How do I create a new column where the values are selected based on existing columns?

Python Pandas Dataframe Numpy

When working with data in Python, particularly with pandas DataFrames, it is often necessary to create new columns based on the values in existing columns. One common task is to create a new column where the values are selected based on the values in other columns.

Problem Description

Let's consider the following DataFrame:

import pandas as pd
        
df = pd.DataFrame({
    'Type': ['A', 'B', 'B', 'C'],
    'Set': ['Z', 'Z', 'X', 'Y']
})
        
print(df)

The DataFrame looks like this:

  Type Set
0    A   Z
1    B   Z
2    B   X
3    C   Y

The task is to add a new column called 'Color' where the values are set to 'green' if 'Set' is equal to 'Z', and 'red' otherwise.

Solution

We can use the apply function in pandas to create the new column based on the values in other columns.

def add_color(row):
    if row['Set'] == 'Z':
        return 'green'
    else:
        return 'red'
        
df['Color'] = df.apply(add_color, axis=1)
print(df)

The output will be:

  Type Set  Color
0    A   Z  green
1    B   Z  green
2    B   X    red
3    C   Y    red

In this solution, we define a function called add_color that takes a row as input. Inside the function, we use an if statement to check if the value in the 'Set' column is equal to 'Z'. If it is, we return 'green'; otherwise, we return 'red'.

We then use the apply function on the DataFrame along the axis 1 (i.e., row-wise). This applies the add_color function to each row and assigns the returned value to the 'Color' column.

By using this method, we can create a new column based on the values in other columns. We can define any custom logic inside the function to determine the values of the new column.

Conclusion

In this article, we have seen how to create a new column in a pandas DataFrame based on the values in existing columns. By using the apply function and defining a custom function, we can easily apply any logic to select the values for the new column.

Remember to adapt this solution to your specific needs and modify the code accordingly. This method can be applied to many different scenarios.

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