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

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

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DOC: Added an example of pitfalls when using astype
pfrcks May 25, 2016
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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
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DOC: Cleaning up
pfrcks May 26, 2016
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DOC: Final touches
pfrcks May 26, 2016
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18 changes: 18 additions & 0 deletions doc/source/basics.rst
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Expand Up @@ -1726,6 +1726,24 @@ then the more *general* one will be used as the result of the operation.
# conversion of dtypes
df3.astype('float32').dtypes

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

.. ipython:: python

df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
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call this dft or something besides df (as that's used a lot)

df.loc[:, ['a', 'b']].astype(np.uint8).dtypes
df.loc[:, ['a', 'b']] = df.loc[:, ['a', 'b']].astype(np.uint8)

To avoid this please take the following approach.

.. ipython:: python
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Can you indent this one (and the following lines) to the same level as 'When trying to convert ...', so the code example is also included in the 'note' frame


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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.

df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
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you already created the df, no need to recreate

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df get's changed in the code example before.

df[['a','b']] = df[['a','b']].astype(np.uint8)
df
df.dtypes

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

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