Jupyter Notebooks are a powerful open-source tool that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. They are widely used in data science, machine learning, and scientific computing for interactive coding and data analysis. This tutorial will guide you through installing Jupyter, using basic features, and performing data analysis interactively.1. Installing Jupyter NotebookTo start using Jupyter Notebooks, you need to install it. You can install Jupyter via Anaconda (recommended for beginners) or pip (for advanced users).Using AnacondaAnaconda is a popular Python distribution that comes with Jupyter Notebook pre-installed.Download and install AnacondaOpen Anaconda Navigator and launch Jupyter NotebookYou should see a dashboard like the one below:Using pipIf you already have Python installed, you can install Jupyter Notebook using pip:Once installed, launch Jupyter Notebook with:2. Navigating the Jupyter InterfaceAfter launching Jupyter Notebook, youll see the Jupyter dashboard. It shows the current directorys files and allows you to create and open notebooks.Click New > Python 3 to create a new notebook.A new notebook consists of cells that can execute code or contain markdown for documentation.3. Running Code in Jupyter NotebookEach notebook consists of cells that can hold code or markdown text.Executing Python CodeTo run a Python command inside a cell, type the code and press Shift + Enter.Using Markdown CellsYou can switch a cell to Markdown (for formatted text) by selecting the cell and pressing Esc + M. Try adding headings, bullet points, or even LaTeX equations:4. Importing and Visualizing DataJupyter is commonly used for data analysis. Lets see how to load and visualize a dataset using Pandas and Matplotlib.Importing Librariesseaborn: To install the seaborn python library you can write the below command in your jupyter notebook:matplotlib: To install the matplotlib and scikit-learn python library you can write the below command in your jupyter notebook:Loading a DatasetThere are many ways to import/load a dataset, either you can download a dataset or you can directly import it using Python library such as Seaborn, Scikit-learn (sklearn), NLTK, etc. The datasets that used here is a Black Friday Sales dataset from Kaggle.5. Data Analysis and VisualizationIt is also an important step as it gives the distribution of the dataset and helps in finding similarities among features. Lets start by looking at the shape of our dataset and concise summary of our dataset, using the below code:Data VisualizationLarge and complex datasets are very difficult to understand but they can be easily understood with the help of graphs. Graphs/Plots can help in determining relationships between different entities and helps in comparing variables/features. Data Visulaisation means presenting the large and complex data in the form of graphs so that they are easily understandable. Begin by creating a bar plot that compares the percentage ratio of tips given by each gender , along with that make another graph to compare the average tips given by individuals of each gender.This code creates a bar plot using Seaborn to visualize the distribution of purchases (Purchase) across different city categories (City_Category) in the DataFrame df_black_friday_sales.This code creates a figure with two subplots side by side. The first subplot displays a count plot of the Age column from the df_black_friday_sales DataFrame, while the second subplot shows a histogram and kernel density estimate (KDE) of the Purchase column.6. Saving and Exporting NotebooksClick File > Save and Export notebook as to save progress and export notebooks in various formats (PDF, HTML, Python script, etc.):7. Best PracticesUse markdown to document your work.Organize your notebook by using headings and sections.Use version control (e.g., GitHub) to track changes.Limit output size for large datasets.ConclusionThis tutorial has covered the fundamental aspects of using Jupyter Notebooks for interactive coding and data analysis. We started with the installation process using both Anaconda and pip, followed by navigating the Jupyter interface. We then explored how to execute Python code, document work using Markdown, and perform data analysis using Pandas and visualization libraries like Matplotlib, scikit-learn and Seaborn.By following the best practices outlined, you can create well-structured, reproducible, and efficient notebooks for your coding and data analysis projects. Now that you have a strong foundation, start experimenting with Jupyter Notebooks and explore its vast capabilities to enhance your workflow! NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. 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