How to plot stacked event duration (Gantt Charts) using Python Pandas?

Question

I have a Pandas DataFrame containing the date that a stream gage started measuring flow and the date that the station was decommissioned. I want to generate a plot showing

Answer 1

It's possible to do this with horizontal bars too: broken_barh(xranges, yrange, **kwargs)

Answer 2

I think you are trying to create a gantt plot. This suggests using hlines:

from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dt

df = pd.read_csv('data.csv')
df.amin = pd.to_datetime(df.amin).astype(datetime)
df.amax = pd.to_datetime(df.amax).astype(datetime)

fig = plt.figure()
ax = fig.add_subplot(111)
ax = ax.xaxis_date()
ax = plt.hlines(df.index, dt.date2num(df.amin), dt.date2num(df.amax))

Answer 3

While I do not know of any way to do this in MatplotLib, you may want to take a look at options with visualizing the data in the way you want by using D3, for example, with this library:

https://github.com/jiahuang/d3-timeline

If you must do it with Matplotlib, here is one way in which it has been done:

Matplotlib timelines

Answer 4

You can use Bokeh (a python library) to make gantt chart and its really beautiful. Here is a code I copied from a twiiter. http://nbviewer.jupyter.org/gist/quebbs/10416d9fb954020688f2

from bokeh.plotting import figure, show, output_notebook, output_file
from bokeh.models import ColumnDataSource, Range1d
from bokeh.models.tools import HoverTool
from datetime import datetime
from bokeh.charts import Bar
output_notebook()
#output_file('GanntChart.html') #use this to create a standalone html file to send to others
import pandas as ps

DF=ps.DataFrame(columns=['Item','Start','End','Color'])
Items=[
    ['Contract Review & Award','2015-7-22','2015-8-7','red'],
    ['Submit SOW','2015-8-10','2015-8-14','gray'],
    ['Initial Field Study','2015-8-17','2015-8-21','gray'],
    ['Topographic Procesing','2015-9-1','2016-6-1','gray'],
    ['Init. Hydrodynamic Modeling','2016-1-2','2016-3-15','gray'],
    ['Prepare Suitability Curves','2016-2-1','2016-3-1','gray'],
    ['Improvement Conceptual Designs','2016-5-1','2016-6-1','gray'],
    ['Retrieve Water Level Data','2016-8-15','2016-9-15','gray'],
    ['Finalize Hydrodynamic Models','2016-9-15','2016-10-15','gray'],
    ['Determine Passability','2016-9-15','2016-10-1','gray'],
    ['Finalize Improvement Concepts','2016-10-1','2016-10-31','gray'],
    ['Stakeholder Meeting','2016-10-20','2016-10-21','blue'],
    ['Completion of Project','2016-11-1','2016-11-30','red']
    ] #first items on bottom

for i,Dat in enumerate(Items[::-1]):
    DF.loc[i]=Dat

#convert strings to datetime fields:
DF['Start_dt']=ps.to_datetime(DF.Start)
DF['End_dt']=ps.to_datetime(DF.End)


G=figure(title='Project Schedule',x_axis_type='datetime',width=800,height=400,y_range=DF.Item.tolist(),
        x_range=Range1d(DF.Start_dt.min(),DF.End_dt.max()), tools='save')

hover=HoverTool(tooltips="Task: @Item<br>\
Start: @Start<br>\
End: @End")
G.add_tools(hover)

DF['ID']=DF.index+0.8
DF['ID1']=DF.index+1.2
CDS=ColumnDataSource(DF)
G.quad(left='Start_dt', right='End_dt', bottom='ID', top='ID1',source=CDS,color="Color")
#G.rect(,"Item",source=CDS)
show(G)