Converting a column within pandas dataframe from int to string

In [16]: df = DataFrame(np.arange(10).reshape(5,2),columns=list('AB'))In [17]: dfOut[17]:    A  B0  0  11  2  32  4  53  6  74  8  9In [18]: df.dtypesOut[18]: A    int64B    int64dtype: object

Convert a series

In [19]: df['A'].apply(str)Out[19]: 0    01    22    43    64    8Name: A, dtype: objectIn [20]: df['A'].apply(str)[0]Out[20]: '0'

Don't forget to assign the result back:

df['A'] = df['A'].apply(str)

Convert the whole frame

In [21]: df.applymap(str)Out[21]:    A  B0  0  11  2  32  4  53  6  74  8  9In [22]: df.applymap(str).iloc[0,0]Out[22]: '0'

df = df.applymap(str)

Change data type of DataFrame column:

To int:

df.column_name = df.column_name.astype(np.int64)

To str:

df.column_name = df.column_name.astype(str)

Warning: Both solutions given ( astype() and apply() ) do not preserve NULL values in either the nan or the None form.

import pandas as pdimport numpy as npdf = pd.DataFrame([None,'string',np.nan,42], index=[0,1,2,3], columns=['A'])df1 = df['A'].astype(str)df2 =  df['A'].apply(str)print df.isnull()print df1.isnull()print df2.isnull()

I believe this is fixed by the implementation of to_string()