In the first comparison, we pick forks and open issues and we show them on a chart.

x = df['forks'] 
y = df['open_issues'] 

We create a plot where forks will be shown on x axis and open issues on y axis. The colors indicate technologies:

• red: Deep learning
• blue: Open source

We will follow the same color code in the next examples:

fig, ax = plt.subplots() 
colors = dict(zip(set(df['technology']), ['red', 'blue']))    
ax.scatter(x=x, y=y, c=df['technology'].apply(lambda x: colors[x]), s = 200, alpha = 0.5) 

There are two additional parameters that we use to obtain prettier results:

• alpha: Transparency level of dots
• s: Size of dots

We add plot descriptions such as title, x axis label and y axis label:

ax.set(title='Deep Learning and Open Source Technologies', xlabel='Number of forks', ylabel='Number of open issues') 

Then, we show the chart.

plt.show()