Logo Questions Linux Laravel Mysql Ubuntu Git Menu
 

add a string prefix to each value in a string column using Pandas

df['col'] = 'str' + df['col'].astype(str)

Example:

>>> df = pd.DataFrame({'col':['a',0]})
>>> df
  col
0   a
1   0
>>> df['col'] = 'str' + df['col'].astype(str)
>>> df
    col
0  stra
1  str0

As an alternative, you can also use an apply combined with format (or better with f-strings) which I find slightly more readable if one e.g. also wants to add a suffix or manipulate the element itself:

df = pd.DataFrame({'col':['a', 0]})

df['col'] = df['col'].apply(lambda x: "{}{}".format('str', x))

which also yields the desired output:

    col
0  stra
1  str0

If you are using Python 3.6+, you can also use f-strings:

df['col'] = df['col'].apply(lambda x: f"str{x}")

yielding the same output.

The f-string version is almost as fast as @RomanPekar's solution (python 3.6.4):

df = pd.DataFrame({'col':['a', 0]*200000})

%timeit df['col'].apply(lambda x: f"str{x}")
117 ms ± 451 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit 'str' + df['col'].astype(str)
112 ms ± 1.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Using format, however, is indeed far slower:

%timeit df['col'].apply(lambda x: "{}{}".format('str', x))
185 ms ± 1.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

You can use pandas.Series.map :

df['col'].map('str{}'.format)

It will apply the word "str" before all your values.


If you load you table file with dtype=str
or convert column type to string df['a'] = df['a'].astype(str)
then you can use such approach:

df['a']= 'col' + df['a'].str[:]

This approach allows prepend, append, and subset string of df.
Works on Pandas v0.23.4, v0.24.1. Don't know about earlier versions.


Donate For Us

If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!