import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

%cd ~/minibook/chapter2/

!wget https://raw.githubusercontent.com/ipython-books/minibook-2nd-data/master/nyc_taxi.zip
!unzip nyc_taxi.zip

%ls data

data_filename = 'data/nyc_data.csv'
fare_filename = 'data/nyc_fare.csv'

data = pd.read_csv(data_filename, parse_dates=['pickup_datetime',
                                               'dropoff_datetime'])
fare = pd.read_csv(fare_filename, parse_dates=['pickup_datetime'])

data.head(3)

data.columns

p_lng = data.pickup_longitude
p_lat = data.pickup_latitude
d_lng = data.dropoff_longitude
d_lat = data.dropoff_latitude

p_lng

def lat_lng_to_pixels(lat, lng):
    lat_rad = lat * np.pi / 180.0
    lat_rad = np.log(np.tan((lat_rad + np.pi / 2.0) / 2.0))
    x = 100 * (lng + 180.0) / 360.0
    y = 100 * (lat_rad - np.pi) / (2.0 * np.pi)
    return (x, y)

px, py = lat_lng_to_pixels(p_lat, p_lng)

px

plt.scatter(px, py)

plt.figure(figsize=(8, 6))
plt.scatter(px, py, s=.1, alpha=.03)
plt.axis('equal')
plt.xlim(29.40, 29.55)
plt.ylim(-37.63, -37.54)
plt.axis('off')

px.count(), px.min(), px.max()

px.mean(), px.median(), px.std()

!conda install seaborn -q -y

import seaborn as sns
sns.__version__

data.trip_distance.hist(bins=np.linspace(0., 10., 100))