Select rows by multiple conditions in Pandas

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In this post, we are going to learn different ways of how Pandas select rows by multiple conditions in Pandas by using dataframe loc[] and by using the column value, loc[] with and operator.

1. loc[] to Select mutiple rows based on column value

To select the row from the pandas dataframe we are using the Datafrmae loc[]. The loc[] access the group of rows and columns by the label.

Syntax

df.loc[df['column name'] condition]

In this example, we have to select a subset of dataframe rows for column ‘Name’ where condition name== ‘Rack’.It will select all the matching rows single or multiple and return a subset of the dataframe.

Program Example

#python 3 program to select subset of dataframe in Pandas
import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,100,100,100,100,100,100],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])
 
#appyling condition selecting subset of dataframe 

selected_students = dfobj.loc[dfobj['Name'] == 'Rack']
print (selected_students)

Output

   Name  Marks  Subject
1  Rack    100     Math
4  Rack    100  Physics
6  Rack    100     math

2. Select Mutiple row using loc[] and & operator

To select the rows based on mutiple condition we can use the & operator.In this example we have passed mutiple conditon using this code dfobj.loc[(dobj[‘Name’] == ‘Rack’) & (dobj[‘Marks’] == 100)].

This code will return a subset of dataframe rows where name=’Rack’ and marks =100.

Program example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,100,100,100,100,100,100],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])
 

selected_students = dfobj.loc[(dobj['Name'] == 'Rack') & (dobj['Marks'] == 100)]
print (selected_students)

output

   Name  Marks  Subject
1  Rack    100     Math
4  Rack    100  Physics
6  Rack    100     math

3. Select row based on Value in Column

We can simply use dataframe column name to select the rows based on column value ,we have used column ‘Name‘ and ,passed condition where name == ‘Rack’ .In this line of code dfobj[(dfobj [‘Name’] == ‘Rack’)].It select all the matching rows single or mutiple.

Program Example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,100,100,100,100,100,100],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])

selected_students = dfobj[(dfobj ['Name'] == 'Rack')]
                         
print (selected_students)

Output

   Name  Marks  Subject
1  Rack    100     Math
4  Rack    100  Physics
6  Rack    100     math

4. Select row based on multiple values in column

To select the rows based on mutiple column value ,we have used column ‘marks‘ and passed mutiple condition using and operator (&) . It will select the rows based on mutiple condition.

Program Example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,90,92,100,100,100,99],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])

subset_of_df = dfobj[(dfobj['Marks'] >=90) & (dfobj['Marks'] < 100) ]
                         
print (subset_of_df)

Output

  Name  Marks Subject
1  Rack     90    Math
2   Max     92   Music
6  Rack     99    math

5. Select row based on any of mutiple condition

Sometimes instead of selecting on mutiple conditions, we have to select all or any matching rows of a given column that we can achieve by using the isin() method and can pass mutiple values.

In this example we have passed mutiple conditions using .isin([‘Math’, ‘Physic’]) ] it will select all rows or any row have subject ‘Math’ or ‘Physic’ and

Program Example

#python 3 program to selectdataframe rows by condition 
import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,90,92,100,100,100,99],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])

subset_of_df = dfobj[dfobj['Subject'].isin(['Math', 'Physic']) ]

                         
print (subset_of_df)

Output

    Name  Marks Subject
0   Jack    100    Math
1   Rack     90    Math
3  David    100  Physic

6. Select Mutiple Rows using OR operator

In this example, we are using the ‘and’ operator to select any or both matching columns value passed in condition.

Program Example

import pandas as pd
 
Student_dict = {
    'Name': ['Jack', 'Rack', 'Max', 'David','Rack','max','Rack'],
    'Marks':[100,90,92,100,100,100,99],
    'Subject': ['Math', 'Math', 'Music', 'Physic','Physics','Music','Math']
}
 
dfobj = pd.DataFrame(Student_dict,columns=['Name','Marks','Subject'])

subset_of_df = dfobj[(dfobj['Name'] == 'Jack') | (dfobj['Subject'] == 'Math')]



                         
print (subset_of_df)

Output

   Name  Marks Subject
0  Jack    100    Math
1  Rack     90    Math
6  Rack     99    Math

Summary

In this post, we have learned multiple ways to Select rows by multiple conditions in Pandas with code example by using dataframe loc[] and by using the column value, loc[] with & and or operator. We can change operators as per need like <=,>=,!=.

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