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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <h1>Multiples Lines, Single Plot</h1>
Step1:  <h2>random low and high temperature data</h2>
Step2:  Next example from
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Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
Explanation: <h1>Multiples Lines, Single Plot</h1>
End of explanation
data_set_size = 15
low_mu, low_sigma = 50, 4.3
low_data_set = low_mu + low_sigma * np.random.randn(data_set_size)
high_mu, high_sigma = 57, 5.2
high_data_set = high_mu + high_sigma * np.random.randn(data_set_size)
days = list(range(1, data_set_size + 1))
plt.plot(days, low_data_set)
plt.show()
plt.plot(days, low_data_set,         
         days, high_data_set)
plt.show()
plt.plot(days, low_data_set,
         days, low_data_set, "vm",
         days, high_data_set, 
         days, high_data_set, "^k")
plt.show()
plt.plot( 
         days, high_data_set, "^k")
plt.show()
plt.plot(days, low_data_set,
         days, low_data_set, "vm",
         days, high_data_set, 
         days, high_data_set, "^k")
plt.xlabel('Day')
plt.ylabel('Temperature: degrees Farenheit')
plt.title('Randomized temperature data')
plt.show()
plt.plot(days, low_data_set,
         
         days, high_data_set
         )
plt.xlabel('Day')
plt.ylabel('Temperature: degrees Farenheit')
plt.title('Randomized temperature data')
plt.show()
plt.plot(
         days, high_data_set, "^k")
plt.xlabel('Day')
plt.ylabel('Temperature: degrees Farenheit')
plt.title('Randomized temperature data')
plt.show()
Explanation: <h2>random low and high temperature data</h2>
End of explanation
t1 = np.arange(0.0, 2.0, 0.1)
t2 = np.arange(0.0, 2.0, 0.01)
# note that plot returns a list of lines.  The "l1, = plot" usage
# extracts the first element of the list into l1 using tuple
# unpacking.  So l1 is a Line2D instance, not a sequence of lines
l1, = plt.plot(t2, np.exp(-t2))
l2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')
l4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')
plt.legend((l2, l4), ('oscillatory', 'damped'), loc='upper right', shadow=True)
plt.xlabel('time')
plt.ylabel('volts')
plt.title('Damped oscillation')
plt.show()
Explanation: Next example from: http://matplotlib.org/examples/pylab_examples/legend_demo2.html
End of explanation
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