1. Detailed Pandas Study

1. Introduction to Pandas

• What is Pandas?: Open-source library for data structures (Series, DataFrames) and tools for data manipulation.

• Key Features:

  • Data cleaning, merging, sorting, and aggregation.

  • Handling missing data (imputation).

2. Core Data Structures

• Series: 1D labeled array (like a column in Excel).

• DataFrame: 2D table (collection of Series with rows/columns).

3. Creating DataFrames

• From Lists/Dictionaries: Convert Python structures to DataFrames.

• From CSV/Excel: Read external files (pd.read_csv()).

4. Data Exploration

• Basic Inspection:

  • head(), tail(): View first/last rows.

  • shape: Dimensions (rows, columns).

  • info(): Data types and missing values.

• Descriptive Statistics:

  • describe(): Summary stats (mean, min, max, etc.).

5. Data Manipulation

• Adding/Removing Columns: Modify DataFrames dynamically.

• Modifying Data:

  • Change column types (astype()).

  • Calculate derived columns (e.g., BMI from height/weight).

• Boolean Indexing: Filter rows conditionally (e.g., df[df['Age'] > 30]).

6. Handling Missing Data

• Detection: isnull(), notnull().

• Imputation: Fill or drop missing values.

7. Advanced Operations

• GroupBy: Aggregate data by categories.

• Merging DataFrames: Combine datasets (joins).

• Pivot Tables: Summarize data interactively.

8. Practical Applications

• Real-world Datasets: Clean and analyze CSV/Excel files.

• Performance Tips: Vectorized operations for speed.

Prerequisites: Basic Python (lists, loops).
Tools: Pandas, NumPy.
Access the full lesson here: https://colab.research.google.com/drive/1bQaP9gZpE-HI8uZ7wi0LoFoiOLeN_6RA?usp=sharing