Gantt Charts in Matplotlib

GanttPlotLove it or hate it, the lack of a tractable options to create Gantt charts warrants frustration at times.  A recent post on Bitbucket provides a nice implementation using matplotlib and python as a platform.  In order to expand the basic functionality a few modifications enable a set of features that highlight the relative contributions of the team participants.  In the example provided above the broad tasks are indicated in yellow while the two inset bars (red:student and blue:PI) illustrate the percent effort.  See the source below for the details.

Creates a simple Gantt chart
Adapted from
BHC 2014

import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import matplotlib.dates
from matplotlib.dates import MONTHLY, DateFormatter, rrulewrapper, RRuleLocator

from pylab import *

def create_date(month,year):
"""Creates the date"""

date = dt.datetime(int(year), int(month), 1)
mdate = matplotlib.dates.date2num(date)

return mdate

# Data

pos = arange(0.5,5.5,0.5)

ylabels = []
ylabels.append('Hardware Design & Review')
ylabels.append('Hardware Construction')
ylabels.append('Integrate and Test Laser Source')
ylabels.append('Objective #1')
ylabels.append('Objective #2')
ylabels.append('Present at ASMS')
ylabels.append('Present Data at Gordon Conference')
ylabels.append('Manuscripts and Final Report')

effort = []
effort.append([0.2, 1.0])
effort.append([0.2, 1.0])
effort.append([0.2, 1.0])
effort.append([0.3, 0.75])
effort.append([0.25, 0.75])
effort.append([0.3, 0.75])
effort.append([0.5, 0.5])
effort.append([0.7, 0.4])

customDates = []

task_dates = {}
for i,task in enumerate(ylabels):
task_dates[task] = customDates[i]
# task_dates['Climatology'] = [create_date(5,2014),create_date(6,2014),create_date(10,2013)]
# task_dates['Structure'] = [create_date(10,2013),create_date(3,2014),create_date(5,2014)]
# task_dates['Impacts'] = [create_date(5,2014),create_date(12,2014),create_date(2,2015)]
# task_dates['Thesis'] = [create_date(2,2015),create_date(5,2015)]

# Initialise plot

fig = plt.figure()
# ax = fig.add_axes([0.15,0.2,0.75,0.3]) #[left,bottom,width,height]
ax = fig.add_subplot(111)

# Plot the data

start_date,end_date = task_dates[ylabels[0]]
ax.barh(0.5, end_date - start_date, left=start_date, height=0.3, align='center', color='blue', alpha = 0.75)
ax.barh(0.45, (end_date - start_date)*effort[0][0], left=start_date, height=0.1, align='center', color='red', alpha = 0.75, label = "PI Effort")
ax.barh(0.55, (end_date - start_date)*effort[0][1], left=start_date, height=0.1, align='center', color='yellow', alpha = 0.75, label = "Student Effort")
for i in range(0,len(ylabels)-1):
labels = ['Analysis','Reporting'] if i == 1 else [None,None]
start_date,mid_date,end_date = task_dates[ylabels[i+1]]
piEffort, studentEffort = effort[i+1]
ax.barh((i*0.5)+1.0, mid_date - start_date, left=start_date, height=0.3, align='center', color='blue', alpha = 0.75)
ax.barh((i*0.5)+1.0-0.05, (mid_date - start_date)*piEffort, left=start_date, height=0.1, align='center', color='red', alpha = 0.75)
ax.barh((i*0.5)+1.0+0.05, (mid_date - start_date)*studentEffort, left=start_date, height=0.1, align='center', color='yellow', alpha = 0.75)
# ax.barh((i*0.5)+1.0, end_date - mid_date, left=mid_date, height=0.3, align='center',label=labels[1], color='yellow')

# Format the y-axis

locsy, labelsy = yticks(pos,ylabels)
plt.setp(labelsy, fontsize = 14)

# Format the x-axis

ax.set_ylim(ymin = -0.1, ymax = 4.5)
ax.grid(color = 'g', linestyle = ':')

ax.xaxis_date() #Tell matplotlib that these are dates...

rule = rrulewrapper(MONTHLY, interval=1)
loc = RRuleLocator(rule)
formatter = DateFormatter("%b '%y")

labelsx = ax.get_xticklabels()
plt.setp(labelsx, rotation=30, fontsize=12)

# Format the legend

font = font_manager.FontProperties(size='small')

# Finish up

XKCD-style Plots in Matplotlib

Now incorporated directly into the latest version of matplotlib (v1.3) here is a great alternative that brings some style to your plotting routines. I haven’t tried it out on plots with a huge number of points but I imagine it should work just fine.  Below are some simple examples.  Simple as matplotlib.pyplot.xkcd()…

Pseudo-Random Sequence with XKCD:




Cheers Jake Vanderplas:

More Examples: