How to plot a time range as a value from Pandas or MatPlotLib

matplotlib, pandas, python-2.7

Solution

import numpy
import pandas

# create an index containing all time stamps
idx1 = pandas.Index(sensor1.Time)
idx2 = pandas.Index(sensor2.Time)
idx3 = pandas.Index(sensor3.Time)
df = pandas.DataFrame(index=idx1.union(idx2).union(idx3))

# create a (constant) Series for each sensor
df['Sensor1'] = df.index.to_series().apply(lambda x: 3 if x >= sensor1.Time.min() and x <= sensor1.Time.max() else numpy.NaN)
df['Sensor2'] = df.index.to_series().apply(lambda x: 2 if x >= sensor2.Time.min() and x <= sensor2.Time.max() else numpy.NaN)
df['Sensor3'] = df.index.to_series().apply(lambda x: 1 if x >= sensor3.Time.min() and x <= sensor3.Time.max() else numpy.NaN)

# plot
p = df.plot(ylim=[0, 4], legend=False)
p.set_yticks([1., 2., 3.])
p.set_yticklabels(['Sensor3', 'Sensor2', 'Sensor1'])

By the way, are you sure you have year 2001 in your timestamps? This will make your Sensor1 plot be invisibly small.

Problem

I have several DataFrames containing time series data and would like to create a simple visualisation of the span of the time ranges for each of those DataFrames. Since I was unable to generate this with code I have included a sketch to illustrate my goal. Here is some code to create three DataFrames that are essentially simplified, smaller versions of the DataFrames I am working with. ``` from pandas import DataFrame from numpy import datetime64, random # example data recorded by two different sensors example_data = random.rand(5,2) example_data2 = random.rand(9,2) example_data3 = random.rand(9,2) # timestamps from sensor1 times = ['2000-01-01 09:00:00', '2000-01-01 09:15:00', '2000-01-01 09:30:00', '2000-01-01 09:45:00', '2000-01-01 10:00:00'] # timestamps from sensor2 times2 = ['2000-01-01 08:45:00', '2000-01-01 09:00:00', '2000-01-01 09:15:00', '2000-01-01 09:30:00', '2000-01-01 09:45:00', '2000-01-01 10:00:00', '2000-01-01 10:15:00', '2000-01-01 10:30:00', '2000-01-01 10:45:00'] # timestamps from sensor2 times3 = ['2000-01-01 09:20:00', '2000-01-01 09:40:00', '2000-01-01 10:00:00', '2000-01-01 10:20:00', '2000-01-01 10:40:00', '2000-01-01 11:00:00', '2000-01-01 11:20:00', '2000-01-01 11:40:00', '2000-01-01 12:00:00'] # create the DataFrame object for sensor1 with the times and data above sensor1 = DataFrame({'Time': times, 'measure1': example_data[:,0], 'measure2': example_data[:,1]}) # create the DataFrame object for sensor2 with the times and data above sensor2 = DataFrame({'Time': times2, 'measure1': example_data2[:,0], 'measure2': example_data2[:,1]}) # create the DataFrame object for sensor2 with the times and data above sensor3 = DataFrame({'Time': times3, 'measure1': example_data3[:,0], 'measure2': example_data3[:,1]}) # coerce the 'Time' column from string to a numpy datetime64 value sensor1['Time'] = sensor1['Time'].astype(datetime64) sensor2['Time'] = sensor2['Time'].astype(datetime64) sensor3['Time'] = sensor3['Time'].astype(datetime64) ``` I have tried taking the min and max datetime value from each of the DataFrames and putting them into a new DataFrame but when I try and plot them I get an error that there are no values to plot. I have also tried to taking just the 'Time' column, and assigning an Integer to a 'value' column (i.e. sensor 1 get's the Int 1 broadcast to the 'value' column, sensor2 get's the Int 2 broadcast and so on), then merging these DataFrames. But this results in lots of duplicate values in the 'Time' column and Nan values in the 'value' column. I have run out of ideas of how to get this to work. EDIT: Corrected a sneaky '2001' timestamp in the code block ;-)

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