Lecture 9 (guest) - Data Visualization with Seaborn

Author: Umberto Mignozzetti


Seaborn is a data visualization library built on the top of matplotlib. It was created by Micheal Waskon at the Center for Neural Science, New York University.

Seaborn has all the attributes of the matplotlib library (it is a child class), making it considerably easy to plot data using Python.

We will learn some of these plots in this class and a few customizations. More about Seaborn can be found in here.

Below you can find a list of functions that we can use to plot data on Seaborn.

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# Importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # This is how you import seaborn

# Datasets

## Political and Economic Risk Dataset
# Info on investment risks in 62 countries in 1992
# courts  : 0 = not independent; 1 = independent
# barb2   : Informal Markets Benefits
# prsexp2 : 0 = very high expropriation risk; 5 = very low
# prscorr2: 0 = very high bribing risk; 5 = very low
# gdpw2   : Log of GDP per capita
perisk = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/perisk.csv')
perisk = perisk.set_index('country')

## Tips Dataset
# Info about tips in a given pub
# totbill : Total Bill
# tip     : Tip
# sex     : F = female; M = male
# smoker  : Yes or No
# day     : Weekday
# time    : Time of the day
# size    : Number of people
tips = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/tips.csv')
tips = tips.set_index('obs')

And here is what we have in these datasets:

courts barb2 prsexp2 prscorr2 gdpw2
Argentina 0 -0.720775 1 3 9.690170
Australia 1 -6.907755 5 4 10.304840
Austria 1 -4.910337 5 4 10.100940
Bangladesh 0 0.775975 1 0 8.379768
Belgium 1 -4.617344 5 4 10.250120
totbill tip sex smoker day time size
1 16.99 1.01 F No Sun Night 2
2 10.34 1.66 M No Sun Night 3
3 21.01 3.50 M No Sun Night 3
4 23.68 3.31 M No Sun Night 2
5 24.59 3.61 F No Sun Night 4

Plotting Data 101

The best way to explore the data is to plot it. However, not all plots are suitable for the variables we want to describe. Starting with a single variable, the first question is what type of variable we are talking about?

Types of variables:

  • Quantitative variables: represent measurement.

    • Discrete: number of children, age in years, etc.

    • Continuous: income, height, GDP per capita, etc.

  • Categorical variables: represent discrete variation

    • Binary: voted for Trump, smokes or not, etc.

    • Nominal: species names, a candidate supported in the primaries, etc.

    • Ordinal: schooling, grade, risk, etc.

For each variable type, there are specific descriptive stats and plots. Below, see an example of the difference between using the right and wrong descriptive stats for continuous and binary variables.

# Summary stats for a continuous variable (good)
count    62.000000
mean      9.041875
std       0.970264
min       7.029973
25%       8.381027
50%       9.185412
75%       9.889280
max      10.410180
Name: gdpw2, dtype: float64
# Frequency table for a continuous variable (bad)
8.727616     1
10.106510    1
10.123670    1
9.701494     1
9.375601     1
7.970049     1
9.414342     1
8.777710     1
8.379768     1
8.228711     1
Name: gdpw2, Length: 62, dtype: int64
# Summary stats for a binary variable (bad)
count    62.000000
mean      0.451613
std       0.501716
min       0.000000
25%       0.000000
50%       0.000000
75%       1.000000
max       1.000000
Name: courts, dtype: float64
# Frequency table for a binary variable (good)
0    34
1    28
Name: courts, dtype: int64

Univariate Plots

Univariate plots are plots for single variables.

Quantitative Variables: Histograms

Starting with numerical variables, one suitable plot is the histogram. It breaks the numerical values into brackets and counts how many values are within each bracket.

The syntax is:

sns.displot(data = the_data_frame,
    x = 'the_variable',
    kind = 'hist',
    kde = [..True or False..], 
    rug = [..True or False..],
    bins = [..number of bins..], 
    stat : [..{"count", "density", "probability"}..],
    [..among others..])

Let’s plot a histogram for the Log of GDP per capita (gdpw2)?

# My code here


We can easily customize the entire plot:

  1. Main title: plt.title('title here')

  2. X-axis title: g.set_xlabels('text') or plt.xlabel('text')

  3. Y-axis title: g.set_ylabels('text') or plt.ylabel('text')

  4. Style: ‘white’, ‘dark’, ‘whitegrid’, ‘darkgrid’, and ‘ticks’. Usage: sns.set_style('stylename')

  5. Remove the spine: g.despine(left = True)

  6. Current Palette + display the palette: sns.palplot(sns.color_palette())

  7. Which palettes: sns.palettes.SEABORN_PALETTES and to change, use set_palette('palette')

  8. Save figure: instead of plt.show() use plt.savefig('figname.png', transparent = False).

  9. Context: set the context between ‘paper’, ‘notebook’, ‘talk’, and ‘poster’. Use sns.set_context('context here')

There are even more customization that we can do. Please check the seaborn documentation for more details.

# My code here

Exercise: Using the histogram, describe the variables totbill and tip in the tips dataset.

## Your answers here

Categorical Variables: Countplot

Countplots are suitable for displaying categorical variables.

The syntax is:

    data = the_data_frame,
    x = 'the_variable', 
    kind = 'count')

Let’s check the risk of expropriation in each of the countries in 1992.

# My code here

All the customizations that we learn apply here as well. We can use them to prettify this plot.

However, since the scale is out of order, we can change the order of the x-axis values using the order parameter.

Even more, for ordinal data, it is customary to use a sequential color scheme, i.e., it gets darker as we increase the categories.

We can use several palettes:

  1. Blues

  2. Greys

  3. PuRd: Light Purple to Dark Red

  4. GnBu: Light Green to Dark Blue

Among others. The syntax to create the color scheme is:

    sns.color_palette("color_scheme", # If want revert add '_r'
                      [..number_of_colors or as_cmap=True..])

For more about color palettes, please check here.

# My code here

Exercise: Do a countplot for the days (day) in the tips dataset.

## Your answers here

Bivariate Plots

Univariate plots are excellent. But in reality, most of the exciting questions in science come from combinations of multiple variables (e.g., cause and effect, correlations, relationships, etc).

For two variables’ plots there are three combinations:

  • discrete x discrete: mosaic plot

  • discrete x continuous: several useful types

  • continuous x continuous: scatterplots

Discrete x Discrete Variables: Mosaicplot

The mosaic plot gives an idea of how the ratio of one variable changes when we change another variable. For instance, one empirical question that we can ask about the perisk dataset is:

Do countries with independent courts have less corruption than countries without independent courts?

The code to test this idea takes two steps. First, we need to prep the data. Then, we plot the data using the kind = 'bar' in the catplot function.

We need to create a table with cumulative values for the two variables we want to study to prep the data. Here is an example of how to do that:

tab = pd.crosstab(df.v1, df.v2, normalize='index') # 1: Crosstab
tab = tab.cumsum(axis = 1).\     # 2: Cummulative sum
      stack().\                  # 3: Stack the results
      reset_index(name = 'dist') # 4: Reset the indexes

Then, we need to plot the results using catplot:

sns.catplot(data = tab,
            x = 'v1', # More variation here
            y = 'dist', # Proportions
            hue = 'v2', # Less variation here
            # Comment hue_order if not displaying well
            hue_order = tab.v2.unique()[::-1], 
            dodge = False,
            kind = 'bar')

Full disclosure: A function exists that builds mosaic plots in one line of code. However, I find the results very ugly. You can Google mosaic plot in python and check that yourself.

# My code here: prepping the data
# My code here: doing the plot

Exercise: Do the number of smokers (variable smoker) vary by the weekday (day)?

## Your answers here

Discrete x Continuous Variables: Boxplots, Swarmplots, Violinplots

Suppose we want to test whether the data distribution varies based on a categorical variable. For example:

Do you think that having an independent judiciary affects the GDP per capita of a country?

We can check if this hypothesis makes sense by looking into the distribution of GDP per capita and segmenting it by the type of judicial institution.

The syntax for building these plots is almost the same as making a single boxplot. The difference is that you add the categorical variable to one of the axes:

    data = data_set, 
    x = 'categorical_variable',
    y = 'continuous_variable',
    kind = 'box') # Or 'violin', 'swarm', 'boxen', 'bar'..
# My code here

Exercise: Are the tips from smokers higher than tips from non-smokers? (the idea is that smokers would compensate non-smokers for the externality caused) Check that in the tips dataset.

## Your answers here

Continuous x Continuous Variables: Scatterplots and Regplots

To plot two continuous variables, one against the other, we can use two functions. First, we can use the relplot function if we want to explore the relationship without fitting any trend line. The syntax is the following:

sns.relplot(data = data_set,
            x = 'independent_axis_continuous_variable',
            y = 'dependent_axis_continuous_variable',
            hue = 'optional_categorical_to_color',
            kind = 'scatter')

And an excellent version of it, with distribution plots on the top and the left, can be built using the jointplot function:

sns.jointplot(data = data_set,
              x = 'independent_axis_continuous_variable',
              y = 'dependent_axis_continuous_variable',
              hue = 'optional_categorical_to_color',
              kind = 'scatter') # Or 'scatter', 'kde', 
                                  'hist', 'hex', 'reg', 

If you want to add a trend line, it is better to use lmplot (instead of ‘reg’ in the plot above). The syntax is the following:

sns.lmplot(data = data_set,
    x = "total_bill", 
    y = "tip", 
    hue = "smoker",
    logistic = ..False or True.., # Logistic fit for discrete y
    order = ..polynomial order.., # Polynomial degree
    lowess = ..False or True..,   # Lowess fit
    ci = ..None..)                # Remove conf. int.
# My code here

Exercise: Are the tips related with total bill in the tips dataset?

## Your answers here

Great job!!!

After-class extras

Excellent job learning seaborn! It is an easy-to-use yet powerful package to generate lovely plots.

Next, you should take a look at the following packages to keep developing your skills:

  • plotnine: Implements the ggplot grammar of graphs in python

  • cartopy: Package to make maps in python.

  • plotly: Builds interactive graphs in python (and other languages). Check also the dash for plotly in python.

Now, try the extra exercises below to sharpen your learning.

## Extra Datasets

## Political Information Dataset
# ANES 2000 Political Information based on interviews
# polInf          : Political Information
# collegeDegree   : College Degree
# female          : Female
# age             : Age in years
# homeOwn         : Own house
# others...
polinf = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/pinf.csv')
pinf_order = ['Very Low', 'Fairly Low', 'Average', 'Fairly High', 'Very High']
polinf['polInf'] = pd.Categorical(polinf.polInf, 

## US Crime data in the 1970's
# Data on violent crime in the US
# Muder: number of murders in the state
# Assault: number of assaults in the state
# others...
usarrests = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/usarrests.csv')
polInf collegeDegree female age homeOwn govt length id
0 Fairly High Yes No 49.0 Yes No 58.400002 1
1 Average No Yes 35.0 Yes No 46.150002 2
2 Very High No Yes 57.0 Yes No 89.519997 3
3 Average No No 63.0 Yes No 92.629997 4
4 Fairly High Yes Yes 40.0 Yes No 58.849998 4
Murder Assault UrbanPop Rape
0 13.2 236 58 21.2
1 10.0 263 48 44.5
2 8.1 294 80 31.0
3 8.8 190 50 19.5
4 9.0 276 91 40.6


  1. (Univariate) In the polinf dataset, make a count plot of the variable polInf. Imagine you want to use this for a talk, so adjust the context. Change the x-axis label and title to appropriate descriptions of the data. (Hint: to rotate the axis tick labels, use plt.xticks(rotation=number_degree_of_your_choice))

  2. (Univariate) In the polinf dataset, make a histogram of the variable age. (Hint: set the context back to notebook before starting)

  3. (Bivariate) Do you think political information varies with a college degree? Check that using the polinf dataset!

  4. (Bivariate) Do you think political information varies with age? Check that using the polinf dataset!

  5. (Bivariate) Do you think there is a correlation between Murder and Assault? Check that using the usarrests dataset!

  6. (Challenge: Multivariate) There are four continuous indicators in the usarrests dataset: Murder, Assault, UrbanPop, and Rape. Do you think you can build a scatterplot matrix? The documentation is in here.

## Your answers here