Skip to content

DOC: Improve DataFrame.select_dtypes examples #19188

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 12, 2018
Merged

DOC: Improve DataFrame.select_dtypes examples #19188

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
39 changes: 20 additions & 19 deletions pandas/core/frame.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2417,17 +2417,18 @@ def select_dtypes(self, include=None, exclude=None):

Notes
-----
* To select all *numeric* types use the numpy dtype ``numpy.number``
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
* To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
* To select timedeltas, use np.timedelta64, 'timedelta' or
'timedelta64'
* To select Pandas categorical dtypes, use 'category'
* To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0),
or a 'datetime64[ns, tz]' string
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``

Examples
--------
Expand All @@ -2436,12 +2437,12 @@ def select_dtypes(self, include=None, exclude=None):
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 0.3962 True 1
1 0.1459 False 2
2 0.2623 True 1
3 0.0764 False 2
4 -0.9703 True 1
5 -1.2094 False 2
0 0.3962 True 1.0
1 0.1459 False 2.0
2 0.2623 True 1.0
3 0.0764 False 2.0
4 -0.9703 True 1.0
5 -1.2094 False 2.0
>>> df.select_dtypes(include='bool')
c
0 True
Expand All @@ -2452,12 +2453,12 @@ def select_dtypes(self, include=None, exclude=None):
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1
1 2
2 1
3 2
4 1
5 2
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['floating'])
b
0 True
Expand Down