
import pandas as pd               #Python Data Analysis Library
import matplotlib.pyplot as plt   #Visualization Library
import numpy as np                #numPy is a general-purpose array-processing package
%matplotlib inline   

times = np.array([ 93.,  96.,  99., 102., 105., 108., 111., 114., 117.,
  120., 123., 126., 129., 132., 135., 138., 141., 144.,
  147., 150., 153., 156., 159., 162.])
temps = np.array([310.7, 308.0, 296.4, 289.5, 288.5, 287.1, 301.1, 308.3,
  311.5, 305.1, 295.6, 292.4, 290.4, 289.1, 299.4, 307.9,
  316.6, 293.9, 291.2, 289.8, 287.1, 285.8, 303.3, 310.])


# Create a figure
fig = plt.figure(figsize=(10, 6))
      
# Ask, out of a 1x1 grid, the first axes.
ax = fig.add_subplot(1, 1, 1)
      
# Plot times as x-variable and temperatures as y-variable
ax.plot(times, temps)


# Add some labels to the plot
ax.set_xlabel('Time')
ax.set_ylabel('Temperature')
    
# Prompt the notebook to re-display the figure after we modify it
fig