Circular barplot with Matplotlib

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

from matplotlib.cm import ScalarMappable
from matplotlib.lines import Line2D
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from textwrap import wrap

path = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/hike_data.csv'
data = pd.read_csv(path)
data.head()

data["region"] = data["location"].str.split("--", n=1, expand=True)[0]

# Make sure there's no leading/trailing whitespace
data["region"] = data["region"].str.strip() 

# Make sure to use .astype(Float) so it is numeric.
data["length_num"] = data["length"].str.split(" ", n=1, expand=True)[0].astype(float)

summary_stats = data.groupby(["region"]).agg(
    sum_length = ("length_num", "sum"),
    mean_gain = ("gain", "mean")
).reset_index()

summary_stats["mean_gain"] = summary_stats["mean_gain"].round(0)

trackNrs = data.groupby("region").size().to_frame('n').reset_index()

summary_all = pd.merge(summary_stats, trackNrs, "left", on = "region")
summary_all.head()

# Bars are sorted by the cumulative track length
df_sorted = summary_all.sort_values("sum_length", ascending=False)

# Values for the x axis
ANGLES = np.linspace(0.05, 2 * np.pi - 0.05, len(df_sorted), endpoint=False)

# Cumulative length
LENGTHS = df_sorted["sum_length"].values

# Mean gain length
MEAN_GAIN = df_sorted["mean_gain"].values

# Region label
REGION = df_sorted["region"].values

# Number of tracks per region
TRACKS_N = df_sorted["n"].values

GREY12 = "#1f1f1f"

# Set default font to Bell MT
plt.rcParams.update({"font.family": "Bell MT"})

# Set default font color to GREY12
plt.rcParams["text.color"] = GREY12

# The minus glyph is not available in Bell MT
# This disables it, and uses a hyphen
plt.rc("axes", unicode_minus=False)

# Colors
COLORS = ["#6C5B7B","#C06C84","#F67280","#F8B195"]

# Colormap
cmap = mpl.colors.LinearSegmentedColormap.from_list("my color", COLORS, N=256)

# Normalizer
norm = mpl.colors.Normalize(vmin=TRACKS_N.min(), vmax=TRACKS_N.max())

# Normalized colors. Each number of tracks is mapped to a color in the 
# color scale 'cmap'
COLORS = cmap(norm(TRACKS_N))

# Some layout stuff ----------------------------------------------
# Initialize layout in polar coordinates
fig, ax = plt.subplots(figsize=(9, 12.6), subplot_kw={"projection": "polar"})

# Set background color to white, both axis and figure.
fig.patch.set_facecolor("white")
ax.set_facecolor("white")

ax.set_theta_offset(1.2 * np.pi / 2)
ax.set_ylim(-1500, 3500)

# Add geometries to the plot -------------------------------------
# See the zorder to manipulate which geometries are on top

# Add bars to represent the cumulative track lengths
ax.bar(ANGLES, LENGTHS, color=COLORS, alpha=0.9, width=0.52, zorder=10)

# Add dashed vertical lines. These are just references
ax.vlines(ANGLES, 0, 3000, color=GREY12, ls=(0, (4, 4)), zorder=11)

# Add dots to represent the mean gain
ax.scatter(ANGLES, MEAN_GAIN, s=60, color=GREY12, zorder=11)


# Add labels for the regions -------------------------------------
# Note the 'wrap()' function.
# The '5' means we want at most 5 consecutive letters in a word, 
# but the 'break_long_words' means we don't want to break words 
# longer than 5 characters.
REGION = ["\n".join(wrap(r, 5, break_long_words=False)) for r in REGION]
REGION

# Set the labels
ax.set_xticks(ANGLES)
ax.set_xticklabels(REGION, size=12);

# Remove unnecesary guides ---------------------------------------

# Remove lines for polar axis (x)
ax.xaxis.grid(False)

# Put grid lines for radial axis (y) at 0, 1000, 2000, and 3000
ax.set_yticklabels([])
ax.set_yticks([0, 1000, 2000, 3000])

# Remove spines
ax.spines["start"].set_color("none")
ax.spines["polar"].set_color("none")


# Adjust padding of the x axis labels ----------------------------
# This is going to add extra space around the labels for the 
# ticks of the x axis.
XTICKS = ax.xaxis.get_major_ticks()
for tick in XTICKS:
    tick.set_pad(10)


# Add custom annotations -----------------------------------------
# The following represent the heights in the values of the y axis
PAD = 10
ax.text(-0.2 * np.pi / 2, 1000 + PAD, "1000", ha="center", size=12)
ax.text(-0.2 * np.pi / 2, 2000 + PAD, "2000", ha="center", size=12)
ax.text(-0.2 * np.pi / 2, 3000 + PAD, "3000", ha="center", size=12)


# Add text to explain the meaning of the height of the bar and the
# height of the dot
ax.text(ANGLES[0], 3100, "Cummulative Length [FT]", rotation=21, 
        ha="center", va="center", size=10, zorder=12)
ax.text(ANGLES[0]+ 0.012, 1300, "Mean Elevation Gain\n[FASL]", rotation=-69, 
        ha="center", va="center", size=10, zorder=12)
fig

# Add legend -----------------------------------------------------

# First, make some room for the legend and the caption in the bottom.
fig.subplots_adjust(bottom=0.175)

# Create an inset axes.
# Width and height are given by the (0.35 and 0.01) in the 
# bbox_to_anchor
cbaxes = inset_axes(
    ax, 
    width="100%", 
    height="100%", 
    loc="center",
    bbox_to_anchor=(0.325, 0.1, 0.35, 0.01),
    bbox_transform=fig.transFigure # Note it uses the figure.
) 

# Create a new norm, which is discrete
bounds = [0, 100, 150, 200, 250, 300]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

# Create the colorbar
cb = fig.colorbar(
    ScalarMappable(norm=norm, cmap=cmap), 
    cax=cbaxes, # Use the inset_axes created above
    orientation = "horizontal",
    ticks=[100, 150, 200, 250]
)

# Remove the outline of the colorbar
cb.outline.set_visible(False)

# Remove tick marks
cb.ax.xaxis.set_tick_params(size=0)

# Set legend label and move it to the top (instead of default bottom)
cb.set_label("Amount of tracks", size=12, labelpad=-40)

# Add annotations ------------------------------------------------

# Make some room for the title and subtitle above.
fig.subplots_adjust(top=0.8)

# Define title, subtitle, and caption
title = "\nHiking Locations in Washington"
subtitle = "\n".join([
    "This Visualisation shows the cummulative length of tracks,",
    "the amount of tracks and the mean gain in elevation per location.\n",
    "If you are an experienced hiker, you might want to go",
    "to the North Cascades since there are a lot of tracks,",
    "higher elevations and total length to overcome."
])
caption = "Data Visualisation by Tobias Stalder\ntobias-stalder.netlify.app\nSource: TidyX Crew (Ellis Hughes, Patrick Ward)\nLink to Data: github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-11-24/readme.md"

# And finally, add them to the plot.
fig.text(0.1, 0.93, title, fontsize=25, weight="bold", ha="left", va="baseline")
fig.text(0.1, 0.9, subtitle, fontsize=14, ha="left", va="top")
fig.text(0.5, 0.025, caption, fontsize=10, ha="center", va="baseline")

# Note: you can use `fig.savefig("plot.png", dpi=300)` to save it with in hihg-quality.
fig