8th day of python challenges 111-117
This commit is contained in:
abd.shallal
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from collections import deque
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from datetime import datetime
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import operator
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import numpy as np
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import pytest
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import pandas as pd
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from pandas.tests.frame.common import _check_mixed_float, _check_mixed_int
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import pandas.util.testing as tm
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# -------------------------------------------------------------------
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# Comparisons
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class TestFrameComparisons:
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# Specifically _not_ flex-comparisons
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def test_comparison_invalid(self):
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def check(df, df2):
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for (x, y) in [(df, df2), (df2, df)]:
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# we expect the result to match Series comparisons for
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# == and !=, inequalities should raise
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result = x == y
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expected = pd.DataFrame(
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{col: x[col] == y[col] for col in x.columns},
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index=x.index,
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columns=x.columns,
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)
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tm.assert_frame_equal(result, expected)
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result = x != y
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expected = pd.DataFrame(
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{col: x[col] != y[col] for col in x.columns},
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index=x.index,
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columns=x.columns,
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)
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tm.assert_frame_equal(result, expected)
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with pytest.raises(TypeError):
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x >= y
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with pytest.raises(TypeError):
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x > y
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with pytest.raises(TypeError):
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x < y
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with pytest.raises(TypeError):
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x <= y
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# GH4968
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# invalid date/int comparisons
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df = pd.DataFrame(np.random.randint(10, size=(10, 1)), columns=["a"])
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df["dates"] = pd.date_range("20010101", periods=len(df))
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df2 = df.copy()
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df2["dates"] = df["a"]
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check(df, df2)
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df = pd.DataFrame(np.random.randint(10, size=(10, 2)), columns=["a", "b"])
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df2 = pd.DataFrame(
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{
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"a": pd.date_range("20010101", periods=len(df)),
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"b": pd.date_range("20100101", periods=len(df)),
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}
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)
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check(df, df2)
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def test_timestamp_compare(self):
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# make sure we can compare Timestamps on the right AND left hand side
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# GH#4982
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df = pd.DataFrame(
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{
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"dates1": pd.date_range("20010101", periods=10),
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"dates2": pd.date_range("20010102", periods=10),
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"intcol": np.random.randint(1000000000, size=10),
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"floatcol": np.random.randn(10),
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"stringcol": list(tm.rands(10)),
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}
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)
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df.loc[np.random.rand(len(df)) > 0.5, "dates2"] = pd.NaT
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ops = {"gt": "lt", "lt": "gt", "ge": "le", "le": "ge", "eq": "eq", "ne": "ne"}
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for left, right in ops.items():
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left_f = getattr(operator, left)
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right_f = getattr(operator, right)
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# no nats
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if left in ["eq", "ne"]:
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expected = left_f(df, pd.Timestamp("20010109"))
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result = right_f(pd.Timestamp("20010109"), df)
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tm.assert_frame_equal(result, expected)
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else:
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with pytest.raises(TypeError):
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left_f(df, pd.Timestamp("20010109"))
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with pytest.raises(TypeError):
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right_f(pd.Timestamp("20010109"), df)
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# nats
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expected = left_f(df, pd.Timestamp("nat"))
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result = right_f(pd.Timestamp("nat"), df)
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tm.assert_frame_equal(result, expected)
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def test_mixed_comparison(self):
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# GH#13128, GH#22163 != datetime64 vs non-dt64 should be False,
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# not raise TypeError
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# (this appears to be fixed before GH#22163, not sure when)
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df = pd.DataFrame([["1989-08-01", 1], ["1989-08-01", 2]])
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other = pd.DataFrame([["a", "b"], ["c", "d"]])
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result = df == other
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assert not result.any().any()
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result = df != other
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assert result.all().all()
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def test_df_boolean_comparison_error(self):
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# GH#4576, GH#22880
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# comparing DataFrame against list/tuple with len(obj) matching
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# len(df.columns) is supported as of GH#22800
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df = pd.DataFrame(np.arange(6).reshape((3, 2)))
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expected = pd.DataFrame([[False, False], [True, False], [False, False]])
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result = df == (2, 2)
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tm.assert_frame_equal(result, expected)
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result = df == [2, 2]
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tm.assert_frame_equal(result, expected)
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def test_df_float_none_comparison(self):
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df = pd.DataFrame(
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np.random.randn(8, 3), index=range(8), columns=["A", "B", "C"]
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)
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result = df.__eq__(None)
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assert not result.any().any()
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def test_df_string_comparison(self):
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df = pd.DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}])
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mask_a = df.a > 1
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tm.assert_frame_equal(df[mask_a], df.loc[1:1, :])
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tm.assert_frame_equal(df[-mask_a], df.loc[0:0, :])
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mask_b = df.b == "foo"
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tm.assert_frame_equal(df[mask_b], df.loc[0:0, :])
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tm.assert_frame_equal(df[-mask_b], df.loc[1:1, :])
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class TestFrameFlexComparisons:
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# TODO: test_bool_flex_frame needs a better name
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def test_bool_flex_frame(self):
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data = np.random.randn(5, 3)
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other_data = np.random.randn(5, 3)
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df = pd.DataFrame(data)
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other = pd.DataFrame(other_data)
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ndim_5 = np.ones(df.shape + (1, 3))
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# Unaligned
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def _check_unaligned_frame(meth, op, df, other):
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part_o = other.loc[3:, 1:].copy()
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rs = meth(part_o)
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xp = op(df, part_o.reindex(index=df.index, columns=df.columns))
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tm.assert_frame_equal(rs, xp)
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# DataFrame
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assert df.eq(df).values.all()
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assert not df.ne(df).values.any()
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for op in ["eq", "ne", "gt", "lt", "ge", "le"]:
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f = getattr(df, op)
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o = getattr(operator, op)
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# No NAs
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tm.assert_frame_equal(f(other), o(df, other))
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_check_unaligned_frame(f, o, df, other)
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# ndarray
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tm.assert_frame_equal(f(other.values), o(df, other.values))
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# scalar
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tm.assert_frame_equal(f(0), o(df, 0))
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# NAs
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msg = "Unable to coerce to Series/DataFrame"
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tm.assert_frame_equal(f(np.nan), o(df, np.nan))
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with pytest.raises(ValueError, match=msg):
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f(ndim_5)
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# Series
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def _test_seq(df, idx_ser, col_ser):
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idx_eq = df.eq(idx_ser, axis=0)
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col_eq = df.eq(col_ser)
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idx_ne = df.ne(idx_ser, axis=0)
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col_ne = df.ne(col_ser)
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tm.assert_frame_equal(col_eq, df == pd.Series(col_ser))
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tm.assert_frame_equal(col_eq, -col_ne)
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tm.assert_frame_equal(idx_eq, -idx_ne)
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tm.assert_frame_equal(idx_eq, df.T.eq(idx_ser).T)
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tm.assert_frame_equal(col_eq, df.eq(list(col_ser)))
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tm.assert_frame_equal(idx_eq, df.eq(pd.Series(idx_ser), axis=0))
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tm.assert_frame_equal(idx_eq, df.eq(list(idx_ser), axis=0))
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idx_gt = df.gt(idx_ser, axis=0)
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col_gt = df.gt(col_ser)
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idx_le = df.le(idx_ser, axis=0)
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col_le = df.le(col_ser)
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tm.assert_frame_equal(col_gt, df > pd.Series(col_ser))
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tm.assert_frame_equal(col_gt, -col_le)
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tm.assert_frame_equal(idx_gt, -idx_le)
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tm.assert_frame_equal(idx_gt, df.T.gt(idx_ser).T)
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idx_ge = df.ge(idx_ser, axis=0)
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col_ge = df.ge(col_ser)
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idx_lt = df.lt(idx_ser, axis=0)
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col_lt = df.lt(col_ser)
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tm.assert_frame_equal(col_ge, df >= pd.Series(col_ser))
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tm.assert_frame_equal(col_ge, -col_lt)
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tm.assert_frame_equal(idx_ge, -idx_lt)
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tm.assert_frame_equal(idx_ge, df.T.ge(idx_ser).T)
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idx_ser = pd.Series(np.random.randn(5))
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col_ser = pd.Series(np.random.randn(3))
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_test_seq(df, idx_ser, col_ser)
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# list/tuple
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_test_seq(df, idx_ser.values, col_ser.values)
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# NA
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df.loc[0, 0] = np.nan
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rs = df.eq(df)
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assert not rs.loc[0, 0]
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rs = df.ne(df)
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assert rs.loc[0, 0]
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rs = df.gt(df)
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assert not rs.loc[0, 0]
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rs = df.lt(df)
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assert not rs.loc[0, 0]
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rs = df.ge(df)
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assert not rs.loc[0, 0]
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rs = df.le(df)
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assert not rs.loc[0, 0]
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# complex
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arr = np.array([np.nan, 1, 6, np.nan])
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arr2 = np.array([2j, np.nan, 7, None])
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df = pd.DataFrame({"a": arr})
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df2 = pd.DataFrame({"a": arr2})
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rs = df.gt(df2)
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assert not rs.values.any()
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rs = df.ne(df2)
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assert rs.values.all()
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arr3 = np.array([2j, np.nan, None])
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df3 = pd.DataFrame({"a": arr3})
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rs = df3.gt(2j)
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assert not rs.values.any()
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# corner, dtype=object
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df1 = pd.DataFrame({"col": ["foo", np.nan, "bar"]})
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df2 = pd.DataFrame({"col": ["foo", datetime.now(), "bar"]})
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result = df1.ne(df2)
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exp = pd.DataFrame({"col": [False, True, False]})
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tm.assert_frame_equal(result, exp)
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def test_flex_comparison_nat(self):
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# GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT,
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# and _definitely_ not be NaN
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df = pd.DataFrame([pd.NaT])
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result = df == pd.NaT
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# result.iloc[0, 0] is a np.bool_ object
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assert result.iloc[0, 0].item() is False
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result = df.eq(pd.NaT)
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assert result.iloc[0, 0].item() is False
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result = df != pd.NaT
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assert result.iloc[0, 0].item() is True
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result = df.ne(pd.NaT)
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assert result.iloc[0, 0].item() is True
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@pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
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def test_df_flex_cmp_constant_return_types(self, opname):
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# GH 15077, non-empty DataFrame
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df = pd.DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
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const = 2
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result = getattr(df, opname)(const).dtypes.value_counts()
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tm.assert_series_equal(result, pd.Series([2], index=[np.dtype(bool)]))
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@pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
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def test_df_flex_cmp_constant_return_types_empty(self, opname):
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# GH 15077 empty DataFrame
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df = pd.DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
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const = 2
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empty = df.iloc[:0]
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result = getattr(empty, opname)(const).dtypes.value_counts()
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tm.assert_series_equal(result, pd.Series([2], index=[np.dtype(bool)]))
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# -------------------------------------------------------------------
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# Arithmetic
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class TestFrameFlexArithmetic:
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def test_df_add_td64_columnwise(self):
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# GH 22534 Check that column-wise addition broadcasts correctly
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dti = pd.date_range("2016-01-01", periods=10)
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tdi = pd.timedelta_range("1", periods=10)
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tser = pd.Series(tdi)
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df = pd.DataFrame({0: dti, 1: tdi})
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result = df.add(tser, axis=0)
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expected = pd.DataFrame({0: dti + tdi, 1: tdi + tdi})
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tm.assert_frame_equal(result, expected)
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def test_df_add_flex_filled_mixed_dtypes(self):
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# GH 19611
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dti = pd.date_range("2016-01-01", periods=3)
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ser = pd.Series(["1 Day", "NaT", "2 Days"], dtype="timedelta64[ns]")
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df = pd.DataFrame({"A": dti, "B": ser})
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other = pd.DataFrame({"A": ser, "B": ser})
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fill = pd.Timedelta(days=1).to_timedelta64()
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result = df.add(other, fill_value=fill)
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expected = pd.DataFrame(
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{
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"A": pd.Series(
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["2016-01-02", "2016-01-03", "2016-01-05"], dtype="datetime64[ns]"
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),
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"B": ser * 2,
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}
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)
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tm.assert_frame_equal(result, expected)
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def test_arith_flex_frame(
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self, all_arithmetic_operators, float_frame, mixed_float_frame
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):
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# one instance of parametrized fixture
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op = all_arithmetic_operators
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def f(x, y):
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# r-versions not in operator-stdlib; get op without "r" and invert
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if op.startswith("__r"):
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return getattr(operator, op.replace("__r", "__"))(y, x)
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return getattr(operator, op)(x, y)
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result = getattr(float_frame, op)(2 * float_frame)
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expected = f(float_frame, 2 * float_frame)
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tm.assert_frame_equal(result, expected)
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# vs mix float
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result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
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expected = f(mixed_float_frame, 2 * mixed_float_frame)
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tm.assert_frame_equal(result, expected)
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_check_mixed_float(result, dtype=dict(C=None))
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@pytest.mark.parametrize("op", ["__add__", "__sub__", "__mul__"])
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def test_arith_flex_frame_mixed(
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self, op, int_frame, mixed_int_frame, mixed_float_frame
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):
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f = getattr(operator, op)
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# vs mix int
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result = getattr(mixed_int_frame, op)(2 + mixed_int_frame)
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expected = f(mixed_int_frame, 2 + mixed_int_frame)
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# no overflow in the uint
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dtype = None
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if op in ["__sub__"]:
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dtype = dict(B="uint64", C=None)
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elif op in ["__add__", "__mul__"]:
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dtype = dict(C=None)
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tm.assert_frame_equal(result, expected)
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_check_mixed_int(result, dtype=dtype)
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# vs mix float
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result = getattr(mixed_float_frame, op)(2 * mixed_float_frame)
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expected = f(mixed_float_frame, 2 * mixed_float_frame)
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tm.assert_frame_equal(result, expected)
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_check_mixed_float(result, dtype=dict(C=None))
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# vs plain int
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result = getattr(int_frame, op)(2 * int_frame)
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expected = f(int_frame, 2 * int_frame)
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tm.assert_frame_equal(result, expected)
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def test_arith_flex_frame_raise(self, all_arithmetic_operators, float_frame):
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# one instance of parametrized fixture
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op = all_arithmetic_operators
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# Check that arrays with dim >= 3 raise
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for dim in range(3, 6):
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arr = np.ones((1,) * dim)
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msg = "Unable to coerce to Series/DataFrame"
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with pytest.raises(ValueError, match=msg):
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getattr(float_frame, op)(arr)
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def test_arith_flex_frame_corner(self, float_frame):
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const_add = float_frame.add(1)
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tm.assert_frame_equal(const_add, float_frame + 1)
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# corner cases
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result = float_frame.add(float_frame[:0])
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tm.assert_frame_equal(result, float_frame * np.nan)
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result = float_frame[:0].add(float_frame)
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tm.assert_frame_equal(result, float_frame * np.nan)
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with pytest.raises(NotImplementedError, match="fill_value"):
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float_frame.add(float_frame.iloc[0], fill_value=3)
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with pytest.raises(NotImplementedError, match="fill_value"):
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float_frame.add(float_frame.iloc[0], axis="index", fill_value=3)
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def test_arith_flex_series(self, simple_frame):
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df = simple_frame
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row = df.xs("a")
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col = df["two"]
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# after arithmetic refactor, add truediv here
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ops = ["add", "sub", "mul", "mod"]
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for op in ops:
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f = getattr(df, op)
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op = getattr(operator, op)
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tm.assert_frame_equal(f(row), op(df, row))
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tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T)
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# special case for some reason
|
||||
tm.assert_frame_equal(df.add(row, axis=None), df + row)
|
||||
|
||||
# cases which will be refactored after big arithmetic refactor
|
||||
tm.assert_frame_equal(df.div(row), df / row)
|
||||
tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)
|
||||
|
||||
# broadcasting issue in GH 7325
|
||||
df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype="int64")
|
||||
expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
|
||||
result = df.div(df[0], axis="index")
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
df = pd.DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype="float64")
|
||||
expected = pd.DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
|
||||
result = df.div(df[0], axis="index")
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_arith_flex_zero_len_raises(self):
|
||||
# GH 19522 passing fill_value to frame flex arith methods should
|
||||
# raise even in the zero-length special cases
|
||||
ser_len0 = pd.Series([])
|
||||
df_len0 = pd.DataFrame(columns=["A", "B"])
|
||||
df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
|
||||
|
||||
with pytest.raises(NotImplementedError, match="fill_value"):
|
||||
df.add(ser_len0, fill_value="E")
|
||||
|
||||
with pytest.raises(NotImplementedError, match="fill_value"):
|
||||
df_len0.sub(df["A"], axis=None, fill_value=3)
|
||||
|
||||
|
||||
class TestFrameArithmetic:
|
||||
def test_df_add_2d_array_rowlike_broadcasts(self):
|
||||
# GH#23000
|
||||
arr = np.arange(6).reshape(3, 2)
|
||||
df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
|
||||
|
||||
rowlike = arr[[1], :] # shape --> (1, ncols)
|
||||
assert rowlike.shape == (1, df.shape[1])
|
||||
|
||||
expected = pd.DataFrame(
|
||||
[[2, 4], [4, 6], [6, 8]],
|
||||
columns=df.columns,
|
||||
index=df.index,
|
||||
# specify dtype explicitly to avoid failing
|
||||
# on 32bit builds
|
||||
dtype=arr.dtype,
|
||||
)
|
||||
result = df + rowlike
|
||||
tm.assert_frame_equal(result, expected)
|
||||
result = rowlike + df
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_df_add_2d_array_collike_broadcasts(self):
|
||||
# GH#23000
|
||||
arr = np.arange(6).reshape(3, 2)
|
||||
df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
|
||||
|
||||
collike = arr[:, [1]] # shape --> (nrows, 1)
|
||||
assert collike.shape == (df.shape[0], 1)
|
||||
|
||||
expected = pd.DataFrame(
|
||||
[[1, 2], [5, 6], [9, 10]],
|
||||
columns=df.columns,
|
||||
index=df.index,
|
||||
# specify dtype explicitly to avoid failing
|
||||
# on 32bit builds
|
||||
dtype=arr.dtype,
|
||||
)
|
||||
result = df + collike
|
||||
tm.assert_frame_equal(result, expected)
|
||||
result = collike + df
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_df_arith_2d_array_rowlike_broadcasts(self, all_arithmetic_operators):
|
||||
# GH#23000
|
||||
opname = all_arithmetic_operators
|
||||
|
||||
arr = np.arange(6).reshape(3, 2)
|
||||
df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
|
||||
|
||||
rowlike = arr[[1], :] # shape --> (1, ncols)
|
||||
assert rowlike.shape == (1, df.shape[1])
|
||||
|
||||
exvals = [
|
||||
getattr(df.loc["A"], opname)(rowlike.squeeze()),
|
||||
getattr(df.loc["B"], opname)(rowlike.squeeze()),
|
||||
getattr(df.loc["C"], opname)(rowlike.squeeze()),
|
||||
]
|
||||
|
||||
expected = pd.DataFrame(exvals, columns=df.columns, index=df.index)
|
||||
|
||||
if opname in ["__rmod__", "__rfloordiv__"]:
|
||||
# exvals will have dtypes [f8, i8, i8] so expected will be
|
||||
# all-f8, but the DataFrame operation will return mixed dtypes
|
||||
# use exvals[-1].dtype instead of "i8" for compat with 32-bit
|
||||
# systems/pythons
|
||||
expected[False] = expected[False].astype(exvals[-1].dtype)
|
||||
|
||||
result = getattr(df, opname)(rowlike)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_df_arith_2d_array_collike_broadcasts(self, all_arithmetic_operators):
|
||||
# GH#23000
|
||||
opname = all_arithmetic_operators
|
||||
|
||||
arr = np.arange(6).reshape(3, 2)
|
||||
df = pd.DataFrame(arr, columns=[True, False], index=["A", "B", "C"])
|
||||
|
||||
collike = arr[:, [1]] # shape --> (nrows, 1)
|
||||
assert collike.shape == (df.shape[0], 1)
|
||||
|
||||
exvals = {
|
||||
True: getattr(df[True], opname)(collike.squeeze()),
|
||||
False: getattr(df[False], opname)(collike.squeeze()),
|
||||
}
|
||||
|
||||
dtype = None
|
||||
if opname in ["__rmod__", "__rfloordiv__"]:
|
||||
# Series ops may return mixed int/float dtypes in cases where
|
||||
# DataFrame op will return all-float. So we upcast `expected`
|
||||
dtype = np.common_type(*[x.values for x in exvals.values()])
|
||||
|
||||
expected = pd.DataFrame(exvals, columns=df.columns, index=df.index, dtype=dtype)
|
||||
|
||||
result = getattr(df, opname)(collike)
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_df_bool_mul_int(self):
|
||||
# GH 22047, GH 22163 multiplication by 1 should result in int dtype,
|
||||
# not object dtype
|
||||
df = pd.DataFrame([[False, True], [False, False]])
|
||||
result = df * 1
|
||||
|
||||
# On appveyor this comes back as np.int32 instead of np.int64,
|
||||
# so we check dtype.kind instead of just dtype
|
||||
kinds = result.dtypes.apply(lambda x: x.kind)
|
||||
assert (kinds == "i").all()
|
||||
|
||||
result = 1 * df
|
||||
kinds = result.dtypes.apply(lambda x: x.kind)
|
||||
assert (kinds == "i").all()
|
||||
|
||||
def test_arith_mixed(self):
|
||||
|
||||
left = pd.DataFrame({"A": ["a", "b", "c"], "B": [1, 2, 3]})
|
||||
|
||||
result = left + left
|
||||
expected = pd.DataFrame({"A": ["aa", "bb", "cc"], "B": [2, 4, 6]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_arith_getitem_commute(self):
|
||||
df = pd.DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]})
|
||||
|
||||
def _test_op(df, op):
|
||||
result = op(df, 1)
|
||||
|
||||
if not df.columns.is_unique:
|
||||
raise ValueError("Only unique columns supported by this test")
|
||||
|
||||
for col in result.columns:
|
||||
tm.assert_series_equal(result[col], op(df[col], 1))
|
||||
|
||||
_test_op(df, operator.add)
|
||||
_test_op(df, operator.sub)
|
||||
_test_op(df, operator.mul)
|
||||
_test_op(df, operator.truediv)
|
||||
_test_op(df, operator.floordiv)
|
||||
_test_op(df, operator.pow)
|
||||
|
||||
_test_op(df, lambda x, y: y + x)
|
||||
_test_op(df, lambda x, y: y - x)
|
||||
_test_op(df, lambda x, y: y * x)
|
||||
_test_op(df, lambda x, y: y / x)
|
||||
_test_op(df, lambda x, y: y ** x)
|
||||
|
||||
_test_op(df, lambda x, y: x + y)
|
||||
_test_op(df, lambda x, y: x - y)
|
||||
_test_op(df, lambda x, y: x * y)
|
||||
_test_op(df, lambda x, y: x / y)
|
||||
_test_op(df, lambda x, y: x ** y)
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"values", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3), deque([1, 2])]
|
||||
)
|
||||
def test_arith_alignment_non_pandas_object(self, values):
|
||||
# GH#17901
|
||||
df = pd.DataFrame({"A": [1, 1], "B": [1, 1]})
|
||||
expected = pd.DataFrame({"A": [2, 2], "B": [3, 3]})
|
||||
result = df + values
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
def test_arith_non_pandas_object(self):
|
||||
df = pd.DataFrame(
|
||||
np.arange(1, 10, dtype="f8").reshape(3, 3),
|
||||
columns=["one", "two", "three"],
|
||||
index=["a", "b", "c"],
|
||||
)
|
||||
|
||||
val1 = df.xs("a").values
|
||||
added = pd.DataFrame(df.values + val1, index=df.index, columns=df.columns)
|
||||
tm.assert_frame_equal(df + val1, added)
|
||||
|
||||
added = pd.DataFrame((df.values.T + val1).T, index=df.index, columns=df.columns)
|
||||
tm.assert_frame_equal(df.add(val1, axis=0), added)
|
||||
|
||||
val2 = list(df["two"])
|
||||
|
||||
added = pd.DataFrame(df.values + val2, index=df.index, columns=df.columns)
|
||||
tm.assert_frame_equal(df + val2, added)
|
||||
|
||||
added = pd.DataFrame((df.values.T + val2).T, index=df.index, columns=df.columns)
|
||||
tm.assert_frame_equal(df.add(val2, axis="index"), added)
|
||||
|
||||
val3 = np.random.rand(*df.shape)
|
||||
added = pd.DataFrame(df.values + val3, index=df.index, columns=df.columns)
|
||||
tm.assert_frame_equal(df.add(val3), added)
|
||||
Reference in New Issue
Block a user