Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.

Jupyter Notebooks and Data Sets for Pandas Library

🚀 The production-ready subclass of `pandas.DataFrame` to support stock statistics and indicators

This repo contains important libraries for data science / machine learning libraries

Data-Science(数据科学)

Leave your regressions behind 🙈 and Let's #makeIThappen with Data Science ⚡

A comprehensive guide to common commands and techniques in Pandas, the powerful data analysis and manipulation library in Python. This cheat sheet provides quick access to essential Pandas functions.

This repository contains the Tirendaz Academy YouTube channel documentation.

Notes taken while learning CoreySchaffers Pandas Tutorial

My Machine learning Journey

This repository contains the work in the AI engineer Cognizant virtual training and internship program from forage

Get lengths and values of runs of equal values in a pandas Series, or vice versa

PP3: Python Essentials: a CLI/TUI application to allow users to manage the dataset of the Project P3 Criteria: Load a Table, Find a Record and Modify Notes and ToDo Status, and update a Definition of Done. This is a first-time, starter python project, as a Neurodiverse software engineer and creative..

pandas is a powerful Python library for data analysis and manipulation. It’s like a Swiss Army knife for handling structured data!

Introducing you to the fundamentals of the quintessential Python data analysis library, pandas, and its core data structures – the Series and DataFrame objects.

This repository contains essential Python scripts and notebooks for data analysis and visualization. It includes: pandas: Data manipulation and analysis, including operations on series and dataframes. NumPy: Efficient numerical computations and array processing. Data Visualization: Creating insightful visualizations using Matplotlib and Seaborn.

Testing and experimenting with some simple Pandas functionality using Flask to serve the parsed data.

How to Install Pandas package into Jupyter Notebook or any of the other Python Packagesa and learn more about Series

Forecast Carbon Emissions with Time-Series data. This repository contains 2x Jupyter Notebooks that predict Carbon Emissions in the United States using Neural Basis Expansion Analysis for Time series (NBeats). The second notebook has an extra pre-processing step of data been scaled and inverse-transformed before final results.

numpy and pandas for data analysis