https://github.com/karthikarajagopal44/pandas-beginner-to-advanced

This repository is designed to be a comprehensive guide to mastering pandas, the powerful data manipulation and analysis library in Python.
https://github.com/karthikarajagopal44/pandas-beginner-to-advanced

data-manipulation datascience eda pandas pandas-dataframe python

Last synced: about 1 month ago
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This repository is designed to be a comprehensive guide to mastering pandas, the powerful data manipulation and analysis library in Python.

• Host: GitHub
• URL: https://github.com/karthikarajagopal44/pandas-beginner-to-advanced
• Owner: KarthikaRajagopal44
• Created: 2024-10-16T09:09:02.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-10-16T09:12:37.000Z (7 months ago)
• Last Synced: 2025-02-01T02:44:40.801Z (3 months ago)
• Topics: data-manipulation, datascience, eda, pandas, pandas-dataframe, python
• Language: Jupyter Notebook
• Homepage:
• Size: 20.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Pandas: Beginner to Advanced

This repository is designed to be a comprehensive guide to mastering pandas, the powerful data manipulation and analysis library in Python. Whether you're just starting out or looking to enhance your data science, machine learning (ML), and deep learning (DL) skills, this repository provides a structured pathway from basic to advanced pandas functionalities.

Beginner Level

File: basic pandas.ipynb

Description: Introduction to pandas, DataFrame and Series Creation, Basic data manipulation and exploration, Handling missing data, Basic statistical operations, Data visualization basics

Intermediate Level

File: intermediate pandas.ipynb

Description: Advanced data selection and filtering, Grouping and aggregation, Merging, joining, and concatenating DataFrames, Pivot tables and cross-tabulations, Time series analysis, Applying functions and lambda expressions, Input and output operations with various file formats

Advanced Level

File: advanced pandas.ipynb

Description: Multi-indexing and hierarchical indexing, Advanced time series manipulations, Rolling, expanding, and exponentially weighted windows, Complex group by operations, Data reshaping and pivoting, Advanced data cleaning and preparation, Performance optimization and memory management, Sparse data handling