len() to get length of the string or numeric data in DataFrame

Length of the string data.
Returns Series or Index of Int

Length of the string column ( NAME )

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data['NAME'].str.len())

Output

Adding one column showing length of the string field ( NAME )

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['LENGTH']=my_data['NAME'].str.len()
print(my_data )

Output

   NAME  ID  MATH  ENGLISH  LENGTH
0  Ravi   1    80       80       4
1  Raju   2    40       70       4
2  Alex   3    70       40       4
3   Ron   4    70       50       3
4  King   5    70       60       4
5  Jack   6    30       30       4

Length of Integer column

We can get the length of one integer column ( Say ENGLISH ) by using len() , but we have to convert the datatype from integer to string by using astype() before using len().

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['LENGTH']=my_data['ENGLISH'].astype(str).str.len()
print(my_data )

Output

   NAME  ID  MATH  ENGLISH  LENGTH
0  Ravi   1    80       80       2
1  Raju   2    40       70       2
2  Alex   3    70       40       2
3   Ron   4    70       50       2
4  King   5    70       60       2
5  Jack   6    30       30       2

Let us use one float datatype and find out the length. Here also we will use astype() to convert float to string data. Here we changed MATH column by adding float data.

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80.34,40.21,70.456,70.123,70.0,30.9],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['LENGTH']=my_data['MATH'].astype(str).str.len()
print(my_data)

Output

   NAME  ID    MATH  ENGLISH  LENGTH
0  Ravi   1  80.340       80       5
1  Raju   2  40.210       70       5
2  Alex   3  70.456       40       6
3   Ron   4  70.123       50       6
4  King   5  70.000       60       4
5  Jack   6  30.900       30       4

Pandas contains() Converting char case slice() 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