Let's say I have a DataFrame like below,

years = [2014, 2014, 2014, 2015, 2015, 2015, 2015]
vehicle_types = ['Truck', 'Truck', 'Car', 'Bike', 'Truck', 'Bike', 'Car']
companies = ["Mercedez",  "Tesla", "Tesla", "Yamaha", "Tesla", "BMW", "Ford"]

df = pd.DataFrame({'year': years,
                    'vehicle_type': vehicle_types,
                    'company': companies
                   })

df.head()

And I want to plot the distribution of vehicle types per year, something like this,

Turns out, this can easily be done in one line with pandas,

df.groupby(['year'])['vehicle_type'].value_counts().unstack().plot.bar()

It's amazing how a single statement takes care of,

• Null counts
• Plotting the histogram bars side by side
• And aesthetics like labels, legends, etc.

The critical part here was the unstack function and how it fits well with the multi-index created by value_counts().