Bar plot with multiple start and end points on a single date

create_bar_plot.py

# Check the following image to see output:
# https://i.imgur.com/miKLguL.png

# Answer to
# https://stackoverflow.com/questions/62376753/how-would-i-create-a-bar-plot-with-multiple-start-and-end-points-on-a-single-dat#62376753

import pandas as pd
import numpy as np
import datetime as dt

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.collections import PolyCollection
import matplotlib.patches as mpatches

data = np.array([
    ["Client", "Task", "Start Time", "End Time"],
    ["client-A", "task-a", "2020-06-10 11:10", "2020-06-10 11:25"],
    ["client-B", "task-b", "2020-06-10 11:30", "2020-06-10 13:54"],
    ["client-B", "task-a", "2020-06-10 17:34", "2020-06-10 18:00"],
    ["client-D", "task-e", "2020-06-11 08:05", "2020-06-11 12:45"],
    ["client-C", "task-d", "2020-06-11 15:15", "2020-06-11 17:01"],
    ["client-A", "task-a", "2020-06-11 19:10", "2020-06-11 20:18"],
    ["client-A", "task-c", "2020-06-11 20:18", "2020-06-11 21:36"],
    ["client-C", "task-a", "2020-06-12 08:02", "2020-06-12 08:25"],
    ["client-D", "task-e", "2020-06-12 08:45", "2020-06-12 09:55"],
    ["client-E", "task-d", "2020-06-12 10:00", "2020-06-12 11:07"],
    ["client-B", "task-c", "2020-06-12 11:11", "2020-06-12 12:30"]
])
data = data[1:, :] # Remove header

# Get all unique days in a sorted list and make dictionary using this
days_list = sorted(list(set([date[:10] for date in data[:, 2]])))[::-1]
days = { day: i+1 for i, day in enumerate(days_list) }

# Make a colormapping based on which client
clients = sorted(list(set(data[:, 0])))
colormapping = { client: f"C{i}" for i, client in enumerate(clients) }

# Save just the start time and end time for each entry as datetime
start_times = [dt.datetime.strptime(date[11:], "%H:%M") for date in data[:, 2]]
end_times = [dt.datetime.strptime(date[11:], "%H:%M") for date in data[:, 3]]

# Go through all data points and add corresponding vertices
verts, colors, texts = [], [], []
for i, d in enumerate(data):
    client, task, date_str = d[0], d[1], d[2]
    day_num = days[date_str[:10]]
    start_date = mdates.date2num(start_times[i])
    end_date = mdates.date2num(end_times[i])

    v = [(start_date, day_num - .4),
         (start_date, day_num + .4),
         (end_date, day_num + .4),
         (end_date, day_num - .4),
         (start_date, day_num - .4)
    ]
    verts.append(v)
    colors.append(colormapping[client])
    texts.append((start_date, day_num, task[-1].upper()))

# Make PolyCollection and scale
bars = PolyCollection(verts, facecolors=colors, edgecolors=("black",))
fig, ax = plt.subplots()
ax.add_collection(bars)
ax.autoscale()

# Set ticks to show every 30 minutes and in specific format
xticks = mdates.MinuteLocator(byminute=[0, 30])
ax.xaxis.set_major_locator(xticks)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
fig.autofmt_xdate()

# Set y-axis to be dates
ax.set_yticks(range(1, len(days_list) + 1))
ax.set_yticklabels(days_list)

# Add task text to plot
for (start_date, day_num, task) in texts:
    plt.text(start_date+.003, day_num-.03, task, color="w")

# Create legend based on clients
plt.legend(handles=[mpatches.Patch(color=color, label=client)
                    for client, color in colormapping.items()])

# Add grid and show
plt.grid()
plt.show()