Pandas DataFrame cast multiple types to columns

Question

I'd like to declare different types for the columns of a pandas DataFrame at instantiation:

frame = pandas.DataFrame({..some data..},dtype=[str,int,int])

This works if dtype is only one type (e.g dtype=float), but not multiple types as above - is there a way to do this?

The common solution seems to be to cast later:

frame['some column'] = frame['some column'].astype(float)

but this has a couple of issues:

It's messy
Looks like it involves an unnecessary copy operation - this could be expensive on large data sets.

in general the types will be correct inferred on creation (or as show below you can explicity create a series/ndarray to that effect) — Jeff
– Jeff, Commented Jun 2, 2014 at 18:07

R. Max · Accepted Answer · 2014-06-02 17:36:05Z

9

As an alternative, you can specify the dtype for each column by creating the Series objects first.

In [2]: df = pd.DataFrame({'x': pd.Series(['1.0', '2.0', '3.0'], dtype=float), 'y': pd.Series(['1', '2', '3'], dtype=int)})

In [3]: df
Out[3]: 
   x  y
0  1  1
1  2  2
2  3  3

[3 rows x 2 columns]

In [4]: df.dtypes
Out[4]: 
x    float64
y      int64
dtype: object

answered Jun 2, 2014 at 17:36

R. Max

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Comments

drastega · Accepted Answer · 2014-06-03 13:18:12Z

8

You can also create a NumPy array with specific dtypes and then convert it to DataFrame.

data = np.zeros((2,),dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])
data[:] = [(1,2.,'Hello'),(2,3.,"World")]
DataFrame(data)

See From structured or record array

answered Jun 3, 2014 at 13:18

drastega

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