from copy import deepcopy from distutils.version import LooseVersion from operator import methodcaller import numpy as np import pytest import pandas.util._test_decorators as td import pandas as pd from pandas import DataFrame, MultiIndex, Series, date_range import pandas.util.testing as tm from pandas.util.testing import ( assert_almost_equal, assert_frame_equal, assert_series_equal, ) from .test_generic import Generic try: import xarray _XARRAY_INSTALLED = True except ImportError: _XARRAY_INSTALLED = False class TestDataFrame(Generic): _typ = DataFrame _comparator = lambda self, x, y: assert_frame_equal(x, y) def test_rename_mi(self): df = DataFrame( [11, 21, 31], index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]), ) df.rename(str.lower) def test_set_axis_name(self): df = pd.DataFrame([[1, 2], [3, 4]]) funcs = ["_set_axis_name", "rename_axis"] for func in funcs: result = methodcaller(func, "foo")(df) assert df.index.name is None assert result.index.name == "foo" result = methodcaller(func, "cols", axis=1)(df) assert df.columns.name is None assert result.columns.name == "cols" def test_set_axis_name_mi(self): df = DataFrame( np.empty((3, 3)), index=MultiIndex.from_tuples([("A", x) for x in list("aBc")]), columns=MultiIndex.from_tuples([("C", x) for x in list("xyz")]), ) level_names = ["L1", "L2"] funcs = ["_set_axis_name", "rename_axis"] for func in funcs: result = methodcaller(func, level_names)(df) assert result.index.names == level_names assert result.columns.names == [None, None] result = methodcaller(func, level_names, axis=1)(df) assert result.columns.names == ["L1", "L2"] assert result.index.names == [None, None] def test_nonzero_single_element(self): # allow single item via bool method df = DataFrame([[True]]) assert df.bool() df = DataFrame([[False]]) assert not df.bool() df = DataFrame([[False, False]]) with pytest.raises(ValueError): df.bool() with pytest.raises(ValueError): bool(df) def test_get_numeric_data_preserve_dtype(self): # get the numeric data o = DataFrame({"A": [1, "2", 3.0]}) result = o._get_numeric_data() expected = DataFrame(index=[0, 1, 2], dtype=object) self._compare(result, expected) def test_metadata_propagation_indiv(self): # groupby df = DataFrame( { "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], "B": ["one", "one", "two", "three", "two", "two", "one", "three"], "C": np.random.randn(8), "D": np.random.randn(8), } ) result = df.groupby("A").sum() self.check_metadata(df, result) # resample df = DataFrame( np.random.randn(1000, 2), index=date_range("20130101", periods=1000, freq="s"), ) result = df.resample("1T") self.check_metadata(df, result) # merging with override # GH 6923 _metadata = DataFrame._metadata _finalize = DataFrame.__finalize__ np.random.seed(10) df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=["a", "b"]) df2 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=["c", "d"]) DataFrame._metadata = ["filename"] df1.filename = "fname1.csv" df2.filename = "fname2.csv" def finalize(self, other, method=None, **kwargs): for name in self._metadata: if method == "merge": left, right = other.left, other.right value = getattr(left, name, "") + "|" + getattr(right, name, "") object.__setattr__(self, name, value) else: object.__setattr__(self, name, getattr(other, name, "")) return self DataFrame.__finalize__ = finalize result = df1.merge(df2, left_on=["a"], right_on=["c"], how="inner") assert result.filename == "fname1.csv|fname2.csv" # concat # GH 6927 DataFrame._metadata = ["filename"] df1 = DataFrame(np.random.randint(0, 4, (3, 2)), columns=list("ab")) df1.filename = "foo" def finalize(self, other, method=None, **kwargs): for name in self._metadata: if method == "concat": value = "+".join( [getattr(o, name) for o in other.objs if getattr(o, name, None)] ) object.__setattr__(self, name, value) else: object.__setattr__(self, name, getattr(other, name, None)) return self DataFrame.__finalize__ = finalize result = pd.concat([df1, df1]) assert result.filename == "foo+foo" # reset DataFrame._metadata = _metadata DataFrame.__finalize__ = _finalize def test_set_attribute(self): # Test for consistent setattr behavior when an attribute and a column # have the same name (Issue #8994) df = DataFrame({"x": [1, 2, 3]}) df.y = 2 df["y"] = [2, 4, 6] df.y = 5 assert df.y == 5 assert_series_equal(df["y"], Series([2, 4, 6], name="y")) @pytest.mark.skipif( not _XARRAY_INSTALLED or _XARRAY_INSTALLED and LooseVersion(xarray.__version__) < LooseVersion("0.10.0"), reason="xarray >= 0.10.0 required", ) @pytest.mark.parametrize( "index", [ "FloatIndex", "IntIndex", "StringIndex", "UnicodeIndex", "DateIndex", "PeriodIndex", "CategoricalIndex", "TimedeltaIndex", ], ) def test_to_xarray_index_types(self, index): from xarray import Dataset index = getattr(tm, "make{}".format(index)) df = DataFrame( { "a": list("abc"), "b": list(range(1, 4)), "c": np.arange(3, 6).astype("u1"), "d": np.arange(4.0, 7.0, dtype="float64"), "e": [True, False, True], "f": pd.Categorical(list("abc")), "g": pd.date_range("20130101", periods=3), "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), } ) df.index = index(3) df.index.name = "foo" df.columns.name = "bar" result = df.to_xarray() assert result.dims["foo"] == 3 assert len(result.coords) == 1 assert len(result.data_vars) == 8 assert_almost_equal(list(result.coords.keys()), ["foo"]) assert isinstance(result, Dataset) # idempotency # categoricals are not preserved # datetimes w/tz are not preserved # column names are lost expected = df.copy() expected["f"] = expected["f"].astype(object) expected["h"] = expected["h"].astype("datetime64[ns]") expected.columns.name = None assert_frame_equal( result.to_dataframe(), expected, check_index_type=False, check_categorical=False, ) @td.skip_if_no("xarray", min_version="0.7.0") def test_to_xarray(self): from xarray import Dataset df = DataFrame( { "a": list("abc"), "b": list(range(1, 4)), "c": np.arange(3, 6).astype("u1"), "d": np.arange(4.0, 7.0, dtype="float64"), "e": [True, False, True], "f": pd.Categorical(list("abc")), "g": pd.date_range("20130101", periods=3), "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), } ) df.index.name = "foo" result = df[0:0].to_xarray() assert result.dims["foo"] == 0 assert isinstance(result, Dataset) # available in 0.7.1 # MultiIndex df.index = pd.MultiIndex.from_product([["a"], range(3)], names=["one", "two"]) result = df.to_xarray() assert result.dims["one"] == 1 assert result.dims["two"] == 3 assert len(result.coords) == 2 assert len(result.data_vars) == 8 assert_almost_equal(list(result.coords.keys()), ["one", "two"]) assert isinstance(result, Dataset) result = result.to_dataframe() expected = df.copy() expected["f"] = expected["f"].astype(object) expected["h"] = expected["h"].astype("datetime64[ns]") expected.columns.name = None assert_frame_equal(result, expected, check_index_type=False) def test_deepcopy_empty(self): # This test covers empty frame copying with non-empty column sets # as reported in issue GH15370 empty_frame = DataFrame(data=[], index=[], columns=["A"]) empty_frame_copy = deepcopy(empty_frame) self._compare(empty_frame_copy, empty_frame)