Pandas

🐼 Pandas Masterclass From Raw Data to Real Insights - A Beginner’s Guide to Data Analysis in Python Who is this for? You’ve done the NumPy Masterclass (or know the basics of Python and arrays). Now you want to work with real tabular data - rows, columns, missing values, merges, and group-level summaries. That’s exactly what Pandas is for. 📌 Table of Contents What is Pandas and Why Do You Need It? Installing and Importing Pandas The Two Core Data Structures Creating DataFrames Reading and Writing Data Exploring a DataFrame Selecting Data - loc, iloc, and Column Access Filtering Rows Adding, Modifying, and Dropping Columns Sorting Data Handling Missing Data String Operations Working with Dates and Times apply(), map(), and applymap() GroupBy - Aggregation and Split-Apply-Combine Merging and Joining DataFrames Pivot Tables and Crosstabs Reshaping - melt() and stack()/unstack() MultiIndex DataFrames Performance Tips Practice Questions What We Covered + What’s Next 1. What is Pandas and Why Do You Need It? NumPy arrays are powerful, but they have one limitation: every column must be the same data type, and there are no column names or row labels. ...

Pandas Masterclass - From Basics to Advanced Prerequisites: Python basics, NumPy fundamentals Install: pip install pandas openpyxl xlrd Import convention: import pandas as pd Table of Contents What is Pandas and Why Use It? Series - The 1D Structure DataFrame - The 2D Structure Loading and Saving Data Exploring Your Data Selecting and Filtering Adding and Modifying Columns Sorting Cleaning Data - Handling Missing Values Cleaning Data - Duplicates and Types String Operations Datetime Operations Groupby and Aggregation Merging, Joining and Concatenating Pivot Tables and Reshaping Apply, Map and Lambda Functions Plotting with Pandas Performance Tips 1. What is Pandas and Why Use It? Pandas provides two core data structures - Series (1D) and DataFrame (2D) - that make working with labeled, tabular data as natural as working with a spreadsheet, but with the full power of Python. ...

Pandas Practice Sheet - IPL Dataset A complete two-table exploration: from match summaries to ball-by-ball intelligence Why IPL Is the Best Dataset for Pandas After Zomato The Zomato dataset taught you how to clean a single messy table. The IPL dataset teaches you something more important: how to work with two related tables and merge them to answer questions neither table can answer alone. This is how data exists in every real company - not as one giant flat file, but as a set of related tables that you must join, aggregate across levels, and reason about carefully. ...

Pandas Practice Sheet - Titanic Dataset 20 Questions from Zero to Advanced, with Full Solutions Getting the Dataset The Titanic dataset is hosted publicly and can be loaded directly into pandas - no download needed. Direct CSV URL (no login required): https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv Load it in one line: import pandas as pd df = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv") Or load via seaborn (also free, slightly different column names): import seaborn as sns df = sns.load_dataset("titanic") Always run df.head() and df.info() before answering any question - knowing your data is half the battle. ...

Pandas Practice Sheet - Zomato Bangalore Dataset A complete guided exploration: from raw messy data to real business insights Why This Dataset? The Titanic dataset is clean, small, and predictable. The Zomato Bangalore dataset is the opposite - it is genuinely messy in the way real-world data is messy. Ratings stored as strings like "3.8/5". Costs formatted as "450" with comma-separators ("1,200"). The cuisines column has multiple cuisines packed into one cell separated by commas. Several columns have inconsistent values, duplicates, and missing data scattered across them. ...