1680 lines
55 KiB
Python
1680 lines
55 KiB
Python
from collections import namedtuple
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from datetime import (
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datetime,
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timedelta,
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)
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import re
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import numpy as np
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import pytest
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from pandas._libs import iNaT
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from pandas.errors import InvalidIndexError
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import is_integer
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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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MultiIndex,
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Series,
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Timestamp,
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date_range,
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isna,
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notna,
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)
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import pandas._testing as tm
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import pandas.core.common as com
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# We pass through a TypeError raised by numpy
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_slice_msg = "slice indices must be integers or None or have an __index__ method"
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class TestDataFrameIndexing:
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def test_getitem(self, float_frame):
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# Slicing
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sl = float_frame[:20]
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assert len(sl.index) == 20
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# Column access
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for _, series in sl.items():
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assert len(series.index) == 20
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assert tm.equalContents(series.index, sl.index)
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for key, _ in float_frame._series.items():
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assert float_frame[key] is not None
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assert "random" not in float_frame
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with pytest.raises(KeyError, match="random"):
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float_frame["random"]
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def test_getitem2(self, float_frame):
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df = float_frame.copy()
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df["$10"] = np.random.randn(len(df))
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ad = np.random.randn(len(df))
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df["@awesome_domain"] = ad
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with pytest.raises(KeyError, match=re.escape("'df[\"$10\"]'")):
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df.__getitem__('df["$10"]')
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res = df["@awesome_domain"]
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tm.assert_numpy_array_equal(ad, res.values)
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def test_setitem_list(self, float_frame):
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float_frame["E"] = "foo"
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data = float_frame[["A", "B"]]
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float_frame[["B", "A"]] = data
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tm.assert_series_equal(float_frame["B"], data["A"], check_names=False)
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tm.assert_series_equal(float_frame["A"], data["B"], check_names=False)
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msg = "Columns must be same length as key"
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with pytest.raises(ValueError, match=msg):
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data[["A"]] = float_frame[["A", "B"]]
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newcolumndata = range(len(data.index) - 1)
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msg = (
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rf"Length of values \({len(newcolumndata)}\) "
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rf"does not match length of index \({len(data)}\)"
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)
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with pytest.raises(ValueError, match=msg):
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data["A"] = newcolumndata
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def test_setitem_list2(self):
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df = DataFrame(0, index=range(3), columns=["tt1", "tt2"], dtype=np.int_)
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df.loc[1, ["tt1", "tt2"]] = [1, 2]
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result = df.loc[df.index[1], ["tt1", "tt2"]]
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expected = Series([1, 2], df.columns, dtype=np.int_, name=1)
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tm.assert_series_equal(result, expected)
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df["tt1"] = df["tt2"] = "0"
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df.loc[df.index[1], ["tt1", "tt2"]] = ["1", "2"]
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result = df.loc[df.index[1], ["tt1", "tt2"]]
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expected = Series(["1", "2"], df.columns, name=1)
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tm.assert_series_equal(result, expected)
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def test_getitem_boolean(self, mixed_float_frame, mixed_int_frame, datetime_frame):
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# boolean indexing
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d = datetime_frame.index[10]
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indexer = datetime_frame.index > d
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indexer_obj = indexer.astype(object)
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subindex = datetime_frame.index[indexer]
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subframe = datetime_frame[indexer]
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tm.assert_index_equal(subindex, subframe.index)
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with pytest.raises(ValueError, match="Item wrong length"):
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datetime_frame[indexer[:-1]]
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subframe_obj = datetime_frame[indexer_obj]
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tm.assert_frame_equal(subframe_obj, subframe)
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with pytest.raises(ValueError, match="Boolean array expected"):
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datetime_frame[datetime_frame]
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# test that Series work
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indexer_obj = Series(indexer_obj, datetime_frame.index)
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subframe_obj = datetime_frame[indexer_obj]
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tm.assert_frame_equal(subframe_obj, subframe)
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# test that Series indexers reindex
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# we are producing a warning that since the passed boolean
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# key is not the same as the given index, we will reindex
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# not sure this is really necessary
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with tm.assert_produces_warning(UserWarning):
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indexer_obj = indexer_obj.reindex(datetime_frame.index[::-1])
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subframe_obj = datetime_frame[indexer_obj]
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tm.assert_frame_equal(subframe_obj, subframe)
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# test df[df > 0]
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for df in [
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datetime_frame,
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mixed_float_frame,
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mixed_int_frame,
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]:
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data = df._get_numeric_data()
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bif = df[df > 0]
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bifw = DataFrame(
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{c: np.where(data[c] > 0, data[c], np.nan) for c in data.columns},
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index=data.index,
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columns=data.columns,
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)
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# add back other columns to compare
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for c in df.columns:
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if c not in bifw:
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bifw[c] = df[c]
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bifw = bifw.reindex(columns=df.columns)
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tm.assert_frame_equal(bif, bifw, check_dtype=False)
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for c in df.columns:
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if bif[c].dtype != bifw[c].dtype:
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assert bif[c].dtype == df[c].dtype
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def test_getitem_boolean_casting(self, datetime_frame):
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# don't upcast if we don't need to
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df = datetime_frame.copy()
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df["E"] = 1
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df["E"] = df["E"].astype("int32")
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df["E1"] = df["E"].copy()
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df["F"] = 1
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df["F"] = df["F"].astype("int64")
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df["F1"] = df["F"].copy()
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casted = df[df > 0]
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result = casted.dtypes
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expected = Series(
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[np.dtype("float64")] * 4
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+ [np.dtype("int32")] * 2
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+ [np.dtype("int64")] * 2,
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index=["A", "B", "C", "D", "E", "E1", "F", "F1"],
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)
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tm.assert_series_equal(result, expected)
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# int block splitting
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df.loc[df.index[1:3], ["E1", "F1"]] = 0
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casted = df[df > 0]
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result = casted.dtypes
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expected = Series(
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[np.dtype("float64")] * 4
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+ [np.dtype("int32")]
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+ [np.dtype("float64")]
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+ [np.dtype("int64")]
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+ [np.dtype("float64")],
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index=["A", "B", "C", "D", "E", "E1", "F", "F1"],
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)
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tm.assert_series_equal(result, expected)
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def test_getitem_boolean_list(self):
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df = DataFrame(np.arange(12).reshape(3, 4))
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def _checkit(lst):
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result = df[lst]
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expected = df.loc[df.index[lst]]
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tm.assert_frame_equal(result, expected)
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_checkit([True, False, True])
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_checkit([True, True, True])
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_checkit([False, False, False])
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def test_getitem_boolean_iadd(self):
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arr = np.random.randn(5, 5)
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df = DataFrame(arr.copy(), columns=["A", "B", "C", "D", "E"])
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df[df < 0] += 1
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arr[arr < 0] += 1
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tm.assert_almost_equal(df.values, arr)
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def test_boolean_index_empty_corner(self):
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# #2096
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blah = DataFrame(np.empty([0, 1]), columns=["A"], index=DatetimeIndex([]))
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# both of these should succeed trivially
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k = np.array([], bool)
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blah[k]
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blah[k] = 0
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def test_getitem_ix_mixed_integer(self):
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df = DataFrame(
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np.random.randn(4, 3), index=[1, 10, "C", "E"], columns=[1, 2, 3]
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)
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result = df.iloc[:-1]
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expected = df.loc[df.index[:-1]]
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tm.assert_frame_equal(result, expected)
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result = df.loc[[1, 10]]
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expected = df.loc[Index([1, 10])]
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tm.assert_frame_equal(result, expected)
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def test_getitem_ix_mixed_integer2(self):
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# 11320
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df = DataFrame(
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{
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"rna": (1.5, 2.2, 3.2, 4.5),
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-1000: [11, 21, 36, 40],
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0: [10, 22, 43, 34],
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1000: [0, 10, 20, 30],
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},
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columns=["rna", -1000, 0, 1000],
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)
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result = df[[1000]]
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expected = df.iloc[:, [3]]
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tm.assert_frame_equal(result, expected)
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result = df[[-1000]]
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expected = df.iloc[:, [1]]
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tm.assert_frame_equal(result, expected)
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def test_getattr(self, float_frame):
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tm.assert_series_equal(float_frame.A, float_frame["A"])
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msg = "'DataFrame' object has no attribute 'NONEXISTENT_NAME'"
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with pytest.raises(AttributeError, match=msg):
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float_frame.NONEXISTENT_NAME
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def test_setattr_column(self):
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df = DataFrame({"foobar": 1}, index=range(10))
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df.foobar = 5
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assert (df.foobar == 5).all()
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def test_setitem(self, float_frame):
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# not sure what else to do here
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series = float_frame["A"][::2]
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float_frame["col5"] = series
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assert "col5" in float_frame
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assert len(series) == 15
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assert len(float_frame) == 30
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exp = np.ravel(np.column_stack((series.values, [np.nan] * 15)))
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exp = Series(exp, index=float_frame.index, name="col5")
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tm.assert_series_equal(float_frame["col5"], exp)
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series = float_frame["A"]
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float_frame["col6"] = series
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tm.assert_series_equal(series, float_frame["col6"], check_names=False)
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# set ndarray
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arr = np.random.randn(len(float_frame))
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float_frame["col9"] = arr
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assert (float_frame["col9"] == arr).all()
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float_frame["col7"] = 5
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assert (float_frame["col7"] == 5).all()
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float_frame["col0"] = 3.14
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assert (float_frame["col0"] == 3.14).all()
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float_frame["col8"] = "foo"
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assert (float_frame["col8"] == "foo").all()
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# this is partially a view (e.g. some blocks are view)
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# so raise/warn
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smaller = float_frame[:2]
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msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
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with pytest.raises(com.SettingWithCopyError, match=msg):
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smaller["col10"] = ["1", "2"]
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assert smaller["col10"].dtype == np.object_
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assert (smaller["col10"] == ["1", "2"]).all()
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def test_setitem2(self):
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# dtype changing GH4204
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df = DataFrame([[0, 0]])
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df.iloc[0] = np.nan
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expected = DataFrame([[np.nan, np.nan]])
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tm.assert_frame_equal(df, expected)
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df = DataFrame([[0, 0]])
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df.loc[0] = np.nan
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tm.assert_frame_equal(df, expected)
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def test_setitem_boolean(self, float_frame):
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df = float_frame.copy()
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values = float_frame.values
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df[df["A"] > 0] = 4
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values[values[:, 0] > 0] = 4
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tm.assert_almost_equal(df.values, values)
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# test that column reindexing works
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series = df["A"] == 4
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series = series.reindex(df.index[::-1])
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df[series] = 1
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values[values[:, 0] == 4] = 1
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tm.assert_almost_equal(df.values, values)
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df[df > 0] = 5
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values[values > 0] = 5
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tm.assert_almost_equal(df.values, values)
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df[df == 5] = 0
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values[values == 5] = 0
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tm.assert_almost_equal(df.values, values)
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# a df that needs alignment first
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df[df[:-1] < 0] = 2
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np.putmask(values[:-1], values[:-1] < 0, 2)
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tm.assert_almost_equal(df.values, values)
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# indexed with same shape but rows-reversed df
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df[df[::-1] == 2] = 3
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values[values == 2] = 3
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tm.assert_almost_equal(df.values, values)
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msg = "Must pass DataFrame or 2-d ndarray with boolean values only"
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with pytest.raises(TypeError, match=msg):
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df[df * 0] = 2
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# index with DataFrame
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mask = df > np.abs(df)
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expected = df.copy()
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df[df > np.abs(df)] = np.nan
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expected.values[mask.values] = np.nan
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tm.assert_frame_equal(df, expected)
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# set from DataFrame
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expected = df.copy()
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df[df > np.abs(df)] = df * 2
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np.putmask(expected.values, mask.values, df.values * 2)
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tm.assert_frame_equal(df, expected)
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def test_setitem_cast(self, float_frame):
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float_frame["D"] = float_frame["D"].astype("i8")
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assert float_frame["D"].dtype == np.int64
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# #669, should not cast?
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# this is now set to int64, which means a replacement of the column to
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# the value dtype (and nothing to do with the existing dtype)
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float_frame["B"] = 0
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assert float_frame["B"].dtype == np.int64
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# cast if pass array of course
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float_frame["B"] = np.arange(len(float_frame))
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assert issubclass(float_frame["B"].dtype.type, np.integer)
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float_frame["foo"] = "bar"
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float_frame["foo"] = 0
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assert float_frame["foo"].dtype == np.int64
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float_frame["foo"] = "bar"
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float_frame["foo"] = 2.5
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assert float_frame["foo"].dtype == np.float64
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float_frame["something"] = 0
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assert float_frame["something"].dtype == np.int64
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float_frame["something"] = 2
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assert float_frame["something"].dtype == np.int64
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float_frame["something"] = 2.5
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assert float_frame["something"].dtype == np.float64
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def test_setitem_corner(self, float_frame):
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# corner case
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df = DataFrame({"B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]}, index=np.arange(3))
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del df["B"]
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df["B"] = [1.0, 2.0, 3.0]
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assert "B" in df
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assert len(df.columns) == 2
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df["A"] = "beginning"
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df["E"] = "foo"
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df["D"] = "bar"
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df[datetime.now()] = "date"
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df[datetime.now()] = 5.0
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# what to do when empty frame with index
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dm = DataFrame(index=float_frame.index)
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dm["A"] = "foo"
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dm["B"] = "bar"
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assert len(dm.columns) == 2
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assert dm.values.dtype == np.object_
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# upcast
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dm["C"] = 1
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assert dm["C"].dtype == np.int64
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dm["E"] = 1.0
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assert dm["E"].dtype == np.float64
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# set existing column
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dm["A"] = "bar"
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assert "bar" == dm["A"][0]
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dm = DataFrame(index=np.arange(3))
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dm["A"] = 1
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dm["foo"] = "bar"
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del dm["foo"]
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dm["foo"] = "bar"
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assert dm["foo"].dtype == np.object_
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dm["coercible"] = ["1", "2", "3"]
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assert dm["coercible"].dtype == np.object_
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def test_setitem_corner2(self):
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data = {
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"title": ["foobar", "bar", "foobar"] + ["foobar"] * 17,
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"cruft": np.random.random(20),
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}
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df = DataFrame(data)
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ix = df[df["title"] == "bar"].index
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df.loc[ix, ["title"]] = "foobar"
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df.loc[ix, ["cruft"]] = 0
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assert df.loc[1, "title"] == "foobar"
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assert df.loc[1, "cruft"] == 0
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def test_setitem_ambig(self):
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# Difficulties with mixed-type data
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from decimal import Decimal
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# Created as float type
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dm = DataFrame(index=range(3), columns=range(3))
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coercable_series = Series([Decimal(1) for _ in range(3)], index=range(3))
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uncoercable_series = Series(["foo", "bzr", "baz"], index=range(3))
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dm[0] = np.ones(3)
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assert len(dm.columns) == 3
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dm[1] = coercable_series
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assert len(dm.columns) == 3
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dm[2] = uncoercable_series
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assert len(dm.columns) == 3
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assert dm[2].dtype == np.object_
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def test_setitem_None(self, float_frame):
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# GH #766
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float_frame[None] = float_frame["A"]
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tm.assert_series_equal(
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float_frame.iloc[:, -1], float_frame["A"], check_names=False
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)
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tm.assert_series_equal(
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float_frame.loc[:, None], float_frame["A"], check_names=False
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)
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tm.assert_series_equal(float_frame[None], float_frame["A"], check_names=False)
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repr(float_frame)
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def test_loc_setitem_boolean_mask_allfalse(self):
|
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# GH 9596
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df = DataFrame(
|
|
{"a": ["1", "2", "3"], "b": ["11", "22", "33"], "c": ["111", "222", "333"]}
|
|
)
|
|
|
|
result = df.copy()
|
|
result.loc[result.b.isna(), "a"] = result.a
|
|
tm.assert_frame_equal(result, df)
|
|
|
|
def test_getitem_fancy_slice_integers_step(self):
|
|
df = DataFrame(np.random.randn(10, 5))
|
|
|
|
# this is OK
|
|
result = df.iloc[:8:2] # noqa
|
|
df.iloc[:8:2] = np.nan
|
|
assert isna(df.iloc[:8:2]).values.all()
|
|
|
|
def test_getitem_setitem_integer_slice_keyerrors(self):
|
|
df = DataFrame(np.random.randn(10, 5), index=range(0, 20, 2))
|
|
|
|
# this is OK
|
|
cp = df.copy()
|
|
cp.iloc[4:10] = 0
|
|
assert (cp.iloc[4:10] == 0).values.all()
|
|
|
|
# so is this
|
|
cp = df.copy()
|
|
cp.iloc[3:11] = 0
|
|
assert (cp.iloc[3:11] == 0).values.all()
|
|
|
|
result = df.iloc[2:6]
|
|
result2 = df.loc[3:11]
|
|
expected = df.reindex([4, 6, 8, 10])
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
tm.assert_frame_equal(result2, expected)
|
|
|
|
# non-monotonic, raise KeyError
|
|
df2 = df.iloc[list(range(5)) + list(range(5, 10))[::-1]]
|
|
with pytest.raises(KeyError, match=r"^3$"):
|
|
df2.loc[3:11]
|
|
with pytest.raises(KeyError, match=r"^3$"):
|
|
df2.loc[3:11] = 0
|
|
|
|
@td.skip_array_manager_invalid_test # already covered in test_iloc_col_slice_view
|
|
def test_fancy_getitem_slice_mixed(self, float_frame, float_string_frame):
|
|
|
|
sliced = float_string_frame.iloc[:, -3:]
|
|
assert sliced["D"].dtype == np.float64
|
|
|
|
# get view with single block
|
|
# setting it triggers setting with copy
|
|
sliced = float_frame.iloc[:, -3:]
|
|
|
|
assert np.shares_memory(sliced["C"]._values, float_frame["C"]._values)
|
|
|
|
msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
|
|
with pytest.raises(com.SettingWithCopyError, match=msg):
|
|
sliced.loc[:, "C"] = 4.0
|
|
|
|
assert (float_frame["C"] == 4).all()
|
|
|
|
def test_getitem_setitem_non_ix_labels(self):
|
|
df = tm.makeTimeDataFrame()
|
|
|
|
start, end = df.index[[5, 10]]
|
|
|
|
result = df.loc[start:end]
|
|
result2 = df[start:end]
|
|
expected = df[5:11]
|
|
tm.assert_frame_equal(result, expected)
|
|
tm.assert_frame_equal(result2, expected)
|
|
|
|
result = df.copy()
|
|
result.loc[start:end] = 0
|
|
result2 = df.copy()
|
|
result2[start:end] = 0
|
|
expected = df.copy()
|
|
expected[5:11] = 0
|
|
tm.assert_frame_equal(result, expected)
|
|
tm.assert_frame_equal(result2, expected)
|
|
|
|
def test_ix_multi_take(self):
|
|
df = DataFrame(np.random.randn(3, 2))
|
|
rs = df.loc[df.index == 0, :]
|
|
xp = df.reindex([0])
|
|
tm.assert_frame_equal(rs, xp)
|
|
|
|
# GH#1321
|
|
df = DataFrame(np.random.randn(3, 2))
|
|
rs = df.loc[df.index == 0, df.columns == 1]
|
|
xp = df.reindex(index=[0], columns=[1])
|
|
tm.assert_frame_equal(rs, xp)
|
|
|
|
def test_getitem_fancy_scalar(self, float_frame):
|
|
f = float_frame
|
|
ix = f.loc
|
|
|
|
# individual value
|
|
for col in f.columns:
|
|
ts = f[col]
|
|
for idx in f.index[::5]:
|
|
assert ix[idx, col] == ts[idx]
|
|
|
|
@td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values
|
|
def test_setitem_fancy_scalar(self, float_frame):
|
|
f = float_frame
|
|
expected = float_frame.copy()
|
|
ix = f.loc
|
|
|
|
# individual value
|
|
for j, col in enumerate(f.columns):
|
|
ts = f[col] # noqa
|
|
for idx in f.index[::5]:
|
|
i = f.index.get_loc(idx)
|
|
val = np.random.randn()
|
|
expected.values[i, j] = val
|
|
|
|
ix[idx, col] = val
|
|
tm.assert_frame_equal(f, expected)
|
|
|
|
def test_getitem_fancy_boolean(self, float_frame):
|
|
f = float_frame
|
|
ix = f.loc
|
|
|
|
expected = f.reindex(columns=["B", "D"])
|
|
result = ix[:, [False, True, False, True]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = f.reindex(index=f.index[5:10], columns=["B", "D"])
|
|
result = ix[f.index[5:10], [False, True, False, True]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
boolvec = f.index > f.index[7]
|
|
expected = f.reindex(index=f.index[boolvec])
|
|
result = ix[boolvec]
|
|
tm.assert_frame_equal(result, expected)
|
|
result = ix[boolvec, :]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = ix[boolvec, f.columns[2:]]
|
|
expected = f.reindex(index=f.index[boolvec], columns=["C", "D"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values
|
|
def test_setitem_fancy_boolean(self, float_frame):
|
|
# from 2d, set with booleans
|
|
frame = float_frame.copy()
|
|
expected = float_frame.copy()
|
|
|
|
mask = frame["A"] > 0
|
|
frame.loc[mask] = 0.0
|
|
expected.values[mask.values] = 0.0
|
|
tm.assert_frame_equal(frame, expected)
|
|
|
|
frame = float_frame.copy()
|
|
expected = float_frame.copy()
|
|
frame.loc[mask, ["A", "B"]] = 0.0
|
|
expected.values[mask.values, :2] = 0.0
|
|
tm.assert_frame_equal(frame, expected)
|
|
|
|
def test_getitem_fancy_ints(self, float_frame):
|
|
result = float_frame.iloc[[1, 4, 7]]
|
|
expected = float_frame.loc[float_frame.index[[1, 4, 7]]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = float_frame.iloc[:, [2, 0, 1]]
|
|
expected = float_frame.loc[:, float_frame.columns[[2, 0, 1]]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_getitem_setitem_boolean_misaligned(self, float_frame):
|
|
# boolean index misaligned labels
|
|
mask = float_frame["A"][::-1] > 1
|
|
|
|
result = float_frame.loc[mask]
|
|
expected = float_frame.loc[mask[::-1]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
cp = float_frame.copy()
|
|
expected = float_frame.copy()
|
|
cp.loc[mask] = 0
|
|
expected.loc[mask] = 0
|
|
tm.assert_frame_equal(cp, expected)
|
|
|
|
def test_getitem_setitem_boolean_multi(self):
|
|
df = DataFrame(np.random.randn(3, 2))
|
|
|
|
# get
|
|
k1 = np.array([True, False, True])
|
|
k2 = np.array([False, True])
|
|
result = df.loc[k1, k2]
|
|
expected = df.loc[[0, 2], [1]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = df.copy()
|
|
df.loc[np.array([True, False, True]), np.array([False, True])] = 5
|
|
expected.loc[[0, 2], [1]] = 5
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_getitem_setitem_float_labels(self):
|
|
index = Index([1.5, 2, 3, 4, 5])
|
|
df = DataFrame(np.random.randn(5, 5), index=index)
|
|
|
|
result = df.loc[1.5:4]
|
|
expected = df.reindex([1.5, 2, 3, 4])
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 4
|
|
|
|
result = df.loc[4:5]
|
|
expected = df.reindex([4, 5]) # reindex with int
|
|
tm.assert_frame_equal(result, expected, check_index_type=False)
|
|
assert len(result) == 2
|
|
|
|
result = df.loc[4:5]
|
|
expected = df.reindex([4.0, 5.0]) # reindex with float
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 2
|
|
|
|
# loc_float changes this to work properly
|
|
result = df.loc[1:2]
|
|
expected = df.iloc[0:2]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df.loc[1:2] = 0
|
|
result = df[1:2]
|
|
assert (result == 0).all().all()
|
|
|
|
# #2727
|
|
index = Index([1.0, 2.5, 3.5, 4.5, 5.0])
|
|
df = DataFrame(np.random.randn(5, 5), index=index)
|
|
|
|
# positional slicing only via iloc!
|
|
msg = (
|
|
"cannot do positional indexing on Float64Index with "
|
|
r"these indexers \[1.0\] of type float"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.iloc[1.0:5]
|
|
|
|
result = df.iloc[4:5]
|
|
expected = df.reindex([5.0])
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 1
|
|
|
|
cp = df.copy()
|
|
|
|
with pytest.raises(TypeError, match=_slice_msg):
|
|
cp.iloc[1.0:5] = 0
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
result = cp.iloc[1.0:5] == 0
|
|
|
|
assert result.values.all()
|
|
assert (cp.iloc[0:1] == df.iloc[0:1]).values.all()
|
|
|
|
cp = df.copy()
|
|
cp.iloc[4:5] = 0
|
|
assert (cp.iloc[4:5] == 0).values.all()
|
|
assert (cp.iloc[0:4] == df.iloc[0:4]).values.all()
|
|
|
|
# float slicing
|
|
result = df.loc[1.0:5]
|
|
expected = df
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 5
|
|
|
|
result = df.loc[1.1:5]
|
|
expected = df.reindex([2.5, 3.5, 4.5, 5.0])
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 4
|
|
|
|
result = df.loc[4.51:5]
|
|
expected = df.reindex([5.0])
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 1
|
|
|
|
result = df.loc[1.0:5.0]
|
|
expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0])
|
|
tm.assert_frame_equal(result, expected)
|
|
assert len(result) == 5
|
|
|
|
cp = df.copy()
|
|
cp.loc[1.0:5.0] = 0
|
|
result = cp.loc[1.0:5.0]
|
|
assert (result == 0).values.all()
|
|
|
|
def test_setitem_single_column_mixed_datetime(self):
|
|
df = DataFrame(
|
|
np.random.randn(5, 3),
|
|
index=["a", "b", "c", "d", "e"],
|
|
columns=["foo", "bar", "baz"],
|
|
)
|
|
|
|
df["timestamp"] = Timestamp("20010102")
|
|
|
|
# check our dtypes
|
|
result = df.dtypes
|
|
expected = Series(
|
|
[np.dtype("float64")] * 3 + [np.dtype("datetime64[ns]")],
|
|
index=["foo", "bar", "baz", "timestamp"],
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# GH#16674 iNaT is treated as an integer when given by the user
|
|
df.loc["b", "timestamp"] = iNaT
|
|
assert not isna(df.loc["b", "timestamp"])
|
|
assert df["timestamp"].dtype == np.object_
|
|
assert df.loc["b", "timestamp"] == iNaT
|
|
|
|
# allow this syntax (as of GH#3216)
|
|
df.loc["c", "timestamp"] = np.nan
|
|
assert isna(df.loc["c", "timestamp"])
|
|
|
|
# allow this syntax
|
|
df.loc["d", :] = np.nan
|
|
assert not isna(df.loc["c", :]).all()
|
|
|
|
def test_setitem_mixed_datetime(self):
|
|
# GH 9336
|
|
expected = DataFrame(
|
|
{
|
|
"a": [0, 0, 0, 0, 13, 14],
|
|
"b": [
|
|
datetime(2012, 1, 1),
|
|
1,
|
|
"x",
|
|
"y",
|
|
datetime(2013, 1, 1),
|
|
datetime(2014, 1, 1),
|
|
],
|
|
}
|
|
)
|
|
df = DataFrame(0, columns=list("ab"), index=range(6))
|
|
df["b"] = pd.NaT
|
|
df.loc[0, "b"] = datetime(2012, 1, 1)
|
|
df.loc[1, "b"] = 1
|
|
df.loc[[2, 3], "b"] = "x", "y"
|
|
A = np.array(
|
|
[
|
|
[13, np.datetime64("2013-01-01T00:00:00")],
|
|
[14, np.datetime64("2014-01-01T00:00:00")],
|
|
]
|
|
)
|
|
df.loc[[4, 5], ["a", "b"]] = A
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_setitem_frame_float(self, float_frame):
|
|
piece = float_frame.loc[float_frame.index[:2], ["A", "B"]]
|
|
float_frame.loc[float_frame.index[-2] :, ["A", "B"]] = piece.values
|
|
result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values
|
|
expected = piece.values
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
def test_setitem_frame_mixed(self, float_string_frame):
|
|
# GH 3216
|
|
|
|
# already aligned
|
|
f = float_string_frame.copy()
|
|
piece = DataFrame(
|
|
[[1.0, 2.0], [3.0, 4.0]], index=f.index[0:2], columns=["A", "B"]
|
|
)
|
|
key = (f.index[slice(None, 2)], ["A", "B"])
|
|
f.loc[key] = piece
|
|
tm.assert_almost_equal(f.loc[f.index[0:2], ["A", "B"]].values, piece.values)
|
|
|
|
def test_setitem_frame_mixed_rows_unaligned(self, float_string_frame):
|
|
# GH#3216 rows unaligned
|
|
f = float_string_frame.copy()
|
|
piece = DataFrame(
|
|
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]],
|
|
index=list(f.index[0:2]) + ["foo", "bar"],
|
|
columns=["A", "B"],
|
|
)
|
|
key = (f.index[slice(None, 2)], ["A", "B"])
|
|
f.loc[key] = piece
|
|
tm.assert_almost_equal(
|
|
f.loc[f.index[0:2:], ["A", "B"]].values, piece.values[0:2]
|
|
)
|
|
|
|
def test_setitem_frame_mixed_key_unaligned(self, float_string_frame):
|
|
# GH#3216 key is unaligned with values
|
|
f = float_string_frame.copy()
|
|
piece = f.loc[f.index[:2], ["A"]]
|
|
piece.index = f.index[-2:]
|
|
key = (f.index[slice(-2, None)], ["A", "B"])
|
|
f.loc[key] = piece
|
|
piece["B"] = np.nan
|
|
tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values)
|
|
|
|
def test_setitem_frame_mixed_ndarray(self, float_string_frame):
|
|
# GH#3216 ndarray
|
|
f = float_string_frame.copy()
|
|
piece = float_string_frame.loc[f.index[:2], ["A", "B"]]
|
|
key = (f.index[slice(-2, None)], ["A", "B"])
|
|
f.loc[key] = piece.values
|
|
tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values)
|
|
|
|
def test_setitem_frame_upcast(self):
|
|
# needs upcasting
|
|
df = DataFrame([[1, 2, "foo"], [3, 4, "bar"]], columns=["A", "B", "C"])
|
|
df2 = df.copy()
|
|
df2.loc[:, ["A", "B"]] = df.loc[:, ["A", "B"]] + 0.5
|
|
expected = df.reindex(columns=["A", "B"])
|
|
expected += 0.5
|
|
expected["C"] = df["C"]
|
|
tm.assert_frame_equal(df2, expected)
|
|
|
|
def test_setitem_frame_align(self, float_frame):
|
|
piece = float_frame.loc[float_frame.index[:2], ["A", "B"]]
|
|
piece.index = float_frame.index[-2:]
|
|
piece.columns = ["A", "B"]
|
|
float_frame.loc[float_frame.index[-2:], ["A", "B"]] = piece
|
|
result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values
|
|
expected = piece.values
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
def test_getitem_setitem_ix_duplicates(self):
|
|
# #1201
|
|
df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"])
|
|
|
|
result = df.loc["foo"]
|
|
expected = df[:2]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.loc["bar"]
|
|
expected = df.iloc[[2, 4]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.loc["baz"]
|
|
expected = df.iloc[3]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_getitem_ix_boolean_duplicates_multiple(self):
|
|
# #1201
|
|
df = DataFrame(np.random.randn(5, 3), index=["foo", "foo", "bar", "baz", "bar"])
|
|
|
|
result = df.loc[["bar"]]
|
|
exp = df.iloc[[2, 4]]
|
|
tm.assert_frame_equal(result, exp)
|
|
|
|
result = df.loc[df[1] > 0]
|
|
exp = df[df[1] > 0]
|
|
tm.assert_frame_equal(result, exp)
|
|
|
|
result = df.loc[df[0] > 0]
|
|
exp = df[df[0] > 0]
|
|
tm.assert_frame_equal(result, exp)
|
|
|
|
@pytest.mark.parametrize("bool_value", [True, False])
|
|
def test_getitem_setitem_ix_bool_keyerror(self, bool_value):
|
|
# #2199
|
|
df = DataFrame({"a": [1, 2, 3]})
|
|
message = f"{bool_value}: boolean label can not be used without a boolean index"
|
|
with pytest.raises(KeyError, match=message):
|
|
df.loc[bool_value]
|
|
|
|
msg = "cannot use a single bool to index into setitem"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.loc[bool_value] = 0
|
|
|
|
# TODO: rename? remove?
|
|
def test_single_element_ix_dont_upcast(self, float_frame):
|
|
float_frame["E"] = 1
|
|
assert issubclass(float_frame["E"].dtype.type, (int, np.integer))
|
|
|
|
result = float_frame.loc[float_frame.index[5], "E"]
|
|
assert is_integer(result)
|
|
|
|
# GH 11617
|
|
df = DataFrame({"a": [1.23]})
|
|
df["b"] = 666
|
|
|
|
result = df.loc[0, "b"]
|
|
assert is_integer(result)
|
|
|
|
expected = Series([666], [0], name="b")
|
|
result = df.loc[[0], "b"]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_iloc_row(self):
|
|
df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))
|
|
|
|
result = df.iloc[1]
|
|
exp = df.loc[2]
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
result = df.iloc[2]
|
|
exp = df.loc[4]
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
# slice
|
|
result = df.iloc[slice(4, 8)]
|
|
expected = df.loc[8:14]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# list of integers
|
|
result = df.iloc[[1, 2, 4, 6]]
|
|
expected = df.reindex(df.index[[1, 2, 4, 6]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_row_slice_view(self, using_array_manager):
|
|
df = DataFrame(np.random.randn(10, 4), index=range(0, 20, 2))
|
|
original = df.copy()
|
|
|
|
# verify slice is view
|
|
# setting it makes it raise/warn
|
|
subset = df.iloc[slice(4, 8)]
|
|
|
|
assert np.shares_memory(df[2], subset[2])
|
|
|
|
msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
|
|
with pytest.raises(com.SettingWithCopyError, match=msg):
|
|
subset.loc[:, 2] = 0.0
|
|
|
|
exp_col = original[2].copy()
|
|
# TODO(ArrayManager) verify it is expected that the original didn't change
|
|
if not using_array_manager:
|
|
exp_col[4:8] = 0.0
|
|
tm.assert_series_equal(df[2], exp_col)
|
|
|
|
def test_iloc_col(self):
|
|
|
|
df = DataFrame(np.random.randn(4, 10), columns=range(0, 20, 2))
|
|
|
|
result = df.iloc[:, 1]
|
|
exp = df.loc[:, 2]
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
result = df.iloc[:, 2]
|
|
exp = df.loc[:, 4]
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
# slice
|
|
result = df.iloc[:, slice(4, 8)]
|
|
expected = df.loc[:, 8:14]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# list of integers
|
|
result = df.iloc[:, [1, 2, 4, 6]]
|
|
expected = df.reindex(columns=df.columns[[1, 2, 4, 6]])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_iloc_col_slice_view(self, using_array_manager):
|
|
df = DataFrame(np.random.randn(4, 10), columns=range(0, 20, 2))
|
|
original = df.copy()
|
|
subset = df.iloc[:, slice(4, 8)]
|
|
|
|
if not using_array_manager:
|
|
# verify slice is view
|
|
|
|
assert np.shares_memory(df[8]._values, subset[8]._values)
|
|
|
|
# and that we are setting a copy
|
|
msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame"
|
|
with pytest.raises(com.SettingWithCopyError, match=msg):
|
|
subset.loc[:, 8] = 0.0
|
|
|
|
assert (df[8] == 0).all()
|
|
else:
|
|
# TODO(ArrayManager) verify this is the desired behaviour
|
|
subset[8] = 0.0
|
|
# subset changed
|
|
assert (subset[8] == 0).all()
|
|
# but df itself did not change (setitem replaces full column)
|
|
tm.assert_frame_equal(df, original)
|
|
|
|
def test_loc_duplicates(self):
|
|
# gh-17105
|
|
|
|
# insert a duplicate element to the index
|
|
trange = date_range(
|
|
start=Timestamp(year=2017, month=1, day=1),
|
|
end=Timestamp(year=2017, month=1, day=5),
|
|
)
|
|
|
|
trange = trange.insert(loc=5, item=Timestamp(year=2017, month=1, day=5))
|
|
|
|
df = DataFrame(0, index=trange, columns=["A", "B"])
|
|
bool_idx = np.array([False, False, False, False, False, True])
|
|
|
|
# assignment
|
|
df.loc[trange[bool_idx], "A"] = 6
|
|
|
|
expected = DataFrame(
|
|
{"A": [0, 0, 0, 0, 6, 6], "B": [0, 0, 0, 0, 0, 0]}, index=trange
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# in-place
|
|
df = DataFrame(0, index=trange, columns=["A", "B"])
|
|
df.loc[trange[bool_idx], "A"] += 6
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_setitem_with_unaligned_tz_aware_datetime_column(self):
|
|
# GH 12981
|
|
# Assignment of unaligned offset-aware datetime series.
|
|
# Make sure timezone isn't lost
|
|
column = Series(date_range("2015-01-01", periods=3, tz="utc"), name="dates")
|
|
df = DataFrame({"dates": column})
|
|
df["dates"] = column[[1, 0, 2]]
|
|
tm.assert_series_equal(df["dates"], column)
|
|
|
|
df = DataFrame({"dates": column})
|
|
df.loc[[0, 1, 2], "dates"] = column[[1, 0, 2]]
|
|
tm.assert_series_equal(df["dates"], column)
|
|
|
|
def test_loc_setitem_datetimelike_with_inference(self):
|
|
# GH 7592
|
|
# assignment of timedeltas with NaT
|
|
|
|
one_hour = timedelta(hours=1)
|
|
df = DataFrame(index=date_range("20130101", periods=4))
|
|
df["A"] = np.array([1 * one_hour] * 4, dtype="m8[ns]")
|
|
df.loc[:, "B"] = np.array([2 * one_hour] * 4, dtype="m8[ns]")
|
|
df.loc[df.index[:3], "C"] = np.array([3 * one_hour] * 3, dtype="m8[ns]")
|
|
df.loc[:, "D"] = np.array([4 * one_hour] * 4, dtype="m8[ns]")
|
|
df.loc[df.index[:3], "E"] = np.array([5 * one_hour] * 3, dtype="m8[ns]")
|
|
df["F"] = np.timedelta64("NaT")
|
|
df.loc[df.index[:-1], "F"] = np.array([6 * one_hour] * 3, dtype="m8[ns]")
|
|
df.loc[df.index[-3] :, "G"] = date_range("20130101", periods=3)
|
|
df["H"] = np.datetime64("NaT")
|
|
result = df.dtypes
|
|
expected = Series(
|
|
[np.dtype("timedelta64[ns]")] * 6 + [np.dtype("datetime64[ns]")] * 2,
|
|
index=list("ABCDEFGH"),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_getitem_boolean_indexing_mixed(self):
|
|
df = DataFrame(
|
|
{
|
|
0: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
|
|
1: {
|
|
35: np.nan,
|
|
40: 0.32632316859446198,
|
|
43: np.nan,
|
|
49: 0.32632316859446198,
|
|
50: 0.39114724480578139,
|
|
},
|
|
2: {
|
|
35: np.nan,
|
|
40: np.nan,
|
|
43: 0.29012581014105987,
|
|
49: np.nan,
|
|
50: np.nan,
|
|
},
|
|
3: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan},
|
|
4: {
|
|
35: 0.34215328467153283,
|
|
40: np.nan,
|
|
43: np.nan,
|
|
49: np.nan,
|
|
50: np.nan,
|
|
},
|
|
"y": {35: 0, 40: 0, 43: 0, 49: 0, 50: 1},
|
|
}
|
|
)
|
|
|
|
# mixed int/float ok
|
|
df2 = df.copy()
|
|
df2[df2 > 0.3] = 1
|
|
expected = df.copy()
|
|
expected.loc[40, 1] = 1
|
|
expected.loc[49, 1] = 1
|
|
expected.loc[50, 1] = 1
|
|
expected.loc[35, 4] = 1
|
|
tm.assert_frame_equal(df2, expected)
|
|
|
|
df["foo"] = "test"
|
|
msg = "not supported between instances|unorderable types"
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df[df > 0.3] = 1
|
|
|
|
def test_type_error_multiindex(self):
|
|
# See gh-12218
|
|
mi = MultiIndex.from_product([["x", "y"], [0, 1]], names=[None, "c"])
|
|
dg = DataFrame(
|
|
[[1, 1, 2, 2], [3, 3, 4, 4]], columns=mi, index=Index([0, 1], name="i")
|
|
)
|
|
with pytest.raises(InvalidIndexError, match="slice"):
|
|
dg[:, 0]
|
|
|
|
index = Index(range(2), name="i")
|
|
columns = MultiIndex(
|
|
levels=[["x", "y"], [0, 1]], codes=[[0, 1], [0, 0]], names=[None, "c"]
|
|
)
|
|
expected = DataFrame([[1, 2], [3, 4]], columns=columns, index=index)
|
|
|
|
result = dg.loc[:, (slice(None), 0)]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
name = ("x", 0)
|
|
index = Index(range(2), name="i")
|
|
expected = Series([1, 3], index=index, name=name)
|
|
|
|
result = dg["x", 0]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_getitem_interval_index_partial_indexing(self):
|
|
# GH#36490
|
|
df = DataFrame(
|
|
np.ones((3, 4)), columns=pd.IntervalIndex.from_breaks(np.arange(5))
|
|
)
|
|
|
|
expected = df.iloc[:, 0]
|
|
|
|
res = df[0.5]
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
res = df.loc[:, 0.5]
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
def test_setitem_array_as_cell_value(self):
|
|
# GH#43422
|
|
df = DataFrame(columns=["a", "b"], dtype=object)
|
|
df.loc[0] = {"a": np.zeros((2,)), "b": np.zeros((2, 2))}
|
|
expected = DataFrame({"a": [np.zeros((2,))], "b": [np.zeros((2, 2))]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
# with AM goes through split-path, loses dtype
|
|
@td.skip_array_manager_not_yet_implemented
|
|
def test_iloc_setitem_nullable_2d_values(self):
|
|
df = DataFrame({"A": [1, 2, 3]}, dtype="Int64")
|
|
orig = df.copy()
|
|
|
|
df.loc[:] = df.values[:, ::-1]
|
|
tm.assert_frame_equal(df, orig)
|
|
|
|
df.loc[:] = pd.core.arrays.PandasArray(df.values[:, ::-1])
|
|
tm.assert_frame_equal(df, orig)
|
|
|
|
df.iloc[:] = df.iloc[:, :]
|
|
tm.assert_frame_equal(df, orig)
|
|
|
|
def test_getitem_segfault_with_empty_like_object(self):
|
|
# GH#46848
|
|
df = DataFrame(np.empty((1, 1), dtype=object))
|
|
df[0] = np.empty_like(df[0])
|
|
# this produces the segfault
|
|
df[[0]]
|
|
|
|
@pytest.mark.parametrize(
|
|
"null", [pd.NaT, pd.NaT.to_numpy("M8[ns]"), pd.NaT.to_numpy("m8[ns]")]
|
|
)
|
|
def test_setting_mismatched_na_into_nullable_fails(
|
|
self, null, any_numeric_ea_dtype
|
|
):
|
|
# GH#44514 don't cast mismatched nulls to pd.NA
|
|
df = DataFrame({"A": [1, 2, 3]}, dtype=any_numeric_ea_dtype)
|
|
ser = df["A"]
|
|
arr = ser._values
|
|
|
|
msg = "|".join(
|
|
[
|
|
r"int\(\) argument must be a string, a bytes-like object or a "
|
|
"(real )?number, not 'NaTType'",
|
|
r"timedelta64\[ns\] cannot be converted to an? (Floating|Integer)Dtype",
|
|
r"datetime64\[ns\] cannot be converted to an? (Floating|Integer)Dtype",
|
|
"object cannot be converted to a FloatingDtype",
|
|
"'values' contains non-numeric NA",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr[0] = null
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
arr[:2] = [null, null]
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser[0] = null
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser[:2] = [null, null]
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.iloc[0] = null
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.iloc[:2] = [null, null]
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.iloc[0, 0] = null
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df.iloc[:2, 0] = [null, null]
|
|
|
|
# Multi-Block
|
|
df2 = df.copy()
|
|
df2["B"] = ser.copy()
|
|
with pytest.raises(TypeError, match=msg):
|
|
df2.iloc[0, 0] = null
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
df2.iloc[:2, 0] = [null, null]
|
|
|
|
def test_loc_expand_empty_frame_keep_index_name(self):
|
|
# GH#45621
|
|
df = DataFrame(columns=["b"], index=Index([], name="a"))
|
|
df.loc[0] = 1
|
|
expected = DataFrame({"b": [1]}, index=Index([0], name="a"))
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_expand_empty_frame_keep_midx_names(self):
|
|
# GH#46317
|
|
df = DataFrame(
|
|
columns=["d"], index=MultiIndex.from_tuples([], names=["a", "b", "c"])
|
|
)
|
|
df.loc[(1, 2, 3)] = "foo"
|
|
expected = DataFrame(
|
|
{"d": ["foo"]},
|
|
index=MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"]),
|
|
)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_internals_not_updated_correctly(self):
|
|
# GH#47867 all steps are necessary to reproduce the initial bug
|
|
df = DataFrame(
|
|
{"bool_col": True, "a": 1, "b": 2.5},
|
|
index=MultiIndex.from_arrays([[1, 2], [1, 2]], names=["idx1", "idx2"]),
|
|
)
|
|
idx = [(1, 1)]
|
|
|
|
df["c"] = 3
|
|
df.loc[idx, "c"] = 0
|
|
|
|
df.loc[idx, "c"]
|
|
df.loc[idx, ["a", "b"]]
|
|
|
|
df.loc[idx, "c"] = 15
|
|
result = df.loc[idx, "c"]
|
|
expected = df = Series(
|
|
15,
|
|
index=MultiIndex.from_arrays([[1], [1]], names=["idx1", "idx2"]),
|
|
name="c",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("val", ["x", 1])
|
|
@pytest.mark.parametrize("idxr", ["a", ["a"]])
|
|
def test_loc_setitem_rhs_frame(self, idxr, val):
|
|
# GH#47578
|
|
df = DataFrame({"a": [1, 2]})
|
|
df.loc[:, "a"] = DataFrame({"a": [val, 11]}, index=[1, 2])
|
|
expected = DataFrame({"a": [np.nan, val]})
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_loc_named_tuple_for_midx(self):
|
|
# GH#48124
|
|
df = DataFrame(
|
|
index=MultiIndex.from_product(
|
|
[["A", "B"], ["a", "b", "c"]], names=["first", "second"]
|
|
)
|
|
)
|
|
indexer_tuple = namedtuple("Indexer", df.index.names)
|
|
idxr = indexer_tuple(first="A", second=["a", "b"])
|
|
result = df.loc[idxr, :]
|
|
expected = DataFrame(
|
|
index=MultiIndex.from_tuples(
|
|
[("A", "a"), ("A", "b")], names=["first", "second"]
|
|
)
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestDataFrameIndexingUInt64:
|
|
def test_setitem(self, uint64_frame):
|
|
|
|
df = uint64_frame
|
|
idx = df["A"].rename("foo")
|
|
|
|
# setitem
|
|
assert "C" not in df.columns
|
|
df["C"] = idx
|
|
tm.assert_series_equal(df["C"], Series(idx, name="C"))
|
|
|
|
assert "D" not in df.columns
|
|
df["D"] = "foo"
|
|
df["D"] = idx
|
|
tm.assert_series_equal(df["D"], Series(idx, name="D"))
|
|
del df["D"]
|
|
|
|
# With NaN: because uint64 has no NaN element,
|
|
# the column should be cast to object.
|
|
df2 = df.copy()
|
|
df2.iloc[1, 1] = pd.NaT
|
|
df2.iloc[1, 2] = pd.NaT
|
|
result = df2["B"]
|
|
tm.assert_series_equal(notna(result), Series([True, False, True], name="B"))
|
|
tm.assert_series_equal(
|
|
df2.dtypes,
|
|
Series(
|
|
[np.dtype("uint64"), np.dtype("O"), np.dtype("O")],
|
|
index=["A", "B", "C"],
|
|
),
|
|
)
|
|
|
|
|
|
def test_object_casting_indexing_wraps_datetimelike(using_array_manager):
|
|
# GH#31649, check the indexing methods all the way down the stack
|
|
df = DataFrame(
|
|
{
|
|
"A": [1, 2],
|
|
"B": date_range("2000", periods=2),
|
|
"C": pd.timedelta_range("1 Day", periods=2),
|
|
}
|
|
)
|
|
|
|
ser = df.loc[0]
|
|
assert isinstance(ser.values[1], Timestamp)
|
|
assert isinstance(ser.values[2], pd.Timedelta)
|
|
|
|
ser = df.iloc[0]
|
|
assert isinstance(ser.values[1], Timestamp)
|
|
assert isinstance(ser.values[2], pd.Timedelta)
|
|
|
|
ser = df.xs(0, axis=0)
|
|
assert isinstance(ser.values[1], Timestamp)
|
|
assert isinstance(ser.values[2], pd.Timedelta)
|
|
|
|
if using_array_manager:
|
|
# remainder of the test checking BlockManager internals
|
|
return
|
|
|
|
mgr = df._mgr
|
|
mgr._rebuild_blknos_and_blklocs()
|
|
arr = mgr.fast_xs(0)
|
|
assert isinstance(arr[1], Timestamp)
|
|
assert isinstance(arr[2], pd.Timedelta)
|
|
|
|
blk = mgr.blocks[mgr.blknos[1]]
|
|
assert blk.dtype == "M8[ns]" # we got the right block
|
|
val = blk.iget((0, 0))
|
|
assert isinstance(val, Timestamp)
|
|
|
|
blk = mgr.blocks[mgr.blknos[2]]
|
|
assert blk.dtype == "m8[ns]" # we got the right block
|
|
val = blk.iget((0, 0))
|
|
assert isinstance(val, pd.Timedelta)
|
|
|
|
|
|
msg1 = r"Cannot setitem on a Categorical with a new category( \(.*\))?, set the"
|
|
msg2 = "Cannot set a Categorical with another, without identical categories"
|
|
|
|
|
|
class TestLocILocDataFrameCategorical:
|
|
@pytest.fixture
|
|
def orig(self):
|
|
cats = Categorical(["a", "a", "a", "a", "a", "a", "a"], categories=["a", "b"])
|
|
idx = Index(["h", "i", "j", "k", "l", "m", "n"])
|
|
values = [1, 1, 1, 1, 1, 1, 1]
|
|
orig = DataFrame({"cats": cats, "values": values}, index=idx)
|
|
return orig
|
|
|
|
@pytest.fixture
|
|
def exp_single_row(self):
|
|
# The expected values if we change a single row
|
|
cats1 = Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"])
|
|
idx1 = Index(["h", "i", "j", "k", "l", "m", "n"])
|
|
values1 = [1, 1, 2, 1, 1, 1, 1]
|
|
exp_single_row = DataFrame({"cats": cats1, "values": values1}, index=idx1)
|
|
return exp_single_row
|
|
|
|
@pytest.fixture
|
|
def exp_multi_row(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)
|
|
return exp_multi_row
|
|
|
|
@pytest.fixture
|
|
def exp_parts_cats_col(self):
|
|
# changed part of the cats column
|
|
cats3 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"])
|
|
idx3 = Index(["h", "i", "j", "k", "l", "m", "n"])
|
|
values3 = [1, 1, 1, 1, 1, 1, 1]
|
|
exp_parts_cats_col = DataFrame({"cats": cats3, "values": values3}, index=idx3)
|
|
return exp_parts_cats_col
|
|
|
|
@pytest.fixture
|
|
def exp_single_cats_value(self):
|
|
# changed single value in cats col
|
|
cats4 = Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"])
|
|
idx4 = Index(["h", "i", "j", "k", "l", "m", "n"])
|
|
values4 = [1, 1, 1, 1, 1, 1, 1]
|
|
exp_single_cats_value = DataFrame(
|
|
{"cats": cats4, "values": values4}, index=idx4
|
|
)
|
|
return exp_single_cats_value
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_loc_iloc_setitem_list_of_lists(self, orig, exp_multi_row, indexer):
|
|
# - assign multiple rows (mixed values) -> exp_multi_row
|
|
df = orig.copy()
|
|
|
|
key = slice(2, 4)
|
|
if indexer is tm.loc:
|
|
key = slice("j", "k")
|
|
|
|
indexer(df)[key, :] = [["b", 2], ["b", 2]]
|
|
tm.assert_frame_equal(df, exp_multi_row)
|
|
|
|
df = orig.copy()
|
|
with pytest.raises(TypeError, match=msg1):
|
|
indexer(df)[key, :] = [["c", 2], ["c", 2]]
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc, tm.at, tm.iat])
|
|
def test_loc_iloc_at_iat_setitem_single_value_in_categories(
|
|
self, orig, exp_single_cats_value, indexer
|
|
):
|
|
# - assign a single value -> exp_single_cats_value
|
|
df = orig.copy()
|
|
|
|
key = (2, 0)
|
|
if indexer in [tm.loc, tm.at]:
|
|
key = (df.index[2], df.columns[0])
|
|
|
|
# "b" is among the categories for df["cat"}]
|
|
indexer(df)[key] = "b"
|
|
tm.assert_frame_equal(df, exp_single_cats_value)
|
|
|
|
# "c" is not among the categories for df["cat"]
|
|
with pytest.raises(TypeError, match=msg1):
|
|
indexer(df)[key] = "c"
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_loc_iloc_setitem_mask_single_value_in_categories(
|
|
self, orig, exp_single_cats_value, indexer
|
|
):
|
|
# mask with single True
|
|
df = orig.copy()
|
|
|
|
mask = df.index == "j"
|
|
key = 0
|
|
if indexer is tm.loc:
|
|
key = df.columns[key]
|
|
|
|
indexer(df)[mask, key] = "b"
|
|
tm.assert_frame_equal(df, exp_single_cats_value)
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_loc_iloc_setitem_full_row_non_categorical_rhs(
|
|
self, orig, exp_single_row, indexer
|
|
):
|
|
# - assign a complete row (mixed values) -> exp_single_row
|
|
df = orig.copy()
|
|
|
|
key = 2
|
|
if indexer is tm.loc:
|
|
key = df.index[2]
|
|
|
|
# not categorical dtype, but "b" _is_ among the categories for df["cat"]
|
|
indexer(df)[key, :] = ["b", 2]
|
|
tm.assert_frame_equal(df, exp_single_row)
|
|
|
|
# "c" is not among the categories for df["cat"]
|
|
with pytest.raises(TypeError, match=msg1):
|
|
indexer(df)[key, :] = ["c", 2]
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_loc_iloc_setitem_partial_col_categorical_rhs(
|
|
self, orig, exp_parts_cats_col, indexer
|
|
):
|
|
# assign a part of a column with dtype == categorical ->
|
|
# exp_parts_cats_col
|
|
df = orig.copy()
|
|
|
|
key = (slice(2, 4), 0)
|
|
if indexer is tm.loc:
|
|
key = (slice("j", "k"), df.columns[0])
|
|
|
|
# same categories as we currently have in df["cats"]
|
|
compat = Categorical(["b", "b"], categories=["a", "b"])
|
|
indexer(df)[key] = compat
|
|
tm.assert_frame_equal(df, exp_parts_cats_col)
|
|
|
|
# categories do not match df["cat"]'s, but "b" is among them
|
|
semi_compat = Categorical(list("bb"), categories=list("abc"))
|
|
with pytest.raises(TypeError, match=msg2):
|
|
# different categories but holdable values
|
|
# -> not sure if this should fail or pass
|
|
indexer(df)[key] = semi_compat
|
|
|
|
# categories do not match df["cat"]'s, and "c" is not among them
|
|
incompat = Categorical(list("cc"), categories=list("abc"))
|
|
with pytest.raises(TypeError, match=msg2):
|
|
# different values
|
|
indexer(df)[key] = incompat
|
|
|
|
@pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
|
|
def test_loc_iloc_setitem_non_categorical_rhs(
|
|
self, orig, exp_parts_cats_col, indexer
|
|
):
|
|
# assign a part of a column with dtype != categorical -> exp_parts_cats_col
|
|
df = orig.copy()
|
|
|
|
key = (slice(2, 4), 0)
|
|
if indexer is tm.loc:
|
|
key = (slice("j", "k"), df.columns[0])
|
|
|
|
# "b" is among the categories for df["cat"]
|
|
indexer(df)[key] = ["b", "b"]
|
|
tm.assert_frame_equal(df, exp_parts_cats_col)
|
|
|
|
# "c" not part of the categories
|
|
with pytest.raises(TypeError, match=msg1):
|
|
indexer(df)[key] = ["c", "c"]
|
|
|
|
def test_loc_on_multiindex_one_level(self):
|
|
# GH#45779
|
|
df = DataFrame(
|
|
data=[[0], [1]],
|
|
index=MultiIndex.from_tuples([("a",), ("b",)], names=["first"]),
|
|
)
|
|
expected = DataFrame(
|
|
data=[[0]], index=MultiIndex.from_tuples([("a",)], names=["first"])
|
|
)
|
|
result = df.loc["a"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
class TestDepreactedIndexers:
|
|
@pytest.mark.parametrize(
|
|
"key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})]
|
|
)
|
|
def test_getitem_dict_and_set_deprecated(self, key):
|
|
# GH#42825
|
|
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.loc[key]
|
|
|
|
@pytest.mark.parametrize(
|
|
"key",
|
|
[
|
|
{1},
|
|
{1: 1},
|
|
(({1}, 2), "a"),
|
|
(({1: 1}, 2), "a"),
|
|
((1, 2), {"a"}),
|
|
((1, 2), {"a": "a"}),
|
|
],
|
|
)
|
|
def test_getitem_dict_and_set_deprecated_multiindex(self, key):
|
|
# GH#42825
|
|
df = DataFrame(
|
|
[[1, 2], [3, 4]],
|
|
columns=["a", "b"],
|
|
index=MultiIndex.from_tuples([(1, 2), (3, 4)]),
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.loc[key]
|
|
|
|
@pytest.mark.parametrize(
|
|
"key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})]
|
|
)
|
|
def test_setitem_dict_and_set_deprecated(self, key):
|
|
# GH#42825
|
|
df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.loc[key] = 1
|
|
|
|
@pytest.mark.parametrize(
|
|
"key",
|
|
[
|
|
{1},
|
|
{1: 1},
|
|
(({1}, 2), "a"),
|
|
(({1: 1}, 2), "a"),
|
|
((1, 2), {"a"}),
|
|
((1, 2), {"a": "a"}),
|
|
],
|
|
)
|
|
def test_setitem_dict_and_set_deprecated_multiindex(self, key):
|
|
# GH#42825
|
|
df = DataFrame(
|
|
[[1, 2], [3, 4]],
|
|
columns=["a", "b"],
|
|
index=MultiIndex.from_tuples([(1, 2), (3, 4)]),
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.loc[key] = 1
|