https://github.com/barasedih11/dr.-semmelweis-and-the-discovery-of-handwashing

Applying Data Manipulation with pandas
https://github.com/barasedih11/dr.-semmelweis-and-the-discovery-of-handwashing

csv data-manipulation pandas python

Last synced: 3 months ago
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Applying Data Manipulation with pandas

• Host: GitHub
• URL: https://github.com/barasedih11/dr.-semmelweis-and-the-discovery-of-handwashing
• Owner: BaraSedih11
• Created: 2024-05-17T03:12:58.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:35:17.000Z (9 months ago)
• Last Synced: 2024-05-17T04:27:57.772Z (9 months ago)
• Topics: csv, data-manipulation, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 155 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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This repository contains an implementation of Dr.-Semmelweis-and-the-Discovery-of-Handwashing applying data manipulation with pandas in python.

## Overview

In this repository, we demonstrate how to perform data manipulation with pandas. We utilize the pandas library. Additionally, we provide a simple example along with explanations to help you understand how to apply data manipulation to your own datasets.

## Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

- pandas

You can install these libraries using pip:

```bash

pip install pandas 

```

## Usage

1. Clone this repository to your local machine:

```bash

git clone https://github.com/BaraSedih11/Dr.-Semmelweis-and-the-Discovery-of-Handwashing.git

```

2. Navigate to the repository directory:

```bash

cd Dr.-Semmelweis-and-the-Discovery-of-Handwashing

```

3. Open and run the Jupyter Notebook `notebook.ipynb` using Jupyter Notebook or JupyterLab.

4. Follow along with the code and comments in the notebook to understand how data manipulation in pandas is implemented using Python.

## Acknowledgements

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.