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What is pandas?

Introduction to pandas as a data manipulation and analysis library for Python.
Installing pandas:

How to install pandas using package managers like pip or conda.
pandas Data Structures:

Exploring the two primary data structures in pandas: Series and DataFrame.
Creating Series and DataFrames:

Creating Series from lists or arrays.
Creating DataFrames from dictionaries, lists, or NumPy arrays.
Basic DataFrame Operations:

Understanding the structure of a DataFrame (rows, columns, and indices).
Accessing data in a DataFrame using indexing and slicing.
Data Exploration:

Methods for getting an overview of data, such as head(), tail(), info(), and describe().
Data Cleaning:

Handling missing data with methods like dropna() and fillna().
Removing duplicates using drop_duplicates().
Data Selection and Filtering:

Selecting columns and rows based on criteria.
Using conditions and Boolean indexing.
Data Manipulation:

Applying functions to data using apply().
Adding, deleting, and renaming columns.
Reordering columns and rows.
Grouping and Aggregation:

Grouping data using groupby().
Performing aggregation operations like sum(), mean(), and custom aggregations.
Merging and Joining Data:

Combining DataFrames using merge() and concat().
Reshaping Data:

Pivoting and melting data with pivot() and melt().
Time Series Data (if applicable):

Handling date and time data using pandas.
Data Visualization:

Basic data visualization using pandas built-in plotting functions.
Working with External Data Sources:

Reading and writing data to/from various formats like CSV, Excel, SQL databases, and JSON.
Case Studies and Practical Examples:

Real-world examples and use cases that demonstrate the power of pandas in data analysis.