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Adding a prefix to column values in Pandas

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Pandas
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Data Manipulation Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Consider the following DataFrame:

df = pd.DataFrame({"A":["a","b"],"B":[4,5]})
df
A B
0 a 4
1 b 5

Adding prefix to a single column

To add a prefix to each value in column A:

df["A"] = "c" + df["A"]
df
A B
0 ca 4
1 cb 5

For non-string typed columns, you must first convert its type to string using astype(str):

df["B"] = "d" + df["B"].astype(str)
df
A B
0 a d4
1 b d5

Adding prefix to multiple columns

To add a prefix to multiple columns:

df[["A","B"]] = "c" + df[["A","B"]].astype(str)
df
A B
0 ca c4
1 cb c5

Here, df[["A","B"]] returns a DataFrame, and we convert the type of all its columns to string using astype(str).

Adding padding to reach a fixed width

Consider the following DataFrame:

df = pd.DataFrame({"A":["aa","b"],"B":[4,55]})
df
A B
0 aa 4
1 b 55

Single column

To add a padding to each value in a column until the desired width is reached, use Series' str.pad(~) method:

df["A"] = df["A"].str.pad(width=3, fillchar="#") # side="left"
df
A B
0 #aa 4
1 ##b 55

Note that str.pad(~) throws an error if all the column values are not of string type. To pad non-string columns, use astype(str) to convert them to string type:

df["B"] = df["B"].astype(str).str.pad(width=3, fillchar="#")
df
A B
0 aa ##4
1 b #55

Multiple columns

Consider the following DataFrame:

df = pd.DataFrame({"A":["aa","b"],"B":["5","66"]})
df
A B
0 aa 5
1 b 66

The method str.pad(~) is only available for Series, and so we cannot pad multiple columns at the same time by simply extracting them as a DataFrame (df[["A","B"]]).

Instead, we can loop through a list of column labels like so:

columns_to_pad = ["A","B"]
for col_label in columns_to_pad:
df[col_label] = df[col_label].str.pad(width=3, fillchar="#")
df
A B
0 #aa ##5
1 ##b #66
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Published by Isshin Inada
Edited by 0 others
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