Ways to filter Pandas DataFrame by column values

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 pandas
import pandas as pd

# declare a dictionary
record = {

 '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 dataframe
dataframe = pd.DataFrame(record,
                         columns = ['Name', 'Age',
                                    'Stream', 'Percentage'])
# show the Dataframe
print("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 condition
rslt_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 condition
rslt_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 condition
rslt_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 condition
rslt_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 condition
rslt_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 condition
rslt_df = dataframe.loc[(dataframe['Age'] == 22) &
              dataframe['Stream'].isin(options)]

print('\nResult dataframe :\n',
      rslt_df)

Output: