zfill() to prepending string with 0

Prepending string with '0'
Returns string with filled 0

If we have integer column then we have to first change the object to string dtype by using astype().

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex'],'ID':[1,2,3],
         'MATH':[30,40,50],'ENGLISH':[20,30,40]}
df = pd.DataFrame(data=my_dict)
df['MATH']=df['MATH'].astype(str) # to string dtype 
#print(df.MATH.str.zfill(3))
df['MATH']=df.MATH.str.zfill(3)
print(df)

Output

   NAME  ID MATH  ENGLISH
0  Ravi   1  030       20
1  Raju   2  040       30
2  Alex   3  050       40

Handling special chars , negative numbers and big numbers

Here we have negative number, single negative number, string and more than 3 char numbers. Check the output

my_dict={'NAME':['Ravi','Raju','Alex','King','Queen'],'ID':[1,2,3,4,5],
         'MATH':[-3,-40,5,'abc',6000],'ENGLISH':[20,30,40,50,10]}
df = pd.DataFrame(data=my_dict)
df['MATH']=df['MATH'].astype(str)
df['MATH']=df.MATH.str.zfill(3)
print(df)

Output

    NAME  ID  MATH  ENGLISH
0   Ravi   1   -03       20
1   Raju   2   -40       30
2   Alex   3   005       40
3   King   4   abc       50
4  Queen   5  6000       10

Example: Padding Numeric Strings with Leading Zeros

The `str.zfill()` method can be used to ensure consistent numeric formatting, such as ID codes or product numbers.

import pandas as pd
data = {'Product': ['A', 'B', 'C'], 'Code': [7, 58, 105]}
df = pd.DataFrame(data)
df['Code'] = df['Code'].astype(str).str.zfill(5)
print(df)

Output

  Product   Code
0       A  00007
1       B  00058
2       C  00105

Use Case: Handling Mixed Data Types

If your column contains a mix of integers, strings, or special characters, `zfill()` can still apply padding without affecting non-numeric data.

data = {'Category': ['alpha', 'beta', 'gamma'], 'ID': [5, 'x12', 'abc']}
df = pd.DataFrame(data)
df['ID'] = df['ID'].astype(str).str.zfill(4)
print(df)

Output

  Category    ID
0    alpha  0005
1     beta  0x12
2    gamma  0abc

Advanced Feature: Using `str.zfill()` for Alphanumeric Data

We can apply `zfill()` to alphanumeric codes, ensuring uniform padding for better sorting and display.

codes = pd.Series(['A1', 'B12', 'C3'])
padded_codes = codes.str.zfill(4)
print(padded_codes)

Output

0    00A1
1    0B12
2    00C3
dtype: object

Pandas contains() Converting char case split()

Numpy arrays Python & MySQL Python- Tutorials

Subhendu Mohapatra

Author

🎥 Join me live on YouTube

Passionate about coding and teaching, I publish practical tutorials on PHP, Python, JavaScript, SQL, and web development. My goal is to make learning simple, engaging, and project‑oriented with real examples and source code.

Subscribe to our YouTube Channel here