Creating a histogram

We want to see how we can get the distribution of a sample of data and get an idea of where values are concentrated, as well as how much variability/spread it has. We will do this by creating a histogram.

As always, we'll start with the simplest possible example:

1. We open the poverty DataFrame and create a subset of it, containing only countries and data from the year 2015:

   import pandas as pd
   poverty = pd.read_csv('data/poverty.csv')
   df = poverty[poverty['is_country'] & poverty['year'].eq(2015)]

2. Import Plotly Express and run the histogram function with df as the argument to the data_frame parameter and the indicator of our choice for the x parameter:

   import plotly.express as px
   gini = 'GINI index (World Bank estimate)'
   px.histogram(data_frame=df, x=gini)

   As a result, we get the histogram that you can see in Figure 8.1:

Figure 8.1 – A histogram of the Gini indicator...