Python Pandas Lambda Function
Lambda functions in Python are small, anonymous functions defined using the lambda keyword.Â
They are typically used for short, throwaway functions that are needed for a brief period, such as within map(), filter(), or sorted() calls.Â
A lambda can take any number of arguments but only one expression, which is evaluated and returned.
For example, lambda x: x *2 defines a simple function that doubles it’s input.
Lambdas help make code more concise and readable in cases where defining a full function is unnecessary.
import pandas as pd import numpy as np import seaborn as sns We use the sns.get_dataset_names() to get different datasets.
Next we load the ‘car_crashes’ dataset using sns.load_dataset
sns.get_dataset_names() 
df = sns.load_dataset('car_crashes') df.head(10) 
Here we create a new column in the DataFrame where each value is the result of multiplying the corresponding value in the total column by 100.
df['total100'] = df.apply(lambda x: x['total']*100, axis = 1) Here, we also create a new column by multiplying values in the total column by 100. This time using a more direct approach.
df['total100v2'] = df['total'].apply(lambda x: x*100) df.head(10) 
Here each value in the ‘ins_premium_type’ column is evaulated and assigned High, Medium or Low based on it’s amount.
df['ins_premium_type'] = df['ins_premium'].apply(lambda x: 'High' if x > 1000 else 'Medium' if x > 800 else 'Low') df.head(10) 
Next we have Lambda with Two Columns.
Here, we create a new column by summing values from two existing columns: speeding and alcohol.
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Using the apply() with a lambda function is a flexible way to perform row-wise operations in pandas.
Here, we create a new column called speedalcohol by adding together the values from the speeding and alcohol columns for each row.
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df['speedalcohol'] = df.apply(lambda x: x['speeding'] + x['alcohol'], axis = 1) df.head(10) 
Next we update rows
Here we convert all the values abbrev column to lowercase. i.e updating the abbrev.
df['abbrev'] = df['abbrev'].apply(lambda x: x.lower()) df.head(10) 
Here, we converted to uppercase ‘fl’, ‘ga’, ‘sc’ , ‘nc’, while others remain unchanged.
df['abbrev'] = df['abbrev'].apply(lambda x: x.upper() if x in ['fl', 'ga', 'sc', 'nc'] else x) df.head(10) This line subtracts 3 from values in the speedalcohol column only if the value is exactly 3.599; otherwise, it leaves the value unchanged.
['speedalcohol'] = df['speedalcohol'].apply(lambda x: x-3 if x == 3.599 else x) df.head(10) 
