A Guide to Visualizing Data with Matplotlib

Prerequisite

This guide assumes that you have Python 3 installed as well as the pandas, numpy, and matplotlib packages.

Setup

The python packages necessary for this guide are:

• pandas
• numpy
• matplotlib
• palmerpenguins

These can be installed via pip.

def ggplot2_defaults():
    [s.set_visible(False) for s in ax.spines.values()]
    # ax.spines['top'].set_visible(False)
    # ax.spines['bottom'].set_visible(False)
    # ax.spines['left'].set_visible(False)
    # ax.spines['right'].set_visible(False)

    ax.set_axisbelow(True) ## draws the grid below the bars
    ax.grid(visible = True, color = "white") ## turns on grid and sets lines to white
    plt.gca().patch.set_facecolor('gainsboro') ## sets grid fill color

We’ll begin by loading these packages

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rdatasets import data
from palmerpenguins import load_penguins

and then reading in the data

penguins = load_penguins()
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

Visualizations

Categorical Variables

Barchart

count_by_species = penguins.groupby("species").size()

fig, ax = plt.subplots()
ax.bar(x = count_by_species.index, height = count_by_species.values, color = "dimgray");
ax.set_ylabel("count")
ax.set_xlabel("species")
ggplot2_defaults()

## sort the bars
count_by_species.sort_values(ascending = False, inplace = True)

fig, ax = plt.subplots()
ax.bar(x = count_by_species.index, height = count_by_species.values, color = "dimgray");
ax.set_ylabel("count")
ax.set_xlabel("species")

ggplot2_defaults()

Side-by-Side Barchart

count_by_species_and_year = penguins.groupby(["species", "year"]).size()
count_by_species_and_year = count_by_species_and_year.reset_index()
count_by_species_and_year.rename(columns = {0:'value'}, inplace = True)

width = 0.25  # the width of the bars
x = np.arange(count_by_species_and_year["species"].nunique())
multiplier = 0
fig, ax = plt.subplots()

for yr in count_by_species_and_year["year"].unique():
    ht = count_by_species_and_year[count_by_species_and_year["year"] == yr]["value"]
    offset = width * multiplier
    rects = ax.bar(x + offset, ht, width, label = yr)
    multiplier += 1
    
ax.set_ylabel('count')
ax.set_xlabel('species')
ax.set_xticks(x + width, count_by_species_and_year["species"].unique())
ax.legend(ncols=3)
ax.set_ylim(0, 60)

ggplot2_defaults()

Stacked Barchart

width = 0.5

fig, ax = plt.subplots()
bottom = np.zeros(3)

for yr in count_by_species_and_year["year"].unique():
    tmp = count_by_species_and_year[count_by_species_and_year["year"] == yr]
    species = np.array(tmp["species"])
    value = np.array(tmp["value"])
    p = ax.bar(species, value, width, label=yr, bottom=bottom)
    bottom += value

ax.set_ylabel('count')
ax.set_xlabel('species')
ax.set_xticks(count_by_species_and_year["species"].unique())
ax.legend(ncols=3)
ax.set_ylim(0, 160)

ggplot2_defaults()

Mosaic Plot

tot_by_grp = count_by_species_and_year.groupby(["species"])["value"].sum().reset_index()
tot_by_grp.rename(columns = {"value":"tot"}, inplace = True)
count_by_species_and_year = count_by_species_and_year.merge(tot_by_grp, on = "species", how = "left")
count_by_species_and_year["prop"] = count_by_species_and_year["value"] / count_by_species_and_year["tot"]

totals = count_by_species_and_year["tot"].unique()
scaled_totals = totals / totals.min()

width = scaled_totals
fig, ax = plt.subplots()
bottom = np.zeros(3)
species_loc = [0, (width[0]/2 + width[1]/2) + .005, (width[0]/2 + width[1] + .005 + width[2]/2) + .005]

for yr in count_by_species_and_year["year"].unique():
    tmp = count_by_species_and_year[count_by_species_and_year["year"] == yr]
    prop = np.array(tmp["prop"])
    p = ax.bar(species_loc, prop, width, label=yr, bottom=bottom, edgecolor = "white")
    bottom += np.array(tmp["prop"])
    
ax.set_ylabel('proportion')
ax.set_xlabel('species')

box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])

ax.set_xticks(ticks = species_loc, labels = count_by_species_and_year["species"].unique())
ax.legend(ncol = 1, loc = "center left", bbox_to_anchor=(1, 0.5))

ggplot2_defaults()

Piecharts

fig, ax = plt.subplots()
ax.pie(x = count_by_species.values, labels = count_by_species.index, startangle = 90);

Numerical Variables

Histograms

fig, ax = plt.subplots()
ax.hist(penguins["bill_length_mm"], edgecolor = "white", color = "#cc4778");

ax.set_ylabel('count')
ax.set_xlabel('bill length (mm)')

ggplot2_defaults()

Boxplots

fig, ax = plt.subplots()

ax.boxplot(penguins["bill_length_mm"].dropna(),
    patch_artist = True,
    medianprops = {"color": "black"},
    boxprops = {"facecolor": "white"})
plt.xticks([]);
ggplot2_defaults()

Boxplot with points

fig, ax = plt.subplots()
n = len(penguins["bill_length_mm"].dropna())

ax.boxplot(penguins["bill_length_mm"].dropna(),
    patch_artist = True,
    medianprops = {"color": "black"},
    boxprops = {"fill": None})
ax.scatter(y = penguins["bill_length_mm"].dropna(), x = [1] * n + np.random.normal(size = n, scale = 1/100), color = "black", alpha = 1/4)

plt.xticks([]);
ggplot2_defaults()