import datetime from datetime import timedelta import re import numpy as np import pytest from pandas._libs.tslibs import Timestamp import pandas.util._test_decorators as td import pandas as pd from pandas import ( DataFrame, Series, _testing as tm, concat, date_range, read_hdf, ) from pandas.tests.io.pytables.common import ( _maybe_remove, ensure_clean_store, ) pytestmark = pytest.mark.single_cpu tables = pytest.importorskip("tables") @pytest.mark.filterwarnings("ignore::tables.NaturalNameWarning") def test_append(setup_path): with ensure_clean_store(setup_path) as store: # this is allowed by almost always don't want to do it # tables.NaturalNameWarning): df = tm.makeTimeDataFrame() _maybe_remove(store, "df1") store.append("df1", df[:10]) store.append("df1", df[10:]) tm.assert_frame_equal(store["df1"], df) _maybe_remove(store, "df2") store.put("df2", df[:10], format="table") store.append("df2", df[10:]) tm.assert_frame_equal(store["df2"], df) _maybe_remove(store, "df3") store.append("/df3", df[:10]) store.append("/df3", df[10:]) tm.assert_frame_equal(store["df3"], df) # this is allowed by almost always don't want to do it # tables.NaturalNameWarning _maybe_remove(store, "/df3 foo") store.append("/df3 foo", df[:10]) store.append("/df3 foo", df[10:]) tm.assert_frame_equal(store["df3 foo"], df) # dtype issues - mizxed type in a single object column df = DataFrame(data=[[1, 2], [0, 1], [1, 2], [0, 0]]) df["mixed_column"] = "testing" df.loc[2, "mixed_column"] = np.nan _maybe_remove(store, "df") store.append("df", df) tm.assert_frame_equal(store["df"], df) # uints - test storage of uints uint_data = DataFrame( { "u08": Series( np.random.default_rng(2).integers(0, high=255, size=5), dtype=np.uint8, ), "u16": Series( np.random.default_rng(2).integers(0, high=65535, size=5), dtype=np.uint16, ), "u32": Series( np.random.default_rng(2).integers(0, high=2**30, size=5), dtype=np.uint32, ), "u64": Series( [2**58, 2**59, 2**60, 2**61, 2**62], dtype=np.uint64, ), }, index=np.arange(5), ) _maybe_remove(store, "uints") store.append("uints", uint_data) tm.assert_frame_equal(store["uints"], uint_data, check_index_type=True) # uints - test storage of uints in indexable columns _maybe_remove(store, "uints") # 64-bit indices not yet supported store.append("uints", uint_data, data_columns=["u08", "u16", "u32"]) tm.assert_frame_equal(store["uints"], uint_data, check_index_type=True) def test_append_series(setup_path): with ensure_clean_store(setup_path) as store: # basic ss = tm.makeStringSeries() ts = tm.makeTimeSeries() ns = Series(np.arange(100)) store.append("ss", ss) result = store["ss"] tm.assert_series_equal(result, ss) assert result.name is None store.append("ts", ts) result = store["ts"] tm.assert_series_equal(result, ts) assert result.name is None ns.name = "foo" store.append("ns", ns) result = store["ns"] tm.assert_series_equal(result, ns) assert result.name == ns.name # select on the values expected = ns[ns > 60] result = store.select("ns", "foo>60") tm.assert_series_equal(result, expected) # select on the index and values expected = ns[(ns > 70) & (ns.index < 90)] result = store.select("ns", "foo>70 and index<90") tm.assert_series_equal(result, expected, check_index_type=True) # multi-index mi = DataFrame(np.random.default_rng(2).standard_normal((5, 1)), columns=["A"]) mi["B"] = np.arange(len(mi)) mi["C"] = "foo" mi.loc[3:5, "C"] = "bar" mi.set_index(["C", "B"], inplace=True) s = mi.stack(future_stack=True) s.index = s.index.droplevel(2) store.append("mi", s) tm.assert_series_equal(store["mi"], s, check_index_type=True) def test_append_some_nans(setup_path): with ensure_clean_store(setup_path) as store: df = DataFrame( { "A": Series(np.random.default_rng(2).standard_normal(20)).astype( "int32" ), "A1": np.random.default_rng(2).standard_normal(20), "A2": np.random.default_rng(2).standard_normal(20), "B": "foo", "C": "bar", "D": Timestamp("2001-01-01").as_unit("ns"), "E": Timestamp("2001-01-02").as_unit("ns"), }, index=np.arange(20), ) # some nans _maybe_remove(store, "df1") df.loc[0:15, ["A1", "B", "D", "E"]] = np.nan store.append("df1", df[:10]) store.append("df1", df[10:]) tm.assert_frame_equal(store["df1"], df, check_index_type=True) # first column df1 = df.copy() df1["A1"] = np.nan _maybe_remove(store, "df1") store.append("df1", df1[:10]) store.append("df1", df1[10:]) tm.assert_frame_equal(store["df1"], df1, check_index_type=True) # 2nd column df2 = df.copy() df2["A2"] = np.nan _maybe_remove(store, "df2") store.append("df2", df2[:10]) store.append("df2", df2[10:]) tm.assert_frame_equal(store["df2"], df2, check_index_type=True) # datetimes df3 = df.copy() df3["E"] = np.nan _maybe_remove(store, "df3") store.append("df3", df3[:10]) store.append("df3", df3[10:]) tm.assert_frame_equal(store["df3"], df3, check_index_type=True) def test_append_all_nans(setup_path): with ensure_clean_store(setup_path) as store: df = DataFrame( { "A1": np.random.default_rng(2).standard_normal(20), "A2": np.random.default_rng(2).standard_normal(20), }, index=np.arange(20), ) df.loc[0:15, :] = np.nan # nan some entire rows (dropna=True) _maybe_remove(store, "df") store.append("df", df[:10], dropna=True) store.append("df", df[10:], dropna=True) tm.assert_frame_equal(store["df"], df[-4:], check_index_type=True) # nan some entire rows (dropna=False) _maybe_remove(store, "df2") store.append("df2", df[:10], dropna=False) store.append("df2", df[10:], dropna=False) tm.assert_frame_equal(store["df2"], df, check_index_type=True) # tests the option io.hdf.dropna_table with pd.option_context("io.hdf.dropna_table", False): _maybe_remove(store, "df3") store.append("df3", df[:10]) store.append("df3", df[10:]) tm.assert_frame_equal(store["df3"], df) with pd.option_context("io.hdf.dropna_table", True): _maybe_remove(store, "df4") store.append("df4", df[:10]) store.append("df4", df[10:]) tm.assert_frame_equal(store["df4"], df[-4:]) # nan some entire rows (string are still written!) df = DataFrame( { "A1": np.random.default_rng(2).standard_normal(20), "A2": np.random.default_rng(2).standard_normal(20), "B": "foo", "C": "bar", }, index=np.arange(20), ) df.loc[0:15, :] = np.nan _maybe_remove(store, "df") store.append("df", df[:10], dropna=True) store.append("df", df[10:], dropna=True) tm.assert_frame_equal(store["df"], df, check_index_type=True) _maybe_remove(store, "df2") store.append("df2", df[:10], dropna=False) store.append("df2", df[10:], dropna=False) tm.assert_frame_equal(store["df2"], df, check_index_type=True) # nan some entire rows (but since we have dates they are still # written!) df = DataFrame( { "A1": np.random.default_rng(2).standard_normal(20), "A2": np.random.default_rng(2).standard_normal(20), "B": "foo", "C": "bar", "D": Timestamp("2001-01-01").as_unit("ns"), "E": Timestamp("2001-01-02").as_unit("ns"), }, index=np.arange(20), ) df.loc[0:15, :] = np.nan _maybe_remove(store, "df") store.append("df", df[:10], dropna=True) store.append("df", df[10:], dropna=True) tm.assert_frame_equal(store["df"], df, check_index_type=True) _maybe_remove(store, "df2") store.append("df2", df[:10], dropna=False) store.append("df2", df[10:], dropna=False) tm.assert_frame_equal(store["df2"], df, check_index_type=True) def test_append_frame_column_oriented(setup_path): with ensure_clean_store(setup_path) as store: # column oriented df = tm.makeTimeDataFrame() df.index = df.index._with_freq(None) # freq doesn't round-trip _maybe_remove(store, "df1") store.append("df1", df.iloc[:, :2], axes=["columns"]) store.append("df1", df.iloc[:, 2:]) tm.assert_frame_equal(store["df1"], df) result = store.select("df1", "columns=A") expected = df.reindex(columns=["A"]) tm.assert_frame_equal(expected, result) # selection on the non-indexable result = store.select("df1", ("columns=A", "index=df.index[0:4]")) expected = df.reindex(columns=["A"], index=df.index[0:4]) tm.assert_frame_equal(expected, result) # this isn't supported msg = re.escape( "passing a filterable condition to a non-table indexer " "[Filter: Not Initialized]" ) with pytest.raises(TypeError, match=msg): store.select("df1", "columns=A and index>df.index[4]") def test_append_with_different_block_ordering(setup_path): # GH 4096; using same frames, but different block orderings with ensure_clean_store(setup_path) as store: for i in range(10): df = DataFrame( np.random.default_rng(2).standard_normal((10, 2)), columns=list("AB") ) df["index"] = range(10) df["index"] += i * 10 df["int64"] = Series([1] * len(df), dtype="int64") df["int16"] = Series([1] * len(df), dtype="int16") if i % 2 == 0: del df["int64"] df["int64"] = Series([1] * len(df), dtype="int64") if i % 3 == 0: a = df.pop("A") df["A"] = a df.set_index("index", inplace=True) store.append("df", df) # test a different ordering but with more fields (like invalid # combinations) with ensure_clean_store(setup_path) as store: df = DataFrame( np.random.default_rng(2).standard_normal((10, 2)), columns=list("AB"), dtype="float64", ) df["int64"] = Series([1] * len(df), dtype="int64") df["int16"] = Series([1] * len(df), dtype="int16") store.append("df", df) # store additional fields in different blocks df["int16_2"] = Series([1] * len(df), dtype="int16") msg = re.escape( "cannot match existing table structure for [int16] on appending data" ) with pytest.raises(ValueError, match=msg): store.append("df", df) # store multiple additional fields in different blocks df["float_3"] = Series([1.0] * len(df), dtype="float64") msg = re.escape( "cannot match existing table structure for [A,B] on appending data" ) with pytest.raises(ValueError, match=msg): store.append("df", df) def test_append_with_strings(setup_path): with ensure_clean_store(setup_path) as store: def check_col(key, name, size): assert ( getattr(store.get_storer(key).table.description, name).itemsize == size ) # avoid truncation on elements df = DataFrame([[123, "asdqwerty"], [345, "dggnhebbsdfbdfb"]]) store.append("df_big", df) tm.assert_frame_equal(store.select("df_big"), df) check_col("df_big", "values_block_1", 15) # appending smaller string ok df2 = DataFrame([[124, "asdqy"], [346, "dggnhefbdfb"]]) store.append("df_big", df2) expected = concat([df, df2]) tm.assert_frame_equal(store.select("df_big"), expected) check_col("df_big", "values_block_1", 15) # avoid truncation on elements df = DataFrame([[123, "asdqwerty"], [345, "dggnhebbsdfbdfb"]]) store.append("df_big2", df, min_itemsize={"values": 50}) tm.assert_frame_equal(store.select("df_big2"), df) check_col("df_big2", "values_block_1", 50) # bigger string on next append store.append("df_new", df) df_new = DataFrame([[124, "abcdefqhij"], [346, "abcdefghijklmnopqrtsuvwxyz"]]) msg = ( r"Trying to store a string with len \[26\] in " r"\[values_block_1\] column but\n" r"this column has a limit of \[15\]!\n" "Consider using min_itemsize to preset the sizes on these " "columns" ) with pytest.raises(ValueError, match=msg): store.append("df_new", df_new) # min_itemsize on Series index (GH 11412) df = tm.makeMixedDataFrame().set_index("C") store.append("ss", df["B"], min_itemsize={"index": 4}) tm.assert_series_equal(store.select("ss"), df["B"]) # same as above, with data_columns=True store.append("ss2", df["B"], data_columns=True, min_itemsize={"index": 4}) tm.assert_series_equal(store.select("ss2"), df["B"]) # min_itemsize in index without appending (GH 10381) store.put("ss3", df, format="table", min_itemsize={"index": 6}) # just make sure there is a longer string: df2 = df.copy().reset_index().assign(C="longer").set_index("C") store.append("ss3", df2) tm.assert_frame_equal(store.select("ss3"), concat([df, df2])) # same as above, with a Series store.put("ss4", df["B"], format="table", min_itemsize={"index": 6}) store.append("ss4", df2["B"]) tm.assert_series_equal(store.select("ss4"), concat([df["B"], df2["B"]])) # with nans _maybe_remove(store, "df") df = tm.makeTimeDataFrame() df["string"] = "foo" df.loc[df.index[1:4], "string"] = np.nan df["string2"] = "bar" df.loc[df.index[4:8], "string2"] = np.nan df["string3"] = "bah" df.loc[df.index[1:], "string3"] = np.nan store.append("df", df) result = store.select("df") tm.assert_frame_equal(result, df) with ensure_clean_store(setup_path) as store: df = DataFrame({"A": "foo", "B": "bar"}, index=range(10)) # a min_itemsize that creates a data_column _maybe_remove(store, "df") store.append("df", df, min_itemsize={"A": 200}) check_col("df", "A", 200) assert store.get_storer("df").data_columns == ["A"] # a min_itemsize that creates a data_column2 _maybe_remove(store, "df") store.append("df", df, data_columns=["B"], min_itemsize={"A": 200}) check_col("df", "A", 200) assert store.get_storer("df").data_columns == ["B", "A"] # a min_itemsize that creates a data_column2 _maybe_remove(store, "df") store.append("df", df, data_columns=["B"], min_itemsize={"values": 200}) check_col("df", "B", 200) check_col("df", "values_block_0", 200) assert store.get_storer("df").data_columns == ["B"] # infer the .typ on subsequent appends _maybe_remove(store, "df") store.append("df", df[:5], min_itemsize=200) store.append("df", df[5:], min_itemsize=200) tm.assert_frame_equal(store["df"], df) # invalid min_itemsize keys df = DataFrame(["foo", "foo", "foo", "barh", "barh", "barh"], columns=["A"]) _maybe_remove(store, "df") msg = re.escape( "min_itemsize has the key [foo] which is not an axis or data_column" ) with pytest.raises(ValueError, match=msg): store.append("df", df, min_itemsize={"foo": 20, "foobar": 20}) def test_append_with_empty_string(setup_path): with ensure_clean_store(setup_path) as store: # with all empty strings (GH 12242) df = DataFrame({"x": ["a", "b", "c", "d", "e", "f", ""]}) store.append("df", df[:-1], min_itemsize={"x": 1}) store.append("df", df[-1:], min_itemsize={"x": 1}) tm.assert_frame_equal(store.select("df"), df) def test_append_with_data_columns(setup_path): with ensure_clean_store(setup_path) as store: df = tm.makeTimeDataFrame() df.iloc[0, df.columns.get_loc("B")] = 1.0 _maybe_remove(store, "df") store.append("df", df[:2], data_columns=["B"]) store.append("df", df[2:]) tm.assert_frame_equal(store["df"], df) # check that we have indices created assert store._handle.root.df.table.cols.index.is_indexed is True assert store._handle.root.df.table.cols.B.is_indexed is True # data column searching result = store.select("df", "B>0") expected = df[df.B > 0] tm.assert_frame_equal(result, expected) # data column searching (with an indexable and a data_columns) result = store.select("df", "B>0 and index>df.index[3]") df_new = df.reindex(index=df.index[4:]) expected = df_new[df_new.B > 0] tm.assert_frame_equal(result, expected) # data column selection with a string data_column df_new = df.copy() df_new["string"] = "foo" df_new.loc[df_new.index[1:4], "string"] = np.nan df_new.loc[df_new.index[5:6], "string"] = "bar" _maybe_remove(store, "df") store.append("df", df_new, data_columns=["string"]) result = store.select("df", "string='foo'") expected = df_new[df_new.string == "foo"] tm.assert_frame_equal(result, expected) # using min_itemsize and a data column def check_col(key, name, size): assert ( getattr(store.get_storer(key).table.description, name).itemsize == size ) with ensure_clean_store(setup_path) as store: _maybe_remove(store, "df") store.append("df", df_new, data_columns=["string"], min_itemsize={"string": 30}) check_col("df", "string", 30) _maybe_remove(store, "df") store.append("df", df_new, data_columns=["string"], min_itemsize=30) check_col("df", "string", 30) _maybe_remove(store, "df") store.append("df", df_new, data_columns=["string"], min_itemsize={"values": 30}) check_col("df", "string", 30) with ensure_clean_store(setup_path) as store: df_new["string2"] = "foobarbah" df_new["string_block1"] = "foobarbah1" df_new["string_block2"] = "foobarbah2" _maybe_remove(store, "df") store.append( "df", df_new, data_columns=["string", "string2"], min_itemsize={"string": 30, "string2": 40, "values": 50}, ) check_col("df", "string", 30) check_col("df", "string2", 40) check_col("df", "values_block_1", 50) with ensure_clean_store(setup_path) as store: # multiple data columns df_new = df.copy() df_new.iloc[0, df_new.columns.get_loc("A")] = 1.0 df_new.iloc[0, df_new.columns.get_loc("B")] = -1.0 df_new["string"] = "foo" sl = df_new.columns.get_loc("string") df_new.iloc[1:4, sl] = np.nan df_new.iloc[5:6, sl] = "bar" df_new["string2"] = "foo" sl = df_new.columns.get_loc("string2") df_new.iloc[2:5, sl] = np.nan df_new.iloc[7:8, sl] = "bar" _maybe_remove(store, "df") store.append("df", df_new, data_columns=["A", "B", "string", "string2"]) result = store.select("df", "string='foo' and string2='foo' and A>0 and B<0") expected = df_new[ (df_new.string == "foo") & (df_new.string2 == "foo") & (df_new.A > 0) & (df_new.B < 0) ] tm.assert_frame_equal(result, expected, check_freq=False) # FIXME: 2020-05-07 freq check randomly fails in the CI # yield an empty frame result = store.select("df", "string='foo' and string2='cool'") expected = df_new[(df_new.string == "foo") & (df_new.string2 == "cool")] tm.assert_frame_equal(result, expected) with ensure_clean_store(setup_path) as store: # doc example df_dc = df.copy() df_dc["string"] = "foo" df_dc.loc[df_dc.index[4:6], "string"] = np.nan df_dc.loc[df_dc.index[7:9], "string"] = "bar" df_dc["string2"] = "cool" df_dc["datetime"] = Timestamp("20010102").as_unit("ns") df_dc.loc[df_dc.index[3:5], ["A", "B", "datetime"]] = np.nan _maybe_remove(store, "df_dc") store.append( "df_dc", df_dc, data_columns=["B", "C", "string", "string2", "datetime"] ) result = store.select("df_dc", "B>0") expected = df_dc[df_dc.B > 0] tm.assert_frame_equal(result, expected) result = store.select("df_dc", ["B > 0", "C > 0", "string == foo"]) expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == "foo")] tm.assert_frame_equal(result, expected, check_freq=False) # FIXME: 2020-12-07 intermittent build failures here with freq of # None instead of BDay(4) with ensure_clean_store(setup_path) as store: # doc example part 2 index = date_range("1/1/2000", periods=8) df_dc = DataFrame( np.random.default_rng(2).standard_normal((8, 3)), index=index, columns=["A", "B", "C"], ) df_dc["string"] = "foo" df_dc.loc[df_dc.index[4:6], "string"] = np.nan df_dc.loc[df_dc.index[7:9], "string"] = "bar" df_dc[["B", "C"]] = df_dc[["B", "C"]].abs() df_dc["string2"] = "cool" # on-disk operations store.append("df_dc", df_dc, data_columns=["B", "C", "string", "string2"]) result = store.select("df_dc", "B>0") expected = df_dc[df_dc.B > 0] tm.assert_frame_equal(result, expected) result = store.select("df_dc", ["B > 0", "C > 0", 'string == "foo"']) expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == "foo")] tm.assert_frame_equal(result, expected) def test_append_hierarchical(tmp_path, setup_path, multiindex_dataframe_random_data): df = multiindex_dataframe_random_data df.columns.name = None with ensure_clean_store(setup_path) as store: store.append("mi", df) result = store.select("mi") tm.assert_frame_equal(result, df) # GH 3748 result = store.select("mi", columns=["A", "B"]) expected = df.reindex(columns=["A", "B"]) tm.assert_frame_equal(result, expected) path = tmp_path / "test.hdf" df.to_hdf(path, "df", format="table") result = read_hdf(path, "df", columns=["A", "B"]) expected = df.reindex(columns=["A", "B"]) tm.assert_frame_equal(result, expected) def test_append_misc(setup_path): with ensure_clean_store(setup_path) as store: df = tm.makeDataFrame() store.append("df", df, chunksize=1) result = store.select("df") tm.assert_frame_equal(result, df) store.append("df1", df, expectedrows=10) result = store.select("df1") tm.assert_frame_equal(result, df) @pytest.mark.parametrize("chunksize", [10, 200, 1000]) def test_append_misc_chunksize(setup_path, chunksize): # more chunksize in append tests df = tm.makeDataFrame() df["string"] = "foo" df["float322"] = 1.0 df["float322"] = df["float322"].astype("float32") df["bool"] = df["float322"] > 0 df["time1"] = Timestamp("20130101").as_unit("ns") df["time2"] = Timestamp("20130102").as_unit("ns") with ensure_clean_store(setup_path, mode="w") as store: store.append("obj", df, chunksize=chunksize) result = store.select("obj") tm.assert_frame_equal(result, df) def test_append_misc_empty_frame(setup_path): # empty frame, GH4273 with ensure_clean_store(setup_path) as store: # 0 len df_empty = DataFrame(columns=list("ABC")) store.append("df", df_empty) with pytest.raises(KeyError, match="'No object named df in the file'"): store.select("df") # repeated append of 0/non-zero frames df = DataFrame(np.random.default_rng(2).random((10, 3)), columns=list("ABC")) store.append("df", df) tm.assert_frame_equal(store.select("df"), df) store.append("df", df_empty) tm.assert_frame_equal(store.select("df"), df) # store df = DataFrame(columns=list("ABC")) store.put("df2", df) tm.assert_frame_equal(store.select("df2"), df) # TODO(ArrayManager) currently we rely on falling back to BlockManager, but # the conversion from AM->BM converts the invalid object dtype column into # a datetime64 column no longer raising an error @td.skip_array_manager_not_yet_implemented def test_append_raise(setup_path): with ensure_clean_store(setup_path) as store: # test append with invalid input to get good error messages # list in column df = tm.makeDataFrame() df["invalid"] = [["a"]] * len(df) assert df.dtypes["invalid"] == np.object_ msg = re.escape( """Cannot serialize the column [invalid] because its data contents are not [string] but [mixed] object dtype""" ) with pytest.raises(TypeError, match=msg): store.append("df", df) # multiple invalid columns df["invalid2"] = [["a"]] * len(df) df["invalid3"] = [["a"]] * len(df) with pytest.raises(TypeError, match=msg): store.append("df", df) # datetime with embedded nans as object df = tm.makeDataFrame() s = Series(datetime.datetime(2001, 1, 2), index=df.index) s = s.astype(object) s[0:5] = np.nan df["invalid"] = s assert df.dtypes["invalid"] == np.object_ msg = "too many timezones in this block, create separate data columns" with pytest.raises(TypeError, match=msg): store.append("df", df) # directly ndarray msg = "value must be None, Series, or DataFrame" with pytest.raises(TypeError, match=msg): store.append("df", np.arange(10)) # series directly msg = re.escape( "cannot properly create the storer for: " "[group->df,value->]" ) with pytest.raises(TypeError, match=msg): store.append("df", Series(np.arange(10))) # appending an incompatible table df = tm.makeDataFrame() store.append("df", df) df["foo"] = "foo" msg = re.escape( "invalid combination of [non_index_axes] on appending data " "[(1, ['A', 'B', 'C', 'D', 'foo'])] vs current table " "[(1, ['A', 'B', 'C', 'D'])]" ) with pytest.raises(ValueError, match=msg): store.append("df", df) # incompatible type (GH 41897) _maybe_remove(store, "df") df["foo"] = Timestamp("20130101") store.append("df", df) df["foo"] = "bar" msg = re.escape( "invalid combination of [values_axes] on appending data " "[name->values_block_1,cname->values_block_1," "dtype->bytes24,kind->string,shape->(1, 30)] " "vs current table " "[name->values_block_1,cname->values_block_1," "dtype->datetime64,kind->datetime64,shape->None]" ) with pytest.raises(ValueError, match=msg): store.append("df", df) def test_append_with_timedelta(setup_path): # GH 3577 # append timedelta ts = Timestamp("20130101").as_unit("ns") df = DataFrame( { "A": ts, "B": [ts + timedelta(days=i, seconds=10) for i in range(10)], } ) df["C"] = df["A"] - df["B"] df.loc[3:5, "C"] = np.nan with ensure_clean_store(setup_path) as store: # table _maybe_remove(store, "df") store.append("df", df, data_columns=True) result = store.select("df") tm.assert_frame_equal(result, df) result = store.select("df", where="C<100000") tm.assert_frame_equal(result, df) result = store.select("df", where="C0", "B>0"], selector="df1" ) expected = df[(df.A > 0) & (df.B > 0)] tm.assert_frame_equal(result, expected) def test_append_to_multiple_dropna(setup_path): df1 = tm.makeTimeDataFrame() df2 = tm.makeTimeDataFrame().rename(columns="{}_2".format) df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan df = concat([df1, df2], axis=1) with ensure_clean_store(setup_path) as store: # dropna=True should guarantee rows are synchronized store.append_to_multiple( {"df1": ["A", "B"], "df2": None}, df, selector="df1", dropna=True ) result = store.select_as_multiple(["df1", "df2"]) expected = df.dropna() tm.assert_frame_equal(result, expected, check_index_type=True) tm.assert_index_equal(store.select("df1").index, store.select("df2").index) def test_append_to_multiple_dropna_false(setup_path): df1 = tm.makeTimeDataFrame() df2 = tm.makeTimeDataFrame().rename(columns="{}_2".format) df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan df = concat([df1, df2], axis=1) with ensure_clean_store(setup_path) as store, pd.option_context( "io.hdf.dropna_table", True ): # dropna=False shouldn't synchronize row indexes store.append_to_multiple( {"df1a": ["A", "B"], "df2a": None}, df, selector="df1a", dropna=False ) msg = "all tables must have exactly the same nrows!" with pytest.raises(ValueError, match=msg): store.select_as_multiple(["df1a", "df2a"]) assert not store.select("df1a").index.equals(store.select("df2a").index) def test_append_to_multiple_min_itemsize(setup_path): # GH 11238 df = DataFrame( { "IX": np.arange(1, 21), "Num": np.arange(1, 21), "BigNum": np.arange(1, 21) * 88, "Str": ["a" for _ in range(20)], "LongStr": ["abcde" for _ in range(20)], } ) expected = df.iloc[[0]] with ensure_clean_store(setup_path) as store: store.append_to_multiple( { "index": ["IX"], "nums": ["Num", "BigNum"], "strs": ["Str", "LongStr"], }, df.iloc[[0]], "index", min_itemsize={"Str": 10, "LongStr": 100, "Num": 2}, ) result = store.select_as_multiple(["index", "nums", "strs"]) tm.assert_frame_equal(result, expected, check_index_type=True)