How to Select rows in Pandas DataFrame Based on Conditions

In this tutorial, we will learn how a user can select rows in Pandas DataFrame based on conditions using Python.

Users can select rows based on a particular column value using '>', '=', '<=', '>=', '!=' operators.

Conditions:

We will discuss different conditions that can be applied to the Pandas DataFrame.

Condition 1:

Select all the rows from the DataFrame in which 'Percentage' is greater than 70 using the basic method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subject_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 
  
# Then we will select rows based on condition
result_DataFrame = Data_Frame[Data_Frame['Percentage_1'] > 70]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
5    John     24          ADS            78
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Condition 2:

Select all the rows from the DataFrame in which 'Percentage' is greater than 70 by using the "loc[]" method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 
  
# Then we will select rows based on condition, That is, Using loc[] method
result_DataFrame = Data_Frame.loc[Data_Frame['Percentage_1'] > 70]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
5    John     24          ADS            78
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Condition 3:

Select all the rows from the DataFrame in which 'Percentage' is not equal to 71 using the "loc[]" method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 
  
# Then we will select rows based on condition, That is, Using loc[] method
result_DataFrame = Data_Frame.loc[Data_Frame['Percentage_1'] != 71]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
    Name_1  Age_1 Subjects_1  Percentage_1
0    Anuj     23       DBMS            88
1    Ashu     24        ADS            62
2   Yashi     21       ASPM            85
4  Joshua     21       MFCS            55
5    John     24        ADS            78
6     Ray     25       ASPM            70
7   Lilly     22        TOC            66
9  Rachel     22       OOPS            89

Now, we will learn how to select those rows whose column value is present in the list by using the "isin()" function of the DataFrame.

Condition 4:

Select all the rows from the given DataFrame in which column value of "Subjects_1" is present in the "Subjects_2" list by using the basic method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 

Subjects_2 = ['ASPM', 'ADS', 'TOC']
  
# Then we will select rows based on condition, That is, Using isin[] method
result_DataFrame = Data_Frame[Data_Frame['Subjects_1'].isin(Subjects_2)]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
   Name_1  Age_1 Subjects_1  Percentage_1
1   Ashu     24        ADS            62
2  Yashi     21       ASPM            85
5   John     24        ADS            78
6    Ray     25       ASPM            70
7  Lilly     22        TOC            66

Condition 5:

Select all the rows from the given DataFrame in which column value of "Subjects_1" is present in the "Subjects_2" list by using the "loc[]" method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 

Subjects_2 = ['ASPM', 'ADS', 'TOC']
  
# Then we will select rows based on condition, That is, Using isin[] method
result_DataFrame = Data_Frame.loc[Data_Frame['Subjects_1'].isin(Subjects_2)]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
   Name_1  Age_1 Subjects_1  Percentage_1
1   Ashu     24        ADS            62
2  Yashi     21       ASPM            85
5   John     24        ADS            78
6    Ray     25       ASPM            70
7  Lilly     22        TOC            66

Condition 6:

Select all the rows from the given DataFrame in which column value of "Subjects_1" is not present in the "Subjects_2" list by using the "loc[]" method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 24, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 62, 85, 71, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 

Subjects_2 = ['ASPM', 'ADS', 'TOC']
  
# Then we will select rows based on condition, That is, Using isin[] method
result_DataFrame = Data_Frame.loc[~Data_Frame['Subjects_1'].isin(Subjects_2)]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     24          ADS            62
2   Yashi     21         ASPM            85
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
3    Mark     19          BCM            71
4  Joshua     21         MFCS            55
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Now, we will learn how to select rows based on multiple column conditions by using the "&" operator.

Condition 7:

Select all the rows from the given DataFrame in which "Percentage_1" is equal to "71" and "Subject_1" is present in the "Subject_2" list by using the basic method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
  
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 21, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 71, 71, 82, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 

Subjects_2 = ['ASPM', 'ADS', 'TOC']
  
# Then we will select rows based on condition, That is, Using isin[] method
result_DataFrame = Data_Frame[(Data_Frame['Percentage_1'] == 71) &
                              Data_Frame['Subjects_1'].isin(Subjects_2)]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     21          ADS            71
2   Yashi     21         ASPM            71
3    Mark     19          BCM            82
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
   Name_1  Age_1 Subjects_1  Percentage_1
1   Ashu     21        ADS            71
2  Yashi     21       ASPM            71

Condition 8:

Select all the rows from the given DataFrame in which "Percentage_1" is equal to "71" and "Subject_1" is present in the "Subject_2" list by using the "loc[]" method.

Code:

# First, import pandas
import pandas as pnd
record_1 = {
   
 'Name_1': ['Anuj', 'Ashu', 'Yashi', 'Mark', 'Joshua', 'John', 'Ray', 'Lilly', 'Rose', 'Rachel' ],
 'Age_1': [23, 21, 21, 19, 21, 24, 25, 22, 23, 22],
 'Subjects_1': ['DBMS', 'ADS', 'ASPM', 'BCM', 'MFCS', 'ADS', 'ASPM', 'TOC', 'Data Mining', 'OOPS'],
 'Percentage_1': [88, 71, 71, 82, 55, 78, 70, 66, 71, 89] }
  
# Now, we are creating a dataframe
Data_Frame = pnd.DataFrame(record_1, columns = ['Name_1', 'Age_1', 'Subjects_1', 'Percentage_1'])
  
print("Given DataFrame: \n", Data_Frame) 

Subjects_2 = ['ASPM', 'ADS', 'TOC']
  
# Then we will select rows based on condition, That is, Using isin[] method
result_DataFrame = Data_Frame.loc[(Data_Frame['Percentage_1'] == 71) &
                              Data_Frame['Subjects_1'].isin(Subjects_2)]
  
print('\nFollowing is the Result DataFrame: \n', result_DataFrame)

Output:

Given DataFrame: 
    Name_1  Age_1   Subjects_1  Percentage_1
0    Anuj     23         DBMS            88
1    Ashu     21          ADS            71
2   Yashi     21         ASPM            71
3    Mark     19          BCM            82
4  Joshua     21         MFCS            55
5    John     24          ADS            78
6     Ray     25         ASPM            70
7   Lilly     22          TOC            66
8    Rose     23  Data Mining            71
9  Rachel     22         OOPS            89

Following is the Result DataFrame: 
   Name_1  Age_1 Subjects_1  Percentage_1
1   Ashu     21        ADS            71
2  Yashi     21       ASPM            71