You can use the following methods to filter for rows that contain a string with a specific length in a pandas DataFrame:
Method 1: Filter Rows Based on String Length in One Column
#filter rows where col1 has a string length of 5 df.loc[df['col1'].str.len() == 5]
Method 2: Filter Rows Based on String Length of Multiple Columns
#filter rows where col1 has string length of 5 and col2 has string length of 7 df.loc[(df['col1'].str.len() == 5) & (df['col2'].str.len() == 7)]
The following examples show how to use each method in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'conf': ['East', 'East', 'North', 'West', 'North', 'South'], 'pos': ['Guard', 'Guard', 'Forward', 'Center', 'Center', 'Forward'], 'points': [5, 7, 7, 9, 12, 9]}) #view DataFrame print(df) conf pos points 0 East Guard 5 1 East Guard 7 2 North Forward 7 3 West Center 9 4 North Center 12 5 South Forward 9
Example 1: Filter Rows Based on String Length in One Column
The following code shows how to filter for rows in the DataFrame that have a string length of 5 in the conf column:
#filter rows where conf has a string length of 5 df.loc[df['conf'].str.len() == 5] conf pos points 2 North Forward 7 4 North Center 12 5 South Forward 9
Only the rows where the conf column has a string length of 5 are returned.
We can see that two different strings met this criteria in the conf column:
- “North”
- “South”
Both strings have a length of 5.
Example 2: Filter Rows Based on String Length of Multiple Columns
The following code shows how to filter for rows in the DataFrame that have a string length of 5 in the conf column and a string length of 7 in the pos column:
#filter rows where conf has string length of 5 and pos has string length of 7 df.loc[(df['conf'].str.len() == 5) & (df['pos'].str.len() == 7)] conf pos points 2 North Forward 7 5 South Forward 9
Only the rows where the conf column has a string length of 5 and the pos column has a strength length of 7 are returned.
Note: You can find the complete documentation for the str.len() function in pandas here.
Additional Resources
The following tutorials explain how to perform other common operations in pandas:
How to Drop Rows in Pandas DataFrame Based on Condition
How to Filter a Pandas DataFrame on Multiple Conditions
How to Use “NOT IN” Filter in Pandas DataFrame