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.