400 lines
13 KiB
Python
400 lines
13 KiB
Python
import numpy as np
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import pytest
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import pandas as pd
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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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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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class TestDataFrameDescribe:
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def test_describe_bool_in_mixed_frame(self):
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df = DataFrame(
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{
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"string_data": ["a", "b", "c", "d", "e"],
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"bool_data": [True, True, False, False, False],
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"int_data": [10, 20, 30, 40, 50],
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}
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)
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# Integer data are included in .describe() output,
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# Boolean and string data are not.
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result = df.describe()
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expected = DataFrame(
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{"int_data": [5, 30, df.int_data.std(), 10, 20, 30, 40, 50]},
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_frame_equal(result, expected)
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# Top value is a boolean value that is False
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result = df.describe(include=["bool"])
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expected = DataFrame(
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{"bool_data": [5, 2, False, 3]}, index=["count", "unique", "top", "freq"]
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)
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tm.assert_frame_equal(result, expected)
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def test_describe_empty_object(self):
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# GH#27183
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df = DataFrame({"A": [None, None]}, dtype=object)
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result = df.describe()
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expected = DataFrame(
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{"A": [0, 0, np.nan, np.nan]},
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dtype=object,
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index=["count", "unique", "top", "freq"],
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)
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tm.assert_frame_equal(result, expected)
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result = df.iloc[:0].describe()
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tm.assert_frame_equal(result, expected)
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def test_describe_bool_frame(self):
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# GH#13891
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df = DataFrame(
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{
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"bool_data_1": [False, False, True, True],
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"bool_data_2": [False, True, True, True],
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}
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)
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result = df.describe()
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expected = DataFrame(
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{"bool_data_1": [4, 2, False, 2], "bool_data_2": [4, 2, True, 3]},
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index=["count", "unique", "top", "freq"],
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)
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tm.assert_frame_equal(result, expected)
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df = DataFrame(
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{
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"bool_data": [False, False, True, True, False],
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"int_data": [0, 1, 2, 3, 4],
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}
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)
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result = df.describe()
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expected = DataFrame(
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{"int_data": [5, 2, df.int_data.std(), 0, 1, 2, 3, 4]},
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_frame_equal(result, expected)
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df = DataFrame(
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{"bool_data": [False, False, True, True], "str_data": ["a", "b", "c", "a"]}
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)
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result = df.describe()
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expected = DataFrame(
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{"bool_data": [4, 2, False, 2], "str_data": [4, 3, "a", 2]},
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index=["count", "unique", "top", "freq"],
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)
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tm.assert_frame_equal(result, expected)
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def test_describe_categorical(self):
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df = DataFrame({"value": np.random.randint(0, 10000, 100)})
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labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)]
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cat_labels = Categorical(labels, labels)
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df = df.sort_values(by=["value"], ascending=True)
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df["value_group"] = pd.cut(
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df.value, range(0, 10500, 500), right=False, labels=cat_labels
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)
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cat = df
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# Categoricals should not show up together with numerical columns
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result = cat.describe()
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assert len(result.columns) == 1
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# In a frame, describe() for the cat should be the same as for string
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# arrays (count, unique, top, freq)
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cat = Categorical(
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["a", "b", "b", "b"], categories=["a", "b", "c"], ordered=True
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)
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s = Series(cat)
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result = s.describe()
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expected = Series([4, 2, "b", 3], index=["count", "unique", "top", "freq"])
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tm.assert_series_equal(result, expected)
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cat = Series(Categorical(["a", "b", "c", "c"]))
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df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]})
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result = df3.describe()
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tm.assert_numpy_array_equal(result["cat"].values, result["s"].values)
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def test_describe_empty_categorical_column(self):
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# GH#26397
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# Ensure the index of an empty categorical DataFrame column
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# also contains (count, unique, top, freq)
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df = DataFrame({"empty_col": Categorical([])})
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result = df.describe()
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expected = DataFrame(
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{"empty_col": [0, 0, np.nan, np.nan]},
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index=["count", "unique", "top", "freq"],
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dtype="object",
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)
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tm.assert_frame_equal(result, expected)
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# ensure NaN, not None
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assert np.isnan(result.iloc[2, 0])
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assert np.isnan(result.iloc[3, 0])
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def test_describe_categorical_columns(self):
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# GH#11558
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columns = pd.CategoricalIndex(["int1", "int2", "obj"], ordered=True, name="XXX")
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df = DataFrame(
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{
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"int1": [10, 20, 30, 40, 50],
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"int2": [10, 20, 30, 40, 50],
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"obj": ["A", 0, None, "X", 1],
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},
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columns=columns,
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)
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result = df.describe()
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exp_columns = pd.CategoricalIndex(
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["int1", "int2"],
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categories=["int1", "int2", "obj"],
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ordered=True,
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name="XXX",
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)
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expected = DataFrame(
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{
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"int1": [5, 30, df.int1.std(), 10, 20, 30, 40, 50],
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"int2": [5, 30, df.int2.std(), 10, 20, 30, 40, 50],
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},
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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columns=exp_columns,
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)
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tm.assert_frame_equal(result, expected)
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tm.assert_categorical_equal(result.columns.values, expected.columns.values)
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def test_describe_datetime_columns(self):
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columns = pd.DatetimeIndex(
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["2011-01-01", "2011-02-01", "2011-03-01"],
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freq="MS",
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tz="US/Eastern",
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name="XXX",
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)
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df = DataFrame(
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{
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0: [10, 20, 30, 40, 50],
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1: [10, 20, 30, 40, 50],
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2: ["A", 0, None, "X", 1],
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}
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)
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df.columns = columns
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result = df.describe()
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exp_columns = pd.DatetimeIndex(
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["2011-01-01", "2011-02-01"], freq="MS", tz="US/Eastern", name="XXX"
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)
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expected = DataFrame(
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{
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0: [5, 30, df.iloc[:, 0].std(), 10, 20, 30, 40, 50],
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1: [5, 30, df.iloc[:, 1].std(), 10, 20, 30, 40, 50],
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},
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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expected.columns = exp_columns
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tm.assert_frame_equal(result, expected)
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assert result.columns.freq == "MS"
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assert result.columns.tz == expected.columns.tz
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def test_describe_timedelta_values(self):
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# GH#6145
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t1 = pd.timedelta_range("1 days", freq="D", periods=5)
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t2 = pd.timedelta_range("1 hours", freq="H", periods=5)
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df = DataFrame({"t1": t1, "t2": t2})
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expected = DataFrame(
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{
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"t1": [
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5,
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pd.Timedelta("3 days"),
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df.iloc[:, 0].std(),
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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pd.Timedelta("3 days"),
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pd.Timedelta("4 days"),
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pd.Timedelta("5 days"),
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],
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"t2": [
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5,
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pd.Timedelta("3 hours"),
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df.iloc[:, 1].std(),
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pd.Timedelta("1 hours"),
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pd.Timedelta("2 hours"),
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pd.Timedelta("3 hours"),
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pd.Timedelta("4 hours"),
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pd.Timedelta("5 hours"),
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],
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},
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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result = df.describe()
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tm.assert_frame_equal(result, expected)
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exp_repr = (
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" t1 t2\n"
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"count 5 5\n"
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"mean 3 days 00:00:00 0 days 03:00:00\n"
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"std 1 days 13:56:50.394919273 0 days 01:34:52.099788303\n"
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"min 1 days 00:00:00 0 days 01:00:00\n"
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"25% 2 days 00:00:00 0 days 02:00:00\n"
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"50% 3 days 00:00:00 0 days 03:00:00\n"
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"75% 4 days 00:00:00 0 days 04:00:00\n"
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"max 5 days 00:00:00 0 days 05:00:00"
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)
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assert repr(result) == exp_repr
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def test_describe_tz_values(self, tz_naive_fixture):
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# GH#21332
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tz = tz_naive_fixture
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s1 = Series(range(5))
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start = Timestamp(2018, 1, 1)
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end = Timestamp(2018, 1, 5)
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s2 = Series(date_range(start, end, tz=tz))
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df = DataFrame({"s1": s1, "s2": s2})
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expected = DataFrame(
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{
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"s1": [5, 2, 0, 1, 2, 3, 4, 1.581139],
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"s2": [
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5,
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Timestamp(2018, 1, 3).tz_localize(tz),
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start.tz_localize(tz),
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s2[1],
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s2[2],
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s2[3],
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end.tz_localize(tz),
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np.nan,
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],
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},
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index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
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)
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result = df.describe(include="all", datetime_is_numeric=True)
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tm.assert_frame_equal(result, expected)
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def test_datetime_is_numeric_includes_datetime(self):
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df = DataFrame({"a": date_range("2012", periods=3), "b": [1, 2, 3]})
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result = df.describe(datetime_is_numeric=True)
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expected = DataFrame(
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{
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"a": [
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3,
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Timestamp("2012-01-02"),
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Timestamp("2012-01-01"),
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Timestamp("2012-01-01T12:00:00"),
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Timestamp("2012-01-02"),
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Timestamp("2012-01-02T12:00:00"),
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Timestamp("2012-01-03"),
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np.nan,
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],
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"b": [3, 2, 1, 1.5, 2, 2.5, 3, 1],
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},
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index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"],
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)
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tm.assert_frame_equal(result, expected)
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def test_describe_tz_values2(self):
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tz = "CET"
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s1 = Series(range(5))
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start = Timestamp(2018, 1, 1)
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end = Timestamp(2018, 1, 5)
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s2 = Series(date_range(start, end, tz=tz))
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df = DataFrame({"s1": s1, "s2": s2})
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s1_ = s1.describe()
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s2_ = Series(
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[
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5,
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5,
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s2.value_counts().index[0],
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1,
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start.tz_localize(tz),
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end.tz_localize(tz),
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],
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index=["count", "unique", "top", "freq", "first", "last"],
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)
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idx = [
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"count",
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"unique",
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"top",
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"freq",
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"first",
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"last",
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"mean",
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"std",
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"min",
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"25%",
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"50%",
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"75%",
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"max",
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]
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expected = pd.concat([s1_, s2_], axis=1, keys=["s1", "s2"]).loc[idx]
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with tm.assert_produces_warning(FutureWarning):
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result = df.describe(include="all")
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tm.assert_frame_equal(result, expected)
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def test_describe_percentiles_integer_idx(self):
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# GH#26660
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df = DataFrame({"x": [1]})
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pct = np.linspace(0, 1, 10 + 1)
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result = df.describe(percentiles=pct)
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expected = DataFrame(
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{"x": [1.0, 1.0, np.NaN, 1.0, *(1.0 for _ in pct), 1.0]},
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index=[
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"count",
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"mean",
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"std",
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"min",
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"0%",
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"10%",
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"20%",
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"30%",
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"40%",
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"50%",
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"60%",
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"70%",
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"80%",
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"90%",
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"100%",
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"max",
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],
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)
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tm.assert_frame_equal(result, expected)
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def test_describe_does_not_raise_error_for_dictlike_elements(self):
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# GH#32409
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df = DataFrame([{"test": {"a": "1"}}, {"test": {"a": "2"}}])
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expected = DataFrame(
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{"test": [2, 2, {"a": "1"}, 1]}, index=["count", "unique", "top", "freq"]
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)
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result = df.describe()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("exclude", ["x", "y", ["x", "y"], ["x", "z"]])
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def test_describe_when_include_all_exclude_not_allowed(self, exclude):
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"""
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When include is 'all', then setting exclude != None is not allowed.
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"""
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df = DataFrame({"x": [1], "y": [2], "z": [3]})
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msg = "exclude must be None when include is 'all'"
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with pytest.raises(ValueError, match=msg):
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df.describe(include="all", exclude=exclude)
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def test_describe_with_duplicate_columns(self):
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df = DataFrame(
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[[1, 1, 1], [2, 2, 2], [3, 3, 3]],
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columns=["bar", "a", "a"],
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dtype="float64",
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)
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result = df.describe()
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ser = df.iloc[:, 0].describe()
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expected = pd.concat([ser, ser, ser], keys=df.columns, axis=1)
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tm.assert_frame_equal(result, expected)
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