Chapter 1: Introduction to Google Colaboratory

In 2017, a significant advancement in Python programming emerged with the introduction of Google Colaboratory. This web-based Integrated Development Environment (IDE), built on a Jupyter notebook framework, enables users to code in Python while leveraging cloud computing and machine learning capabilities. This guide will explore how to effectively code and create visualizations using Google Colaboratory, along with its advantages and disadvantages. If you're looking to learn programming in a user-friendly manner or create graphs for business purposes, this guide is tailored for you.

Google Colaboratory functions as an extension of Google Drive, akin to Google Docs, Sheets, and Slides. With Colab, you can write executable Python code while benefiting from Google Drive's features, such as real-time collaboration, cloud storage, and easy sharing. Most importantly, there's no need for any software installation—everything operates seamlessly in the cloud.

One of the most remarkable advantages of Google Colaboratory is its accessibility. Traditionally, programmers relied on high-performance computers due to the intensive computing requirements of coding tasks. However, with Colab being a cloud-based tool, you can execute code on virtually any device, from a 2008 computer to your smartphone. This makes it an exceptional choice for anyone interested in coding, regardless of their hardware capabilities.

To set up Google Colaboratory on your Drive, simply right-click on your Drive and select "Connect more apps." Then search for "Colab" and click install.

Screenshot by Emilio Sainz Williams

Once installed, you can create a new Colab document by right-clicking again and selecting the Google Colaboratory option.

Screenshot by Emilio Sainz Williams

Chapter 2: Importing Data and Creating Graphs

With your Colab document ready, you can start executing Python code. For this demonstration, we'll focus on creating visualizations. Begin by importing your desired data in CSV format. Create a dedicated folder for Google Colaboratory, and upload your CSV file. For this example, we'll use a CSV containing data about Netflix movies. You can use your own dataset or download one from the internet for practice.

Screenshot by Emilio Sainz Williams

Next, run the following code to import your CSV file into the Colab document:

from google.colab import drive

drive.mount('/content/drive', force_remount=True)

import pandas as pd

path = "/content/drive/MyDrive/Colab Notebooks/movies.csv"

df = pd.read_csv(path, encoding='cp1252')

Screenshot by Emilio Sainz Williams

The code sets a path to access your CSV file. Make sure to replace "movies.csv" with the name of your file. After running the code, you will be prompted for permission, and your CSV will be imported into the document.

To verify that your data has been successfully imported, execute the following command:

print(df.head())

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This command prints the first few entries of your CSV, allowing you to confirm that the data was imported correctly and identify the x and y values for graphing. To create a simple histogram, use the following code:

fig = px.histogram(df, x='country', y='budget', title='Budget by Country')

fig.show()

Screenshot by Emilio Sainz Williams

In summary, using Google Colaboratory for data visualization is straightforward and efficient. Import your data, apply the graphing code from the provided resources, and visualize your business or personal data with ease.

Explore how to create various charts in Google Colab, including line plots, histograms, and more.

Learn about interactive graphs, tables, and widgets in Google Colaboratory for enhanced data visualization.