In [1]:

    

import pandas as pd
import numpy as np
import solution


    

In [2]:

    

df = solution.get_train_df()


    

In [3]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [4]:

    

df.sort_values('charge_time').plot(
    x='charge_time', y='charge_last', ylim=[0, 9])


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x10e4d7290>






    

In [5]:

    

predict = solution.predictor(df)
predicted = {v: predict(v) for v in np.arange(0.1, 10.0, 0.15)}

dfp = pd.DataFrame(predicted.items(), columns=['charge_time', 'charge_last'])


    

In [6]:

    

dfp.sort_values('charge_time').plot(
    x='charge_time', y='charge_last', ylim=[0, 9])


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x10e506610>