Filtering a Pandas DataFrame by way of column values is a commonplace operation while running with information in Python. You can use various methods and techniques to achieve this. Here are numerous ways to filter out a Pandas DataFrame through column values.
In this post, we will see different ways to filter Pandas Dataframe by column values. First, Let’s create a Dataframe:
Python3
# importing pandasimport pandas as pd # declare a dictionaryrecord = { 'Name' : ['Ankit', 'Swapnil', 'Aishwarya', 'Priyanka', 'Shivangi', 'Shaurya' ], 'Age' : [22, 20, 21, 19, 18, 22], 'Stream' : ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'], 'Percentage' : [90, 90, 96, 75, 70, 80] } # create a dataframedataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])# show the Dataframeprint("Given Dataframe :\n", dataframe) |
Output:
Selecting rows of Pandas Dataframe based on particular column value using ‘>’, ‘=’, ‘=’, ‘<=’, ‘!=’ operator.
Example 1: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 75 using [ ].
Python3
# selecting rows based on conditionrslt_df = dataframe[dataframe['Percentage'] > 70] print('\nResult dataframe :\n', rslt_df) |
Output:
Example 2: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 70 using loc[ ].
Python3
# selecting rows based on conditionrslt_df = dataframe.loc[dataframe['Percentage'] > 70] print('\nResult dataframe :\n', rslt_df) |
Output:
Selecting those rows of Pandas Dataframe whose column value is present in the list using isin() method of the dataframe.
Example 1: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using [ ].
Python3
options = ['Science', 'Commerce'] # selecting rows based on conditionrslt_df = dataframe[dataframe['Stream'].isin(options)] print('\nResult dataframe :\n', rslt_df) |
Output:
Example 2: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using loc[ ].
Python
options = ['Science', 'Commerce'] # selecting rows based on conditionrslt_df = dataframe.loc[dataframe['Stream'].isin(options)] print('\nResult dataframe :\n', rslt_df) |
Output:
Selecting rows of Pandas Dataframe based on multiple column conditions using ‘&’ operator.
Example1: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using [ ].
Python3
options = ['Commerce' ,'Science'] # selecting rows based on conditionrslt_df = dataframe[(dataframe['Age'] == 22) & dataframe['Stream'].isin(options)] print('\nResult dataframe :\n', rslt_df) |
Output:
Example 2: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using loc[ ].
Python3
options = ['Commerce', 'Science'] # selecting rows based on conditionrslt_df = dataframe.loc[(dataframe['Age'] == 22) & dataframe['Stream'].isin(options)] print('\nResult dataframe :\n', rslt_df) |
Output:
