How to Apply Lambda Function to Pandas DataFrame

1. Lambda Function
2. Applying Lambda Function on a Single Column Using DataFrame.assign() Method
3. Applying Lambda Function on Multiple Columns Using DataFrame.assign() Method
4. Applying Lambda Function on a Single Row Using DataFrame.apply() Method
5. Filtering Data by Applying Lambda Function
6. Use the map() Function by Applying Lambda Function
7. Use if-else Statement by Applying Lambda Function
8. Conclusion

The lambda function solves various data science problems in Pandas python. We can apply the lambda function on both rows and columns in the pandas DataFrame.

We will explore in this article how to apply the lambda functions to pandas DataFrame.

There are several applications of lambda function on pandas DataFrame such as filter(), map(), and conditional statements that we will explain with the help of some examples in this article.

Lambda Function

Lambda function contains a single expression.

The Lambda function is a small function that can also use as an anonymous function means it doesn’t require any name. The lambda function is useful to solve small problems with less code.

The following syntax is used to apply a lambda function on pandas DataFrame:

dataframe.apply(lambda x: x + 2)

Applying Lambda Function on a Single Column Using DataFrame.assign() Method

The dataframe.assign() method applies the Lambda function on a single column. Let’s take an example.

In the following example, we have applied a lambda function on the column Students Marks. After applying the Lambda function, the student percentages are calculated and stored in a new Percentage column.

See the following implementation to apply a lambda function on a single column in Pandas DataFrame.

Example Code:

import pandas as pd

# initialization of list
students_record = [
    ["Samreena", 900],
    ["Mehwish", 750],
    ["Asif", 895],
    ["Mirha", 800],
    ["Affan", 850],
    ["Raees", 950],
]

# pandas dataframe creation
dataframe = pd.DataFrame(students_record, columns=["Student Names", "Student Marks"])

# using Lambda function
dataframe1 = dataframe.assign(Percentage=lambda x: (x["Student Marks"] / 1000 * 100))

# display dataframe
print(dataframe1)

Output:

	Student Names	Student Marks	Percentage
0	Samreena	             900	90.0
1	Mehwish	                 750	75.0
2	Asif	                 895	89.5
3	Mirha	                 800	80.0
4	Affan	                 850	85.0
5	Raees	                 950	95.0

Applying Lambda Function on Multiple Columns Using DataFrame.assign() Method

We can also apply the Lambda function on multiple columns using the dataframe.assign() method in Pandas DataFrame.

For example, we have four columns Student Names, Computer, Math, and Physics. We applied a Lambda function on multiple subjects columns such as Computer, Math, and Physics to calculate the obtained marks stored in the Marks_Obtained column.

Implement the following example.

Example Code:

import pandas as pd

# nested list initialization
values_list = [
    ["Samreena", 85, 75, 100],
    ["Mehwish", 90, 75, 90],
    ["Asif", 95, 82, 80],
    ["Mirha", 75, 88, 68],
    ["Affan", 80, 63, 70],
    ["Raees", 91, 64, 90],
]

# pandas dataframe creation
df = pd.DataFrame(values_list, columns=["Student Names", "Computer", "Math", "Physics"])

# applying Lambda function

dataframe = df.assign(
    Marks_Obtained=lambda x: (x["Computer"] + x["Math"] + x["Physics"])
)

# display dataframe
print(dataframe)

Output:

Student Names	Computer	Math	Physics	 Marks_Obtained
0	Samreena	85	        75	      100	 260
1	Mehwish	    90	        75	       90	 255
2	Asif	    95	        82	       80	 257
3	Mirha	    75	        88	       68	 231
4	Affan	    80	        63	       70	 213
5	Raees	    91	        64	       90	 245  

Applying Lambda Function on a Single Row Using DataFrame.apply() Method

The dataframe.apply() method applies the Lambda function on a single row.

For example, we applied the lambda function a single row axis=1. Using the lambda function, we incremented each person’s Monthly Income by 1000.

Example Code:

import pandas as pd

df = pd.DataFrame(
    {
        "ID": [1, 2, 3, 4, 5],
        "Names": ["Samreena", "Asif", "Mirha", "Affan", "Mahwish"],
        "Age": [20, 25, 15, 10, 30],
        "Monthly Income": [4000, 6000, 5000, 2000, 8000],
    }
)
df["Monthly Income"] = df.apply(lambda x: x["Monthly Income"] + 1000, axis=1)
print(df)

Output:

	ID	Names	    Age	 Monthly Income
0	1	Samreena	 20	 5000
1	2	Asif	     25	 7000
2	3	Mirha	     15	 6000
3	4	Affan	     10	 3000
4	5	Mahwish	     30	 9000

Filtering Data by Applying Lambda Function

We can also filter the desired data by applying the Lambda function.

The filter() function takes pandas series and a lambda function. The Lambda function applies to the pandas series that returns the specific results after filtering the given series.

In the following example, we have applied the lambda function on the Age column and filtered the age of people under 25 years.

Example Code:

import pandas as pd

df = pd.DataFrame(
    {
        "ID": [1, 2, 3, 4, 5],
        "Names": ["Samreena", "Asif", "Mirha", "Affan", "Mahwish"],
        "Age": [20, 25, 15, 10, 30],
        "Monthly Income": [4000, 6000, 5000, 2000, 8000],
    }
)
print(list(filter(lambda x: x < 25, df["Age"])))

Output:

[20, 15, 10]

Use the map() Function by Applying Lambda Function

We can use the map() and lambda functions.

The lambda function applies on series to map the series based on the input correspondence. This function is useful to substitute or replace a series with other values.

When we use the map() function, the input size will equal the output size. To understand the concept of the map() function, see the following source code implementation.

Example Code:

import pandas as pd

df = pd.DataFrame(
    {
        "ID": [1, 2, 3, 4, 5],
        "Names": ["Samreena", "Asif", "Mirha", "Affan", "Mahwish"],
        "Age": [20, 25, 15, 10, 30],
        "Monthly Income": [4000, 6000, 5000, 2000, 8000],
    }
)
df["Monthly Income"] = list(map(lambda x: int(x + x * 0.5), df["Monthly Income"]))
print(df)

Output:

    ID	  Names	    Age	 Monthly Income
0	1	Samreena	20	6000
1	2	Asif	    25	9000
2	3	Mirha	    15	7500
3	4	Affan	    10	3000
4	5	Mahwish	    30	12000

Use if-else Statement by Applying Lambda Function

We can also apply the conditional statements on pandas dataframes using the lambda function.

We used the conditional statement inside the lambda function in the following example. We applied the condition on the Monthly Income column.

If the monthly income is greater and equal to 5000, add Stable inside the Category column; otherwise, add UnStable.

Example Code:

import pandas as pd

df = pd.DataFrame(
    {
        "ID": [1, 2, 3, 4, 5],
        "Names": ["Samreena", "Asif", "Mirha", "Affan", "Mahwish"],
        "Age": [20, 25, 15, 10, 30],
        "Monthly Income": [4000, 6000, 5000, 2000, 8000],
    }
)
df["Category"] = df["Monthly Income"].apply(
    lambda x: "Stable" if x >= 5000 else "UnStable"
)
print(df)

Output:

    ID	 Names	    Age	 Monthly Income	 Category
0	1	Samreena	20	    4000	    UnStable
1	2	Asif	    25	    6000	    Stable
2	3	Mirha	    15	    5000	    Stable
3	4	Affan	    10	    2000	    UnStable
4	5	Mahwish	    30	    8000	    Stable

Conclusion

We implemented various methods for applying the Lambda function on Pandas DataFrame. We have seen how to apply the lambda function on rows and columns using the dataframe.assign() and dataframe.apply() methods.

We demonstrated the different applications of the lambda function on pandas DataFrame series, such as the filter() function, map() function, conditional statements, and more.