Data Visualization in Python
こんにちは、 ゴーリストのビベックです。

Hello World! This is Vivek from Goalist.

If you want to build a very powerful machine learning algorithm on structured data then the first step to take is to explore the data every which way you can. You draw a Histogram; you draw a Correlogram; you draw Cross Plots, you really want to understand what is in that data what does each variable mean, what its distribution is and ideally how was it collected.

Once you have a real rock solid understanding of what’s in the data, only then can you smoothly into creating your machine learning model.

“roll down the hill”

In this post, let’s go through the different libraries in Python and gain some insight into our data by visualizing it.

Along with me, you may want to try and experiment with the packages that we are about to explore. Use Google Colab to follow along.

Well, you may ask what is Google Colab?

Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

Learn more about Google Colab here…

Follow this URL to create your notebook

So let’s get started…

We’ll be using the following packages to plot different graphs

Matplotlib

Seaborn

Bokeh

Out of these, Matplotlib is the most common charting package, see its documentation for details, and its examples for inspiration.

1) Line Graphs

A line graph is commonly used to show trends over time. It can help readers to understand your data in a few seconds.

Sample code to generate a Line Graph is given below. Go ahead and open the sample code in Colab and experiment with it.
['Vivek Amilkanthawar']
['Machine Learning Tools', 'Data Analysis', 'Python', 'Data Science', 'Data Visualization']