def test_object_dtype_categorical():
cat_series = pd.Series(
pd.Categorical(my_object_vals, categories=my_object_vals))
widget = show_grid(cat_series)
constraints_enum = widget._columns[0]["constraints"]["enum"]
assert not isinstance(constraints_enum[0], dict)
assert not isinstance(constraints_enum[1], dict)
widget._handle_view_msg_helper({
"type": "show_filter_dropdown",
"field": 0,
"search_val": None
})
widget._handle_view_msg_helper({
"field": 0,
"filter_info": {
"field": 0,
"selected": [0],
"type": "text",
"excluded": [],
},
"type": "change_filter",
})
assert len(widget._df) == 1
assert widget._df[0][0] == cat_series[0]
def test_crosstab():
a = np.array(
[
"foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar", "foo",
"foo", "foo"
],
dtype=object,
)
b = np.array(
[
"one", "one", "one", "two", "one", "one", "one", "two", "two",
"two", "one"
],
dtype=object,
)
c = np.array(
[
"dull",
"dull",
"shiny",
"dull",
"dull",
"shiny",
"shiny",
"dull",
"shiny",
"shiny",
"shiny",
],
dtype=object,
)
with warns_that_defaulting_to_pandas():
df = pd.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"])
assert isinstance(df, pd.DataFrame)
foo = pd.Categorical(["a", "b"], categories=["a", "b", "c"])
bar = pd.Categorical(["d", "e"], categories=["d", "e", "f"])
with warns_that_defaulting_to_pandas():
df = pd.crosstab(foo, bar)
assert isinstance(df, pd.DataFrame)
with warns_that_defaulting_to_pandas():
df = pd.crosstab(foo, bar, dropna=False)
assert isinstance(df, pd.DataFrame)
def get_test_data():
return {
"A": 1.0,
"B": pd.Timestamp("20130102"),
"C": pd.Series(1, index=list(range(4)), dtype="float32"),
"D": np.array([3] * 4, dtype="int32"),
"E": pd.Categorical(["test", "train", "foo", "bar"]),
"F": ["foo", "bar", "buzz", "fox"],
}
def test_unique():
modin_result = pd.unique([2, 1, 3, 3])
pandas_result = pandas.unique([2, 1, 3, 3])
assert_array_equal(modin_result, pandas_result)
modin_result = pd.unique(pd.Series([2] + [1] * 5))
pandas_result = pandas.unique(pandas.Series([2] + [1] * 5))
assert_array_equal(modin_result, pandas_result)
modin_result = pd.unique(
pd.Series([pd.Timestamp("20160101"), pd.Timestamp("20160101")])
)
pandas_result = pandas.unique(
pandas.Series([pandas.Timestamp("20160101"), pandas.Timestamp("20160101")])
)
assert_array_equal(modin_result, pandas_result)
modin_result = pd.unique(
pd.Series(
[
pd.Timestamp("20160101", tz="US/Eastern"),
pd.Timestamp("20160101", tz="US/Eastern"),
]
)
)
pandas_result = pandas.unique(
pandas.Series(
[
pandas.Timestamp("20160101", tz="US/Eastern"),
pandas.Timestamp("20160101", tz="US/Eastern"),
]
)
)
assert_array_equal(modin_result, pandas_result)
modin_result = pd.unique(
pd.Index(
[
pd.Timestamp("20160101", tz="US/Eastern"),
pd.Timestamp("20160101", tz="US/Eastern"),
]
)
)
pandas_result = pandas.unique(
pandas.Index(
[
pandas.Timestamp("20160101", tz="US/Eastern"),
pandas.Timestamp("20160101", tz="US/Eastern"),
]
)
)
assert_array_equal(modin_result, pandas_result)
modin_result = pd.unique(pd.Series(pd.Categorical(list("baabc"))))
pandas_result = pandas.unique(pandas.Series(pandas.Categorical(list("baabc"))))
assert_array_equal(modin_result, pandas_result)
def create_df():
return pd.DataFrame({
"A":
1.0,
"Date":
pd.Timestamp("20130102"),
"C":
pd.Series(1, index=list(range(4)), dtype="float32"),
"D":
np.array([3] * 4, dtype="int32"),
"E":
pd.Categorical(["test", "train", "foo", "bar"]),
"F": ["foo", "bar", "buzz", "fox"],
})
def test_2195(datetime_is_numeric, has_numeric_column):
data = {
"categorical": pd.Categorical(["d"] * 10**2),
"date": [np.datetime64("2000-01-01")] * 10**2,
}
if has_numeric_column:
data.update({"numeric": [5] * 10**2})
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
eval_general(
modin_df,
pandas_df,
lambda df: df.describe(datetime_is_numeric=datetime_is_numeric),
)
