In this article, we are going to see how to filter Pandas Dataframe based on index. We can filter Dataframe based on indexes with the help of filter(). This method is used to Subset rows or columns of the Dataframe according to labels in the specified index. We can use the below syntax to filter Dataframe based on index.
Syntax: DataFrame.filter ( items=None, like=None, regex=None, axis=None )
Parameters:
- items : List of info axis to restrict to (must not all be present).
- like : Keep info axis where “arg in col == True”
- regex : Keep info axis with re.search(regex, col) == True
- axis : The axis to filter on. By default this is the info axis, ‘index’ for Series, ‘columns’ for DataFrame
Returns : This method is return same type of object as input object.
Example 1:
The following program is to understand how to filter Dataframe based on numeric value indexes.
Python
# import pandasimport pandas as pd# define datadata = {"Name": ["Mukul", "Suraj", "Rohit", "Rahul", "Mohit", "Nishu", "Rishi", "Manoj", "Mukesh", "Rohan"], "Age": [22, 23, 25, 21, 27, 24, 26, 23, 21, 27], "Qualification": ["BBA", "BCA", "BBA", "BBA", "MBA", "BCA", "MBA", "BBA", "BCA", "MBA"] }# define dataframedf = pd.DataFrame(data, columns=['Name', 'Age', 'Qualification'])# display original dataframeprint("\n Original Dataframe \n", df)# filter 5 index valuedf_1 = df.filter(items=[5], axis=0)# display resultprint("\n Display only 5 index value \n", df_1)# filter only 5 and 8 index valuedf_2 = df.filter(items=[5, 8], axis=0)# display resultprint("\n Display only 5 and 8 index value \n", df_2) |
Output:
Example 2:
The following program is to know how to filter Dataframe based on non-numeric value indexes.
Python
# import pandasimport pandas as pd# define datadata = {"Name": ["Mukul", "Suraj", "Rohit", "Rahul", "Mohit"], "Age": [22, 23, 25, 21, 27], "Qualification": ["BBA", "BCA", "BBA", "BBA", "MBA"] }# define dataframedf = pd.DataFrame(data, columns=['Name', 'Age', 'Qualification'], index=[ 'Person_A', 'Person_B', 'Person_C', 'Person_D', 'Person_E'])# display original dataframeprint("\n Original Dataframe \n", df)# filter Person_B index valuedf_1 = df.filter(items=['Person_B'], axis=0)# display resultprint("\n Display only Person_B index value \n", df_1)# filter only Person_B and Person_D index valuedf_2 = df.filter(items=['Person_B', 'Person_D'], axis=0)# display resultprint("\n Display Person_B and Person_D index value \n", df_2) |
Output:
Example 3:
The following program is to understand how to filter Dataframe for indexes that contain a specific string.
Python
# import pandasimport pandas as pd# define datadata = {"Name": ["Mukul", "Suraj", "Rohit", "Rahul", "Mohit"], "Age": [22, 23, 25, 21, 27], "Qualification": ["BBA", "BCA", "BBA", "BBA", "MBA"] }# define dataframedf = pd.DataFrame(data, columns=['Name', 'Age', 'Qualification'], index=[ 'Person_A', 'Person_B', 'Person_AB', 'Person_c', 'Person_AC'])# display original dataframeprint("\n Original Dataframe \n", df)# filter index that contain Person_A string.df_1 = df.filter(like='Person_A', axis=0)# display resultprint("\n display index that contain Person_A string \n", df_1) |
Output
Example 4:
The following program is to know how to filter Dataframe for indexes that contain a specific character.
Python
# import pandasimport pandas as pd# define datadata = {"Name": ["Mukul", "Suraj", "Rohit", "Rahul", "Mohit"], "Age": [22, 23, 25, 21, 27], "Qualification": ["BBA", "BCA", "BBA", "BBA", "MBA"] }# define dataframedf = pd.DataFrame(data, columns=['Name', 'Age', 'Qualification'], index=[ 'Person_A', 'Person_B', 'Person_AB', 'Person_C', 'Person_BC'])# display original dataframeprint("\n Original Dataframe \n", df)# filter index that contain an specific character.df_1 = df.filter(like='B', axis=0)# display resultprint("\n display all indexes that contain Specific character \n", df_1) |
Output
