A lollipop chart can be used to display ranking in the data. It is similar to an ordered bar chart. 

Let's consider the carDF dataset. It can be found in the GitHub repository for chapter 2. Alternatively, it can be used from the GitHub link directly, as mention in the following code:

1. Load the dataset:

#Read the dataset

carDF = pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/hands-on-exploratory-data-analysis-with-python/master/Chapter%202/cardata.csv')

2. Group the dataset by manufacturer. For now, if it does not make sense, just remember that the following snippet groups the entries by a particular field (we will go through groupby functions in detail in Chapter 4, Data Transformation):

#Group by manufacturer and take average mileage
processedDF = carDF[['cty','manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())

3. Sort the values by cty and reset the index (again, we will go through sorting and how we reset the index in Chapter 4, Data Transformation):

#Sort the values by cty and reset index
processedDF.sort_values('cty', inplace=True)
processedDF.reset_index(inplace=True)

4. Plot the graph:

#Plot the graph
fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.vlines(x=processedDF.index, ymin=0, ymax=processedDF.cty, color='firebrick', alpha=0.7, linewidth=2)
ax.scatter(x=processedDF.index, y=processedDF.cty, s=75, color='firebrick', alpha=0.7)

5. Annotate the title:

#Annotate Title
ax.set_title('Lollipop Chart for Highway Mileage using car dataset', fontdict={'size':22})

6. Annotate labels, xticks, and ylims:

ax.set_ylabel('Miles Per Gallon')
ax.set_xticks(processedDF.index)
ax.set_xticklabels(processedDF.manufacturer.str.upper(), rotation=65, fontdict={'horizontalalignment': 'right', 'size':12})
ax.set_ylim(0, 30)

7. Write the actual mean values in the plot, and display the plot:

#Write the values in the plot
for row in processedDF.itertuples():
    ax.text(row.Index, row.cty+.5, s=round(row.cty, 2), horizontalalignment= 'center', verticalalignment='bottom', fontsize=14)

#Display the plot on the screen
plt.show()

The lollipop chart generated by the preceding snippet is as follows:

Having seen the preceding output, you now know why it is called a lollipop chart, don't you? The line and the circle on the top gives a nice illustration of different types of cars and their associated miles per gallon consumption. Now, the data makes more sense, doesn't it?