Pandas if-else statements in Python

Pandas is a powerful library in Python used for data manipulation and analysis. It provides various functionalities to manipulate data frames and perform operations on them. In this article, we will discuss if-else statements in pandas and how they can be used to manipulate data frames.

Syntax

The syntax for if-else statements in pandas is as follows:

df.loc[df['column_name'] condition, 'new_column_name'] = 'value_if_true' if df['column_name'] condition else 'value_if_false'

where,

• df is the name of the dataframe
• column_name is the name of the column on which the condition is to be applied
• condition is the conditional statement to be checked
• new_column_name is the name of the new column to be created
• value_if_true is the value to be assigned if the condition is true
• value_if_false is the value to be assigned if the condition is false

Example

Let's consider the following data frame:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Mark', 'Jessica', 'Sam'],
        'Age': [22, 24, 21, 19, 23],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Marks': [75, 80, 64, 78, 82]}
df = pd.DataFrame(data)
print(df)

Output:

      Name  Age  Gender  Marks
0     John   22    Male     75
1     Emma   24  Female     80
2     Mark   21    Male     64
3  Jessica   19  Female     78
4      Sam   23    Male     82

Let's say we want to add a column named Pass/Fail based on the marks obtained. If marks are greater than or equal to 70, the value assigned should be Pass, otherwise Fail. We can achieve this using if-else statements in pandas.

df.loc[df['Marks'] >= 70, 'Pass/Fail'] = 'Pass'
df.loc[df['Marks'] < 70, 'Pass/Fail'] = 'Fail'
print(df)

Output:

      Name  Age  Gender  Marks Pass/Fail
0     John   22    Male     75      Pass
1     Emma   24  Female     80      Pass
2     Mark   21    Male     64      Fail
3  Jessica   19  Female     78      Pass
4      Sam   23    Male     82      Pass

In the above example, we have used if-else statements in pandas to create a new column named Pass/Fail based on the marks obtained by the students. If the marks are greater than or equal to 70, the value assigned is Pass, otherwise Fail.

Conclusion

Pandas if-else statements are a useful tool for manipulating data frames in Python. They can be used to create new columns based on conditional statements, which can help in data analysis and visualization.