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DOC: astype now takes dict mapping col names to datatypes (#14761) #14837

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Dec 22, 2016
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DOC: astype now takes dict mapping col names to datatypes (#14761) #14837

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astype method now takes dict mapping col names to datatypes #14761
m-charlton Dec 9, 2016
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Updates after review
m-charlton Dec 15, 2016
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Corrections to docs after review feedback
m-charlton Dec 15, 2016
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Merge branch 'update_docs_astype_with_dict_#14761' of https://github.…
m-charlton Dec 15, 2016
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Merge branch 'update_docs_astype_with_dict_#14761'
m-charlton Dec 15, 2016
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m-charlton Dec 16, 2016
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Removing uneeded changes
m-charlton Dec 15, 2016
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Merge remote-tracking branch 'upstream/master'
m-charlton Dec 19, 2016
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Correcting after review
m-charlton Dec 19, 2016
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4 changes: 3 additions & 1 deletion doc/source/basics.rst
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Expand Up @@ -1755,12 +1755,14 @@ then the more *general* one will be used as the result of the operation.
# conversion of dtypes
df3.astype('float32').dtypes

.. versionadded:: 0.20.0
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0.20 -> 0.19

and I would also clarify what was added in 0.20.0 (only the ability to specify different dtypes for different columns with a dict)

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I think I understand this

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I'm a little confused. I tried using a dict with column/dtype mapping as an
argument to astype and this works in 0.19.0:

>>> import numpy as np
>>> import pandas as pd
>>> pd.__version__
'0.19.0'
>>> dft = pd.DataFrame({'a': [1,0,1], 'b': [4,5,6], 'c': [7, 8, 9]})
>>> dft = dft.astype({'a': np.bool, 'c': np.uint8})
>>> dft
       a  b  c
0   True  4  7
1  False  5  8
2   True  6  9
>>> dft.dtypes
a     bool
b    int64
c    uint8
dtype: object

I think I'm missing what new functionality has been introduced in v0.20

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@m-charlton As I said above, this feature was already introduced in 0.19.0. That's the reason I asked to change 0.20.0 to 0.19.0 in the versionadded here

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That makes sense. Will submit changes today.


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.astype({'a': np.float64, 'c': np.uint8})
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Can you leave the previous example there as well, and add this line (the single dtype is still a valid case)

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So I would leave this example, but just add a new one below this one, also beginning with an explaining sentence, like "Convert certain columns to as specific dtype by passing a dict to astype: ..."

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Will make relevant changes and resubmit. Old example will be left in place as is.

dft
dft.dtypes

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10 changes: 10 additions & 0 deletions doc/source/whatsnew/v0.20.0.txt
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Expand Up @@ -43,6 +43,16 @@ fixed-width text files, and :func:`read_excel` for parsing Excel files.
pd.read_fwf(StringIO(data)).dtypes
pd.read_fwf(StringIO(data), dtype={'a':'float64', 'b':'object'}).dtypes

You can now pass a dictionary mapping column names to desired data types for that
column to :meth:`~DataFrame.astype`.

.. ipython:: python

dft = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6], 'c': [7, 8, 9]})
dft = dft.astype({'a': np.float64, 'c': np.uint8})
dft
dft.dtypes

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@m-charlton This is not needed, as this enhancement was already added in 0.19.0, and is mentioned in the whatsnew of 0.19.0 (see the changes in the PR https://github.com/pandas-dev/pandas/pull/13375/files)

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Will leave this file unchanged.

.. _whatsnew_0200.enhancements.other:

Other enhancements
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