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DeaMariaLeon
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im-vinicius
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DOC: Fixing EX01 - Added examples (pandas-dev#53561)
* testing example in template * Added max example * Added groupby examples * Updated code_checks * Corrected alignment
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+164
-12
lines changed

2 files changed

+164
-12
lines changed

‎ci/code_checks.sh

-8
Original file line numberDiff line numberDiff line change
@@ -265,35 +265,27 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
265265
pandas.api.indexers.VariableOffsetWindowIndexer \
266266
pandas.core.groupby.DataFrameGroupBy.diff \
267267
pandas.core.groupby.DataFrameGroupBy.ffill \
268-
pandas.core.groupby.DataFrameGroupBy.max \
269268
pandas.core.groupby.DataFrameGroupBy.median \
270-
pandas.core.groupby.DataFrameGroupBy.min \
271269
pandas.core.groupby.DataFrameGroupBy.ohlc \
272270
pandas.core.groupby.DataFrameGroupBy.pct_change \
273-
pandas.core.groupby.DataFrameGroupBy.prod \
274271
pandas.core.groupby.DataFrameGroupBy.sem \
275272
pandas.core.groupby.DataFrameGroupBy.shift \
276273
pandas.core.groupby.DataFrameGroupBy.size \
277274
pandas.core.groupby.DataFrameGroupBy.skew \
278275
pandas.core.groupby.DataFrameGroupBy.std \
279-
pandas.core.groupby.DataFrameGroupBy.sum \
280276
pandas.core.groupby.DataFrameGroupBy.var \
281277
pandas.core.groupby.SeriesGroupBy.diff \
282278
pandas.core.groupby.SeriesGroupBy.fillna \
283279
pandas.core.groupby.SeriesGroupBy.ffill \
284-
pandas.core.groupby.SeriesGroupBy.max \
285280
pandas.core.groupby.SeriesGroupBy.median \
286-
pandas.core.groupby.SeriesGroupBy.min \
287281
pandas.core.groupby.SeriesGroupBy.nunique \
288282
pandas.core.groupby.SeriesGroupBy.ohlc \
289283
pandas.core.groupby.SeriesGroupBy.pct_change \
290-
pandas.core.groupby.SeriesGroupBy.prod \
291284
pandas.core.groupby.SeriesGroupBy.sem \
292285
pandas.core.groupby.SeriesGroupBy.shift \
293286
pandas.core.groupby.SeriesGroupBy.size \
294287
pandas.core.groupby.SeriesGroupBy.skew \
295288
pandas.core.groupby.SeriesGroupBy.std \
296-
pandas.core.groupby.SeriesGroupBy.sum \
297289
pandas.core.groupby.SeriesGroupBy.var \
298290
pandas.core.groupby.SeriesGroupBy.hist \
299291
pandas.core.groupby.DataFrameGroupBy.plot \

‎pandas/core/groupby/groupby.py

+164-4
Original file line numberDiff line numberDiff line change
@@ -341,6 +341,10 @@ class providing the base-class of operations.
341341
-------
342342
Series or DataFrame
343343
Computed {fname} of values within each group.
344+
345+
Examples
346+
--------
347+
{example}
344348
"""
345349

346350
_pipe_template = """
@@ -2550,7 +2554,46 @@ def size(self) -> DataFrame | Series:
25502554
return result
25512555

25522556
@final
2553-
@doc(_groupby_agg_method_template, fname="sum", no=False, mc=0)
2557+
@doc(
2558+
_groupby_agg_method_template,
2559+
fname="sum",
2560+
no=False,
2561+
mc=0,
2562+
example=dedent(
2563+
"""\
2564+
For SeriesGroupBy:
2565+
2566+
>>> lst = ['a', 'a', 'b', 'b']
2567+
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
2568+
>>> ser
2569+
a 1
2570+
a 2
2571+
b 3
2572+
b 4
2573+
dtype: int64
2574+
>>> ser.groupby(level=0).sum()
2575+
a 3
2576+
b 7
2577+
dtype: int64
2578+
2579+
For DataFrameGroupBy:
2580+
2581+
>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
2582+
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],
2583+
... index=["tiger", "leopard", "cheetah", "lion"])
2584+
>>> df
2585+
a b c
2586+
tiger 1 8 2
2587+
leopard 1 2 5
2588+
cheetah 2 5 8
2589+
lion 2 6 9
2590+
>>> df.groupby("a").sum()
2591+
b c
2592+
a
2593+
1 10 7
2594+
2 11 17"""
2595+
),
2596+
)
25542597
def sum(
25552598
self,
25562599
numeric_only: bool = False,
@@ -2580,14 +2623,92 @@ def sum(
25802623
return self._reindex_output(result, fill_value=0)
25812624

25822625
@final
2583-
@doc(_groupby_agg_method_template, fname="prod", no=False, mc=0)
2626+
@doc(
2627+
_groupby_agg_method_template,
2628+
fname="prod",
2629+
no=False,
2630+
mc=0,
2631+
example=dedent(
2632+
"""\
2633+
For SeriesGroupBy:
2634+
2635+
>>> lst = ['a', 'a', 'b', 'b']
2636+
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
2637+
>>> ser
2638+
a 1
2639+
a 2
2640+
b 3
2641+
b 4
2642+
dtype: int64
2643+
>>> ser.groupby(level=0).prod()
2644+
a 2
2645+
b 12
2646+
dtype: int64
2647+
2648+
For DataFrameGroupBy:
2649+
2650+
>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
2651+
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],
2652+
... index=["tiger", "leopard", "cheetah", "lion"])
2653+
>>> df
2654+
a b c
2655+
tiger 1 8 2
2656+
leopard 1 2 5
2657+
cheetah 2 5 8
2658+
lion 2 6 9
2659+
>>> df.groupby("a").prod()
2660+
b c
2661+
a
2662+
1 16 10
2663+
2 30 72"""
2664+
),
2665+
)
25842666
def prod(self, numeric_only: bool = False, min_count: int = 0):
25852667
return self._agg_general(
25862668
numeric_only=numeric_only, min_count=min_count, alias="prod", npfunc=np.prod
25872669
)
25882670

25892671
@final
2590-
@doc(_groupby_agg_method_template, fname="min", no=False, mc=-1)
2672+
@doc(
2673+
_groupby_agg_method_template,
2674+
fname="min",
2675+
no=False,
2676+
mc=-1,
2677+
example=dedent(
2678+
"""\
2679+
For SeriesGroupBy:
2680+
2681+
>>> lst = ['a', 'a', 'b', 'b']
2682+
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
2683+
>>> ser
2684+
a 1
2685+
a 2
2686+
b 3
2687+
b 4
2688+
dtype: int64
2689+
>>> ser.groupby(level=0).min()
2690+
a 1
2691+
b 3
2692+
dtype: int64
2693+
2694+
For DataFrameGroupBy:
2695+
2696+
>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
2697+
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],
2698+
... index=["tiger", "leopard", "cheetah", "lion"])
2699+
>>> df
2700+
a b c
2701+
tiger 1 8 2
2702+
leopard 1 2 5
2703+
cheetah 2 5 8
2704+
lion 2 6 9
2705+
>>> df.groupby("a").min()
2706+
b c
2707+
a
2708+
1 2 2
2709+
2 5 8"""
2710+
),
2711+
)
25912712
def min(
25922713
self,
25932714
numeric_only: bool = False,
@@ -2608,7 +2729,46 @@ def min(
26082729
)
26092730

26102731
@final
2611-
@doc(_groupby_agg_method_template, fname="max", no=False, mc=-1)
2732+
@doc(
2733+
_groupby_agg_method_template,
2734+
fname="max",
2735+
no=False,
2736+
mc=-1,
2737+
example=dedent(
2738+
"""\
2739+
For SeriesGroupBy:
2740+
2741+
>>> lst = ['a', 'a', 'b', 'b']
2742+
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
2743+
>>> ser
2744+
a 1
2745+
a 2
2746+
b 3
2747+
b 4
2748+
dtype: int64
2749+
>>> ser.groupby(level=0).max()
2750+
a 2
2751+
b 4
2752+
dtype: int64
2753+
2754+
For DataFrameGroupBy:
2755+
2756+
>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
2757+
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],
2758+
... index=["tiger", "leopard", "cheetah", "lion"])
2759+
>>> df
2760+
a b c
2761+
tiger 1 8 2
2762+
leopard 1 2 5
2763+
cheetah 2 5 8
2764+
lion 2 6 9
2765+
>>> df.groupby("a").max()
2766+
b c
2767+
a
2768+
1 8 5
2769+
2 6 9"""
2770+
),
2771+
)
26122772
def max(
26132773
self,
26142774
numeric_only: bool = False,

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