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Get all rows in a Pandas DataFrame containing given substring

  • Difficulty Level : Easy
  • Last Updated : 24 Dec, 2018

Let’s see how to get all rows in a Pandas DataFrame containing given substring with the help of different examples.

Code #1: Check the values PG in column Position

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# importing pandas 
import pandas as pd
  
# Creating the dataframe with dict of lists
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],
                   'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],
                   'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],
                   'Number': [3, 4, 7, 11, 5],
                   'Age': [33, 25, 34, 35, 28],
                   'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],
                   'Weight': [89, 79, 113, 78, 84],
                   'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],
                   'Salary': [99999, 99994, 89999, 78889, 87779]},
                   index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5'])
print(df, "\n")
  
print("Check PG values in Position column:\n")
df1 = df['Position'].str.contains("PG")
print(df1)

Output:



But this result doesn’t seem very helpful, as it returns the bool values with the index. Let’s see if we can do something better.
 

Code #2: Getting the rows satisfying condition




# importing pandas as pd
import pandas as pd
  
# Creating the dataframe with dict of lists
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],
                   'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],
                   'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],
                   'Number': [3, 4, 7, 11, 5],
                   'Age': [33, 25, 34, 35, 28],
                   'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],
                   'Weight': [89, 79, 113, 78, 84],
                   'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],
                   'Salary': [99999, 99994, 89999, 78889, 87779]},
                   index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5'])
  
df1 = df[df['Position'].str.contains("PG")]
print(df1)

Output:

 

Code #3: Filter all rows where either Team contains ‘Boston’ or College contains ‘MIT’.




# importing pandas
import pandas as pd
  
# Creating the dataframe with dict of lists
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'],
                   'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'],
                   'Position': ['PG', 'PG', 'UG', 'PG', 'UG'],
                   'Number': [3, 4, 7, 11, 5],
                   'Age': [33, 25, 34, 35, 28],
                   'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'],
                   'Weight': [89, 79, 113, 78, 84],
                   'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'],
                   'Salary': [99999, 99994, 89999, 78889, 87779]},
                   index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5'])
  
  
df1 = df[df['Team'].str.contains("Boston") | df['College'].str.contains('MIT')]
print(df1)

Output:

 
Code #4: Filter rows checking Team name contains ‘Boston and Position must be PG.




# importing pandas module 
import pandas as pd 
    
# making data frame 
  
  
df1 = df[df['Team'].str.contains('Boston') & df['Position'].str.contains('PG')]
df1

Output:

 

Code #5: Filter rows checking Position contains PG and College must contains like UC.




# importing pandas module 
import pandas as pd 
    
# making data frame 
  
  
df1 = df[df['Position'].str.contains("PG") & df['College'].str.contains('UC')]
df1

Output:

 




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