google_searches_4.py

d = {"search": searches,
    "time": dates}
googled = pd.DataFrame(d)

dt = datetime.datetime(2014, 10, 1)
end = datetime.datetime(2017, 3, 5)
step = datetime.timedelta(days=7)

weekly = []

while dt < end:
    weekly.append(dt.strftime('%Y-%m-%d %H:%M:%S'))
    dt += step

# finding/smoothing/normalizing weekly data only shown for 'trump' but same process applies to the other terms
trump_weeks = []
for i in range(len(weekly)-1):
    trump_weeks.append(sum((googled['time'] > weekly[i]) & 
              (googled['time'] < weekly[i+1]) &
              (googled['search'].str.contains('trump'))))

term = len(trump_weeks)-1
trump_weeks_smooth = [(trump_weeks[i] + trump_weeks[i-1] +trump_weeks[i+1])/3 for i in range(1, term)]
trump_weeks_smooth_norm = [i/float(max(trump_weeks_smooth)) for i in trump_weeks_smooth]

plt.plot(range(term-1), trump_weeks_smooth_norm, label='trump', linewidth=5.0)
plt.plot(range(term-1), warriors_weeks_smooth_norm, label='warriors', linewidth=5.0)
plt.plot(range(term-1), ibm_weeks_smooth_norm, label='ibm', linewidth=5.0)
plt.plot(range(term-1), python_weeks_smooth_norm, label='python', linewidth=5.0)
plt.xticks([30,60,90,120], ['May 2015','November 2015', 'June 2016', 'January 2017'], fontsize=15)
plt.legend()

plt.gcf().set_size_inches(18.5, 10.5, forward=True)
plt.show()