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test groupby.indices for multiple groupby and mix of types #38273

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104 changes: 104 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -7,6 +7,8 @@

from pandas.errors import PerformanceWarning

from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64_any_dtype

import pandas as pd
from pandas import (
DataFrame,
Expand Down Expand Up @@ -361,6 +363,108 @@ def f3(x):
df2.groupby("a").apply(f3)


def test_single_groupby_indices_output():
cols = [
"int",
"int_cat",
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Replace this with data dictionary instead, use list(data.keys()) where cols is used now.

"float",
"float_cat",
"dt",
"dt_cat",
"dttz",
"dttz_cat",
"period",
"period_cat",
]

int_ = Series([1, 2, 3])
dt_ = pd.to_datetime(["2018Q1", "2018Q2", "2018Q3"])
dttz_ = dt_.tz_localize("Europe/Berlin")
df = DataFrame(
data={
"int": int_,
"int_cat": int_.astype("category"),
"float": int_.astype("float"),
"float_cat": int_.astype("float").astype("category"),
"dt": dt_,
"dt_cat": dt_.astype("category"),
"dttz": dttz_,
"dttz_cat": dttz_.astype("category"),
"period": dt_.to_period("Q"),
"period_cat": dt_.to_period("Q").astype("category"),
},
columns=cols,
)
for col in df.columns:
col_vals = list(df[col].unique())

if is_datetime64_any_dtype(df[col]):
col_vals = [Timestamp(el) for el in col_vals]

target = {key: np.array([i]) for i, key in enumerate(col_vals)}

indices = df.groupby(col).indices

assert set(target.keys()) == set(indices.keys())
for key in target.keys():
assert pd.core.dtypes.missing.array_equivalent(target[key], indices[key])


def test_multiple_groupby_indices_output():
cols = [
"int",
"int_cat",
"float",
"float_cat",
"dt",
"dt_cat",
"dttz",
"dttz_cat",
"period",
"period_cat",
"value",
]

int_ = Series([1, 2, 3])
dt_ = pd.to_datetime(["2018Q1", "2018Q2", "2018Q3"])
dttz_ = dt_.tz_localize("Europe/Berlin")
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these test are overly complicated. can you simplify this by hard-coding the expected index locations and just doing a single comparision at the end.

df = DataFrame(
data={
"int": int_,
"int_cat": int_.astype("category"),
"float": int_.astype("float"),
"float_cat": int_.astype("float").astype("category"),
"dt": dt_,
"dt_cat": dt_.astype("category"),
"dttz": dttz_,
"dttz_cat": dttz_.astype("category"),
"period": dt_.to_period("Q"),
"period_cat": dt_.to_period("Q").astype("category"),
"value": Series([1, 2, 3]),
},
columns=cols,
)
groupby_cols = cols[:-1]
col_vals = {col: list(df[col].unique()) for col in groupby_cols}

for col in groupby_cols:
is_dt = is_datetime64_any_dtype(df[col])
is_cat_dt = is_categorical_dtype(df[col]) and is_datetime64_any_dtype(
df[col].cat.categories
)
if is_dt or is_cat_dt:
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I think is_cat_dt can be removed here, can you check?

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@rhshadrach rhshadrach Dec 20, 2020 •
edited
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If this can be removed, then I think these tests can be combined using @pytest.mark.parametrize

col_vals[col] = [Timestamp(el) for el in col_vals[col]]

it = zip(*(col_vals[col] for col in groupby_cols))
target = {key: np.array([i]) for i, key in enumerate(it)}

indices = df.groupby(groupby_cols).indices

assert set(target.keys()) == set(indices.keys())
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use self.assert_numpy_array_equal for the comparisions.

pls simply as much as possible.

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I don't follow. What is self in this? Also that's a comparison on the keys, which are single objects or tuples, depending on how many columns are in the groupby.

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sorry, tm.assert_assert_numpy_array_equal

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Why use set? Is the order not expected to be the same?

for key in target.keys():
assert pd.core.dtypes.missing.array_equivalent(target[key], indices[key])


def test_attr_wrapper(ts):
grouped = ts.groupby(lambda x: x.weekday())

Expand Down