In [1]:

    

import pandas as pd


    

In [2]:

    

%matplotlib inline


    

In [4]:

    

overall_drain = pd.Series([1.31,1.89,1.81,1.87,0.74,0.91,0.62,1.11,1.0])


    

In [5]:

    

overall_drain


    

    
Out[5]:





0    1.31
1    1.89
2    1.81
3    1.87
4    0.74
5    0.91
6    0.62
7    1.11
8    1.00
dtype: float64

In [6]:

    

overall_drain.describe()


    

    
Out[6]:





count    9.000000
mean     1.251111
std      0.495718
min      0.620000
25%      0.910000
50%      1.110000
75%      1.810000
max      1.890000
dtype: float64

In [9]:

    

overall_drain.plot(kind="bar", title = "Screen Off Discharge Rate (%/hr)")


    

    
Out[9]:





<matplotlib.axes.AxesSubplot at 0x109a82190>






    

In [13]:

    

cfc_drain = pd.DataFrame([{'drain': 4.07, 'hours': 6.0},
 {'drain': 1.86, 'hours': 5.0},
 {'drain': 3.44, 'hours': 6.0},
 {'drain': 8.14, 'hours': 13.0},
 {'drain': 4.36, 'hours': 7.0},
 {'drain': 8.03, 'hours': 13.0},
 {'drain': 11.03, 'hours': 19.0},
 {'drain': 4.67, 'hours': 8.0},
 {'drain': 3.52, 'hours': 5.0}])


    

In [14]:

    

cfc_drain.hourly = cfc_drain.drain/cfc_drain.hours


    

In [18]:

    

cfc_drain.hourly


    

    
Out[18]:





0    0.678333
1    0.372000
2    0.573333
3    0.626154
4    0.622857
5    0.617692
6    0.580526
7    0.583750
8    0.704000
dtype: float64

In [19]:

    

cfc_drain.hourly.plot(kind="bar", title = "cfctracker discharge rate (%/hr)")


    

    
Out[19]:





<matplotlib.axes.AxesSubplot at 0x109c794d0>






    

In [ ]: