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

Question

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 ;-)

Answer 1

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.