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DOC: Added an example of pitfalls when using astype #13278

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705e1a5
DOC: Added an example of pitfalls when using astype
pfrcks May 25, 2016
f394045
DOC: Cleaned up the documentation
pfrcks May 25, 2016
e1877bf
DOC: Restructured the documentation
pfrcks May 25, 2016
278e922
DOC: Some cleaning up
pfrcks May 25, 2016
c30209d
DOC: Cleaning up
pfrcks May 26, 2016
035a177
DOC: Final touches
pfrcks May 26, 2016
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22 changes: 22 additions & 0 deletions doc/source/basics.rst
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Expand Up @@ -1726,6 +1726,28 @@ then the more *general* one will be used as the result of the operation.
# conversion of dtypes
df3.astype('float32').dtypes

Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`

.. ipython:: python

dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
dft[['a','b']] = dft[['a','b']].astype(np.uint8)
dft
dft.dtypes

.. note::

When trying to convert a subset of columns to a specified type using :meth:`~DataFrame.astype` and :meth:`~DataFrame.loc`, upcasting occurs.

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flip these around showing the correct method of assignment using astype.

then you can put a ::note for the explanation. People reading this just want the answer; some want the explanation, so highliting that differently helps.

:meth:`~DataFrame.loc` tries to fit in what we are assigning to the current dtypes, while ``[]`` will overwrite them taking the dtype from the right hand side. Therefore the following piece of code produces the unintended result.

.. ipython:: python

dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
dft.loc[:, ['a', 'b']].astype(np.uint8).dtypes
dft.loc[:, ['a', 'b']] = dft.loc[:, ['a', 'b']].astype(np.uint8)
dft.dtypes

object conversion
~~~~~~~~~~~~~~~~~

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