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DOC: Update to docstring of DataFrame(dtype) (#14764) #16487

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May 31, 2017
Merged

DOC: Update to docstring of DataFrame(dtype) (#14764) #16487

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b1b51ba
Merge pull request #1 from pandas-dev/master
VincentLa May 22, 2017
9ea8c0d
Merge pull request #2 from pandas-dev/master
VincentLa May 24, 2017
ccbbad6
Adding some more documentation on dataframe with regards to dtype
VincentLa May 24, 2017
7f6b7f4
Making example for creating dataframe from np matrix easier
VincentLa May 24, 2017
f8435fa
Merge branch 'master' into issue-14764
VincentLa May 28, 2017
6108d69
small edit
jorisvandenbossche May 31, 2017
edc1c04
another edit
jorisvandenbossche May 31, 2017
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42 changes: 36 additions & 6 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -241,17 +241,47 @@ class DataFrame(NDFrame):
Column labels to use for resulting frame. Will default to
np.arange(n) if no column labels are provided
dtype : dtype, default None
Data type to force, otherwise infer
Data type to force. Only a single dtype is allowed. If None, infer
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this is not right. you can pass a dict of dtypes.

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Oh interesting. How? This does not work for me:

d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d, dtype={'col1': np.int64, 'col2': np.int64})

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According to #4464 and #9133 it seems like this is not yet implemented.

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#13375 implemented his for astype
hasn't been implemented for construction

copy : boolean, default False
Copy data from inputs. Only affects DataFrame / 2d ndarray input

Examples
--------
>>> d = {'col1': ts1, 'col2': ts2}
>>> df = DataFrame(data=d, index=index)
>>> df2 = DataFrame(np.random.randn(10, 5))
>>> df3 = DataFrame(np.random.randn(10, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
Constructing DataFrame from a dictionary.

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2
0 1 3
1 2 4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1 int64
col2 int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1 int8
col2 int8
dtype: object

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(np.random.randint(low=0, high=10, size=(5, 5)),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df2
a b c d e
0 2 8 8 3 4
1 4 2 9 0 9
2 1 0 7 8 0
3 5 1 7 1 3
4 6 0 2 4 2

See also
--------
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