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DOC: Improve groupby().ngroup() explanation for missing groups #50049

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merged 4 commits into from
Jan 3, 2023
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DOC: Improve groupby().ngroup() explanation for missing groups #50049

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11ba535
DOC: Improve groupby().ngroup() explanation for missing groups
NickCrews Dec 4, 2022
9466d4d
DOC: fixup PR suggestions
NickCrews Dec 31, 2022
66ae02e
DOC: fixup: update order of labels
NickCrews Jan 2, 2023
e5d0075
DOC: fixup: restore ascending=false example in docstring
NickCrews Jan 3, 2023
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47 changes: 25 additions & 22 deletions pandas/core/groupby/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3212,6 +3212,9 @@ def ngroup(self, ascending: bool = True):
would be seen when iterating over the groupby object, not the
order they are first observed.

Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN`
and will be skipped from the count.

Parameters
----------
ascending : bool, default True
Expand All @@ -3228,38 +3231,38 @@ def ngroup(self, ascending: bool = True):

Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
color
0 red
1 None
2 red
3 blue
4 blue
5 red
>>> df.groupby("color").ngroup()
0 1.0
1 NaN
2 1.0
3 0.0
4 0.0
5 1.0
dtype: float64
>>> df.groupby("color", dropna=False).ngroup()
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I dropped the ascending example, I figured that was obvious enough. Think I should keep it in?

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@rhshadrach rhshadrach Jan 3, 2023 •
edited
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I think we typically demonstrate all the features of a method (ignoring methods with a large amount of arguments), regardless of how obvious they might seem. I think it should be retained.

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I restored

        >>> df.groupby("color", dropna=False).ngroup(ascending=False)
        0    1
        1    0
        2    1
        3    2
        4    2
        5    1
        dtype: int64

I chose to use dropna=False because I wanted to show
that NA keys are placed BEFORE other keys.

I figured the dropna=True example was obvious enough from this and
I didn't need that one as well.

0 1
1 1
1 2
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
>>> df.groupby("color", dropna=False).ngroup(ascending=False)
0 1
1 0
2 1
3 3
3 2
4 2
5 0
5 1
dtype: int64
"""
with self._group_selection_context():
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