mirror of
https://github.com/aykhans/AzSuicideDataVisualization.git
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360 lines
10 KiB
Python
360 lines
10 KiB
Python
# Tests specifically aimed at detecting bad arguments.
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# This file is organized by reason for exception.
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# 1. always invalid argument values
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# 2. missing column(s)
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# 3. incompatible ops/dtype/args/kwargs
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# 4. invalid result shape/type
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# If your test does not fit into one of these categories, add to this list.
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from itertools import chain
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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 import (
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Categorical,
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DataFrame,
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Series,
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date_range,
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notna,
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)
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import pandas._testing as tm
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from pandas.core.base import SpecificationError
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@pytest.mark.parametrize("result_type", ["foo", 1])
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def test_result_type_error(result_type, int_frame_const_col):
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# allowed result_type
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df = int_frame_const_col
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msg = (
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"invalid value for result_type, must be one of "
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"{None, 'reduce', 'broadcast', 'expand'}"
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)
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with pytest.raises(ValueError, match=msg):
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df.apply(lambda x: [1, 2, 3], axis=1, result_type=result_type)
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def test_apply_invalid_axis_value():
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df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=["a", "a", "c"])
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msg = "No axis named 2 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.apply(lambda x: x, 2)
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def test_applymap_invalid_na_action(float_frame):
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# GH 23803
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with pytest.raises(ValueError, match="na_action must be .*Got 'abc'"):
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float_frame.applymap(lambda x: len(str(x)), na_action="abc")
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def test_agg_raises():
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# GH 26513
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df = DataFrame({"A": [0, 1], "B": [1, 2]})
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msg = "Must provide"
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with pytest.raises(TypeError, match=msg):
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df.agg()
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def test_map_with_invalid_na_action_raises():
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# https://github.com/pandas-dev/pandas/issues/32815
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s = Series([1, 2, 3])
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msg = "na_action must either be 'ignore' or None"
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with pytest.raises(ValueError, match=msg):
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s.map(lambda x: x, na_action="____")
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def test_map_categorical_na_action():
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values = Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True)
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s = Series(values, name="XX", index=list("abcdefg"))
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with pytest.raises(NotImplementedError, match=tm.EMPTY_STRING_PATTERN):
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s.map(lambda x: x, na_action="ignore")
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def test_map_datetimetz_na_action():
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values = date_range("2011-01-01", "2011-01-02", freq="H").tz_localize("Asia/Tokyo")
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s = Series(values, name="XX")
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with pytest.raises(NotImplementedError, match=tm.EMPTY_STRING_PATTERN):
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s.map(lambda x: x, na_action="ignore")
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@pytest.mark.parametrize("box", [DataFrame, Series])
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@pytest.mark.parametrize("method", ["apply", "agg", "transform"])
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@pytest.mark.parametrize("func", [{"A": {"B": "sum"}}, {"A": {"B": ["sum"]}}])
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def test_nested_renamer(box, method, func):
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# GH 35964
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obj = box({"A": [1]})
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match = "nested renamer is not supported"
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with pytest.raises(SpecificationError, match=match):
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getattr(obj, method)(func)
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@pytest.mark.parametrize(
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"renamer",
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[{"foo": ["min", "max"]}, {"foo": ["min", "max"], "bar": ["sum", "mean"]}],
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)
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def test_series_nested_renamer(renamer):
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s = Series(range(6), dtype="int64", name="series")
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msg = "nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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s.agg(renamer)
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def test_apply_dict_depr():
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tsdf = DataFrame(
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np.random.randn(10, 3),
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columns=["A", "B", "C"],
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index=date_range("1/1/2000", periods=10),
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)
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msg = "nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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tsdf.A.agg({"foo": ["sum", "mean"]})
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@pytest.mark.parametrize("method", ["agg", "transform"])
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def test_dict_nested_renaming_depr(method):
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df = DataFrame({"A": range(5), "B": 5})
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# nested renaming
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msg = r"nested renamer is not supported"
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with pytest.raises(SpecificationError, match=msg):
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getattr(df, method)({"A": {"foo": "min"}, "B": {"bar": "max"}})
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@pytest.mark.parametrize("method", ["apply", "agg", "transform"])
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@pytest.mark.parametrize("func", [{"B": "sum"}, {"B": ["sum"]}])
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def test_missing_column(method, func):
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# GH 40004
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obj = DataFrame({"A": [1]})
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match = re.escape("Column(s) ['B'] do not exist")
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with pytest.raises(KeyError, match=match):
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getattr(obj, method)(func)
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def test_transform_mixed_column_name_dtypes():
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# GH39025
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df = DataFrame({"a": ["1"]})
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msg = r"Column\(s\) \[1, 'b'\] do not exist"
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with pytest.raises(KeyError, match=msg):
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df.transform({"a": int, 1: str, "b": int})
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@pytest.mark.parametrize(
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"how, args", [("pct_change", ()), ("nsmallest", (1, ["a", "b"])), ("tail", 1)]
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)
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def test_apply_str_axis_1_raises(how, args):
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# GH 39211 - some ops don't support axis=1
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df = DataFrame({"a": [1, 2], "b": [3, 4]})
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msg = f"Operation {how} does not support axis=1"
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with pytest.raises(ValueError, match=msg):
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df.apply(how, axis=1, args=args)
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def test_transform_axis_1_raises():
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# GH 35964
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msg = "No axis named 1 for object type Series"
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with pytest.raises(ValueError, match=msg):
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Series([1]).transform("sum", axis=1)
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def test_apply_modify_traceback():
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data = DataFrame(
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{
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"A": [
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"foo",
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"foo",
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"foo",
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"foo",
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"bar",
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"bar",
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"bar",
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"bar",
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"foo",
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"foo",
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"foo",
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],
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"B": [
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"one",
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"one",
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"one",
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"two",
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"one",
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"one",
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"one",
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"two",
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"two",
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"two",
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"one",
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],
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"C": [
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"dull",
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"dull",
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"shiny",
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"dull",
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"dull",
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"shiny",
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"shiny",
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"dull",
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"shiny",
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"shiny",
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"shiny",
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],
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"D": np.random.randn(11),
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"E": np.random.randn(11),
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"F": np.random.randn(11),
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}
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)
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data.loc[4, "C"] = np.nan
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def transform(row):
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if row["C"].startswith("shin") and row["A"] == "foo":
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row["D"] = 7
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return row
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def transform2(row):
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if notna(row["C"]) and row["C"].startswith("shin") and row["A"] == "foo":
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row["D"] = 7
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return row
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msg = "'float' object has no attribute 'startswith'"
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with pytest.raises(AttributeError, match=msg):
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data.apply(transform, axis=1)
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@pytest.mark.parametrize(
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"df, func, expected",
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tm.get_cython_table_params(
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DataFrame([["a", "b"], ["b", "a"]]), [["cumprod", TypeError]]
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),
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)
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def test_agg_cython_table_raises_frame(df, func, expected, axis):
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# GH 21224
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msg = "can't multiply sequence by non-int of type 'str'"
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with pytest.raises(expected, match=msg):
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df.agg(func, axis=axis)
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@pytest.mark.parametrize(
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"series, func, expected",
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chain(
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tm.get_cython_table_params(
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Series("a b c".split()),
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[
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("mean", TypeError), # mean raises TypeError
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("prod", TypeError),
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("std", TypeError),
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("var", TypeError),
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("median", TypeError),
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("cumprod", TypeError),
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],
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)
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),
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)
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def test_agg_cython_table_raises_series(series, func, expected):
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# GH21224
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msg = r"[Cc]ould not convert|can't multiply sequence by non-int of type"
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with pytest.raises(expected, match=msg):
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# e.g. Series('a b'.split()).cumprod() will raise
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series.agg(func)
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def test_agg_none_to_type():
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# GH 40543
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df = DataFrame({"a": [None]})
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msg = re.escape("int() argument must be a string")
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with pytest.raises(TypeError, match=msg):
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df.agg({"a": int})
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def test_transform_none_to_type():
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# GH#34377
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df = DataFrame({"a": [None]})
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msg = "Transform function failed"
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with pytest.raises(TypeError, match=msg):
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df.transform({"a": int})
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@pytest.mark.parametrize(
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"func",
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[
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lambda x: np.array([1, 2]).reshape(-1, 2),
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lambda x: [1, 2],
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lambda x: Series([1, 2]),
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],
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)
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def test_apply_broadcast_error(int_frame_const_col, func):
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df = int_frame_const_col
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# > 1 ndim
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msg = "too many dims to broadcast|cannot broadcast result"
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with pytest.raises(ValueError, match=msg):
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df.apply(func, axis=1, result_type="broadcast")
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def test_transform_and_agg_err_agg(axis, float_frame):
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# cannot both transform and agg
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msg = "cannot combine transform and aggregation operations"
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with pytest.raises(ValueError, match=msg):
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with np.errstate(all="ignore"):
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float_frame.agg(["max", "sqrt"], axis=axis)
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@pytest.mark.parametrize(
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"func, msg",
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[
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(["sqrt", "max"], "cannot combine transform and aggregation"),
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(
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{"foo": np.sqrt, "bar": "sum"},
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"cannot perform both aggregation and transformation",
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),
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],
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)
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def test_transform_and_agg_err_series(string_series, func, msg):
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# we are trying to transform with an aggregator
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with pytest.raises(ValueError, match=msg):
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with np.errstate(all="ignore"):
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string_series.agg(func)
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@pytest.mark.parametrize("func", [["max", "min"], ["max", "sqrt"]])
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def test_transform_wont_agg_frame(axis, float_frame, func):
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# GH 35964
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# cannot both transform and agg
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msg = "Function did not transform"
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with pytest.raises(ValueError, match=msg):
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float_frame.transform(func, axis=axis)
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@pytest.mark.parametrize("func", [["min", "max"], ["sqrt", "max"]])
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def test_transform_wont_agg_series(string_series, func):
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# GH 35964
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# we are trying to transform with an aggregator
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msg = "Function did not transform"
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warn = RuntimeWarning if func[0] == "sqrt" else None
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warn_msg = "invalid value encountered in sqrt"
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with pytest.raises(ValueError, match=msg):
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with tm.assert_produces_warning(warn, match=warn_msg):
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string_series.transform(func)
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@pytest.mark.parametrize(
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"op_wrapper", [lambda x: x, lambda x: [x], lambda x: {"A": x}, lambda x: {"A": [x]}]
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)
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@pytest.mark.filterwarnings("ignore:.*Select only valid:FutureWarning")
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def test_transform_reducer_raises(all_reductions, frame_or_series, op_wrapper):
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# GH 35964
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op = op_wrapper(all_reductions)
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obj = DataFrame({"A": [1, 2, 3]})
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obj = tm.get_obj(obj, frame_or_series)
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msg = "Function did not transform"
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with pytest.raises(ValueError, match=msg):
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obj.transform(op)
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