Print

49 of 100: Heatmap in matplotlib

At the beginning of the year I challenged myself to create all 100 visualizations using python and matplotlib from the 1 dataset,100 visualizations project and I am sharing with you the code for all the visualizations.

Note: Data Viz Project is copyright Ferdio and available under a Creative Commons Attribution – Non Commercial – No Derivatives 4.0 International license. I asked Ferdio and they told me they used a Design tool to create all the plots.

Collaborate

There are a ton of improvements that can be made on the code, so let me know in the comments any improvements you make and I will update the post accordingly!

This is the original viz that we are trying to recreate in matplotlib

Import the packages

We will need the following packages:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.colors

Generate the data

We could actually go from numpy to matplotlib, but most data projects use pandas to transform the data, so I am using a pandas dataframe as the starting point.

color_dict = {"Norway": "#CC5A43", "Denmark": "#3B4D83", "Sweden": "#5375D4", }

label_color, datalabels_color ='#757C85', "#FFFFFF"

data = {
    "year": [2004, 2022, 2004, 2022, 2004, 2022],
    "countries" : [ "Denmark", "Denmark", "Norway", "Norway","Sweden", "Sweden",],
    "sites": [4,10,5,8,13,15]
}
df= pd.DataFrame(data)
indexyearcountriessites
02004Sweden13
12022Sweden15
22004Denmark4
32022Denmark10
42004Norway5
52022Norway8

We need to create the year labels, the country codes and then sort the data.

sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1, 2004:4, 2022:5}
df = df.sort_values(by=['year','countries',], key=lambda x: x.map(sort_order_dict))
df['ctry_code'] = df.countries.astype(str).str[:2].astype(str).str.upper()
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
df['color']=df.countries.map(color_dict)
yearcountriessitesctry_codeyear_lblcolor
22004Norway5NO’04#CC5A43
02004Denmark4DE’04#3B4D83
42004Sweden13SW’04#5375D4
32022Norway8NO’22#CC5A43
12022Denmark10DE’22#3B4D83
52022Sweden15SW’22#5375D4

Define the variables

sites =  np.array(df.groupby(['year'], sort=False).sites.apply(list).tolist())
countries = df.countries.unique()
years = df.year.unique()
colors = df.color.unique()

cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", colors[::-1],df.sites.max()) 
#df.sites.max adds the discrete option to the colorbar

Plot the chart

fig, ax = plt.subplots()

#specify the range of the colormap regarless of the values of the plot
im = ax.imshow(sites, cmap = cmap,
               vmin=0, vmax=15,) 

# Loop over data dimensions and create text annotations.
for i in range(len(years)):
    for j in range(len(countries)):
        text = ax.text(j, i, sites[i, j],
                       size = 12,
                       ha="center", va="center", color=datalabels_color)

cbar = ax.figure.colorbar(im, ax=ax, shrink=0.9,
                          ticks = [0,15],
                          
                           location='bottom' )
cbar.outline.set_visible(False)
cbar.ax.tick_params(size=0)
cbar.ax.set_xticklabels(cbar.ax.get_xticklabels(), color = label_color,)


ax.tick_params(axis='both', which='major',labeltop=True,labelbottom=False,length=0,labelsize=12,colors= label_color, pad = 20)
ax.yaxis.set_ticks(range(len(years)), labels = years)
ax.xaxis.set_ticks(range(len(countries)), labels = countries)
ax.spines[['left','right','bottom','top']].set_visible(False)

The result:

49 of 100: Heatmap in matplotlib
Was this helpful?

Reader Interactions

Leave a Reply

Your email address will not be published. Required fields are marked *

Table of Contents