In [ ]:

    

date_start = date(2014,10,13)
date_end = date(2016,8,31)
dates = [date_start + timedelta(days=x) for x in range((date_end-date_start).days + 1)]

daily = []
for d in dates:
    temp = instanteanous_variation.loc[(instanteanous_variation.time.dt.date==d),['incrementation']].cumsum().values
    daily = np.append(daily,temp) 
instanteanous_variation['daily_variation'] = pd.Series(daily, index=instanteanous_variation.index)
display(instanteanous_variation.iloc[16:22,:])


    

In [ ]:

    

#Visualization of instanteanous_variation
week_start = date(2016,6,13)
week_end = date(2016,6,19)
week_to_visualize = [week_start + timedelta(days=x) for x in range((week_end-week_start).days + 1)]
#Compute Boolean Vector to select among our data
boolean_selection = False
for d in week_to_visualize:
    boolean_selection = boolean_selection | (instanteanous_variation.time.dt.date==d)

instanteanous_variation_1week = instanteanous_variation.loc[(boolean_selection),['time','daily_variation']]
instanteanous_variation_1week = instanteanous_variation_1week.set_index('time')

plt.figure(figsize=(30,10))
plt.plot_date(instanteanous_variation_1week.index, instanteanous_variation_1week.values)
plt.title('Instanteanous Daily Variation Over 1 Week', fontsize=20)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.ylabel('Instanteanous Daily Variation', fontsize=15)
plt.show()