import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline
data = pd.read_csv('data/nyc_data.csv', parse_dates=['pickup_datetime',
                                                     'dropoff_datetime'])
fare = pd.read_csv('data/nyc_fare.csv', parse_dates=['pickup_datetime'])

weekly = data.groupby(data.pickup_datetime.dt.weekofyear)

len(weekly)

y = weekly.size()
y.head(3)

x = weekly.pickup_datetime.first()
x.head(3)

pd.Series(y.values, index=x).plot()
plt.ylim(0)  # Set the lower y value to 0.
plt.xlabel('Week')  # Label of the x axis.
plt.ylabel('Taxi rides')  # Label of the y axis.

tip = fare[['medallion', 'tip_amount']] \
      .loc[fare.tip_amount>0].groupby('medallion').mean()
print(len(tip))
tip.head(3)

tip.hist(bins=np.linspace(0., 6., 100))
plt.xlabel('Average tip')
plt.ylabel('Number of taxis')

data_merged = pd.merge(data, tip, how='left', left_on='medallion', right_index=True)
data_merged.head(3)