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# Test GroupBy._positional_selector positional grouped indexing GH#42864
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize(
"arg, expected_rows",
[
[0, [0, 1, 4]],
[2, [5]],
[5, []],
[-1, [3, 4, 7]],
[-2, [1, 6]],
[-6, []],
],
)
def test_int(slice_test_df, slice_test_grouped, arg, expected_rows):
# Test single integer
result = slice_test_grouped._positional_selector[arg]
expected = slice_test_df.iloc[expected_rows]
tm.assert_frame_equal(result, expected)
def test_slice(slice_test_df, slice_test_grouped):
# Test single slice
result = slice_test_grouped._positional_selector[0:3:2]
expected = slice_test_df.iloc[[0, 1, 4, 5]]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"arg, expected_rows",
[
[[0, 2], [0, 1, 4, 5]],
[[0, 2, -1], [0, 1, 3, 4, 5, 7]],
[range(0, 3, 2), [0, 1, 4, 5]],
[{0, 2}, [0, 1, 4, 5]],
],
ids=[
"list",
"negative",
"range",
"set",
],
)
def test_list(slice_test_df, slice_test_grouped, arg, expected_rows):
# Test lists of integers and integer valued iterables
result = slice_test_grouped._positional_selector[arg]
expected = slice_test_df.iloc[expected_rows]
tm.assert_frame_equal(result, expected)
def test_ints(slice_test_df, slice_test_grouped):
# Test tuple of ints
result = slice_test_grouped._positional_selector[0, 2, -1]
expected = slice_test_df.iloc[[0, 1, 3, 4, 5, 7]]
tm.assert_frame_equal(result, expected)
def test_slices(slice_test_df, slice_test_grouped):
# Test tuple of slices
result = slice_test_grouped._positional_selector[:2, -2:]
expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
tm.assert_frame_equal(result, expected)
def test_mix(slice_test_df, slice_test_grouped):
# Test mixed tuple of ints and slices
result = slice_test_grouped._positional_selector[0, 1, -2:]
expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"arg, expected_rows",
[
[0, [0, 1, 4]],
[[0, 2, -1], [0, 1, 3, 4, 5, 7]],
[(slice(None, 2), slice(-2, None)), [0, 1, 2, 3, 4, 6, 7]],
],
)
def test_as_index(slice_test_df, arg, expected_rows):
# Test the default as_index behaviour
result = slice_test_df.groupby("Group", sort=False)._positional_selector[arg]
expected = slice_test_df.iloc[expected_rows]
tm.assert_frame_equal(result, expected)
def test_doc_examples():
# Test the examples in the documentation
df = pd.DataFrame(
[["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]], columns=["A", "B"]
)
grouped = df.groupby("A", as_index=False)
result = grouped._positional_selector[1:2]
expected = pd.DataFrame([["a", 2], ["b", 5]], columns=["A", "B"], index=[1, 4])
tm.assert_frame_equal(result, expected)
result = grouped._positional_selector[1, -1]
expected = pd.DataFrame(
[["a", 2], ["a", 3], ["b", 5]], columns=["A", "B"], index=[1, 2, 4]
)
tm.assert_frame_equal(result, expected)
@pytest.fixture()
def multiindex_data():
rng = np.random.default_rng(2)
ndates = 100
nitems = 20
dates = pd.date_range("20130101", periods=ndates, freq="D")
items = [f"item {i}" for i in range(nitems)]
data = {}
for date in dates:
nitems_for_date = nitems - rng.integers(0, 12)
levels = [
(item, rng.integers(0, 10000) / 100, rng.integers(0, 10000) / 100)
for item in items[:nitems_for_date]
]
levels.sort(key=lambda x: x[1])
data[date] = levels
return data
def _make_df_from_data(data):
rows = {}
for date in data:
for level in data[date]:
rows[(date, level[0])] = {"A": level[1], "B": level[2]}
df = pd.DataFrame.from_dict(rows, orient="index")
df.index.names = ("Date", "Item")
return df
def test_multiindex(multiindex_data):
# Test the multiindex mentioned as the use-case in the documentation
df = _make_df_from_data(multiindex_data)
result = df.groupby("Date", as_index=False).nth(slice(3, -3))
sliced = {date: multiindex_data[date][3:-3] for date in multiindex_data}
expected = _make_df_from_data(sliced)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("arg", [1, 5, 30, 1000, -1, -5, -30, -1000])
@pytest.mark.parametrize("method", ["head", "tail"])
@pytest.mark.parametrize("simulated", [True, False])
def test_against_head_and_tail(arg, method, simulated):
# Test gives the same results as grouped head and tail
n_groups = 100
n_rows_per_group = 30
data = {
"group": [
f"group {g}" for j in range(n_rows_per_group) for g in range(n_groups)
],
"value": [
f"group {g} row {j}"
for j in range(n_rows_per_group)
for g in range(n_groups)
],
}
df = pd.DataFrame(data)
grouped = df.groupby("group", as_index=False)
size = arg if arg >= 0 else n_rows_per_group + arg
if method == "head":
result = grouped._positional_selector[:arg]
if simulated:
indices = [
j * n_groups + i
for j in range(size)
for i in range(n_groups)
if j * n_groups + i < n_groups * n_rows_per_group
]
expected = df.iloc[indices]
else:
expected = grouped.head(arg)
else:
result = grouped._positional_selector[-arg:]
if simulated:
indices = [
(n_rows_per_group + j - size) * n_groups + i
for j in range(size)
for i in range(n_groups)
if (n_rows_per_group + j - size) * n_groups + i >= 0
]
expected = df.iloc[indices]
else:
expected = grouped.tail(arg)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("start", [None, 0, 1, 10, -1, -10])
@pytest.mark.parametrize("stop", [None, 0, 1, 10, -1, -10])
@pytest.mark.parametrize("step", [None, 1, 5])
def test_against_df_iloc(start, stop, step):
# Test that a single group gives the same results as DataFrame.iloc
n_rows = 30
data = {
"group": ["group 0"] * n_rows,
"value": list(range(n_rows)),
}
df = pd.DataFrame(data)
grouped = df.groupby("group", as_index=False)
result = grouped._positional_selector[start:stop:step]
expected = df.iloc[start:stop:step]
tm.assert_frame_equal(result, expected)
def test_series():
# Test grouped Series
ser = pd.Series([1, 2, 3, 4, 5], index=["a", "a", "a", "b", "b"])
grouped = ser.groupby(level=0)
result = grouped._positional_selector[1:2]
expected = pd.Series([2, 5], index=["a", "b"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("step", [1, 2, 3, 4, 5])
def test_step(step):
# Test slice with various step values
data = [["x", f"x{i}"] for i in range(5)]
data += [["y", f"y{i}"] for i in range(4)]
data += [["z", f"z{i}"] for i in range(3)]
df = pd.DataFrame(data, columns=["A", "B"])
grouped = df.groupby("A", as_index=False)
result = grouped._positional_selector[::step]
data = [["x", f"x{i}"] for i in range(0, 5, step)]
data += [["y", f"y{i}"] for i in range(0, 4, step)]
data += [["z", f"z{i}"] for i in range(0, 3, step)]
index = [0 + i for i in range(0, 5, step)]
index += [5 + i for i in range(0, 4, step)]
index += [9 + i for i in range(0, 3, step)]
expected = pd.DataFrame(data, columns=["A", "B"], index=index)
tm.assert_frame_equal(result, expected)
@pytest.fixture()
def column_group_df():
return pd.DataFrame(
[[0, 1, 2, 3, 4, 5, 6], [0, 0, 1, 0, 1, 0, 2]],
columns=["A", "B", "C", "D", "E", "F", "G"],
)
def test_column_axis(column_group_df):
msg = "DataFrame.groupby with axis=1"
with tm.assert_produces_warning(FutureWarning, match=msg):
g = column_group_df.groupby(column_group_df.iloc[1], axis=1)
result = g._positional_selector[1:-1]
expected = column_group_df.iloc[:, [1, 3]]
tm.assert_frame_equal(result, expected)
def test_columns_on_iter():
# GitHub issue #44821
df = pd.DataFrame({k: range(10) for k in "ABC"})
# Group-by and select columns
cols = ["A", "B"]
for _, dg in df.groupby(df.A < 4)[cols]:
tm.assert_index_equal(dg.columns, pd.Index(cols))
assert "C" not in dg.columns
@pytest.mark.parametrize("func", [list, pd.Index, pd.Series, np.array])
def test_groupby_duplicated_columns(func):
# GH#44924
df = pd.DataFrame(
{
"A": [1, 2],
"B": [3, 3],
"C": ["G", "G"],
}
)
result = df.groupby("C")[func(["A", "B", "A"])].mean()
expected = pd.DataFrame(
[[1.5, 3.0, 1.5]], columns=["A", "B", "A"], index=pd.Index(["G"], name="C")
)
tm.assert_frame_equal(result, expected)
def test_groupby_get_nonexisting_groups():
# GH#32492
df = pd.DataFrame(
data={
"A": ["a1", "a2", None],
"B": ["b1", "b2", "b1"],
"val": [1, 2, 3],
}
)
grps = df.groupby(by=["A", "B"])
msg = "('a2', 'b1')"
with pytest.raises(KeyError, match=msg):
grps.get_group(("a2", "b1"))
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