Add Leading Zeros to Strings in Pandas Dataframe

str attribute contains most of the methods in string.

df['ID'] = df['ID'].str.zfill(15)

See more: http://pandas.pydata.org/pandas-docs/stable/text.html

It can be achieved with a single line while initialization. Just use converters argument.

df = pd.read_excel('filename.xlsx', converters={'ID': '{:0>15}'.format})

so you’ll reduce the code length by half 🙂

PS: read_csv have this argument as well.

With Python 3.6+, you can also use f-strings:

df['ID'] = df['ID'].map(lambda x: f'{x:0>15}')

Performance is comparable or slightly worse versus df['ID'].map('{:0>15}'.format). On the other hand, f-strings permit more complex output, and you can use them more efficiently via a list comprehension.

Performance benchmarking

# Python 3.6.0, Pandas 0.19.2

df = pd.concat([df]*1000)

%timeit df['ID'].map('{:0>15}'.format)                  # 4.06 ms per loop
%timeit df['ID'].map(lambda x: f'{x:0>15}')             # 5.46 ms per loop
%timeit df['ID'].astype(str).str.zfill(15)              # 18.6 ms per loop

%timeit list(map('{:0>15}'.format, df['ID'].values))    # 7.91 ms per loop
%timeit ['{:0>15}'.format(x) for x in df['ID'].values]  # 7.63 ms per loop
%timeit [f'{x:0>15}' for x in df['ID'].values]          # 4.87 ms per loop
%timeit [str(x).zfill(15) for x in df['ID'].values]     # 21.2 ms per loop

# check results are the same
x = df['ID'].map('{:0>15}'.format)
y = df['ID'].map(lambda x: f'{x:0>15}')
z = df['ID'].astype(str).str.zfill(15)

assert (x == y).all() and (x == z).all()

If you are encountering the error:

Pandas error: Can only use .str accessor with string values, which use np.object_ dtype in pandas

df['ID'] = df['ID'].astype(str).str.zfill(15)

If you want a more customizable solution to this problem, you can try pandas.Series.str.pad

df['ID'] = df['ID'].astype(str).str.pad(15, side='left', fillchar='0')

str.zfill(n) is a special case equivalent to str.pad(n, side='left', fillchar='0')

rjust worked for me:

df['ID']= df['ID'].str.rjust(15,'0')