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DOC: update the DataFrame.iat[] docstring #20219

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merged 7 commits into from
Mar 11, 2018
Merged

DOC: update the DataFrame.iat[] docstring #20219

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DOC: update the DataFrame.iat[] docstring
akosel Mar 10, 2018
fa0edf7
Update based on PR comments
akosel Mar 10, 2018
2565642
Update based on PR comments
akosel Mar 10, 2018
e8233e5
Singular not plural
akosel Mar 10, 2018
88c23c0
Update to account for use with Series. Add example using Series.
akosel Mar 10, 2018
6b1fc48
Update indexing.py
jorisvandenbossche Mar 11, 2018
aa387ad
PEP8
TomAugspurger Mar 11, 2018
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29 changes: 26 additions & 3 deletions pandas/core/indexing.py
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Expand Up @@ -1920,11 +1920,34 @@ def _convert_key(self, key, is_setter=False):


class _iAtIndexer(_ScalarAccessIndexer):
"""Fast integer location scalar accessor.
"""
Access a single value for a row/column pair by integer position.

Similarly to ``iloc``, ``iat`` provides **integer** based lookups.
You can also set using these indexers.
Similar to ``iloc``, in that both provide integer-based lookups. Use
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this also works on a Series, can you re-word to accomodate

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@akosel akosel Mar 10, 2018 •
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Great point, I specified that iat works for both a DataFrame and a Series (below the comment, so Github isn't collapsing it here). Let me know if you want be to reword it any differently. I also added an example for using it on Series.

``iat`` if you only need to get or set a single value in a DataFrame.

See Also
--------
DataFrame.at : Access a single value for a row/column label pair
DataFrame.loc : Access a group of rows and columns by label(s)
DataFrame.iloc : Access a group of rows and columns by integer position(s)

Examples
--------
>>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
... columns=['A', 'B', 'C'])
>>> df
A B C
0 0 2 3
1 0 4 1
2 10 20 30

>>> df.iat[1, 2]
1

>>> df.iat[1, 2] = 10
>>> df.iat[1, 2]
10
"""

_takeable = True
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