
|
arr = np.zeros((100, 1))
|
arr[-4:] = 1
|
expected = DataFrame(arr)
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc])
|
@pytest.mark.parametrize("box", [Series, np.array, list, pd.array])
|
@pytest.mark.parametrize("n", [1, 2, 3])
|
def test_setitem_slice_indexer_broadcasting_rhs(self, n, box, indexer):
|
# GH#40440
|
df = DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"])
|
indexer(df)[1:] = box([10, 11, 12])
|
expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"])
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("box", [Series, np.array, list, pd.array])
|
@pytest.mark.parametrize("n", [1, 2, 3])
|
def test_setitem_list_indexer_broadcasting_rhs(self, n, box):
|
# GH#40440
|
df = DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"])
|
df.iloc[list(range(1, n + 1))] = box([10, 11, 12])
|
expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"])
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc])
|
@pytest.mark.parametrize("box", [Series, np.array, list, pd.array])
|
@pytest.mark.parametrize("n", [1, 2, 3])
|
def test_setitem_slice_broadcasting_rhs_mixed_dtypes(self, n, box, indexer):
|
# GH#40440
|
df = DataFrame(
|
[[1, 3, 5], ["x", "y", "z"]] + [[2, 4, 6]] * n, columns=["a", "b", "c"]
|
)
|
indexer(df)[1:] = box([10, 11, 12])
|
expected = DataFrame(
|
[[1, 3, 5]] + [[10, 11, 12]] * (n + 1),
|
columns=["a", "b", "c"],
|
dtype="object",
|
)
|
tm.assert_frame_equal(df, expected)
|
|
|
class TestDataFrameSetItemCallable:
|
def test_setitem_callable(self):
|
# GH#12533
|
df = DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]})
|
df[lambda x: "A"] = [11, 12, 13, 14]
|
|
exp = DataFrame({"A": [11, 12, 13, 14], "B": [5, 6, 7, 8]})
|
tm.assert_frame_equal(df, exp)
|
|
def test_setitem_other_callable(self):
|
# GH#13299
|
def inc(x):
|
return x + 1
|
|
df = DataFrame([[-1, 1], [1, -1]])
|
df[df > 0] = inc
|
|
expected = DataFrame([[-1, inc], [inc, -1]])
|
tm.assert_frame_equal(df, expected)
|
|
|
class TestDataFrameSetItemBooleanMask:
|
@td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values
|
@pytest.mark.parametrize(
|
"mask_type",
|
[lambda df: df > np.abs(df) / 2, lambda df: (df > np.abs(df) / 2).values],
|
ids=["dataframe", "array"],
|
)
|
def test_setitem_boolean_mask(self, mask_type, float_frame):
|
# Test for issue #18582
|
df = float_frame.copy()
|
mask = mask_type(df)
|
|
# index with boolean mask
|
result = df.copy()
|
result[mask] = np.nan
|
|
expected = df.values.copy()
|
expected[np.array(mask)] = np.nan
|
expected = DataFrame(expected, index=df.index, columns=df.columns)
|
tm.assert_frame_equal(result, expected)
|
|
@pytest.mark.xfail(reason="Currently empty indexers are treated as all False")
|
@pytest.mark.parametrize("box", [list, np.array, Series])
|
def test_setitem_loc_empty_indexer_raises_with_non_empty_value(self, box):
|
# GH#37672
|
df = DataFrame({"a": ["a"], "b": [1], "c": [1]})
|
if box == Series:
|
indexer = box([], dtype="object")
|
else:
|
indexer = box([])
|
msg = "Must have equal len keys and value when setting with an iterable"
|
with pytest.raises(ValueError, match=msg):
|
df.loc[indexer, ["b"]] = [1]
|
|
@pytest.mark.parametrize("box", [list, np.array, Series])
|
def test_setitem_loc_only_false_indexer_dtype_changed(self, box):
|
# GH#37550
|
# Dtype is only changed when value to set is a Series and indexer is
|
# empty/bool all False
|
df = DataFrame({"a": ["a"], "b": [1], "c": [1]})
|
indexer = box([False])
|
df.loc[indexer, ["b"]] = 10 - df["c"]
|
expected = DataFrame({"a": ["a"], "b": [1], "c": [1]})
|
tm.assert_frame_equal(df, expected)
|
|
df.loc[indexer, ["b"]] = 9
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize("indexer", [tm.setitem, tm.loc])
|
def test_setitem_boolean_mask_aligning(self, indexer):
|
# GH#39931
|
df = DataFrame({"a": [1, 4, 2, 3], "b": [5, 6, 7, 8]})
|
expected = df.copy()
|
mask = df["a"] >= 3
|
indexer(df)[mask] = indexer(df)[mask].sort_values("a")
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_mask_categorical(self):
|
# assign multiple rows (mixed values) (-> array) -> exp_multi_row
|
# changed multiple rows
|
cats2 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"])
|
idx2 = Index(["h", "i", "j", "k", "l", "m", "n"])
|
values2 = [1, 1, 2, 2, 1, 1, 1]
|
exp_multi_row = DataFrame({"cats": cats2, "values": values2}, index=idx2)
|
|
catsf = Categorical(
|
["a", "a", "c", "c", "a", "a", "a"], categories=["a", "b", "c"]
|
)
|
idxf = Index(["h", "i", "j", "k", "l", "m", "n"])
|
valuesf = [1, 1, 3, 3, 1, 1, 1]
|
df = DataFrame({"cats": catsf, "values": valuesf}, index=idxf)
|
|
exp_fancy = exp_multi_row.copy()
|
exp_fancy["cats"] = exp_fancy["cats"].cat.set_categories(["a", "b", "c"])
|
|
mask = df["cats"] == "c"
|
df[mask] = ["b", 2]
|
# category c is kept in .categories
|
tm.assert_frame_equal(df, exp_fancy)
|
|
@pytest.mark.parametrize("dtype", ["float", "int64"])
|
@pytest.mark.parametrize("kwargs", [{}, {"index": [1]}, {"columns": ["A"]}])
|
def test_setitem_empty_frame_with_boolean(self, dtype, kwargs):
|
# see GH#10126
|
kwargs["dtype"] = dtype
|
df = DataFrame(**kwargs)
|
|
df2 = df.copy()
|
df[df > df2] = 47
|
tm.assert_frame_equal(df, df2)
|
|
def test_setitem_boolean_indexing(self):
|
idx = list(range(3))
|
cols = ["A", "B", "C"]
|
df1 = DataFrame(
|
index=idx,
|
columns=cols,
|
data=np.array(
|
[[0.0, 0.5, 1.0], [1.5, 2.0, 2.5], [3.0, 3.5, 4.0]], dtype=float
|
),
|
)
|
df2 = DataFrame(index=idx, columns=cols, data=np.ones((len(idx), len(cols))))
|
|
expected = DataFrame(
|
index=idx,
|
columns=cols,
|
data=np.array([[0.0, 0.5, 1.0], [1.5, 2.0, -1], [-1, -1, -1]], dtype=float),
|
)
|
|
df1[df1 > 2.0 * df2] = -1
|
tm.assert_frame_equal(df1, expected)
|
with pytest.raises(ValueError, match="Item wrong length"):
|
df1[df1.index[:-1] > 2] = -1
|
|
def test_loc_setitem_all_false_boolean_two_blocks(self):
|
# GH#40885
|
df = DataFrame({"a": [1, 2], "b": [3, 4], "c": "a"})
|
expected = df.copy()
|
indexer = Series([False, False], name="c")
|
df.loc[indexer, ["b"]] = DataFrame({"b": [5, 6]}, index=[0, 1])
|
tm.assert_frame_equal(df, expected)
|
|
def test_setitem_ea_boolean_mask(self):
|
# GH#47125
|
df = DataFrame([[-1, 2], [3, -4]])
|
expected = DataFrame([[0, 2], [3, 0]])
|
boolean_indexer = DataFrame(
|
{
|
0: Series([True, False], dtype="boolean"),
|
1: Series([pd.NA, True], dtype="boolean"),
|
}
|
)
|
df[boolean_indexer] = 0
|
tm.assert_frame_equal(df, expected)
|
|
|
class TestDataFrameSetitemCopyViewSemantics:
|
def test_setitem_always_copy(self, float_frame):
|
assert "E" not in float_frame.columns
|
s = float_frame["A"].copy()
|
float_frame["E"] = s
|
|
float_frame.iloc[5:10, float_frame.columns.get_loc("E")] = np.nan
|
assert notna(s[5:10]).all()
|
|
@pytest.mark.parametrize("consolidate", [True, False])
|
def test_setitem_partial_column_inplace(
|
self, consolidate, using_array_manager, using_copy_on_write
|
):
|
# This setting should be in-place, regardless of whether frame is
|
# single-block or multi-block
|
# GH#304 this used to be incorrectly not-inplace, in which case
|
# we needed to ensure _item_cache was cleared.
|
|
df = DataFrame(
|
{"x": [1.1, 2.1, 3.1, 4.1], "y": [5.1, 6.1, 7.1, 8.1]}, index=[0, 1, 2, 3]
|
)
|
df.insert(2, "z", np.nan)
|
if not using_array_manager:
|
if consolidate:
|
df._consolidate_inplace()
|
assert len(df._mgr.blocks) == 1
|
else:
|
assert len(df._mgr.blocks) == 2
|
|
zvals = df["z"]._values
|
|
df.loc[2:, "z"] = 42
|
|
expected = Series([np.nan, np.nan, 42, 42], index=df.index, name="z")
|
tm.assert_series_equal(df["z"], expected)
|
|
# check setting occurred in-place
|
if not using_copy_on_write:
|
tm.assert_numpy_array_equal(zvals, expected.values)
|
assert np.shares_memory(zvals, df["z"]._values)
|
|
def test_setitem_duplicate_columns_not_inplace(self):
|
# GH#39510
|
cols = ["A", "B"] * 2
|
df = DataFrame(0.0, index=[0], columns=cols)
|
df_copy = df.copy()
|
df_view = df[:]
|
df["B"] = (2, 5)
|
|
expected = DataFrame([[0.0, 2, 0.0, 5]], columns=cols)
|
tm.assert_frame_equal(df_view, df_copy)
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize(
|
"value", [1, np.array([[1], [1]], dtype="int64"), [[1], [1]]]
|
)
|
def test_setitem_same_dtype_not_inplace(self, value, using_array_manager):
|
# GH#39510
|
cols = ["A", "B"]
|
df = DataFrame(0, index=[0, 1], columns=cols)
|
df_copy = df.copy()
|
df_view = df[:]
|
df[["B"]] = value
|
|
expected = DataFrame([[0, 1], [0, 1]], columns=cols)
|
tm.assert_frame_equal(df, expected)
|
tm.assert_frame_equal(df_view, df_copy)
|
|
@pytest.mark.parametrize("value", [1.0, np.array([[1.0], [1.0]]), [[1.0], [1.0]]])
|
def test_setitem_listlike_key_scalar_value_not_inplace(self, value):
|
# GH#39510
|
cols = ["A", "B"]
|
df = DataFrame(0, index=[0, 1], columns=cols)
|
df_copy = df.copy()
|
df_view = df[:]
|
df[["B"]] = value
|
|
expected = DataFrame([[0, 1.0], [0, 1.0]], columns=cols)
|
tm.assert_frame_equal(df_view, df_copy)
|
tm.assert_frame_equal(df, expected)
|
|
@pytest.mark.parametrize(
|
"indexer",
|
[
|
"a",
|
["a"],
|
pytest.param(
|
[True, False],
|
marks=pytest.mark.xfail(
|
reason="Boolean indexer incorrectly setting inplace",
|
strict=False, # passing on some builds, no obvious pattern
|
),
|
),
|
],
|
)
|
@pytest.mark.parametrize(
|
"value, set_value",
|
[
|
(1, 5),
|
(1.0, 5.0),
|
(Timestamp("2020-12-31"), Timestamp("2021-12-31")),
|
("a", "b"),
|
],
|
)
|
def test_setitem_not_operating_inplace(self, value, set_value, indexer):
|
# GH#43406
|
df = DataFrame({"a": value}, index=[0, 1])
|
expected = df.copy()
|
view = df[:]
|
df[indexer] = set_value
|
tm.assert_frame_equal(view, expected)
|
|
@td.skip_array_manager_invalid_test
|
def test_setitem_column_update_inplace(self, using_copy_on_write):
|
# https://github.com/pandas-dev/pandas/issues/47172
|
|
labels = [f"c{i}" for i in range(10)]
|
df = DataFrame({col: np.zeros(len(labels)) for col in labels}, index=labels)
|
values = df._mgr.blocks[0].values
|
|
if not using_copy_on_write:
|
for label in df.columns:
|
df[label][label] = 1
|
|
# diagonal values all updated
|
assert np.all(values[np.arange(10), np.arange(10)] == 1)
|
else:
|
with tm.raises_chained_assignment_error():
|
for label in df.columns:
|
df[label][label] = 1
|
# original dataframe not updated
|
assert np.all(values[np.arange(10), np.arange(10)] == 0)
|
