https://github.com/v41bh4vr4jput/data-analysis-with-python
This repository is a comprehensive collection of data analysis projects and tutorials using Python's most powerful libraries: NumPy, Pandas, Seaborn, and Matplotlib. It is designed to help you explore, clean, visualize, and analyze data efficiently.
https://github.com/v41bh4vr4jput/data-analysis-with-python
api data data-analysis data-visualization matplotlib numpy pandas python sakila-db seaborn
Last synced: 2 months ago
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This repository is a comprehensive collection of data analysis projects and tutorials using Python's most powerful libraries: NumPy, Pandas, Seaborn, and Matplotlib. It is designed to help you explore, clean, visualize, and analyze data efficiently.
- Host: GitHub
- URL: https://github.com/v41bh4vr4jput/data-analysis-with-python
- Owner: V41BH4VR4JPUT
- Created: 2025-03-16T06:19:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-26T08:31:58.000Z (about 1 year ago)
- Last Synced: 2025-03-26T09:36:13.891Z (about 1 year ago)
- Topics: api, data, data-analysis, data-visualization, matplotlib, numpy, pandas, python, sakila-db, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 171 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Analysis with Python
## 📌 Overview
This repository contains a collection of **Jupyter Notebooks** covering various aspects of **data analysis using Python**, including **data cleaning, handling missing data, visualization, and reading different file formats (CSV, Excel, SQL, HTML, etc.)**. The main libraries used in this repository include **Pandas, NumPy, Matplotlib, and Seaborn**.
## 📂 Directory Structure & File Descriptions
```
└── v41bh4vr4jput-data-analysis-with-python/
├── README.md
├── Cleaning_not_null_values.ipynb
├── Handling_missing_data.ipynb
├── Pandas_Dataframe.ipynb
├── Pandas_series.ipynb
├── Matplotlib/
│ └── Visualization.ipynb
├── Reading and Extracting data/
│ └── data/
│ ├── btc-market-price.csv
│ ├── eth-price.csv
│ ├── Reading_External_data_and_Plottng.ipynb
│ └── .ipynb_checkpoints/
│ └── btc-market-price-checkpoint.csv
├── Reading CSV and TXT files/
│ ├── btc-market-price.csv
│ ├── exam_review.csv
│ ├── Main.ipynb
│ └── out.csv
├── Reading Data from Relational databases/
│ ├── chinook.db
│ └── main.ipynb
├── Reading Excel Files/
│ ├── main.ipynb
│ ├── out.xlsx
│ └── products.xlsx
└── Reading HTML tables/
└── Main.ipynb
```
### 📝 **Notebooks & Descriptions**
#### 1️⃣ **Data Cleaning & Handling Missing Data**
- **Cleaning_not_null_values.ipynb** → Techniques for handling and cleaning data with non-null values.
- **Handling_missing_data.ipynb** → Methods for dealing with missing values in datasets using Pandas and NumPy.
#### 2️⃣ **Pandas Basics: DataFrame & Series**
- **Pandas_Dataframe.ipynb** → Introduction to Pandas DataFrames, data manipulation, and transformations.
- **Pandas_series.ipynb** → Understanding Pandas Series, operations, and indexing.
#### 3️⃣ **Data Visualization**
- **Matplotlib/Visualization.ipynb** → Creating various visualizations using **Matplotlib and Seaborn**, including bar charts, histograms, line plots, and scatter plots.
#### 4️⃣ **Reading and Extracting Data**
- **Reading_External_data_and_Plottng.ipynb** → How to read external datasets (CSV) and visualize data trends.
- **btc-market-price.csv** & **eth-price.csv** → Sample datasets for Bitcoin and Ethereum price trends.
#### 5️⃣ **Reading Different File Formats**
- **Reading CSV and TXT files/Main.ipynb** → Techniques for reading and processing CSV and TXT files.
- **Reading Data from Relational databases/main.ipynb** → Using Pandas and SQLAlchemy to extract data from **SQLite databases (chinook.db)**.
- **Reading Excel Files/main.ipynb** → Working with Excel files (**out.xlsx, products.xlsx**) using Pandas.
- **Reading HTML tables/Main.ipynb** → Extracting and parsing data from **HTML tables**.
## 🔧 **Setup & Installation**
### **Prerequisites**
Ensure you have **Python 3.8+** installed along with the following libraries:
```bash
pip install numpy pandas matplotlib seaborn jupyterlab
```
### **Run Jupyter Notebook**
Navigate to the project directory and launch Jupyter Lab:
```bash
cd v41bh4vr4jput-data-analysis-with-python
jupyter lab
```
## 🏆 **Key Features**
✅ **Comprehensive Data Handling** – Cleaning, missing data handling, and manipulation.
✅ **Data Visualization** – Plotting and analyzing trends with Matplotlib & Seaborn.
✅ **File Handling** – Read and process CSV, Excel, SQL, and HTML tables.
✅ **Real-world Data** – Work with datasets related to finance, e-commerce, and reviews.