plotting multiple time series simultaneously in matplotlib

Question

I have data divided into different periods of time. The time-series is continuous for eg(one data set is for february then next on April and next in Jan 2016) But how can I reduce the gap of the year 2015 in between> It really just wastes the space and shrinks the graph

I can plot them together like the image below. Any way to remove the time-stamps in between?

Either you'll have to modify your x-values to remove the gaps, a adjust the x-ticks and labels, or you could create "gaps" in your x-axis as in stackoverflow.com/questions/32185411/… — Diziet Asahi
– Diziet Asahi, Commented Sep 26, 2017 at 12:17
As an alternative, you could generate a row of three subplots, one per continuous subrange (see matplotlib.org/examples/pylab_examples/subplots_demo.html for examples). — Hans
– Hans, Commented Sep 26, 2017 at 12:20
Is the date itself important? It's hard to tell from your question, but perhaps changing the x-axis to be "days from start of monitoring period" may be more appropriate. That would then allow all three of your series to be plotted along the same axis. Generally having a non-continuous date axis is very confusing to interpret and can be quite misleading. — Ken Syme
– Ken Syme, Commented Sep 26, 2017 at 16:02
@DavidG that was my next idea. But I wanted to see if it's possible without doing that — Yash
– Yash, Commented Sep 27, 2017 at 3:57

SiHa · Accepted Answer · 2017-09-27 07:40:45Z

I found a solution, which isn't exactly what I was looking for but fulfills the purpose. I modified the solution posted of this link:

fig=plt.figure()
fig,(ax,ax2,ax3) = plt.subplots(1,3,sharey=True)
fig.subplots_adjust(wspace=0.05)
fig.set_size_inches(20, 3, forward=True)

ax.plot(healthyde['timestamp'],healthyde['value'],label='healthy',color='g')
ax2.plot(healthynde['timestamp'],healthynde['value'],label='detection',color='y')
ax3.plot(healthylde['timestamp'],healthylde['value'],label='fault',color='r')

ax.set_xlim(healthyde['timestamp'].iloc[0], healthyde['timestamp'].iloc[-1] )
ax2.set_xlim(healthynde['timestamp'].iloc[0], healthynde['timestamp'].iloc[-1] )
ax3.set_xlim(healthylde['timestamp'].iloc[0], healthylde['timestamp'].iloc[-1] )

labels = ax.get_xticklabels()
for label in labels:
    label.set_rotation(30)

labels = ax2.get_xticklabels()
for label in labels:
    label.set_rotation(30)

labels = ax3.get_xticklabels()
for label in labels:
    label.set_rotation(30)

ax.legend()
ax2.legend()
ax3.legend()

plt.show()

Output: plot

Little Bobby Tables · Accepted Answer · 2017-09-27 09:42:39Z

0

If you are using a pandas DataFrame then this should work and it is a little tidier than the above answer:

fig = plt.figure()
fig, axes = plt.subplots(1, 3, sharey=True)

for (col, data), ax, c in zip(df.iteritems(), axes):
    data.dropna().plot(ax=ax, rot=30, legend=True)

edited Sep 27, 2017 at 9:42

answered Sep 27, 2017 at 7:39

Little Bobby Tables

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3 Comments

Yash Over a year ago

No, DateTime Index is important, I don't want to remove it. Having to keep date-time index is why the problem occurred in first place

Little Bobby Tables Over a year ago

Sorry, this wasn't clear. I your answer might be best but with some tidying. I will edit.

Little Bobby Tables Over a year ago

@Yash this should work and is a lot more readable and concise :)

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