346 lines
11 KiB
Python
346 lines
11 KiB
Python
from pathlib import Path
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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.tslibs import Timestamp
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from pandas.compat import is_platform_windows
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import pandas as pd
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from pandas import (
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DataFrame,
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HDFStore,
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Index,
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Series,
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_testing as tm,
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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_path,
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ensure_clean_store,
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)
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from pandas.util import _test_decorators as td
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from pandas.io.pytables import TableIterator
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pytestmark = pytest.mark.single_cpu
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def test_read_missing_key_close_store(setup_path):
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# GH 25766
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with ensure_clean_path(setup_path) as path:
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df = DataFrame({"a": range(2), "b": range(2)})
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df.to_hdf(path, "k1")
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with pytest.raises(KeyError, match="'No object named k2 in the file'"):
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read_hdf(path, "k2")
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# smoke test to test that file is properly closed after
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# read with KeyError before another write
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df.to_hdf(path, "k2")
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def test_read_missing_key_opened_store(setup_path):
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# GH 28699
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with ensure_clean_path(setup_path) as path:
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df = DataFrame({"a": range(2), "b": range(2)})
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df.to_hdf(path, "k1")
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with HDFStore(path, "r") as store:
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with pytest.raises(KeyError, match="'No object named k2 in the file'"):
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read_hdf(store, "k2")
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# Test that the file is still open after a KeyError and that we can
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# still read from it.
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read_hdf(store, "k1")
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def test_read_column(setup_path):
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df = tm.makeTimeDataFrame()
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with ensure_clean_store(setup_path) as store:
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_maybe_remove(store, "df")
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# GH 17912
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# HDFStore.select_column should raise a KeyError
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# exception if the key is not a valid store
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with pytest.raises(KeyError, match="No object named df in the file"):
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store.select_column("df", "index")
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store.append("df", df)
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# error
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with pytest.raises(
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KeyError, match=re.escape("'column [foo] not found in the table'")
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):
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store.select_column("df", "foo")
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msg = re.escape("select_column() got an unexpected keyword argument 'where'")
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with pytest.raises(TypeError, match=msg):
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store.select_column("df", "index", where=["index>5"])
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# valid
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result = store.select_column("df", "index")
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tm.assert_almost_equal(result.values, Series(df.index).values)
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assert isinstance(result, Series)
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# not a data indexable column
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msg = re.escape(
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"column [values_block_0] can not be extracted individually; "
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"it is not data indexable"
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)
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with pytest.raises(ValueError, match=msg):
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store.select_column("df", "values_block_0")
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# a data column
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df2 = df.copy()
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df2["string"] = "foo"
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store.append("df2", df2, data_columns=["string"])
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result = store.select_column("df2", "string")
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tm.assert_almost_equal(result.values, df2["string"].values)
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# a data column with NaNs, result excludes the NaNs
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df3 = df.copy()
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df3["string"] = "foo"
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df3.loc[df3.index[4:6], "string"] = np.nan
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store.append("df3", df3, data_columns=["string"])
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result = store.select_column("df3", "string")
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tm.assert_almost_equal(result.values, df3["string"].values)
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# start/stop
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result = store.select_column("df3", "string", start=2)
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tm.assert_almost_equal(result.values, df3["string"].values[2:])
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result = store.select_column("df3", "string", start=-2)
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tm.assert_almost_equal(result.values, df3["string"].values[-2:])
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result = store.select_column("df3", "string", stop=2)
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tm.assert_almost_equal(result.values, df3["string"].values[:2])
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result = store.select_column("df3", "string", stop=-2)
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tm.assert_almost_equal(result.values, df3["string"].values[:-2])
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result = store.select_column("df3", "string", start=2, stop=-2)
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tm.assert_almost_equal(result.values, df3["string"].values[2:-2])
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result = store.select_column("df3", "string", start=-2, stop=2)
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tm.assert_almost_equal(result.values, df3["string"].values[-2:2])
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# GH 10392 - make sure column name is preserved
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df4 = DataFrame({"A": np.random.randn(10), "B": "foo"})
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store.append("df4", df4, data_columns=True)
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expected = df4["B"]
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result = store.select_column("df4", "B")
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tm.assert_series_equal(result, expected)
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def test_pytables_native_read(datapath, setup_path):
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with ensure_clean_store(
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datapath("io", "data", "legacy_hdf/pytables_native.h5"), mode="r"
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) as store:
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d2 = store["detector/readout"]
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assert isinstance(d2, DataFrame)
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@pytest.mark.skipif(is_platform_windows(), reason="native2 read fails oddly on windows")
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def test_pytables_native2_read(datapath, setup_path):
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with ensure_clean_store(
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datapath("io", "data", "legacy_hdf", "pytables_native2.h5"), mode="r"
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) as store:
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str(store)
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d1 = store["detector"]
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assert isinstance(d1, DataFrame)
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def test_legacy_table_fixed_format_read_py2(datapath, setup_path):
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# GH 24510
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# legacy table with fixed format written in Python 2
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with ensure_clean_store(
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datapath("io", "data", "legacy_hdf", "legacy_table_fixed_py2.h5"), mode="r"
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) as store:
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result = store.select("df")
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expected = DataFrame(
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[[1, 2, 3, "D"]],
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columns=["A", "B", "C", "D"],
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index=Index(["ABC"], name="INDEX_NAME"),
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)
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tm.assert_frame_equal(expected, result)
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def test_legacy_table_fixed_format_read_datetime_py2(datapath, setup_path):
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# GH 31750
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# legacy table with fixed format and datetime64 column written in Python 2
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with ensure_clean_store(
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datapath("io", "data", "legacy_hdf", "legacy_table_fixed_datetime_py2.h5"),
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mode="r",
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) as store:
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result = store.select("df")
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expected = DataFrame(
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[[Timestamp("2020-02-06T18:00")]],
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columns=["A"],
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index=Index(["date"]),
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)
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tm.assert_frame_equal(expected, result)
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def test_legacy_table_read_py2(datapath, setup_path):
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# issue: 24925
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# legacy table written in Python 2
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with ensure_clean_store(
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datapath("io", "data", "legacy_hdf", "legacy_table_py2.h5"), mode="r"
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) as store:
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result = store.select("table")
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expected = DataFrame({"a": ["a", "b"], "b": [2, 3]})
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tm.assert_frame_equal(expected, result)
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def test_read_hdf_open_store(setup_path):
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# GH10330
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# No check for non-string path_or-buf, and no test of open store
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df = DataFrame(np.random.rand(4, 5), index=list("abcd"), columns=list("ABCDE"))
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df.index.name = "letters"
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df = df.set_index(keys="E", append=True)
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with ensure_clean_path(setup_path) as path:
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df.to_hdf(path, "df", mode="w")
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direct = read_hdf(path, "df")
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store = HDFStore(path, mode="r")
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indirect = read_hdf(store, "df")
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tm.assert_frame_equal(direct, indirect)
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assert store.is_open
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store.close()
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def test_read_hdf_iterator(setup_path):
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df = DataFrame(np.random.rand(4, 5), index=list("abcd"), columns=list("ABCDE"))
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df.index.name = "letters"
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df = df.set_index(keys="E", append=True)
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with ensure_clean_path(setup_path) as path:
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df.to_hdf(path, "df", mode="w", format="t")
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direct = read_hdf(path, "df")
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iterator = read_hdf(path, "df", iterator=True)
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assert isinstance(iterator, TableIterator)
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indirect = next(iterator.__iter__())
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tm.assert_frame_equal(direct, indirect)
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iterator.store.close()
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def test_read_nokey(setup_path):
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# GH10443
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df = DataFrame(np.random.rand(4, 5), index=list("abcd"), columns=list("ABCDE"))
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# Categorical dtype not supported for "fixed" format. So no need
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# to test with that dtype in the dataframe here.
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with ensure_clean_path(setup_path) as path:
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df.to_hdf(path, "df", mode="a")
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reread = read_hdf(path)
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tm.assert_frame_equal(df, reread)
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df.to_hdf(path, "df2", mode="a")
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msg = "key must be provided when HDF5 file contains multiple datasets."
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with pytest.raises(ValueError, match=msg):
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read_hdf(path)
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def test_read_nokey_table(setup_path):
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# GH13231
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df = DataFrame({"i": range(5), "c": Series(list("abacd"), dtype="category")})
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with ensure_clean_path(setup_path) as path:
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df.to_hdf(path, "df", mode="a", format="table")
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reread = read_hdf(path)
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tm.assert_frame_equal(df, reread)
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df.to_hdf(path, "df2", mode="a", format="table")
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msg = "key must be provided when HDF5 file contains multiple datasets."
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with pytest.raises(ValueError, match=msg):
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read_hdf(path)
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def test_read_nokey_empty(setup_path):
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with ensure_clean_path(setup_path) as path:
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store = HDFStore(path)
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store.close()
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msg = re.escape(
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"Dataset(s) incompatible with Pandas data types, not table, or no "
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"datasets found in HDF5 file."
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)
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with pytest.raises(ValueError, match=msg):
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read_hdf(path)
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def test_read_from_pathlib_path(setup_path):
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# GH11773
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expected = DataFrame(
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np.random.rand(4, 5), index=list("abcd"), columns=list("ABCDE")
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)
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with ensure_clean_path(setup_path) as filename:
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path_obj = Path(filename)
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expected.to_hdf(path_obj, "df", mode="a")
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actual = read_hdf(path_obj, "df")
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tm.assert_frame_equal(expected, actual)
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@td.skip_if_no("py.path")
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def test_read_from_py_localpath(setup_path):
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# GH11773
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from py.path import local as LocalPath
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expected = DataFrame(
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np.random.rand(4, 5), index=list("abcd"), columns=list("ABCDE")
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)
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with ensure_clean_path(setup_path) as filename:
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path_obj = LocalPath(filename)
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expected.to_hdf(path_obj, "df", mode="a")
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actual = read_hdf(path_obj, "df")
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tm.assert_frame_equal(expected, actual)
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@pytest.mark.parametrize("format", ["fixed", "table"])
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def test_read_hdf_series_mode_r(format, setup_path):
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# GH 16583
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# Tests that reading a Series saved to an HDF file
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# still works if a mode='r' argument is supplied
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series = tm.makeFloatSeries()
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with ensure_clean_path(setup_path) as path:
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series.to_hdf(path, key="data", format=format)
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result = read_hdf(path, key="data", mode="r")
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tm.assert_series_equal(result, series)
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def test_read_py2_hdf_file_in_py3(datapath):
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# GH 16781
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# tests reading a PeriodIndex DataFrame written in Python2 in Python3
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# the file was generated in Python 2.7 like so:
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#
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# df = DataFrame([1.,2,3], index=pd.PeriodIndex(
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# ['2015-01-01', '2015-01-02', '2015-01-05'], freq='B'))
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# df.to_hdf('periodindex_0.20.1_x86_64_darwin_2.7.13.h5', 'p')
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expected = DataFrame(
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[1.0, 2, 3],
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index=pd.PeriodIndex(["2015-01-01", "2015-01-02", "2015-01-05"], freq="B"),
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)
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with ensure_clean_store(
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datapath(
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"io", "data", "legacy_hdf", "periodindex_0.20.1_x86_64_darwin_2.7.13.h5"
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),
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mode="r",
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) as store:
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result = store["p"]
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tm.assert_frame_equal(result, expected)
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