Beyond Python Logo

How to install Python Notebooks

Written by Matthew Yeager
4-minute read (350 words)
Published: Thu Jul 04 2019
Python Notebook Installation with JupyterInstall with a single download. Start plotting your first Python visuals!

Python Notebooks are a way to code using your web browser. You will be able to write Python code and see results in a familiar environment. No need to install a special editor or worry about learning a new interface.

We will go through the step-by-step download and installation process. Providing a visual guide for each screen and setting. Soon you will be using some of the powerful charting libraries within Python to plot your first visual!

Step-by-step Python installation guidePython Notesbooks with Anaconda download

Instead of installing the Python language, an editor to work in, and then each library you want to use, it has all been packaged up into a single framework called Anaconda. You won't need to worry about finding specific libraries - Anaconda has bundled the best Python packages used by the professionals, students, and broader Python community. Anaconda has already tested each tool to make sure they are working.

Take a look at the Anaconda Download section and make sure you pick the correct operating system (Windows, macOS, Linux). Focus on the latest Python version available, Python 3.7 and click the download button.

Anaconda Download Options

Once the download has completed, launch the installer and follow along with the prompts.

Visual Walkthrough:

Anaconda Install_Setup
Anaconda Install_License
Anaconda Install_Type
Anaconda Install_Location
Anaconda Install_Options
Anaconda Install_Installing
Anaconda Install_Completed
Anaconda Install_Jetbrains
Anaconda Install_Thanks
Running your first Python codeAccessing your Python Notebook and running code
After installation you can now search for Anaconda Navigator within your system. Anaconda comes with a few other languages, but we will keep focused on Python Jupyter Notebooks.

Anaconda Navigator Launcher

Clicking on the Launch button for Jupyter Notesbook will bring us to the Jupyter Dashboard. You will notice this has opened right up in your web browser and looks like a familiar file explorer. We can now create a new folder for your work, and then a new Python 3 Notebook. Look below for a each step of the process in images. It might be tricky to find and rename your folder, but we have the details!

Your first bit of code to just display a quick message so we know Python is up and running. Click on that text box in the middle of your page. Now you can type the code below to output the message "Look around!".

print("Look around!")
To execute the code and get back the result, we look at the toolbar above our textbox. There will be a button with a play icon and the word "Run". Clicking the "Run" button as highlighted in the walkthrough will run your code and output the results.

Python Notebook Print Text

That was fast! You have Python installed and setup! You have typed in your first code and ran in within your web browser. We got back the results right away and will be able to start using some of the installed libraries for visualization!

To help demonstrate some of the power you have unlocked, here are a few more lines that generate both a line chart and histogram. You won't need to understand everything that is going on here yet. We just want to make sure you have access to these libraries and can see how accessible Python code can be.

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Produce a few columns of random data
df = pd.DataFrame({
    'a': np.random.randn(400) + 2,
    'b': np.random.randn(400) - 1,
    'c': np.random.randn(400),
    'd': np.random.randn(400),
    'e': np.random.randn(400)
}, columns=['a', 'b', 'c', 'd', 'e'])

# Produce 2 subplots for displaying
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))

# Cumulative sum line chart
df.cumsum()[['c', 'd', 'e']].plot(ax=ax1);

# Histograph with transparency
df.plot.hist(ax=ax2, alpha=0.5);

Python Notebook Plot Samples

Visual Walkthrough:

Jupyter Files_Explorer
Jupyter Files_New_Folder
Jupyter Files_Rename_Folder
Jupyter Files_New_Name
Jupyter Files_New_Python_3
Jupyter Files_Blank
Jupyter Notebook_Hello_World
Jupyter Notebook_New_Cell
Jupyter Notebook_Advanced_Plot

Beyond Python Visual Newsletter

Enjoying the content? We send step-by-step visual Python tutorials to your inbox! Be notified when new content is available by the Beyond Python team.



Understanding Practical PythonMaybe you do want to learn how to create quick Python visualization!

Python doesn't need to be complicated work for programmers. We just installed Python using Anaconda and ran code in our web browser. If you enjoyed removing all the technical aspects and jumping right into accomplishing results, then check out our eBook!

Python for the Office explains each line of code so that you can start building ontop of the plots you are making! You can signup for a free guide - it's about 20 pages and has you reading data and producing charts immediately.




Questions, Comments, Concerns?

Thanks for reading! If you've made it this far then you are probably interested in the material that we will be producing. We have an idea of what we believe will be most valuable to our readers, but hearing from you directly would be even better.

Send us an email at questions@beyondpython.com or reach out to us on twitter @BeyondPython

If you have a topic that you are struggling with, a file that you can't seem to work with, or even a dataset that just seems impossible to wrangle, then please let us know. We want to provide you with useful and practical information so you can start using Python today.

Beyond Python Visual Newsletter

Enjoying the content? We send step-by-step visual Python tutorials to your inbox! Be notified when new content is available by the Beyond Python team.



Disclosures & Privacy
All Rights Reserved
© 2019 Beyond Python