#!/usr/bin/env python
# coding: utf-8

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# install
# pip install seaborn


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import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

get_ipython().run_line_magic('matplotlib', 'inline')


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# setting sns default
sns.set()
sns.set_style('darkgrid')


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X = np.random.random_integers(10, 100, 15)
plt.plot(X)


# ** Getting Data and Preprocessing **

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names = [
       'mpg'
    ,  'cylinders'
    ,  'displacement'
    ,  'horsepower'
    ,  'weight'
    ,  'acceleration'
    ,  'model_year'
    ,  'origin'
    ,  'car_name'
]

# reading the file and assigning the header
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", sep='\s+', names=names)

df['maker'] = df.car_name.map(lambda x: x.split()[0])
df.origin = df.origin.map({1: 'America', 2: 'Europe', 3: 'Asia'})
df=df.applymap(lambda x: np.nan if x == '?' else x).dropna()
df['horsepower'] = df.horsepower.astype(float)
df.head()


# # factorplot and FacetGrid

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sns.factorplot(data=df, x="model_year", y="mpg")


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sns.factorplot(data=df, x="model_year", y="mpg", col="origin")


# http://tomaugspurger.github.io/modern-6-visualization.html  
# http://blog.insightdatalabs.com/advanced-functionality-in-seaborn/  
# https://github.com/InsightDataLabs/ipython-notebooks/blob/master/seaborn.ipynb  
# 

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