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How to Select rows in Pandas DataFrame Based on Conditions

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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:

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:

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:

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:

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:

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:

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:

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:

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  

Conclusion

In this tutorial, we have discussed how to select different rows of Pandas DataFrame based on various conditions.


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