21 Days Python Pandas Challenge

Explore Python Pandas, a powerful library for data manipulation and analysis. Here's what we covered in this structured journey:

1. Pandas Basics: Introduction to DataFrames, Series, and creating datasets.
2. Data Cleaning: Handling missing values, dropping duplicates, and standardizing data.
3. Data Selection and Filtering: Using loc, iloc, and conditional filtering.
4. Aggregation and Grouping: Applying groupby(), aggregation functions, and summarizing data.
5. String Operations: Manipulating string data with methods like split(), slice(), and replace().
6. Handling Dates and Times: Working with datetime objects, extracting components, and resampling.
7. Data Transformation: Techniques like pivot tables, melt(), and stacking/unstacking.
8. Merging and Joining: Combining datasets with merge(), join(), and concat().
9. Window Functions: Rolling and expanding operations for time-series analysis.
10. Visualization with Pandas: Creating quick visualizations using Pandas’ built-in plotting.
11. Iterating over Data: Efficient row and column-wise operations using apply(), map(), and iterrows().
12. Advanced Indexing: MultiIndex and hierarchical indexing techniques.
13. Performance Optimization: Tips on improving the performance of large datasets.
14. Working with External Data: Reading and writing CSV, Excel, and SQL databases.
15. Pandas in ETL: Using Pandas for Extract, Transform, and Load processes.
16. Data Validation: Ensuring data quality and consistency.
17. Exploratory Data Analysis (EDA): Generating insights using descriptive statistics.
18. Handling Categorical Data: Working with categorical and dummy variables.
19. Error Handling: Managing exceptions in Pandas workflows.
20. Custom Functions: Writing and applying user-defined functions on datasets.