Exploring Lambda Functions in Python

Lambda functions in Python are small, anonymous functions that can take any number of arguments but can only have one expression. They are ideal for situations where a small, temporary function is needed without the overhead of defining a formal function. They offer a quick and concise way to create small, reusable pieces of code directly within code, particularly in functional programming paradigms, enhancing readability and conciseness.

Syntax:

lambda arguments: expression

Arguments: Arguments in a lambda function represent the input values or variables on which the function performs its operation.

Expression: Expression in a lambda function is the computation or operation performed on the input arguments, producing the result of the function.

Importing Library

import pandas as pd

Importing Dataset

df=pd.read_csv("Dataset.csv")
df.head()

	Row ID	Order Date	Ship Mode	Customer ID	Postal Code	Category	Sub-Category	Product Name	Sales
0	4010	01-01-18	Standard Class	SC-20725	90036.0	Furniture	Furnishings	Howard Miller 11-1/2" Diameter Brentwood Wall ...	474.430
1	8070	01-01-18	Standard Class	JM-15250	77340.0	Office Supplies	Storage	SAFCO Boltless Steel Shelving	454.560
2	8071	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Furnishings	Tenex Carpeted, Granite-Look or Clear Contempo...	141.420
3	8072	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Chairs	Office Star - Contemporary Task Swivel Chair	310.744
4	8073	01-01-18	Standard Class	JM-15250	77340.0	Office Supplies	Art	Fluorescent Highlighters by Dixon	12.736

Let’s try out few examples using lambda function:

Using Lambda Function as Filter for Data Selection

cat_furniture=df.loc[lambda x: x['Category'] == 'Furniture']
cat_furniture.head()

	Row ID	Order Date	Ship Mode	Customer ID	Postal Code	Category	Sub-Category	Product Name	Sales
0	4010	01-01-18	Standard Class	SC-20725	90036.0	Furniture	Furnishings	Howard Miller 11-1/2" Diameter Brentwood Wall ...	474.430
2	8071	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Furnishings	Tenex Carpeted, Granite-Look or Clear Contempo...	141.420
3	8072	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Chairs	Office Star - Contemporary Task Swivel Chair	310.744
8	849	01-01-18	Standard Class	GA-14725	44052.0	Furniture	Furnishings	Linden 10" Round Wall Clock, Black	48.896
14	8363	02-01-18	Second Class	TS-21655	79907.0	Furniture	Tables	Bevis Oval Conference Table, Walnut	913.430

Using Lambda Function to Categorize Data Based on Conditions

df['Sales_Category']=df['Sales'].apply(lambda Sales: 'Very High Sales' if Sales > 10000 else('High Sales'
                                                                                            if Sales > 5000 else 'Low Sales'))
df.head()

	Row ID	Order Date	Ship Mode	Customer ID	Postal Code	Category	Sub-Category	Product Name	Sales	Sales_Category
0	4010	01-01-18	Standard Class	SC-20725	90036.0	Furniture	Furnishings	Howard Miller 11-1/2" Diameter Brentwood Wall ...	474.430	Low Sales
1	8070	01-01-18	Standard Class	JM-15250	77340.0	Office Supplies	Storage	SAFCO Boltless Steel Shelving	454.560	Low Sales
2	8071	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Furnishings	Tenex Carpeted, Granite-Look or Clear Contempo...	141.420	Low Sales
3	8072	01-01-18	Standard Class	JM-15250	77340.0	Furniture	Chairs	Office Star - Contemporary Task Swivel Chair	310.744	Low Sales
4	8073	01-01-18	Standard Class	JM-15250	77340.0	Office Supplies	Art	Fluorescent Highlighters by Dixon	12.736	Low Sales

Using Lambda Function for Aggregating Summary Statistics

total_sales = df.groupby("Customer ID").apply(lambda x: pd.Series([x["Sales"].sum(), x["Sales"].count()],
                                                                  index=["Total Sales", "Count of Sales per Customer"]))
total_sales.head()

	Total Sales	Count of Sales per Customer
Customer ID
AA-10315	374.480	2.0
AA-10375	206.732	5.0
AA-10480	15.552	1.0
AA-10645	12.960	1.0
AB-10060	2936.264	8.0

Using Lambda Function in a For Loop

total_sales_per_category = {}

# Calculating Total Sales for Each Product Category
for category, category_data in df.groupby('Category'):
    total_sales_per_category[category] = round(sum(map(lambda x: x[1], category_data['Sales'].items())),2)
    
total_sales_per_category

{'Furniture': 212313.79, 'Office Supplies': 240363.94, 'Technology': 269370.69}

Conclusion:

Lambda functions represent a key feature of Python’s expressive syntax, enabling developers to write compact and efficient code for a wide range of tasks. Their simplicity and flexibility make them invaluable for tasks such as data manipulation, sorting, filtering, and more. By embracing lambda functions, Python programmers can embrace a more functional style of programming and enhance their productivity and code maintainability.