I am looking to draw a timeline bar graph using matplotlib that will show the things a person did in one day. I am adding the code below's output and an expected output that I am looking for. Any library can be used, in my case the closest I could get to was using matplotlib. Any help would be greatly appreciated.
import datetime as dtimport pandas as pdimport matplotlib.pyplot as pltimport numpy as npdata = [ (dt.datetime(2018, 7, 17, 0, 15), dt.datetime(2018, 7, 17, 0, 30), 'sleep'),(dt.datetime(2018, 7, 17, 0, 30), dt.datetime(2018, 7, 17, 0, 45), 'eat'),(dt.datetime(2018, 7, 17, 0, 45), dt.datetime(2018, 7, 17, 1, 0), 'work'),(dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 30), 'sleep'),(dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30), 'eat'), (dt.datetime(2018, 7, 17, 1, 30), dt.datetime(2018, 7, 17, 1, 45), 'work')]rng=[]for i in range(len(data)):rng.append((data[i][0]).strftime('%H:%M'))index={}activity = []for i in range(len(data)):index[(data[i][2])]=[]activity.append(data[i][2])for i in range(len(index)):for j in range(len(activity)):if activity[j]==index.keys()[i]:index[index.keys()[i]].append(15)else:index[index.keys()[i]].append(0) data = list(index.values())df = pd.DataFrame(data,index=list(index.keys()))df.plot.barh(stacked=True, sharex=False)plt.show()My Output:
Using matplotlib this is what I was getting
Expected Output:
I got this using google charts' Timeline graph but I need this using python and the data used for generating both graphs is not exactly the same, I hope you get the point
Best Answer
You may create a PolyCollection of "bars". For this you would need to convert your dates to numbers (matplotlib.dates.date2num).
import datetime as dtimport matplotlib.pyplot as pltimport matplotlib.dates as mdatesfrom matplotlib.collections import PolyCollectiondata = [ (dt.datetime(2018, 7, 17, 0, 15), dt.datetime(2018, 7, 17, 0, 30), 'sleep'),(dt.datetime(2018, 7, 17, 0, 30), dt.datetime(2018, 7, 17, 0, 45), 'eat'),(dt.datetime(2018, 7, 17, 0, 45), dt.datetime(2018, 7, 17, 1, 0), 'work'),(dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 30), 'sleep'),(dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30), 'eat'), (dt.datetime(2018, 7, 17, 1, 30), dt.datetime(2018, 7, 17, 1, 45), 'work')]cats = {"sleep" : 1, "eat" : 2, "work" : 3}colormapping = {"sleep" : "C0", "eat" : "C1", "work" : "C2"}verts = []colors = []for d in data:v = [(mdates.date2num(d[0]), cats[d[2]]-.4),(mdates.date2num(d[0]), cats[d[2]]+.4),(mdates.date2num(d[1]), cats[d[2]]+.4),(mdates.date2num(d[1]), cats[d[2]]-.4),(mdates.date2num(d[0]), cats[d[2]]-.4)]verts.append(v)colors.append(colormapping[d[2]])bars = PolyCollection(verts, facecolors=colors)fig, ax = plt.subplots()ax.add_collection(bars)ax.autoscale()loc = mdates.MinuteLocator(byminute=[0,15,30,45])ax.xaxis.set_major_locator(loc)ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))ax.set_yticks([1,2,3])ax.set_yticklabels(["sleep", "eat", "work"])plt.show()
Note that such plots can equally be generated with a Broken Bar plot (broken_barh), however, the (unsorted) data used here, make it a bit easier using a PolyCollection.
And now you would need to explain to me how you can sleep and eat at the same time - something I can never quite get at, as hard as I try.
My solution using Altair (example):
import altair as altimport datetime as dtimport pandas as pdalt.renderers.enable('jupyterlab')data = pd.DataFrame()data['from'] = [dt.datetime(2018, 7, 17, 0, 15),dt.datetime(2018, 7, 17, 0, 30),dt.datetime(2018, 7, 17, 0, 45), dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30)]data['to'] = [dt.datetime(2018, 7, 17, 0, 30),dt.datetime(2018, 7, 17, 0, 45),dt.datetime(2018, 7, 17, 1, 0), dt.datetime(2018, 7, 17, 1, 15), dt.datetime(2018, 7, 17, 1, 30), dt.datetime(2018, 7, 17, 1, 45)]data['activity'] = ['sleep','eat','work','sleep','eat','work']#dataalt.Chart(data).mark_bar().encode(x='from',x2='to',y='activity',color=alt.Color('activity', scale=alt.Scale(scheme='dark2')))Output:
