Mixed types of elements in DataFrame's column

Question

Consider the following three DataFrame's:

df1 = pd.DataFrame([[1,2],[4,3]])
df2 = pd.DataFrame([[1,.2],[4,3]])
df3 = pd.DataFrame([[1,'a'],[4,3]])

Here are the types of the second column of the DataFrame's:

In [56]: map(type,df1[1])
Out[56]: [numpy.int64, numpy.int64]

In [57]: map(type,df2[1])
Out[57]: [numpy.float64, numpy.float64]

In [58]: map(type,df3[1])
Out[58]: [str, int]

In the first case, all int's are casted to numpy.int64. Fine. In the third case, there is basically no casting. However, in the second case, the integer (3) is casted to numpy.float64; probably since the other number is a float.

How can I control the casting? In the second case, I want to have either [float64, int64] or [float, int] as types.

Workaround:

Using a callable printing function there can be a workaround as showed here.

def printFloat(x):
    if np.modf(x)[0] == 0:
        return str(int(x))
    else:
        return str(x)
pd.options.display.float_format = printFloat

Answer 1

The columns of a pandas DataFrame (or a Series) are homogeneously of type. You can inspect this with dtype (or DataFrame.dtypes):

In [14]: df1[1].dtype
Out[14]: dtype('int64')

In [15]: df2[1].dtype
Out[15]: dtype('float64')

In [16]: df3[1].dtype
Out[16]: dtype('O')

Only the generic 'object' dtype can hold any python object, and in this way can also contain mixed types:

In [18]: df2 = pd.DataFrame([[1,.2],[4,3]], dtype='object')

In [19]: df2[1].dtype
Out[19]: dtype('O')

In [20]: map(type,df2[1])
Out[20]: [float, int]

But this is really not recommended, as this defeats the purpose (or at least the performance) of pandas.

Is there a reason you specifically want both ints and floats in the same column?