Get all rows in a Pandas DataFrame containing given substring - GeeksforGeeks

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
# importing pandas import pandas as pd  # Creating the dataframe with dict of listsdf = 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 pdimport pandas as pd  # Creating the dataframe with dict of listsdf = 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 pandasimport pandas as pd  # Creating the dataframe with dict of listsdf = 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 df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")     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 df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")     df1 = df[df['Position'].str.contains("PG") & df['College'].str.contains('UC')]df1
Output: 
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