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316 lines
8.9 KiB
316 lines
8.9 KiB
# Test GroupBy._positional_selector positional grouped indexing GH#42864
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import random
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import numpy as np
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import pytest
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import pandas as pd
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import pandas._testing as tm
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@pytest.mark.parametrize(
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"arg, expected_rows",
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[
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[0, [0, 1, 4]],
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[2, [5]],
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[5, []],
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[-1, [3, 4, 7]],
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[-2, [1, 6]],
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[-6, []],
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],
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)
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def test_int(slice_test_df, slice_test_grouped, arg, expected_rows):
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# Test single integer
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result = slice_test_grouped._positional_selector[arg]
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expected = slice_test_df.iloc[expected_rows]
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tm.assert_frame_equal(result, expected)
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def test_slice(slice_test_df, slice_test_grouped):
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# Test single slice
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result = slice_test_grouped._positional_selector[0:3:2]
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expected = slice_test_df.iloc[[0, 1, 4, 5]]
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"arg, expected_rows",
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[
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[[0, 2], [0, 1, 4, 5]],
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[[0, 2, -1], [0, 1, 3, 4, 5, 7]],
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[range(0, 3, 2), [0, 1, 4, 5]],
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[{0, 2}, [0, 1, 4, 5]],
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],
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ids=[
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"list",
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"negative",
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"range",
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"set",
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],
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)
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def test_list(slice_test_df, slice_test_grouped, arg, expected_rows):
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# Test lists of integers and integer valued iterables
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result = slice_test_grouped._positional_selector[arg]
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expected = slice_test_df.iloc[expected_rows]
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tm.assert_frame_equal(result, expected)
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def test_ints(slice_test_df, slice_test_grouped):
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# Test tuple of ints
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result = slice_test_grouped._positional_selector[0, 2, -1]
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expected = slice_test_df.iloc[[0, 1, 3, 4, 5, 7]]
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tm.assert_frame_equal(result, expected)
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def test_slices(slice_test_df, slice_test_grouped):
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# Test tuple of slices
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result = slice_test_grouped._positional_selector[:2, -2:]
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expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
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tm.assert_frame_equal(result, expected)
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def test_mix(slice_test_df, slice_test_grouped):
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# Test mixed tuple of ints and slices
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result = slice_test_grouped._positional_selector[0, 1, -2:]
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expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"arg, expected_rows",
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[
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[0, [0, 1, 4]],
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[[0, 2, -1], [0, 1, 3, 4, 5, 7]],
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[(slice(None, 2), slice(-2, None)), [0, 1, 2, 3, 4, 6, 7]],
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],
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)
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def test_as_index(slice_test_df, arg, expected_rows):
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# Test the default as_index behaviour
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result = slice_test_df.groupby("Group", sort=False)._positional_selector[arg]
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expected = slice_test_df.iloc[expected_rows]
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tm.assert_frame_equal(result, expected)
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def test_doc_examples():
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# Test the examples in the documentation
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df = pd.DataFrame(
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[["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]], columns=["A", "B"]
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)
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grouped = df.groupby("A", as_index=False)
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result = grouped._positional_selector[1:2]
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expected = pd.DataFrame([["a", 2], ["b", 5]], columns=["A", "B"], index=[1, 4])
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tm.assert_frame_equal(result, expected)
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result = grouped._positional_selector[1, -1]
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expected = pd.DataFrame(
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[["a", 2], ["a", 3], ["b", 5]], columns=["A", "B"], index=[1, 2, 4]
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)
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tm.assert_frame_equal(result, expected)
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@pytest.fixture()
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def multiindex_data():
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ndates = 100
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nitems = 20
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dates = pd.date_range("20130101", periods=ndates, freq="D")
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items = [f"item {i}" for i in range(nitems)]
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data = {}
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for date in dates:
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nitems_for_date = nitems - random.randint(0, 12)
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levels = [
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(item, random.randint(0, 10000) / 100, random.randint(0, 10000) / 100)
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for item in items[:nitems_for_date]
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]
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levels.sort(key=lambda x: x[1])
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data[date] = levels
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return data
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def _make_df_from_data(data):
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rows = {}
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for date in data:
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for level in data[date]:
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rows[(date, level[0])] = {"A": level[1], "B": level[2]}
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df = pd.DataFrame.from_dict(rows, orient="index")
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df.index.names = ("Date", "Item")
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return df
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def test_multiindex(multiindex_data):
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# Test the multiindex mentioned as the use-case in the documentation
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df = _make_df_from_data(multiindex_data)
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result = df.groupby("Date", as_index=False).nth(slice(3, -3))
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sliced = {date: multiindex_data[date][3:-3] for date in multiindex_data}
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expected = _make_df_from_data(sliced)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("arg", [1, 5, 30, 1000, -1, -5, -30, -1000])
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@pytest.mark.parametrize("method", ["head", "tail"])
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@pytest.mark.parametrize("simulated", [True, False])
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def test_against_head_and_tail(arg, method, simulated):
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# Test gives the same results as grouped head and tail
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n_groups = 100
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n_rows_per_group = 30
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data = {
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"group": [
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f"group {g}" for j in range(n_rows_per_group) for g in range(n_groups)
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],
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"value": [
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f"group {g} row {j}"
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for j in range(n_rows_per_group)
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for g in range(n_groups)
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],
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}
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df = pd.DataFrame(data)
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grouped = df.groupby("group", as_index=False)
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size = arg if arg >= 0 else n_rows_per_group + arg
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if method == "head":
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result = grouped._positional_selector[:arg]
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if simulated:
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indices = []
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for j in range(size):
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for i in range(n_groups):
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if j * n_groups + i < n_groups * n_rows_per_group:
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indices.append(j * n_groups + i)
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expected = df.iloc[indices]
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else:
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expected = grouped.head(arg)
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else:
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result = grouped._positional_selector[-arg:]
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if simulated:
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indices = []
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for j in range(size):
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for i in range(n_groups):
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if (n_rows_per_group + j - size) * n_groups + i >= 0:
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indices.append((n_rows_per_group + j - size) * n_groups + i)
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expected = df.iloc[indices]
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else:
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expected = grouped.tail(arg)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("start", [None, 0, 1, 10, -1, -10])
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@pytest.mark.parametrize("stop", [None, 0, 1, 10, -1, -10])
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@pytest.mark.parametrize("step", [None, 1, 5])
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def test_against_df_iloc(start, stop, step):
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# Test that a single group gives the same results as DataFame.iloc
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n_rows = 30
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data = {
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"group": ["group 0"] * n_rows,
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"value": list(range(n_rows)),
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}
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df = pd.DataFrame(data)
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grouped = df.groupby("group", as_index=False)
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result = grouped._positional_selector[start:stop:step]
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expected = df.iloc[start:stop:step]
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tm.assert_frame_equal(result, expected)
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def test_series():
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# Test grouped Series
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ser = pd.Series([1, 2, 3, 4, 5], index=["a", "a", "a", "b", "b"])
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grouped = ser.groupby(level=0)
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result = grouped._positional_selector[1:2]
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expected = pd.Series([2, 5], index=["a", "b"])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("step", [1, 2, 3, 4, 5])
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def test_step(step):
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# Test slice with various step values
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data = [["x", f"x{i}"] for i in range(5)]
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data += [["y", f"y{i}"] for i in range(4)]
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data += [["z", f"z{i}"] for i in range(3)]
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df = pd.DataFrame(data, columns=["A", "B"])
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grouped = df.groupby("A", as_index=False)
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result = grouped._positional_selector[::step]
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data = [["x", f"x{i}"] for i in range(0, 5, step)]
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data += [["y", f"y{i}"] for i in range(0, 4, step)]
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data += [["z", f"z{i}"] for i in range(0, 3, step)]
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index = [0 + i for i in range(0, 5, step)]
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index += [5 + i for i in range(0, 4, step)]
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index += [9 + i for i in range(0, 3, step)]
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expected = pd.DataFrame(data, columns=["A", "B"], index=index)
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tm.assert_frame_equal(result, expected)
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@pytest.fixture()
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def column_group_df():
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return pd.DataFrame(
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[[0, 1, 2, 3, 4, 5, 6], [0, 0, 1, 0, 1, 0, 2]],
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columns=["A", "B", "C", "D", "E", "F", "G"],
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)
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def test_column_axis(column_group_df):
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g = column_group_df.groupby(column_group_df.iloc[1], axis=1)
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result = g._positional_selector[1:-1]
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expected = column_group_df.iloc[:, [1, 3]]
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tm.assert_frame_equal(result, expected)
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def test_columns_on_iter():
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# GitHub issue #44821
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df = pd.DataFrame({k: range(10) for k in "ABC"})
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# Group-by and select columns
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cols = ["A", "B"]
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for _, dg in df.groupby(df.A < 4)[cols]:
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tm.assert_index_equal(dg.columns, pd.Index(cols))
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assert "C" not in dg.columns
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@pytest.mark.parametrize("func", [list, pd.Index, pd.Series, np.array])
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def test_groupby_duplicated_columns(func):
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# GH#44924
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df = pd.DataFrame(
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{
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"A": [1, 2],
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"B": [3, 3],
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"C": ["G", "G"],
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}
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)
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result = df.groupby("C")[func(["A", "B", "A"])].mean()
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expected = pd.DataFrame(
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[[1.5, 3.0, 1.5]], columns=["A", "B", "A"], index=pd.Index(["G"], name="C")
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)
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tm.assert_frame_equal(result, expected)
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