import numpy as np
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import pytest
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from pandas import (
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Categorical,
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DataFrame,
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Series,
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_testing as tm,
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concat,
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read_hdf,
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)
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from pandas.tests.io.pytables.common import (
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_maybe_remove,
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ensure_clean_store,
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)
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pytestmark = [
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pytest.mark.single_cpu,
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]
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def test_categorical(setup_path):
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with ensure_clean_store(setup_path) as store:
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# Basic
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_maybe_remove(store, "s")
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s = Series(
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Categorical(
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["a", "b", "b", "a", "a", "c"],
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categories=["a", "b", "c", "d"],
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ordered=False,
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)
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)
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store.append("s", s, format="table")
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result = store.select("s")
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tm.assert_series_equal(s, result)
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_maybe_remove(store, "s_ordered")
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s = Series(
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Categorical(
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["a", "b", "b", "a", "a", "c"],
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categories=["a", "b", "c", "d"],
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ordered=True,
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)
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)
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store.append("s_ordered", s, format="table")
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result = store.select("s_ordered")
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tm.assert_series_equal(s, result)
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_maybe_remove(store, "df")
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df = DataFrame({"s": s, "vals": [1, 2, 3, 4, 5, 6]})
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store.append("df", df, format="table")
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result = store.select("df")
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tm.assert_frame_equal(result, df)
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# Dtypes
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_maybe_remove(store, "si")
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s = Series([1, 1, 2, 2, 3, 4, 5]).astype("category")
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store.append("si", s)
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result = store.select("si")
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tm.assert_series_equal(result, s)
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_maybe_remove(store, "si2")
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s = Series([1, 1, np.nan, 2, 3, 4, 5]).astype("category")
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store.append("si2", s)
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result = store.select("si2")
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tm.assert_series_equal(result, s)
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# Multiple
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_maybe_remove(store, "df2")
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df2 = df.copy()
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df2["s2"] = Series(list("abcdefg")).astype("category")
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store.append("df2", df2)
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result = store.select("df2")
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tm.assert_frame_equal(result, df2)
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# Make sure the metadata is OK
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info = store.info()
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assert "/df2 " in info
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# df2._mgr.blocks[0] and df2._mgr.blocks[2] are Categorical
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assert "/df2/meta/values_block_0/meta" in info
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assert "/df2/meta/values_block_2/meta" in info
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# unordered
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_maybe_remove(store, "s2")
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s = Series(
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Categorical(
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["a", "b", "b", "a", "a", "c"],
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categories=["a", "b", "c", "d"],
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ordered=False,
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)
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)
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store.append("s2", s, format="table")
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result = store.select("s2")
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tm.assert_series_equal(result, s)
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# Query
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_maybe_remove(store, "df3")
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store.append("df3", df, data_columns=["s"])
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expected = df[df.s.isin(["b", "c"])]
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result = store.select("df3", where=['s in ["b","c"]'])
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tm.assert_frame_equal(result, expected)
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expected = df[df.s.isin(["b", "c"])]
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result = store.select("df3", where=['s = ["b","c"]'])
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tm.assert_frame_equal(result, expected)
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expected = df[df.s.isin(["d"])]
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result = store.select("df3", where=['s in ["d"]'])
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tm.assert_frame_equal(result, expected)
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expected = df[df.s.isin(["f"])]
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result = store.select("df3", where=['s in ["f"]'])
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tm.assert_frame_equal(result, expected)
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# Appending with same categories is ok
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store.append("df3", df)
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df = concat([df, df])
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expected = df[df.s.isin(["b", "c"])]
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result = store.select("df3", where=['s in ["b","c"]'])
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tm.assert_frame_equal(result, expected)
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# Appending must have the same categories
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df3 = df.copy()
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df3["s"] = df3["s"].cat.remove_unused_categories()
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msg = "cannot append a categorical with different categories to the existing"
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with pytest.raises(ValueError, match=msg):
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store.append("df3", df3)
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# Remove, and make sure meta data is removed (its a recursive
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# removal so should be).
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result = store.select("df3/meta/s/meta")
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assert result is not None
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store.remove("df3")
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with pytest.raises(
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KeyError, match="'No object named df3/meta/s/meta in the file'"
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):
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store.select("df3/meta/s/meta")
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def test_categorical_conversion(tmp_path, setup_path):
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# GH13322
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# Check that read_hdf with categorical columns doesn't return rows if
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# where criteria isn't met.
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obsids = ["ESP_012345_6789", "ESP_987654_3210"]
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imgids = ["APF00006np", "APF0001imm"]
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data = [4.3, 9.8]
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# Test without categories
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df = DataFrame({"obsids": obsids, "imgids": imgids, "data": data})
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# We are expecting an empty DataFrame matching types of df
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expected = df.iloc[[], :]
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path = tmp_path / setup_path
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df.to_hdf(path, "df", format="table", data_columns=True)
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result = read_hdf(path, "df", where="obsids=B")
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tm.assert_frame_equal(result, expected)
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# Test with categories
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df.obsids = df.obsids.astype("category")
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df.imgids = df.imgids.astype("category")
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# We are expecting an empty DataFrame matching types of df
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expected = df.iloc[[], :]
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path = tmp_path / setup_path
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df.to_hdf(path, "df", format="table", data_columns=True)
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result = read_hdf(path, "df", where="obsids=B")
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tm.assert_frame_equal(result, expected)
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def test_categorical_nan_only_columns(tmp_path, setup_path):
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# GH18413
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# Check that read_hdf with categorical columns with NaN-only values can
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# be read back.
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df = DataFrame(
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{
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"a": ["a", "b", "c", np.nan],
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"b": [np.nan, np.nan, np.nan, np.nan],
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"c": [1, 2, 3, 4],
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"d": Series([None] * 4, dtype=object),
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}
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)
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df["a"] = df.a.astype("category")
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df["b"] = df.b.astype("category")
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df["d"] = df.b.astype("category")
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expected = df
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path = tmp_path / setup_path
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df.to_hdf(path, "df", format="table", data_columns=True)
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result = read_hdf(path, "df")
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"where, df, expected",
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[
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('col=="q"', DataFrame({"col": ["a", "b", "s"]}), DataFrame({"col": []})),
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('col=="a"', DataFrame({"col": ["a", "b", "s"]}), DataFrame({"col": ["a"]})),
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],
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)
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def test_convert_value(
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tmp_path, setup_path, where: str, df: DataFrame, expected: DataFrame
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):
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# GH39420
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# Check that read_hdf with categorical columns can filter by where condition.
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df.col = df.col.astype("category")
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max_widths = {"col": 1}
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categorical_values = sorted(df.col.unique())
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expected.col = expected.col.astype("category")
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expected.col = expected.col.cat.set_categories(categorical_values)
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path = tmp_path / setup_path
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df.to_hdf(path, "df", format="table", min_itemsize=max_widths)
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result = read_hdf(path, where=where)
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tm.assert_frame_equal(result, expected)
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