964 lines
34 KiB
Python
964 lines
34 KiB
Python
"""
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test with the TimeGrouper / grouping with datetimes
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"""
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from datetime import (
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datetime,
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timedelta,
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)
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import numpy as np
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import pytest
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import pytz
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import pandas as pd
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from pandas import (
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DataFrame,
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DatetimeIndex,
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Index,
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MultiIndex,
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Series,
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Timestamp,
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date_range,
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offsets,
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)
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import pandas._testing as tm
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from pandas.core.groupby.grouper import Grouper
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from pandas.core.groupby.ops import BinGrouper
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@pytest.fixture
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def frame_for_truncated_bingrouper():
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"""
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DataFrame used by groupby_with_truncated_bingrouper, made into
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a separate fixture for easier reuse in
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test_groupby_apply_timegrouper_with_nat_apply_squeeze
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"""
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df = DataFrame(
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{
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"Quantity": [18, 3, 5, 1, 9, 3],
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"Date": [
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Timestamp(2013, 9, 1, 13, 0),
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Timestamp(2013, 9, 1, 13, 5),
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Timestamp(2013, 10, 1, 20, 0),
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Timestamp(2013, 10, 3, 10, 0),
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pd.NaT,
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Timestamp(2013, 9, 2, 14, 0),
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],
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}
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)
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return df
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@pytest.fixture
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def groupby_with_truncated_bingrouper(frame_for_truncated_bingrouper):
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"""
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GroupBy object such that gb._grouper is a BinGrouper and
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len(gb._grouper.result_index) < len(gb._grouper.group_keys_seq)
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Aggregations on this groupby should have
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dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date")
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As either the index or an index level.
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"""
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df = frame_for_truncated_bingrouper
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tdg = Grouper(key="Date", freq="5D")
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gb = df.groupby(tdg)
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# check we're testing the case we're interested in
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assert len(gb._grouper.result_index) != len(gb._grouper.group_keys_seq)
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return gb
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class TestGroupBy:
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def test_groupby_with_timegrouper(self):
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# GH 4161
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# TimeGrouper requires a sorted index
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# also verifies that the resultant index has the correct name
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df_original = DataFrame(
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{
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"Buyer": "Carl Carl Carl Carl Joe Carl".split(),
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"Quantity": [18, 3, 5, 1, 9, 3],
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"Date": [
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datetime(2013, 9, 1, 13, 0),
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datetime(2013, 9, 1, 13, 5),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 3, 10, 0),
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datetime(2013, 12, 2, 12, 0),
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datetime(2013, 9, 2, 14, 0),
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],
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}
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)
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# GH 6908 change target column's order
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df_reordered = df_original.sort_values(by="Quantity")
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for df in [df_original, df_reordered]:
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df = df.set_index(["Date"])
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exp_dti = date_range(
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"20130901",
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"20131205",
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freq="5D",
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name="Date",
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inclusive="left",
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unit=df.index.unit,
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)
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expected = DataFrame(
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{"Buyer": 0, "Quantity": 0},
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index=exp_dti,
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)
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# Cast to object to avoid implicit cast when setting entry to "CarlCarlCarl"
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expected = expected.astype({"Buyer": object})
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expected.iloc[0, 0] = "CarlCarlCarl"
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expected.iloc[6, 0] = "CarlCarl"
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expected.iloc[18, 0] = "Joe"
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expected.iloc[[0, 6, 18], 1] = np.array([24, 6, 9], dtype="int64")
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result1 = df.resample("5D").sum()
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tm.assert_frame_equal(result1, expected)
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df_sorted = df.sort_index()
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result2 = df_sorted.groupby(Grouper(freq="5D")).sum()
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tm.assert_frame_equal(result2, expected)
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result3 = df.groupby(Grouper(freq="5D")).sum()
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tm.assert_frame_equal(result3, expected)
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@pytest.mark.parametrize("should_sort", [True, False])
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def test_groupby_with_timegrouper_methods(self, should_sort):
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# GH 3881
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# make sure API of timegrouper conforms
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df = DataFrame(
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{
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"Branch": "A A A A A B".split(),
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"Buyer": "Carl Mark Carl Joe Joe Carl".split(),
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"Quantity": [1, 3, 5, 8, 9, 3],
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"Date": [
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datetime(2013, 1, 1, 13, 0),
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datetime(2013, 1, 1, 13, 5),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 2, 10, 0),
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datetime(2013, 12, 2, 12, 0),
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datetime(2013, 12, 2, 14, 0),
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],
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}
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)
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if should_sort:
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df = df.sort_values(by="Quantity", ascending=False)
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df = df.set_index("Date", drop=False)
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g = df.groupby(Grouper(freq="6ME"))
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assert g.group_keys
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assert isinstance(g._grouper, BinGrouper)
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groups = g.groups
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assert isinstance(groups, dict)
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assert len(groups) == 3
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def test_timegrouper_with_reg_groups(self):
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# GH 3794
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# allow combination of timegrouper/reg groups
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df_original = DataFrame(
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{
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"Branch": "A A A A A A A B".split(),
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"Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
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"Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
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"Date": [
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datetime(2013, 1, 1, 13, 0),
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datetime(2013, 1, 1, 13, 5),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 2, 10, 0),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 2, 10, 0),
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datetime(2013, 12, 2, 12, 0),
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datetime(2013, 12, 2, 14, 0),
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],
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}
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).set_index("Date")
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df_sorted = df_original.sort_values(by="Quantity", ascending=False)
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for df in [df_original, df_sorted]:
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expected = DataFrame(
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{
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"Buyer": "Carl Joe Mark".split(),
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"Quantity": [10, 18, 3],
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"Date": [
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datetime(2013, 12, 31, 0, 0),
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datetime(2013, 12, 31, 0, 0),
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datetime(2013, 12, 31, 0, 0),
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],
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}
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).set_index(["Date", "Buyer"])
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msg = "The default value of numeric_only"
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result = df.groupby([Grouper(freq="YE"), "Buyer"]).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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expected = DataFrame(
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{
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"Buyer": "Carl Mark Carl Joe".split(),
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"Quantity": [1, 3, 9, 18],
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"Date": [
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datetime(2013, 1, 1, 0, 0),
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datetime(2013, 1, 1, 0, 0),
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datetime(2013, 7, 1, 0, 0),
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datetime(2013, 7, 1, 0, 0),
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],
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}
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).set_index(["Date", "Buyer"])
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result = df.groupby([Grouper(freq="6MS"), "Buyer"]).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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df_original = DataFrame(
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{
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"Branch": "A A A A A A A B".split(),
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"Buyer": "Carl Mark Carl Carl Joe Joe Joe Carl".split(),
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"Quantity": [1, 3, 5, 1, 8, 1, 9, 3],
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"Date": [
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datetime(2013, 10, 1, 13, 0),
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datetime(2013, 10, 1, 13, 5),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 2, 10, 0),
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datetime(2013, 10, 1, 20, 0),
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datetime(2013, 10, 2, 10, 0),
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datetime(2013, 10, 2, 12, 0),
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datetime(2013, 10, 2, 14, 0),
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],
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}
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).set_index("Date")
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df_sorted = df_original.sort_values(by="Quantity", ascending=False)
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for df in [df_original, df_sorted]:
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expected = DataFrame(
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{
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"Buyer": "Carl Joe Mark Carl Joe".split(),
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"Quantity": [6, 8, 3, 4, 10],
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"Date": [
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datetime(2013, 10, 1, 0, 0),
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datetime(2013, 10, 1, 0, 0),
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datetime(2013, 10, 1, 0, 0),
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datetime(2013, 10, 2, 0, 0),
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datetime(2013, 10, 2, 0, 0),
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],
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}
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).set_index(["Date", "Buyer"])
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result = df.groupby([Grouper(freq="1D"), "Buyer"]).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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result = df.groupby([Grouper(freq="1ME"), "Buyer"]).sum(numeric_only=True)
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expected = DataFrame(
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{
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"Buyer": "Carl Joe Mark".split(),
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"Quantity": [10, 18, 3],
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"Date": [
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datetime(2013, 10, 31, 0, 0),
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datetime(2013, 10, 31, 0, 0),
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datetime(2013, 10, 31, 0, 0),
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],
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}
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).set_index(["Date", "Buyer"])
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tm.assert_frame_equal(result, expected)
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# passing the name
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df = df.reset_index()
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result = df.groupby([Grouper(freq="1ME", key="Date"), "Buyer"]).sum(
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numeric_only=True
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)
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tm.assert_frame_equal(result, expected)
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with pytest.raises(KeyError, match="'The grouper name foo is not found'"):
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df.groupby([Grouper(freq="1ME", key="foo"), "Buyer"]).sum()
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# passing the level
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df = df.set_index("Date")
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result = df.groupby([Grouper(freq="1ME", level="Date"), "Buyer"]).sum(
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numeric_only=True
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)
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tm.assert_frame_equal(result, expected)
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result = df.groupby([Grouper(freq="1ME", level=0), "Buyer"]).sum(
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numeric_only=True
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)
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tm.assert_frame_equal(result, expected)
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with pytest.raises(ValueError, match="The level foo is not valid"):
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df.groupby([Grouper(freq="1ME", level="foo"), "Buyer"]).sum()
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# multi names
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df = df.copy()
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df["Date"] = df.index + offsets.MonthEnd(2)
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result = df.groupby([Grouper(freq="1ME", key="Date"), "Buyer"]).sum(
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numeric_only=True
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)
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expected = DataFrame(
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{
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"Buyer": "Carl Joe Mark".split(),
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"Quantity": [10, 18, 3],
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"Date": [
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datetime(2013, 11, 30, 0, 0),
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datetime(2013, 11, 30, 0, 0),
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datetime(2013, 11, 30, 0, 0),
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],
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}
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).set_index(["Date", "Buyer"])
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tm.assert_frame_equal(result, expected)
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# error as we have both a level and a name!
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msg = "The Grouper cannot specify both a key and a level!"
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with pytest.raises(ValueError, match=msg):
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df.groupby(
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[Grouper(freq="1ME", key="Date", level="Date"), "Buyer"]
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).sum()
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# single groupers
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expected = DataFrame(
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[[31]],
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columns=["Quantity"],
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index=DatetimeIndex(
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[datetime(2013, 10, 31, 0, 0)], freq=offsets.MonthEnd(), name="Date"
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),
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)
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result = df.groupby(Grouper(freq="1ME")).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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result = df.groupby([Grouper(freq="1ME")]).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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expected.index = expected.index.shift(1)
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assert expected.index.freq == offsets.MonthEnd()
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result = df.groupby(Grouper(freq="1ME", key="Date")).sum(numeric_only=True)
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tm.assert_frame_equal(result, expected)
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result = df.groupby([Grouper(freq="1ME", key="Date")]).sum(
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numeric_only=True
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("freq", ["D", "ME", "YE", "QE-APR"])
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def test_timegrouper_with_reg_groups_freq(self, freq):
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# GH 6764 multiple grouping with/without sort
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df = DataFrame(
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{
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"date": pd.to_datetime(
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[
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"20121002",
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"20121007",
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"20130130",
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"20130202",
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"20130305",
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"20121002",
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"20121207",
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"20130130",
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"20130202",
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"20130305",
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"20130202",
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"20130305",
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]
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),
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"user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
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"whole_cost": [
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1790,
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364,
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280,
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259,
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201,
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623,
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90,
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312,
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359,
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301,
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359,
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801,
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],
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"cost1": [12, 15, 10, 24, 39, 1, 0, 90, 45, 34, 1, 12],
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}
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).set_index("date")
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expected = (
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df.groupby("user_id")["whole_cost"]
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.resample(freq)
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.sum(min_count=1) # XXX
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.dropna()
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.reorder_levels(["date", "user_id"])
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.sort_index()
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.astype("int64")
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)
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expected.name = "whole_cost"
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result1 = (
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df.sort_index().groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
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)
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tm.assert_series_equal(result1, expected)
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result2 = df.groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
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tm.assert_series_equal(result2, expected)
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|
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def test_timegrouper_get_group(self):
|
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# GH 6914
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|
|
df_original = DataFrame(
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{
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"Buyer": "Carl Joe Joe Carl Joe Carl".split(),
|
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"Quantity": [18, 3, 5, 1, 9, 3],
|
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"Date": [
|
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datetime(2013, 9, 1, 13, 0),
|
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datetime(2013, 9, 1, 13, 5),
|
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datetime(2013, 10, 1, 20, 0),
|
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datetime(2013, 10, 3, 10, 0),
|
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datetime(2013, 12, 2, 12, 0),
|
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datetime(2013, 9, 2, 14, 0),
|
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],
|
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}
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)
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df_reordered = df_original.sort_values(by="Quantity")
|
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|
|
# single grouping
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expected_list = [
|
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df_original.iloc[[0, 1, 5]],
|
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df_original.iloc[[2, 3]],
|
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df_original.iloc[[4]],
|
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]
|
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dt_list = ["2013-09-30", "2013-10-31", "2013-12-31"]
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|
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for df in [df_original, df_reordered]:
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grouped = df.groupby(Grouper(freq="ME", key="Date"))
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for t, expected in zip(dt_list, expected_list):
|
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dt = Timestamp(t)
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result = grouped.get_group(dt)
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tm.assert_frame_equal(result, expected)
|
|
|
|
# multiple grouping
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expected_list = [
|
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df_original.iloc[[1]],
|
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df_original.iloc[[3]],
|
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df_original.iloc[[4]],
|
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]
|
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g_list = [("Joe", "2013-09-30"), ("Carl", "2013-10-31"), ("Joe", "2013-12-31")]
|
|
|
|
for df in [df_original, df_reordered]:
|
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grouped = df.groupby(["Buyer", Grouper(freq="ME", key="Date")])
|
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for (b, t), expected in zip(g_list, expected_list):
|
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dt = Timestamp(t)
|
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result = grouped.get_group((b, dt))
|
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tm.assert_frame_equal(result, expected)
|
|
|
|
# with index
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|
df_original = df_original.set_index("Date")
|
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df_reordered = df_original.sort_values(by="Quantity")
|
|
|
|
expected_list = [
|
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df_original.iloc[[0, 1, 5]],
|
|
df_original.iloc[[2, 3]],
|
|
df_original.iloc[[4]],
|
|
]
|
|
|
|
for df in [df_original, df_reordered]:
|
|
grouped = df.groupby(Grouper(freq="ME"))
|
|
for t, expected in zip(dt_list, expected_list):
|
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dt = Timestamp(t)
|
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result = grouped.get_group(dt)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_timegrouper_apply_return_type_series(self):
|
|
# Using `apply` with the `TimeGrouper` should give the
|
|
# same return type as an `apply` with a `Grouper`.
|
|
# Issue #11742
|
|
df = DataFrame({"date": ["10/10/2000", "11/10/2000"], "value": [10, 13]})
|
|
df_dt = df.copy()
|
|
df_dt["date"] = pd.to_datetime(df_dt["date"])
|
|
|
|
def sumfunc_series(x):
|
|
return Series([x["value"].sum()], ("sum",))
|
|
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
|
expected = df.groupby(Grouper(key="date")).apply(sumfunc_series)
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
|
result = df_dt.groupby(Grouper(freq="ME", key="date")).apply(sumfunc_series)
|
|
tm.assert_frame_equal(
|
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result.reset_index(drop=True), expected.reset_index(drop=True)
|
|
)
|
|
|
|
def test_timegrouper_apply_return_type_value(self):
|
|
# Using `apply` with the `TimeGrouper` should give the
|
|
# same return type as an `apply` with a `Grouper`.
|
|
# Issue #11742
|
|
df = DataFrame({"date": ["10/10/2000", "11/10/2000"], "value": [10, 13]})
|
|
df_dt = df.copy()
|
|
df_dt["date"] = pd.to_datetime(df_dt["date"])
|
|
|
|
def sumfunc_value(x):
|
|
return x.value.sum()
|
|
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
|
expected = df.groupby(Grouper(key="date")).apply(sumfunc_value)
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
|
result = df_dt.groupby(Grouper(freq="ME", key="date")).apply(sumfunc_value)
|
|
tm.assert_series_equal(
|
|
result.reset_index(drop=True), expected.reset_index(drop=True)
|
|
)
|
|
|
|
def test_groupby_groups_datetimeindex(self):
|
|
# GH#1430
|
|
periods = 1000
|
|
ind = date_range(start="2012/1/1", freq="5min", periods=periods)
|
|
df = DataFrame(
|
|
{"high": np.arange(periods), "low": np.arange(periods)}, index=ind
|
|
)
|
|
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
|
|
|
|
# it works!
|
|
groups = grouped.groups
|
|
assert isinstance(next(iter(groups.keys())), datetime)
|
|
|
|
def test_groupby_groups_datetimeindex2(self):
|
|
# GH#11442
|
|
index = date_range("2015/01/01", periods=5, name="date")
|
|
df = DataFrame({"A": [5, 6, 7, 8, 9], "B": [1, 2, 3, 4, 5]}, index=index)
|
|
result = df.groupby(level="date").groups
|
|
dates = ["2015-01-05", "2015-01-04", "2015-01-03", "2015-01-02", "2015-01-01"]
|
|
expected = {
|
|
Timestamp(date): DatetimeIndex([date], name="date") for date in dates
|
|
}
|
|
tm.assert_dict_equal(result, expected)
|
|
|
|
grouped = df.groupby(level="date")
|
|
for date in dates:
|
|
result = grouped.get_group(date)
|
|
data = [[df.loc[date, "A"], df.loc[date, "B"]]]
|
|
expected_index = DatetimeIndex(
|
|
[date], name="date", freq="D", dtype=index.dtype
|
|
)
|
|
expected = DataFrame(data, columns=list("AB"), index=expected_index)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_groups_datetimeindex_tz(self):
|
|
# GH 3950
|
|
dates = [
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
]
|
|
df = DataFrame(
|
|
{
|
|
"label": ["a", "a", "a", "b", "b", "b"],
|
|
"datetime": dates,
|
|
"value1": np.arange(6, dtype="int64"),
|
|
"value2": [1, 2] * 3,
|
|
}
|
|
)
|
|
df["datetime"] = df["datetime"].apply(lambda d: Timestamp(d, tz="US/Pacific"))
|
|
|
|
exp_idx1 = DatetimeIndex(
|
|
[
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
"2011-07-19 09:00:00",
|
|
],
|
|
tz="US/Pacific",
|
|
name="datetime",
|
|
)
|
|
exp_idx2 = Index(["a", "b"] * 3, name="label")
|
|
exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
|
|
expected = DataFrame(
|
|
{"value1": [0, 3, 1, 4, 2, 5], "value2": [1, 2, 2, 1, 1, 2]},
|
|
index=exp_idx,
|
|
columns=["value1", "value2"],
|
|
)
|
|
|
|
result = df.groupby(["datetime", "label"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# by level
|
|
didx = DatetimeIndex(dates, tz="Asia/Tokyo")
|
|
df = DataFrame(
|
|
{"value1": np.arange(6, dtype="int64"), "value2": [1, 2, 3, 1, 2, 3]},
|
|
index=didx,
|
|
)
|
|
|
|
exp_idx = DatetimeIndex(
|
|
["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
|
|
tz="Asia/Tokyo",
|
|
)
|
|
expected = DataFrame(
|
|
{"value1": [3, 5, 7], "value2": [2, 4, 6]},
|
|
index=exp_idx,
|
|
columns=["value1", "value2"],
|
|
)
|
|
|
|
result = df.groupby(level=0).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_frame_datetime64_handling_groupby(self):
|
|
# it works!
|
|
df = DataFrame(
|
|
[(3, np.datetime64("2012-07-03")), (3, np.datetime64("2012-07-04"))],
|
|
columns=["a", "date"],
|
|
)
|
|
result = df.groupby("a").first()
|
|
assert result["date"][3] == Timestamp("2012-07-03")
|
|
|
|
def test_groupby_multi_timezone(self):
|
|
# combining multiple / different timezones yields UTC
|
|
df = DataFrame(
|
|
{
|
|
"value": range(5),
|
|
"date": [
|
|
"2000-01-28 16:47:00",
|
|
"2000-01-29 16:48:00",
|
|
"2000-01-30 16:49:00",
|
|
"2000-01-31 16:50:00",
|
|
"2000-01-01 16:50:00",
|
|
],
|
|
"tz": [
|
|
"America/Chicago",
|
|
"America/Chicago",
|
|
"America/Los_Angeles",
|
|
"America/Chicago",
|
|
"America/New_York",
|
|
],
|
|
}
|
|
)
|
|
|
|
result = df.groupby("tz", group_keys=False).date.apply(
|
|
lambda x: pd.to_datetime(x).dt.tz_localize(x.name)
|
|
)
|
|
|
|
expected = Series(
|
|
[
|
|
Timestamp("2000-01-28 16:47:00-0600", tz="America/Chicago"),
|
|
Timestamp("2000-01-29 16:48:00-0600", tz="America/Chicago"),
|
|
Timestamp("2000-01-30 16:49:00-0800", tz="America/Los_Angeles"),
|
|
Timestamp("2000-01-31 16:50:00-0600", tz="America/Chicago"),
|
|
Timestamp("2000-01-01 16:50:00-0500", tz="America/New_York"),
|
|
],
|
|
name="date",
|
|
dtype=object,
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
tz = "America/Chicago"
|
|
res_values = df.groupby("tz").date.get_group(tz)
|
|
result = pd.to_datetime(res_values).dt.tz_localize(tz)
|
|
exp_values = Series(
|
|
["2000-01-28 16:47:00", "2000-01-29 16:48:00", "2000-01-31 16:50:00"],
|
|
index=[0, 1, 3],
|
|
name="date",
|
|
)
|
|
expected = pd.to_datetime(exp_values).dt.tz_localize(tz)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_groups_periods(self):
|
|
dates = [
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
]
|
|
df = DataFrame(
|
|
{
|
|
"label": ["a", "a", "a", "b", "b", "b"],
|
|
"period": [pd.Period(d, freq="h") for d in dates],
|
|
"value1": np.arange(6, dtype="int64"),
|
|
"value2": [1, 2] * 3,
|
|
}
|
|
)
|
|
|
|
exp_idx1 = pd.PeriodIndex(
|
|
[
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 07:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 08:00:00",
|
|
"2011-07-19 09:00:00",
|
|
"2011-07-19 09:00:00",
|
|
],
|
|
freq="h",
|
|
name="period",
|
|
)
|
|
exp_idx2 = Index(["a", "b"] * 3, name="label")
|
|
exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
|
|
expected = DataFrame(
|
|
{"value1": [0, 3, 1, 4, 2, 5], "value2": [1, 2, 2, 1, 1, 2]},
|
|
index=exp_idx,
|
|
columns=["value1", "value2"],
|
|
)
|
|
|
|
result = df.groupby(["period", "label"]).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# by level
|
|
didx = pd.PeriodIndex(dates, freq="h")
|
|
df = DataFrame(
|
|
{"value1": np.arange(6, dtype="int64"), "value2": [1, 2, 3, 1, 2, 3]},
|
|
index=didx,
|
|
)
|
|
|
|
exp_idx = pd.PeriodIndex(
|
|
["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"],
|
|
freq="h",
|
|
)
|
|
expected = DataFrame(
|
|
{"value1": [3, 5, 7], "value2": [2, 4, 6]},
|
|
index=exp_idx,
|
|
columns=["value1", "value2"],
|
|
)
|
|
|
|
result = df.groupby(level=0).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_first_datetime64(self):
|
|
df = DataFrame([(1, 1351036800000000000), (2, 1351036800000000000)])
|
|
df[1] = df[1].astype("M8[ns]")
|
|
|
|
assert issubclass(df[1].dtype.type, np.datetime64)
|
|
|
|
result = df.groupby(level=0).first()
|
|
got_dt = result[1].dtype
|
|
assert issubclass(got_dt.type, np.datetime64)
|
|
|
|
result = df[1].groupby(level=0).first()
|
|
got_dt = result.dtype
|
|
assert issubclass(got_dt.type, np.datetime64)
|
|
|
|
def test_groupby_max_datetime64(self):
|
|
# GH 5869
|
|
# datetimelike dtype conversion from int
|
|
df = DataFrame({"A": Timestamp("20130101"), "B": np.arange(5)})
|
|
# TODO: can we retain second reso in .apply here?
|
|
expected = df.groupby("A")["A"].apply(lambda x: x.max()).astype("M8[s]")
|
|
result = df.groupby("A")["A"].max()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_datetime64_32_bit(self):
|
|
# GH 6410 / numpy 4328
|
|
# 32-bit under 1.9-dev indexing issue
|
|
|
|
df = DataFrame({"A": range(2), "B": [Timestamp("2000-01-1")] * 2})
|
|
result = df.groupby("A")["B"].transform("min")
|
|
expected = Series([Timestamp("2000-01-1")] * 2, name="B")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_with_timezone_selection(self):
|
|
# GH 11616
|
|
# Test that column selection returns output in correct timezone.
|
|
|
|
df = DataFrame(
|
|
{
|
|
"factor": np.random.default_rng(2).integers(0, 3, size=60),
|
|
"time": date_range("01/01/2000 00:00", periods=60, freq="s", tz="UTC"),
|
|
}
|
|
)
|
|
df1 = df.groupby("factor").max()["time"]
|
|
df2 = df.groupby("factor")["time"].max()
|
|
tm.assert_series_equal(df1, df2)
|
|
|
|
def test_timezone_info(self):
|
|
# see gh-11682: Timezone info lost when broadcasting
|
|
# scalar datetime to DataFrame
|
|
|
|
df = DataFrame({"a": [1], "b": [datetime.now(pytz.utc)]})
|
|
assert df["b"][0].tzinfo == pytz.utc
|
|
df = DataFrame({"a": [1, 2, 3]})
|
|
df["b"] = datetime.now(pytz.utc)
|
|
assert df["b"][0].tzinfo == pytz.utc
|
|
|
|
def test_datetime_count(self):
|
|
df = DataFrame(
|
|
{"a": [1, 2, 3] * 2, "dates": date_range("now", periods=6, freq="min")}
|
|
)
|
|
result = df.groupby("a").dates.count()
|
|
expected = Series([2, 2, 2], index=Index([1, 2, 3], name="a"), name="dates")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_first_last_max_min_on_time_data(self):
|
|
# GH 10295
|
|
# Verify that NaT is not in the result of max, min, first and last on
|
|
# Dataframe with datetime or timedelta values.
|
|
df_test = DataFrame(
|
|
{
|
|
"dt": [
|
|
np.nan,
|
|
"2015-07-24 10:10",
|
|
"2015-07-25 11:11",
|
|
"2015-07-23 12:12",
|
|
np.nan,
|
|
],
|
|
"td": [
|
|
np.nan,
|
|
timedelta(days=1),
|
|
timedelta(days=2),
|
|
timedelta(days=3),
|
|
np.nan,
|
|
],
|
|
}
|
|
)
|
|
df_test.dt = pd.to_datetime(df_test.dt)
|
|
df_test["group"] = "A"
|
|
df_ref = df_test[df_test.dt.notna()]
|
|
|
|
grouped_test = df_test.groupby("group")
|
|
grouped_ref = df_ref.groupby("group")
|
|
|
|
tm.assert_frame_equal(grouped_ref.max(), grouped_test.max())
|
|
tm.assert_frame_equal(grouped_ref.min(), grouped_test.min())
|
|
tm.assert_frame_equal(grouped_ref.first(), grouped_test.first())
|
|
tm.assert_frame_equal(grouped_ref.last(), grouped_test.last())
|
|
|
|
def test_nunique_with_timegrouper_and_nat(self):
|
|
# GH 17575
|
|
test = DataFrame(
|
|
{
|
|
"time": [
|
|
Timestamp("2016-06-28 09:35:35"),
|
|
pd.NaT,
|
|
Timestamp("2016-06-28 16:46:28"),
|
|
],
|
|
"data": ["1", "2", "3"],
|
|
}
|
|
)
|
|
|
|
grouper = Grouper(key="time", freq="h")
|
|
result = test.groupby(grouper)["data"].nunique()
|
|
expected = test[test.time.notnull()].groupby(grouper)["data"].nunique()
|
|
expected.index = expected.index._with_freq(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_scalar_call_versus_list_call(self):
|
|
# Issue: 17530
|
|
data_frame = {
|
|
"location": ["shanghai", "beijing", "shanghai"],
|
|
"time": Series(
|
|
["2017-08-09 13:32:23", "2017-08-11 23:23:15", "2017-08-11 22:23:15"],
|
|
dtype="datetime64[ns]",
|
|
),
|
|
"value": [1, 2, 3],
|
|
}
|
|
data_frame = DataFrame(data_frame).set_index("time")
|
|
grouper = Grouper(freq="D")
|
|
|
|
grouped = data_frame.groupby(grouper)
|
|
result = grouped.count()
|
|
grouped = data_frame.groupby([grouper])
|
|
expected = grouped.count()
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_grouper_period_index(self):
|
|
# GH 32108
|
|
periods = 2
|
|
index = pd.period_range(
|
|
start="2018-01", periods=periods, freq="M", name="Month"
|
|
)
|
|
period_series = Series(range(periods), index=index)
|
|
result = period_series.groupby(period_series.index.month).sum()
|
|
|
|
expected = Series(
|
|
range(periods), index=Index(range(1, periods + 1), name=index.name)
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_apply_timegrouper_with_nat_dict_returns(
|
|
self, groupby_with_truncated_bingrouper
|
|
):
|
|
# GH#43500 case where gb._grouper.result_index and gb._grouper.group_keys_seq
|
|
# have different lengths that goes through the `isinstance(values[0], dict)`
|
|
# path
|
|
gb = groupby_with_truncated_bingrouper
|
|
|
|
res = gb["Quantity"].apply(lambda x: {"foo": len(x)})
|
|
|
|
df = gb.obj
|
|
unit = df["Date"]._values.unit
|
|
dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date", unit=unit)
|
|
mi = MultiIndex.from_arrays([dti, ["foo"] * len(dti)])
|
|
expected = Series([3, 0, 0, 0, 0, 0, 2], index=mi, name="Quantity")
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
def test_groupby_apply_timegrouper_with_nat_scalar_returns(
|
|
self, groupby_with_truncated_bingrouper
|
|
):
|
|
# GH#43500 Previously raised ValueError bc used index with incorrect
|
|
# length in wrap_applied_result
|
|
gb = groupby_with_truncated_bingrouper
|
|
|
|
res = gb["Quantity"].apply(lambda x: x.iloc[0] if len(x) else np.nan)
|
|
|
|
df = gb.obj
|
|
unit = df["Date"]._values.unit
|
|
dti = date_range("2013-09-01", "2013-10-01", freq="5D", name="Date", unit=unit)
|
|
expected = Series(
|
|
[18, np.nan, np.nan, np.nan, np.nan, np.nan, 5],
|
|
index=dti._with_freq(None),
|
|
name="Quantity",
|
|
)
|
|
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
def test_groupby_apply_timegrouper_with_nat_apply_squeeze(
|
|
self, frame_for_truncated_bingrouper
|
|
):
|
|
df = frame_for_truncated_bingrouper
|
|
|
|
# We need to create a GroupBy object with only one non-NaT group,
|
|
# so use a huge freq so that all non-NaT dates will be grouped together
|
|
tdg = Grouper(key="Date", freq="100YE")
|
|
gb = df.groupby(tdg)
|
|
|
|
# check that we will go through the singular_series path
|
|
# in _wrap_applied_output_series
|
|
assert gb.ngroups == 1
|
|
assert gb._selected_obj._get_axis(gb.axis).nlevels == 1
|
|
|
|
# function that returns a Series
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
|
res = gb.apply(lambda x: x["Quantity"] * 2)
|
|
|
|
dti = Index([Timestamp("2013-12-31")], dtype=df["Date"].dtype, name="Date")
|
|
expected = DataFrame(
|
|
[[36, 6, 6, 10, 2]],
|
|
index=dti,
|
|
columns=Index([0, 1, 5, 2, 3], name="Quantity"),
|
|
)
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
@pytest.mark.single_cpu
|
|
def test_groupby_agg_numba_timegrouper_with_nat(
|
|
self, groupby_with_truncated_bingrouper
|
|
):
|
|
pytest.importorskip("numba")
|
|
|
|
# See discussion in GH#43487
|
|
gb = groupby_with_truncated_bingrouper
|
|
|
|
result = gb["Quantity"].aggregate(
|
|
lambda values, index: np.nanmean(values), engine="numba"
|
|
)
|
|
|
|
expected = gb["Quantity"].aggregate("mean")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result_df = gb[["Quantity"]].aggregate(
|
|
lambda values, index: np.nanmean(values), engine="numba"
|
|
)
|
|
expected_df = gb[["Quantity"]].aggregate("mean")
|
|
tm.assert_frame_equal(result_df, expected_df)
|