Print

53 of 100: Funnel chart 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!

To be improved: I automated the creation of the rectangles, but hardcoded the shadowing. My brain was on fire, sorry… will review and automate later.

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
from matplotlib.path import Path
from matplotlib.patches import PathPatch, Rectangle
import numpy as np
import pandas as pd

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": "#2B314D", "Denmark": "#A54836", "Sweden": "#5375D4", }

xy_ticklabel_color, grand_totals_color, grid_color, datalabels_color ='#757C85',"#101628", "#C8C9C9", "#FFFFFF"

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

We need to create the percentage change then sort the data by a list I supplied.

df['pct_change'] = df.groupby('countries', sort=False)['sites'].apply(
     lambda x: x.pct_change()).to_numpy().round(3)*100
#custom sort a dataframe
sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1, 2004:5, 2022:4}
df = df.sort_values(by=['countries','year',], key=lambda x: x.map(sort_order_dict))

df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
indexyearcountriessitespct_changeyear_lblcolor
52022Norway860.0’22#2B314D
42004Norway5NaN’04#2B314D
32022Denmark10150.0’22#A54836
22004Denmark4NaN’04#A54836
12022Sweden1515.4’22#5375D4
02004Sweden13NaN’04#5375D4

Define the variables

colors = df.color
sites = df.sites
countries = df.countries.unique()
title = np.insert(countries, np.arange(len(countries))+1, [""]*len(countries))
pct_changes = [int(num) if float(num).is_integer() else num for num in df["pct_change"]]
year_labels =df.year_lbl

separation = 10 #distance between the plots
width_of_bar = 2
middle_of_plot = df.sites.max()

Plot the chart


fig, ax = plt.subplots(figsize=(12,12))


for rows, site, year_label, color, country,  in zip(range(6),sites, year_labels, colors, title, ):
    x= middle_of_plot-site/2
    height = separation*rows

    ax.broken_barh([(x, site) ], (height, width_of_bar), facecolors=color)
    #add year label
    ax.text(x-1, height+0.5, year_label, size = 10, color = "#777F87")
    #number of sites
    ax.text(middle_of_plot, height+0.5, site,color = "w",weight = "bold")
    #countries
    ax.text(middle_of_plot-1.2, height+13, country,size = 12, color = "#777F87", clip_on=False)
   


ax.set(xlim=(0,df.sites.max()+middle_of_plot),ylim=(0-separation,separation*len(df.sites)))

# no curve
verts = [(11,1), (11,5), (12.5,5), (12.5,10), (12.5+5,10), (12.5+5,5), (11+8,5),(11+8,1),(11,1), ]
codes = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4,Path.LINETO,Path.CURVE4,Path.CURVE4, Path.CURVE4,Path.CLOSEPOLY]
p = Path(verts,codes)
ax.add_patch(PathPatch(p,fc=colors[4],alpha=0.6, color =colors[4] ))


# den curve
verts = [(10,22), (10,25), (13,25), (13,30), (13+4,30), (13+4,25), (10+10,25),(10+10,22),(10,22), ]
codes = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4,Path.LINETO,Path.CURVE4,Path.CURVE4, Path.CURVE4,Path.CLOSEPOLY]
p = Path(verts,codes)
ax.add_patch(PathPatch(p,fc=colors[2],alpha=0.6, color =colors[2] ))

# swe curve
verts = [(7.5,42), (7.5,45), (8.5,45), (8.5,50), (8.5+13,50), (8.5+13,45), (7.5+15,45),(7.5+15,42),(7.5,42), ]
codes = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4,Path.LINETO,Path.CURVE4,Path.CURVE4, Path.CURVE4,Path.CLOSEPOLY]
p = Path(verts,codes)
ax.add_patch(PathPatch(p,fc=colors[0],alpha=0.6, color =colors[0] ))



for pct,y in zip(pct_changes[0::2],range(6)[0::2]):
    ax.annotate(f'+{pct}%', (middle_of_plot,y*separation +6), color = "w", weight= "bold",
                ha= "center",va = "center",
                 bbox= dict(fc= "#151C32", ec = "#151C32", boxstyle = 'round, pad=0.5'))

ax.set_axis_off()

The result:

53 of 100: Funnel chart in matplotlib
Was this helpful?

Reader Interactions

Leave a Reply

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

Table of Contents