Data Analysis Python Tamil | Data Analysis using Python Tamil #2

Описание: In this Data Analysis Python Tamil Tutorial (Sales Analysis Case Study - Part 2), we continue exploring our retail sales dataset to answer an important business question:
💡 “Which is the Best Month for Sales?”

📌Download Sales Data Here: https://drive.google.com/drive/folder...

✍ Case Study Part 1:    • Data Analysis with Python Tamil | Data Ana...  
✍Pandas Filtering in Tamil:    • Python Pandas Tamil | Learn Pandas in Tami...  
✍Pandas Apply Function Tamil:    • Python Pandas Tamil Tutorial | Pandas Appl...  
✍Pandas Groupby Function Tamil:    • Python Pandas Tamil Tutorial | Learn Panda...  


✅In this episode, we go step by step through the entire process — from data cleaning to visualization — using Pandas, Matplotlib, and Seaborn.

🔥Here’s what you’ll learn in detail 👇

00:00 Introduction: Sales Case Study Data Analysis python Tamil
01:52 Identify the columns that need to be created
03:10 Deriving Month Column from Order Date Column
05:30 Create Function for Month to Extract Month information from Column
07:50 Use Pandas Apply function to Extract Month information
12:13 Check Month Column Data type
12:47 Change Month Column Data type
22:48 Changing Data Type for create Sales Column
26:05 Create Sales Column
28:32 Find Best Month for Maximum Sales
30:46 Data Visualization using Seaborn
34:46 Next Video: Case Study Part 3

🔹 1. Data Cleaning & Type Conversion
We begin by checking the dataset for incorrect or missing values. Then, we convert data types (like changing “Order Date” from string to datetime) to make analysis easier and more accurate.

🔹 2. Creating New Columns
Next, we create two new columns —

Month: Extracted from the “Order Date” column.

Sales: Calculated by multiplying “Quantity Ordered × Price Each”.
These help us derive monthly trends and total revenue.

🔹 3. Grouping and Aggregation
We use the groupby() function to sum up total sales for each month.
This step helps us understand which months drive the highest revenue.

🔹 4. Data Visualization
We then visualize our grouped data using Seaborn’s barplot, which makes it easy to spot the month with the maximum sales visually.
We also add titles, labels, and formatting to make the chart more readable and professional.

🔹 5. Insights and Business Understanding
Finally, we interpret the results — identifying the best performing month, understanding possible seasonal patterns, and discussing how this insight could help businesses plan better.

This tutorial helps you understand how real-world data analysis workflows are built — from data preparation to finding meaningful insights — completely explained in Tamil.

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