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https://github.com/madhurimarawat/data-wrangling

This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.
https://github.com/madhurimarawat/data-wrangling

data-filtering data-wrangling handling-missing-values python

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This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.

Host: GitHub
URL: https://github.com/madhurimarawat/data-wrangling
Owner: madhurimarawat
License: mit
Created: 2024-09-08T15:43:14.000Z (2 months ago)
Default Branch: main
Last Pushed: 2024-09-08T16:01:52.000Z (2 months ago)
Last Synced: 2024-09-08T17:55:13.222Z (2 months ago)
Topics: data-filtering, data-wrangling, handling-missing-values, python
Language: Jupyter Notebook
Homepage:
Size: 104 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Data-Wrangling
This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.

## Python

Python is a high-level, interpreted programming language widely used in data science for data manipulation, analysis, and visualization. Libraries such as Pandas and NumPy provide powerful tools for data wrangling, including handling missing values, filtering, and reshaping datasets.

## Directory Structure 📂

```
Data-Wrangling/
│
├── Experiment 1 - Handling Missing Values/
│ ├── Handling_Missing_Values.ipynb
│
├── Experiment 2 - Data Filtering/
│ ├── Data_Filtering.ipynb
│ ├── Experiment 2 Document.docx
│
├── Experiment 3 - Data Aggregation/
│ ├── Data_Aggregation.ipynb
│ ├── Experiment 3 Document.docx/
│
├── Experiment 4 - Data Concatenation/
│ ├── Data_Concatenation.ipynb
│
├── Experiment 5 - Data Reshaping/
│ ├── Data_Reshaping.ipynb
│
├── Experiment 6 - Data Sampling/
│ ├── Data_Sampling.ipynb
│
├── Experiment 7 - Data Conversion/
│ ├── Data_Conversion.ipynb
│
└── README.md
```

## Table Of Contents 📔 🔖 📑

### 1. [Handling Missing Values](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%201)

**Description:**
Identify and fill missing values in a dataset using methods such as mean imputation or forward/backward filling to ensure data completeness and accuracy.

### 2. [Data Filtering](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%202)

**Description:**
Filter rows or columns based on specified criteria, such as removing outliers or selecting data within a certain range to refine datasets for analysis.

### 3. [Data Aggregation](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%203)

**Description:**
Aggregate data by grouping rows based on specific attributes and computing summary statistics, such as mean, median, count, or sum. This helps to summarize large datasets for easier analysis.

### 4. [Data Concatenation](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%204)

**Description:**
Concatenate multiple datasets either along rows or columns to create a unified dataset. This method is useful when merging datasets from different sources or appending new data to an existing dataset.

### 5. [Data Reshaping](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%205)

**Description:**
Reshape data by pivoting, stacking, or unstacking to convert between wide and long formats. This technique allows for better organization and analysis of data with multiple variables.

### 6. [Data Sampling](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%206)

**Description:**
Randomly sample rows or columns from a dataset to create a smaller subset for analysis. Sampling is useful for exploratory data analysis, testing models, or handling large datasets efficiently.

### 7. [Data Conversion](https://github.com/madhurimarawat/Data-Wrangling/tree/main/Experiment%207)

**Description:**
Convert data types of columns, such as changing categorical variables to numerical representations or converting numerical values into categories, enabling better processing and analysis of the data.

---

## Thanks for Visiting 😄

- Drop a 🌟 if you find this repository useful.

- If you have any doubts or suggestions, feel free to reach me.

📫 How to reach me: [![Linkedin Badge](https://img.shields.io/badge/-madhurima-blue?style=flat&logo=Linkedin&logoColor=white)](https://www.linkedin.com/in/madhurima-rawat/)

- **Contribute and Discuss:** Feel free to open issues 🐛, submit pull requests 🛠️, or start discussions 💬 to help improve this repository!