252 lines
8.3 KiB
Python
252 lines
8.3 KiB
Python
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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 import DataFrame, Index, Series, concat, date_range
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import pandas._testing as tm
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class TestEmptyConcat:
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def test_handle_empty_objects(self, sort):
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df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
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baz = df[:5].copy()
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baz["foo"] = "bar"
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empty = df[5:5]
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frames = [baz, empty, empty, df[5:]]
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concatted = concat(frames, axis=0, sort=sort)
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expected = df.reindex(columns=["a", "b", "c", "d", "foo"])
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expected["foo"] = expected["foo"].astype("O")
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expected.loc[0:4, "foo"] = "bar"
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tm.assert_frame_equal(concatted, expected)
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# empty as first element with time series
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# GH3259
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df = DataFrame(
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{"A": range(10000)}, index=date_range("20130101", periods=10000, freq="s")
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)
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empty = DataFrame()
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result = concat([df, empty], axis=1)
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tm.assert_frame_equal(result, df)
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result = concat([empty, df], axis=1)
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tm.assert_frame_equal(result, df)
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result = concat([df, empty])
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tm.assert_frame_equal(result, df)
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result = concat([empty, df])
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tm.assert_frame_equal(result, df)
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def test_concat_empty_series(self):
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# GH 11082
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s1 = Series([1, 2, 3], name="x")
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s2 = Series(name="y", dtype="float64")
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res = pd.concat([s1, s2], axis=1)
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exp = DataFrame(
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{"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
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index=Index([0, 1, 2], dtype="O"),
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)
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tm.assert_frame_equal(res, exp)
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s1 = Series([1, 2, 3], name="x")
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s2 = Series(name="y", dtype="float64")
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res = pd.concat([s1, s2], axis=0)
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# name will be reset
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exp = Series([1, 2, 3])
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tm.assert_series_equal(res, exp)
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# empty Series with no name
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s1 = Series([1, 2, 3], name="x")
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s2 = Series(name=None, dtype="float64")
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res = pd.concat([s1, s2], axis=1)
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exp = DataFrame(
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{"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
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columns=["x", 0],
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index=Index([0, 1, 2], dtype="O"),
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)
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tm.assert_frame_equal(res, exp)
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@pytest.mark.parametrize("tz", [None, "UTC"])
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@pytest.mark.parametrize("values", [[], [1, 2, 3]])
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def test_concat_empty_series_timelike(self, tz, values):
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# GH 18447
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first = Series([], dtype="M8[ns]").dt.tz_localize(tz)
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dtype = None if values else np.float64
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second = Series(values, dtype=dtype)
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expected = DataFrame(
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{
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0: Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
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1: values,
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}
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)
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result = concat([first, second], axis=1)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"left,right,expected",
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[
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# booleans
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(np.bool_, np.int32, np.int32),
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(np.bool_, np.float32, np.object_),
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# datetime-like
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("m8[ns]", np.bool_, np.object_),
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("m8[ns]", np.int64, np.object_),
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("M8[ns]", np.bool_, np.object_),
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("M8[ns]", np.int64, np.object_),
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# categorical
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("category", "category", "category"),
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("category", "object", "object"),
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],
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)
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def test_concat_empty_series_dtypes(self, left, right, expected):
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result = pd.concat([Series(dtype=left), Series(dtype=right)])
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assert result.dtype == expected
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@pytest.mark.parametrize(
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"dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
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)
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def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
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dtype = np.dtype(dtype)
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result = pd.concat([Series(dtype=dtype)])
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assert result.dtype == dtype
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result = pd.concat([Series(dtype=dtype), Series(dtype=dtype)])
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assert result.dtype == dtype
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def test_concat_empty_series_dtypes_roundtrips(self):
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# round-tripping with self & like self
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dtypes = map(np.dtype, ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"])
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def int_result_type(dtype, dtype2):
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typs = {dtype.kind, dtype2.kind}
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if not len(typs - {"i", "u", "b"}) and (
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dtype.kind == "i" or dtype2.kind == "i"
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):
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return "i"
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elif not len(typs - {"u", "b"}) and (
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dtype.kind == "u" or dtype2.kind == "u"
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):
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return "u"
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return None
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def float_result_type(dtype, dtype2):
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typs = {dtype.kind, dtype2.kind}
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if not len(typs - {"f", "i", "u"}) and (
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dtype.kind == "f" or dtype2.kind == "f"
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):
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return "f"
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return None
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def get_result_type(dtype, dtype2):
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result = float_result_type(dtype, dtype2)
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if result is not None:
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return result
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result = int_result_type(dtype, dtype2)
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if result is not None:
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return result
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return "O"
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for dtype in dtypes:
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for dtype2 in dtypes:
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if dtype == dtype2:
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continue
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expected = get_result_type(dtype, dtype2)
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result = pd.concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
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assert result.kind == expected
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def test_concat_empty_series_dtypes_triple(self):
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assert (
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pd.concat(
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[Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
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).dtype
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== np.object_
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)
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def test_concat_empty_series_dtype_category_with_array(self):
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# GH#18515
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assert (
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pd.concat(
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[Series(np.array([]), dtype="category"), Series(dtype="float64")]
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).dtype
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== "float64"
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)
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def test_concat_empty_series_dtypes_sparse(self):
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result = pd.concat(
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[
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Series(dtype="float64").astype("Sparse"),
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Series(dtype="float64").astype("Sparse"),
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]
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)
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assert result.dtype == "Sparse[float64]"
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result = pd.concat(
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[Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
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)
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# TODO: release-note: concat sparse dtype
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expected = pd.SparseDtype(np.float64)
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assert result.dtype == expected
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result = pd.concat(
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[Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
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)
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# TODO: release-note: concat sparse dtype
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expected = pd.SparseDtype("object")
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assert result.dtype == expected
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def test_concat_empty_df_object_dtype(self):
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# GH 9149
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df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
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df_2 = DataFrame(columns=df_1.columns)
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result = pd.concat([df_1, df_2], axis=0)
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expected = df_1.astype(object)
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tm.assert_frame_equal(result, expected)
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def test_concat_empty_dataframe_dtypes(self):
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df = DataFrame(columns=list("abc"))
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df["a"] = df["a"].astype(np.bool_)
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df["b"] = df["b"].astype(np.int32)
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df["c"] = df["c"].astype(np.float64)
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result = pd.concat([df, df])
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assert result["a"].dtype == np.bool_
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assert result["b"].dtype == np.int32
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assert result["c"].dtype == np.float64
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result = pd.concat([df, df.astype(np.float64)])
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assert result["a"].dtype == np.object_
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assert result["b"].dtype == np.float64
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assert result["c"].dtype == np.float64
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def test_concat_inner_join_empty(self):
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# GH 15328
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df_empty = DataFrame()
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df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
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df_expected = DataFrame({"a": []}, index=[], dtype="int64")
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for how, expected in [("inner", df_expected), ("outer", df_a)]:
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result = pd.concat([df_a, df_empty], axis=1, join=how)
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tm.assert_frame_equal(result, expected)
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def test_empty_dtype_coerce(self):
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# xref to #12411
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# xref to #12045
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# xref to #11594
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# see below
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# 10571
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df1 = DataFrame(data=[[1, None], [2, None]], columns=["a", "b"])
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df2 = DataFrame(data=[[3, None], [4, None]], columns=["a", "b"])
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result = concat([df1, df2])
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expected = df1.dtypes
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tm.assert_series_equal(result.dtypes, expected)
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