Timeline bar graph using python and matplotlib

Question

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

data = [    (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

there's an issue with your code @ index[(data[i][3])]=[] – Yuca Jul 24 '18 at 18:50 — Yuca, Jul 24 '18 at 18:50

ImportanceOfBeingErnest · Accepted Answer · 2018-07-24T19:27:20.940

You may create a PolyCollection of "bars". For this you would need to convert your dates to numbers (matplotlib.dates.date2num).

import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.collections import PolyCollection

data = [    (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.

Haha, when I saw that a new answer was added, I immediately knew that it was you. — Moritz, Jul 24 '18 at 19:19
Thank you for the answer, that was an error by me in data that you pointed out at the end of your answer, thats the reason i added the expected output in case there was any mistake in my code or question — mrb28830, Jul 24 '18 at 19:30
so can we change the x-axis something like 1,2,3..24 hrs or maybe 2,4..24 like in the above example output and plot the graph against it so that it is not clumsy when there is more data throughout the day? — mrb28830, Jul 24 '18 at 19:44
Sure, you may use any Locator and Formatter you like. Possibly an `HourLocator` makes sense and a `DateFormatter("%H")`. — ImportanceOfBeingErnest, Jul 24 '18 at 19:48
Thanks for the code, I tried it but I don't understand what the v variable represents. Could you help me with this? As we want to draw 4 size polygon I'd expect 4 item in the list and not 5 — Simon, Jan 18 '22 at 13:38
Hello, I noticed in your graph that the X-axis tick label is 'DAY HOUR:MIN' and I have the same behaviour. Would it be possible to just have 'HOUR:MIN'? — Simon, Jan 20 '22 at 08:46

score 12 · Answer 2 · edited Dec 05 '19 at 11:30

My solution using Altair (example):

import altair as alt
import datetime as dt
import pandas as pd


alt.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']
#data

alt.Chart(data).mark_bar().encode(
    x='from',
    x2='to',
    y='activity',
    color=alt.Color('activity', scale=alt.Scale(scheme='dark2'))
)

Output:

Timeline bar graph using python and matplotlib

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