ECON 0150 | Economic Data Analysis

The economist’s data analysis pipeline.


Part 1.2 | Summarizing Numerical Variables

Summarizing Numerical Variables

… use the appropriate summary tool for the variable type

Numerical Variables: Histograms

Q. Which age group has the most Starbucks customers?

> the bin sizes aren’t even, making it hard to interpret

Numerical Variables: Histograms

Q. Which age group has the most Starbucks customers?

> the bin sizes aren’t even, making it hard to interpret

Histograms: Use equal sized bins

Q. Which age group has the most Starbucks customers?

> but what if we want to distinguish between a 55 year old and a 60 year old?

Histograms: Use narrow enough bins

Q. Which age group has the most Starbucks customers?

> what if we take this even further?

> what if we compare 44 year olds to 45 year olds?

Histograms: Avoid visualizing noise

Q. Do 44 or 45 year olds spend more at Starbucks?

> we can go too far, introducing statistical noise. how do we fix the problem?

> increase the sample size or the bin width!

Histograms: Balance resolution vs noise

Q. Which age group has the most Starbucks customers?

> larger sample has less noise!

Histograms: Balance resolution vs noise

Q. Which age group has the most Starbucks customers?

> larger bins also has less noise!

Histograms: Summary

… use the right summary tool for the variable type



  • Use histograms to visualize continuous variables.
  • Make histograms with equally sized bins.
  • Histograms with bins that are too narrow increase statistical noise, which can obscure underlying relationships.

Exercise 1.2: Histograms

Q. Which age group among those making $40k or less has the most Starbucks customers?



Lets use the data to examine whether customers between 45 - 55 years old spend the most among customers making less than $40k.

Data: Starbucks_Customer_Profiles_40k.csv

Exercise 1.2: Histograms

Q. Which age group among those making $40k or less has the most Starbucks customers? 

# Histogram with 5 year bins
sns.histplot(customers, x='age', bins=range(20,100,5))

# Save Figure
plt.savefig('exercise_1_2_1.png')

Numerical Variables: Histograms

Q. Which countries drank an average amount of coffee?

> histogram bins make it impossible to see the exact values

Numerical Variables: Histograms

Q. Which countries drank the most coffee in 1999?

> again, histograms make it difficult to see statistical measures

Numerical Variables: Boxplots

Q. Which countries drank the most coffee in 1999?

> as we’ll see, boxplots can tell us about quartiles

> but boxplots are still pretty unclear for our question

Boxplots + Stripplots

Q. Which countries drank the most coffee in 1999?

> here we can see the datapoints directly with the boxplot

> each point represents a country’s coffee consumption

Boxplots + Stripplots

Q. Which countries drank the most coffee in 1999?

> each element of the boxplot represents one of these five quartiles

Boxplots + Stripplots

Which countries consumed more than 8 kg per capita?

Boxplots + Stripplots

Which countries consumed more than 8 kg per capita?

> we can highlight the relevant subsets of the data

Boxplots + Stripplots

Which country consumed the most coffee per capita?

> we can find the exact values according to quartiles

Boxplots + Stripplots

Which country consumed the most coffee per capita?

> we can find the exact values according to quartiles

> Finland consumed the most coffee per capita in 1999

Boxplots + Stripplots

Which country consumed the least coffee per capita?

Boxplots + Stripplots

Which country consumed the least coffee per capita?

> Russia consumed the least coffee per capita in 1999

Boxplots + Stripplots

How about the median?

Boxplots + Stripplots

How about the median?

> the US!

Boxplots + Stripplots

Which country consumes more than exactly 25% of countries?

Boxplots + Stripplots

Which country consumes more than exactly 25% of countries?

> Slovakia!

Boxplots + Stripplots

Which country consumes more than exactly 75% of countries?

Boxplots + Stripplots

Which country consumes more than exactly 75% of countries?

> Netherlands

Boxplots + Stripplots

Boxplots show quartiles; stripplots show the data.

Boxplots + Stripplots: Summary

Boxplots show quartiles; stripplots show the data.



  • Boxplots make it easy to show the quartiles.
  • Stripplots can show the distribution of the data.
  • We can highlight subsets of the data.

Exercise 1.2: Boxplots + Stripplots

Show the distribution of coffee consumption per capita in 2019.



Lets use a boxplot and stripplot to examine the distribution of coffee consumption per capita among coffee-importing countries in 2019.

  • Data: Coffee_Per_Cap_2019.csv

Exercise 1.2: Boxplots + Stripplots

Show the distribution of coffee consumption per capita in 2019. 

# Boxplot with no outliers
sns.boxplot(coffee, x='Coffee_2019', whis=(0,100))
# Stripplot
sns.stripplot(coffee, x='Coffee_2019')

# Save Figure
plt.savefig('exercise_1_2_2.png')