#!/usr/bin/env python2

# -*- coding: utf-8 -*-

"""

Created on Wed Aug 16 12:13:11 2017

@author: leo

This script is the implementation of chapter 7 "Driving Visual Analyses with Automobile Data"

of "Practical Data Science Cookbook" by Tony Ojeda, etc.

"""

#%% Section Analyzing automobile fuel efficiency over time with Python

# -------------------------------------------

import pandas as pd

import numpy as np

vehicles = pd.read_csv("vehicles.csv")

column_names = vehicles.columns.values

column_names[[22, 23, 70, 71, 72, 73]]

len(vehicles)

len(vehicles.columns)

vehicles.columns

len(pd.unique(vehicles.year))

min(vehicles.year)

max(vehicles.year)

pd.value_counts(vehicles.fuelType1)

pd.value_counts(vehicles.trany)

vehicles["trany2"] = vehicles.trany.str[0]

pd.value_counts(vehicles.trany2)

#%% step 1 ~ 4 on Page 202

from ggplot import ggplot, aes, geom_point, xlab, ylab, ggtitle

grouped = vehicles.groupby("year")

averaged = grouped['comb08', 'highway08', 'city08'].agg([np.mean])

averaged.columns = ['comb08_mean', 'highway08_mean', 'city08_mean']

averaged['year'] = averaged.index

print(ggplot(averaged, aes('year', 'comb08_mean')) +

geom_point(color='steelblue') +

xlab('Year') +

ylab('Average MPG') +

ggtitle('All cars'))

#%% step 5

criteria1 = vehicles.fuelType1.isin(['Regular Gasoline', 'Prenium Gasoline', 'Midgrade Gasoline'])

criteria2 = vehicles.fuelType2.isnull()

criteria3 = vehicles.atvType != 'Hybrid'

vehicles_non_hybrid = vehicles[criteria1 & criteria2 & criteria3]

len(vehicles_non_hybrid)

#%% step 6

grouped = vehicles_non_hybrid.groupby(['year'])

averaged = grouped['comb08'].agg([np.mean])

print(averaged)

#%% step 7 ~ 9

pd.unique(vehicles_non_hybrid.displ)

criteria = vehicles_non_hybrid.displ.notnull()

vehicles_non_hybrid = vehicles_non_hybrid[criteria]

vehicles_non_hybrid.displ = vehicles_non_hybrid.displ.astype('float')

criteria = vehicles_non_hybrid.comb08.notnull()

vehicles_non_hybrid = vehicles_non_hybrid[criteria]

vehicles_non_hybrid.comb08 = vehicles_non_hybrid.comb08.astype('float')

print(ggplot(vehicles_non_hybrid, aes('displ', 'comb08')) +

geom_point(color='steelblue') +

xlab('Engine Displacement') +

ylab('Average MPG') +

ggtitle('Gasoline cars'))

#%% step 10

grouped_by_year = vehicles_non_hybrid.groupby(['year'])

avg_grouped_by_year = grouped_by_year['displ', 'comb08'].agg([np.mean])

#%% step 11

avg_grouped_by_year['year'] = avg_grouped_by_year.index

melted_avg_grouped_by_year = pd.melt(avg_grouped_by_year, id_vars='year')

from ggplot import facet_wrap

p = ggplot(aes(x='year', y='value', color='variable_0'), data=melted_avg_grouped_by_year)

p + geom_point() + facet_wrap('variable_0')

#%% Section Investigating the makes and models of automobiles with Python

# ------ step 1, 2 ------------------

pd.unique(vehicles_non_hybrid.cylinders)

vehicles_non_hybrid.cylinders = vehicles_non_hybrid.cylinders.astype('float')

pd.unique(vehicles_non_hybrid.cylinders)

vehicles_non_hybrid_4 = vehicles_non_hybrid[(vehicles_non_hybrid.cylinders == 4.0)]

#%% step 3

import matplotlib.pyplot as plt

%matplotlib inline

grouped_by_year_4_cylinder = vehicles_non_hybrid_4.groupby(['year']).make.nunique()

fig = grouped_by_year_4_cylinder.plot()

fig.set_xlabel('Year')

fig.set_ylabel('Number of 4-Cylinder Makes')

print(fig)

#%% step 4

grouped_by_year_4_cylinder = vehicles_non_hybrid_4.groupby(['year'])

unique_makes = []

for name, group in grouped_by_year_4_cylinder:

unique_makes.append(set(pd.unique(group['make'])))

unique_makes = reduce(set.intersection, unique_makes)

print(unique_makes)

#%% step 5, 6, 7

boolean_mask = []

for index, row in vehicles_non_hybrid_4.iterrows():

make = row['make']

boolean_mask.append(make in unique_makes)

df_common_makes = vehicles_non_hybrid_4[boolean_mask]

df_common_makes_grouped = df_common_makes.groupby(['year', 'make']).agg(np.mean).reset_index()

from ggplot import geom_line

ggplot(aes(x='year', y='comb08'), data = df_common_makes_grouped) + geom_line() + facet_wrap('make')