Adding Trailing Zeros To Row Values To Make Sure There Are 10 Digits

If i have a data frame where max digits in each row is 10 but some IDs are less than 10 because the trailing zeros have been cut off, how do I add trailing zeros in python to make

Solution 1:

You can use str.pad() which I believe works perfect for this scenario:

df['ID'] = df['ID'].str.pad(width=10,side='right',fillchar='0')

In case the dtype of the column is not string, then you can first convert it:

df['ID'] = df['ID'].astype(str).str.pad(width=10,side='right',fillchar='0')

Output:

           ID
0  1234567689
1  1234567680
2  1234567600

Solution 2:

Another way is to use, Series.str.ljust:

df['ID'] = df['ID'].str.ljust(width=10, fillchar='0')

Result:

           ID
0  1234567689
1  1234567680
2  1234567600

Solution 3:

You can use ljust for this:

df = df['ID'].astype(str).str.ljust(10, '0')
print(df)

0    1234567689
1    1234567680
2    1234567600

Solution 4:

I think f-formatting can do that

X = [1234567689, 12345, 123,]
print([f'{item:0<9}' for item in X])

This only works with Python 3.6+. The idea is to get the value and left pad 9 zeros. In Pandas you can do, the following to maintain your field as numeric

df['ID'] = df['ID'].apply(lambda x: f'{x:0<9'}).astype(int)