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Jupyter Notebook

(Stanford University - Alvin Wei-Cheng Wong)

- Overview

Jupyter Notebook is a free, open-source web application that allows users to create interactive documents that contain code, visualizations, equations, and other computational outputs. It was previously known as IPython Notebook, but was renamed in 2014. 

Jupyter Notebook can be used by students, programmers, and data scientists to:

  • Document and demonstrate coding workflows
  • Experiment with code
  • Prototype and explain code
  • Explore and visualize data
  • Share ideas with others 


Jupyter Notebook documents can be both human-readable and executable. Human-readable documents contain analysis descriptions and results, such as figures and tables. Executable documents can be run to perform data analysis. 

Jupyter Notebook offers a multi-user version for research labs, classrooms, and companies. It also allows users to leverage big data tools, such as Apache Spark, from R, Python, and Scala. Users can also explore data with pandas, scikit-learn, ggplot2, and TensorFlow. 

Jupyter Notebook installation requires Python 2.7 or Python 3.3 or greater.

Please refer to the following for more information:


- Jupyter Notebook Installations

Jupyter Notebook can be installed separately, but it's easier to install when either Miniconda or Anaconda is already installed. 


A. Using Miniconda:

Here are some steps to install Jupyter Notebook using Miniconda on Windows:

  • Download Miniconda
  • Install Miniconda
  • Open Command Prompt or PowerShell
  • Update Conda
  • Create a new Conda environment (optional)
  • Install Jupyter Notebook
  • Launch Jupyter Notebook


B. Using Anaconda:

Another way to install Jupyter Notebook is to install a scientific Python distribution that includes scientific Python packages. The most common distribution is called Anaconda. 

To install Python packages in Jupyter Notebook, you can: 

  • Open Jupyter Notebook on your computer
  • Create a new notebook or open an existing one
  • In a code cell, type !pip install <package_name> and run the cell
  • Replace <package_name> with the name of the package you want to install

Jupyter installation requires Python 3.3 or greater, or Python 2.7.


- Jupyter Pandas

Jupyter is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is a popular tool for data science, machine learning, and scientific computing. 

Pandas is a Python library for data manipulation and analysis. It is built on top of NumPy and provides a high-level data structure called a DataFrame. DataFrames are two-dimensional tables with labeled rows and columns. Pandas provides a variety of functions for working with DataFrames, including filtering, sorting, grouping, and merging. 

Jupyter and Pandas are often used together for data analysis. Jupyter provides an interactive environment for exploring and manipulating data, while Pandas provides a powerful library for data analysis tasks. 

Here is an example of how to use Jupyter and Pandas together to merge two DataFrames:

import pandas as pd

# Create two DataFrames

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

df2 = pd.DataFrame({'A': [1, 6], 'D': [7, 8]})

# Merge the DataFrames on the 'A' column

df = pd.merge(df1, df2, on='A')

# Print the merged DataFrame



This code will print the following output:


   A  B  D

0  1  3  7

1  1  4  8


As you can see, Jupyter and Pandas can be used together to perform powerful data analysis tasks.


[More to come ...]



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