Pandas Add Column based on Another Column

How to add a column based on another existing column in Pandas DataFrame. You can add/append a new column to the DataFrame based on the values of another column using df.assign(), df.apply(), and, np.where() functions and return a new DataFrame after adding a new column.

In this article, I will explain how to add/append a column to the DataFrame based on the values of another column using multiple functions with well-defined examples.

Key Points –

You can create a new column by assigning values to df['new_column'], where values are derived from an existing column.
Conditional logic, such as np.where() or apply(), is often used to set values based on conditions in another column.
Using apply() with a custom function provides flexibility but may be slower than vectorized methods.
You can use multiple columns to create a new column by performing operations across columns.
Arithmetic operations on columns are commonly used to create calculated columns.
Avoid using loops to create columns in large DataFrames, as it may lead to performance issues.

Quick Examples of Add Column Based on Another Column

Following are examples of adding a column based on another column.


# Quick examples of add column based on another column

# Example 1: Add column 
# Using arithmetic operation
# Based on existing column 
df["Final_Fee"] = df["Fee"] - df["Discount"]

# Example 2: Add New Column using assign()
df = pd.DataFrame(technologies)
df1 = df.assign(Discount_Percent=lambda x: x.Discount / x.Fee * 100)

# Example 3: Add column using np.where()
df['Discount_rating'] = np.where(df['Discount'] > 2000, 'Good', 'Bad')

# Example 4: Add column using apply()
df['Final_fee'] = df.apply(lambda x: x['Fee'] - x['Discount'], axis=1)

# Example 5: Add column to DataFrame using loc[]
df['Without_discount'] = df.loc[:,['Fee', 'Discount']].sum(axis=1)

Now, let’s create a DataFrame Let’s create a Pandas DataFrame with sample data and execute the above examples.


# Create DataFrame
import pandas as pd
import numpy as np
technologies= {
    'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
    'Fee' :[22000,25000,23000,24000,26000],
    'Discount':[1000,2300,1000,1200,2500]
          }

df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)

Yields below output.

Pandas Add Column Based on Existing Column

To add a new column based on an existing column in Pandas DataFrame use the df[] notation. We can derive a new column by computing arithmetic operations on existing columns and assign the result as a new column to DataFrame. This process is the fastest and simplest way of creating a new column using another column of DataFrame.


# Add column using arithmetic operation
# Based on existing columns 
df["Final_Fee"] = df["Fee"] - df["Discount"]
print("The DataFrame after adding a new Column:\n", df)

Yields below output.

Adding a Column using assign()

In real-time, we are mostly required to add a column to DataFrame by calculating from an existing column, the below example derives the Discount_Percent column from Fee and Discount. Here, I will use lambda to derive a new column from the existing one.


# Add New Column from Existing Column 
df = pd.DataFrame(technologies)
df1 = df.assign(Discount_Percent=lambda x: x.Discount / x.Fee * 100)
print("The DataFrame after adding a new Column:\n", df1)

Yields below output. Similarly, you can also derive multiple columns and add them to a DataFrame in a single statement, I will leave this to you to explore.


# Output:
The DataFrame after adding a new Column:
    Courses    Fee  Discount  Discount_Percent
0    Spark  22000      1000          4.545455
1  PySpark  25000      2300          9.200000
2   Hadoop  23000      1000          4.347826
3   Python  24000      1200          5.000000
4   Pandas  26000      2500          9.615385

Pandas Add Column Using NumPy where()

Alternatively, we can create and add a new column in pandas DataFrames based on the values of existing columns using numpy.where() function. In this example, I will add a new column, that I have to create based on the 'Discount' column of the given DataFrame.


# Add column using np.where()
df['Discount_rating'] = np.where(df['Discount'] > 2000, 'Good', 'Bad')
print("The DataFrame after adding new Column:\n", df)

Yields below output.


# Output:
The DataFrame after adding a new Column:
   Courses    Fee  Discount Discount_rating
 0    Spark  22000      1000             Bad
 1  PySpark  25000      2300            Good
 2   Hadoop  23000      1000             Bad
 3   Python  24000      1200             Bad
 4   Pandas  26000      2500            Good

Add Column Using apply()

Using apply() function we can add a required column to DataFrame by calculating values from an existing column. The below example derives the Final_fee column from Fee and Discount. Here, I will use lambda to derive a new column.


# Add column using apply()
df['Final_fee'] = df.apply(lambda x: x['Fee'] - x['Discount'], axis=1)
print("The DataFrame after adding a new Column:\n", df)

Yields below output.


# Output:
The DataFrame after adding a new Column:
   Courses    Fee  Discount  Final_fee
0    Spark  22000      1000      21000
1  PySpark  25000      2300      22700
2   Hadoop  23000      1000      22000
3   Python  24000      1200      22800
4   Pandas  26000      2500      23500

Add Column to DataFrame using loc[]

Moreover, we can add a column to DataFrame based on the values of existing columns using Pandas.DataFrame.loc[] attribute. loc[] is used to select rows and columns by names/labels of pandas DataFrame. Here, I will select multiple columns of DataFrame using the loc[] attribute and then call the sum() function. This syntax will add the new column to DataFrame.


# Add column to DataFrame using loc[]
df['Without_discount'] = df.loc[:,['Fee', 'Discount']].sum(axis=1)
print("The DataFrame after adding a new Column:\n", df)

Yields below output.


# Output:
The DataFrame after adding a new Column:
   Courses    Fee  Discount  Without_discount
0    Spark  22000      1000             23000
1  PySpark  25000      2300             27300
2   Hadoop  23000      1000             24000
3   Python  24000      1200             25200
4   Pandas  26000      2500             28500

Frequently Asked Questions of Add Column based on Another Column

You can add a new column in Pandas based on the existing column by using df[] notation. You can derive a new column by applying simple arithmetic operations on existing columns and assigning the result to a new column. For example, df['new_column'] = df['existing_column'] * 2.

You can add a new column conditionally based on values from another column using df.apply() method with a custom function or using the numpy.where() function.

You can concatenate or combine values from two existing columns using string concatenation, for example: df['new_column'] = df['column1'] + df['column2'].

You can add a new column by applying a custom function to an existing column using the apply() method, which allows you to apply a function element-wise to each row.

You can perform various date and time operations when creating a new column based on date or time columns. The Pandas library provides many functions for working with date and time data.

Conclusion

In this article, I have explained how to add/append a column to the Pandas DataFrame based on the values of another column using multiple functions and also I explained how to use basic arithmetic operations and other functions.

Happy learning!!

References

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.assign.html

Pandas Add Column based on Another Column

Quick Examples of Add Column Based on Another Column

Pandas Add Column Based on Existing Column

Adding a Column using assign()

Pandas Add Column Using NumPy where()

Add Column Using apply()

Add Column to DataFrame using loc[]

Frequently Asked Questions of Add Column based on Another Column

Conclusion

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References