8+ Jupyter 使い方 For You

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Introduction

Jupyter is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. It is widely used by data scientists, researchers, and educators across the world. In this article, we will explore the basics of Jupyter and how it can be used to streamline your workflow.

Getting Started with Jupyter

To get started with Jupyter, you need to install it on your local machine. You can install it using pip, conda, or Docker. Once you have installed Jupyter, you can launch it by typing 'jupyter notebook' in your terminal. This will open a Jupyter notebook in your default browser.

Creating a New Notebook

Once you have launched Jupyter, you can create a new notebook by clicking on the 'New' button and selecting 'Python 3' or any other programming language you want to use. This will open a new notebook where you can start writing your code.

Writing and Running Code

You can write your code in the cells of the notebook. To run the code, you can either click on the 'Run' button or press 'Shift+Enter'. Jupyter will execute the code and display the output below the cell.

Working with Markdown Cells

You can add markdown cells to your notebook to write text, format it, and add images. Markdown is a lightweight markup language that is easy to learn. You can add headings, bold and italic text, lists, tables, and more using markdown syntax.

Adding Visualizations

Jupyter supports various libraries for data visualization, such as matplotlib, seaborn, and bokeh. You can create interactive charts, graphs, and maps using these libraries and add them to your notebook. This makes it easy to analyze and communicate your findings.

Sharing Your Notebook

Jupyter allows you to share your notebook with others by exporting it to various formats, such as HTML, PDF, or Markdown. You can also share it on GitHub or the Jupyter Notebook Viewer, which allows others to view and interact with your notebook online.

Using Jupyter for Data Science

Jupyter is widely used by data scientists for exploratory data analysis, machine learning, and data visualization. You can import and manipulate data using libraries such as Pandas and Numpy, build predictive models using Scikit-learn or TensorFlow, and create interactive visualizations using Plotly or Bokeh.

Conclusion

Jupyter is a powerful tool for data scientists, researchers, and educators. It allows you to create and share documents containing live code, equations, visualizations, and narrative text. In this article, we have covered the basics of Jupyter and how it can be used to streamline your workflow. We hope this article has been helpful in getting you started with Jupyter.