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lambdaFunctions solve various data science problems in Pandas python. We can DataFrameapply lambda functions on rows and columns in pandas.

In this article, we will explore how to use lambda functions with pandas DataFrame.

DataFrameThere are various applications of lambda functions on pandas such as filter(), map()and which 条件语句we will explain with some examples in this article.

Lambda Function

A Lambda function consists of an expression.

LambdaA function is a small function that can also be used as an anonymous function, which means it does not require any name. lambdaFunctions are useful for solving small problems with less code.

DataFrameThe following syntax is used to apply lambda functions on pandas :

dataframe.apply(lambda x: x + 2)

Use DataFrame.assign()the method to apply a Lambda function on a single column

dataframe.assign()Method applies a Lambda function to a single column. Let's take an example.

In the following example, we Students Markshave applied a lambda function on the column. After applying the Lambda function, the student percentage is calculated and stored in a new 百分比column.

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

Sample 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

DataFrame.assign()Apply Lambda functions on multiple columns using the

We can also apply Lambda functions to multiple columns using the method DataFramein Pandas.dataframe.assign()

For example, we have four columns Student Names, Computer, , Mathand Physics. We apply a Lambda function on multiple subject columns such as Computer, Math, and to calculate the obtained scores stored in the column.PhysicsMarks_Obtained

Implement the following example.

Sample 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  

Use DataFrame.apply()the method to apply a Lambda function on a single line

dataframe.apply()method applies a Lambda function to a single row.

For example, we applied the lambda function to a single row axis=1. Using the lambda function, we increased each person's 月收入value by 1000.

Sample 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 a Lambda function

We can also filter the required data by applying Lambda functions.

filter()The function takes a pandas Series and a lambda function. Lambda functions are applied to pandas Series returning specific results after filtering a given Series.

In the following example, we Agehave applied a lambda function on the column and filtered the age of people below 25 years old.

Sample 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 map()the function by applying a Lambda function

We can use map()and lambda functions.

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

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

Sample 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

By applying a Lambda function using if-elsethe statement

We can also use lambda functions dataframesto apply conditional statements on pandas.

In the following example, we have used conditional statements in lambda functions. We apply the condition to Monthly Incomethe column.

If the monthly income is greater than or equal to 5000, Categoryadd it in the column Stable; otherwise, add it UnStable.

Sample 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

in conclusion

We have implemented DataFramevarious methods to apply Lambda functions on Pandas. We have seen how to apply lambda functions on rows and columns using dataframe.assign()and methods.dataframe.apply()

We DataFramehave demonstrated different applications of lambda functions on pandas series such as filter()functions, map()functions, conditional statements, etc.