What is Pandas?
Pandas is a powerful open-source data analysis and manipulation library built on top of Python. It provides high-performance, easy-to-use data structures such as Series and DataFrame for handling structured data efficiently. Pandas is widely used in data science, machine learning, and data analysis due to its simplicity and flexibility.
Why use Pandas?
Pandas allows users to clean, process, analyze, and visualize data with ease. It supports multiple data formats, including CSV, Excel, JSON, and SQL. With Pandas, users can perform operations like filtering, sorting, merging, and aggregating large datasets quickly. It also integrates well with other libraries such as NumPy, Matplotlib, and Seaborn, making it an essential tool for data professionals.
Installation and Setup
To install Pandas, use the following command:
After installation, you can import Pandas in your Python script
Pandas offers a wide range of functionalities, making data manipulation effortless. In this course, we will cover everything from basic data handling to advanced data operations, ensuring you gain practical knowledge to work with real-world datasets efficiently.
2. Pandas Series
- Creating a Series
- Indexing and Slicing
- Operations on Series
- Handling Missing Values
3. Pandas DataFrame
- Creating DataFrame (from Dictionary, CSV, Excel, JSON)
- Selecting Columns and Rows
- Adding and Removing Columns
- Filtering Data
4. Data Manipulation with Pandas
- Sorting Data
- Grouping and Aggregations
- Merging, Joining, and Concatenation
- Handling Duplicates
5. Working with Missing Data
- Identifying Missing Values
- Filling and Dropping Missing Values
- Handling NaN in DataFrames
6. Data Cleaning and Transformation
- Applying Functions on Data
- Replacing Values
- Renaming Columns and Index
- String Operations in Pandas
7. Working with Time Series Data
- DateTime Indexing
- Resampling and Frequency Conversion
- Time-based Grouping
8. Input and Output Operations
- Reading CSV, Excel, JSON, and SQL
- Writing to CSV, Excel, JSON, and SQL
- Handling Large Datasets
9. Data Visualization with Pandas
- Plotting with Pandas
- Customizing Charts
- Integration with Matplotlib and Seaborn
10. Advanced Pandas
- MultiIndex DataFrames
- Pivot Tables and Cross Tabulation
- Performance Optimization
11. Real-World Projects
- Analyzing Sales Data
- Cleaning and Processing Real-World Datasets
- Automating Data Tasks