Converting a column within pandas dataframe from int to string

Question

I have a dataframe in pandas with mixed int and str data columns. I want to concatenate first the columns within the dataframe. To do that I have to convert an int

Answer 1

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)

Answer 2

In [16]: df = DataFrame(np.arange(10).reshape(5,2),columns=list('AB'))

In [17]: df
Out[17]: 
   A  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9

In [18]: df.dtypes
Out[18]: 
A    int64
B    int64
dtype: object

---

Convert a series

In [19]: df['A'].apply(str)
Out[19]: 
0    0
1    2
2    4
3    6
4    8
Name: A, dtype: object

In [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  B
0  0  1
1  2  3
2  4  5
3  6  7
4  8  9

In [22]: df.applymap(str).iloc[0,0]
Out[22]: '0'

df = df.applymap(str)

Answer 3

Use the following code:

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

Answer 4

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

import pandas as pd
import numpy as np

df = 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()

Answer 5

Just for an additional reference.

All of the above answers will work in case of a data frame. But if you are using lambda while creating / modify a column this won't work, Because there it is considered as a int attribute instead of pandas series. You have to use str( target_attribute ) to make it as a string. Please refer the below example.

def add_zero_in_prefix(df):
    if(df['Hour']<10):
        return '0' + str(df['Hour'])

data['str_hr'] = data.apply(add_zero_in_prefix, axis=1)