Matplotlib is not the only library we can use to plot histograms. Bokeh (available at https://bokeh.pydata.org/en/latest/) is another powerful plotting library, built on top of D3.js, which allows you to interactively play with your charts.

Here's how you plot with Bokeh:

%%local
from bokeh.io import show
from bokeh.plotting import figure
from bokeh.io import output_notebook
output_notebook()

labels = [str(round(e, 2)) for e in hist_MPG['bins']]

p = figure(
    x_range=labels, 
    plot_height=350, 
    title='Histogram of fuel economy'
)

p.vbar(x=labels, top=hist_MPG['counts'], width=0.9)

show(p)

First, we load all the necessary components of Bokeh; the output_notebook() method makes sure that we produce the chart inline in the notebook instead of opening a new window each time. Next, we produce the labels to put on our chart. Then, we define our figure: the x_range parameter specifies the number of points on the x axis and the plot_height sets the height of our plot. Finally, we use the .vbar(...) method to draw the bars of our histogram; the x parameter is the labels to put on our plot, and the top parameter specifies the counts. 

The result looks as follows:

It's the same information, but you can interact with this chart in your browser.