test groupby.indices for multiple groupby and mix of types

Creates tests for GH26859

Author: alexifm

pandas/tests/groupby/test_groupby.py

@@ -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,
@@ -361,6 +363,108 @@ def f3(x):
         df2.groupby("a").apply(f3)
 
 
+def test_single_groupby_indices_output():
+    cols = [
+        "int",
+        "int_cat",
+        "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")
+    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:
+            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())
+    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())