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The main libraries used in this repository include **Pandas, NumPy, Matplotlib, and Seaborn**.\n\n## 📂 Directory Structure \u0026 File Descriptions\n```\n└── v41bh4vr4jput-data-analysis-with-python/\n    ├── README.md\n    ├── Cleaning_not_null_values.ipynb\n    ├── Handling_missing_data.ipynb\n    ├── Pandas_Dataframe.ipynb\n    ├── Pandas_series.ipynb\n    ├── Matplotlib/\n    │   └── Visualization.ipynb\n    ├── Reading and Extracting data/\n    │   └── data/\n    │       ├── btc-market-price.csv\n    │       ├── eth-price.csv\n    │       ├── Reading_External_data_and_Plottng.ipynb\n    │       └── .ipynb_checkpoints/\n    │           └── btc-market-price-checkpoint.csv\n    ├── Reading CSV and TXT files/\n    │   ├── btc-market-price.csv\n    │   ├── exam_review.csv\n    │   ├── Main.ipynb\n    │   └── out.csv\n    ├── Reading Data from Relational databases/\n    │   ├── chinook.db\n    │   └── main.ipynb\n    ├── Reading Excel Files/\n    │   ├── main.ipynb\n    │   ├── out.xlsx\n    │   └── products.xlsx\n    └── Reading HTML tables/\n        └── Main.ipynb\n```\n\n### 📝 **Notebooks \u0026 Descriptions**\n\n#### 1️⃣ **Data Cleaning \u0026 Handling Missing Data**\n- **Cleaning_not_null_values.ipynb** → Techniques for handling and cleaning data with non-null values.\n- **Handling_missing_data.ipynb** → Methods for dealing with missing values in datasets using Pandas and NumPy.\n\n#### 2️⃣ **Pandas Basics: DataFrame \u0026 Series**\n- **Pandas_Dataframe.ipynb** → Introduction to Pandas DataFrames, data manipulation, and transformations.\n- **Pandas_series.ipynb** → Understanding Pandas Series, operations, and indexing.\n\n#### 3️⃣ **Data Visualization**\n- **Matplotlib/Visualization.ipynb** → Creating various visualizations using **Matplotlib and Seaborn**, including bar charts, histograms, line plots, and scatter plots.\n\n#### 4️⃣ **Reading and Extracting Data**\n- **Reading_External_data_and_Plottng.ipynb** → How to read external datasets (CSV) and visualize data trends.\n- **btc-market-price.csv** \u0026 **eth-price.csv** → Sample datasets for Bitcoin and Ethereum price trends.\n\n#### 5️⃣ **Reading Different File Formats**\n- **Reading CSV and TXT files/Main.ipynb** → Techniques for reading and processing CSV and TXT files.\n- **Reading Data from Relational databases/main.ipynb** → Using Pandas and SQLAlchemy to extract data from **SQLite databases (chinook.db)**.\n- **Reading Excel Files/main.ipynb** → Working with Excel files (**out.xlsx, products.xlsx**) using Pandas.\n- **Reading HTML tables/Main.ipynb** → Extracting and parsing data from **HTML tables**.\n\n## 🔧 **Setup \u0026 Installation**\n### **Prerequisites**\nEnsure you have **Python 3.8+** installed along with the following libraries:\n```bash\npip install numpy pandas matplotlib seaborn jupyterlab\n```\n\n### **Run Jupyter Notebook**\nNavigate to the project directory and launch Jupyter Lab:\n```bash\ncd v41bh4vr4jput-data-analysis-with-python\njupyter lab\n```\n\n## 🏆 **Key Features**\n✅ **Comprehensive Data Handling** – Cleaning, missing data handling, and manipulation.  \n✅ **Data Visualization** – Plotting and analyzing trends with Matplotlib \u0026 Seaborn.  \n✅ **File Handling** – Read and process CSV, Excel, SQL, and HTML tables.  \n✅ **Real-world Data** – Work with datasets related to finance, e-commerce, and reviews.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv41bh4vr4jput%2Fdata-analysis-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fv41bh4vr4jput%2Fdata-analysis-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fv41bh4vr4jput%2Fdata-analysis-with-python/lists"}