In [1]:

    

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


    

In [2]:

    

records = pd.read_csv('../data/fucking_final_dataset.csv')
records = records[records.pub_year > 1899]
records = records[records.pub_year < 2000]


    

In [3]:

    

len(records)


    

    
Out[3]:





5443655

In [4]:

    

records.head(1)


    

    
Out[4]:






  

    

      

      
Unnamed: 0
      
Unnamed: 0.1
      
Unnamed: 0.1
      
Unnamed: 0.1.1
      
control_number
      
title
      
uniform_title
      
author
      
publisher
      
pub_location
      
pub_year
      
translation
      
prev_language
      
title_auth_slug
      
canonical_title
      
canonical_author
      
slug
      
canonical_city
      
canonical_country
      
full_text_slug
    
  
  

    

      
0
      
0
      
0
      
0
      
0
      
1000686
      
chao
      
NaN
      
sauvajon, marc-gilbert
      
escelicer
      
madrid
      
1972
      
NaN
      
fre
      
chao|sauvajon, marc-gilbert
      
chao
      
sauvajon, marc-gilbert
      
madrid,spain
      
madrid
      
spain
      
chao,sauvajon, marc-gilbert,madrid,1972,escelicer
    
  

In [5]:

    

plt.rcParams['figure.figsize'] = (12.0, 6.0)


    

In [6]:

    

records.groupby('canonical_country').count()['control_number'].sort_values(inplace=False, ascending=False).ix[:10].plot(kind="bar")


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8e7a9614e0>






    

In [7]:

    

records.groupby('slug').count()['control_number'].sort_values(inplace=False, ascending=False).ix[:25].plot(kind="bar")


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fdf6605ab38>






    

In [8]:

    

records.sort_values('pub_year').groupby('pub_year').count()['control_number'].plot()


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fdf1823a710>






    

In [9]:

    

top_slugs = records.groupby('slug').count()['control_number'].sort_values(inplace=False, ascending=False).ix[:10].index
top_slugs


    

    
Out[9]:





Index(['madrid,spain', 'barcelona,spain', 'mexico,mexico',
       'buenos aires,argentina', 'santiago,chile', 'rio de janeiro,brazil',
       'sao paulo,brazil', 'bogota,colombia', 'lisbon,portugal',
       'caracas,venezuela'],
      dtype='object', name='slug')

In [10]:

    

top_producers = records[records.slug.isin(top_slugs)]


    

In [11]:

    

group_top_producers = top_producers.sort_values('pub_year').groupby(['slug', 'pub_year']).count()['control_number']


    

In [12]:

    

top_producer_df = pd.DataFrame({
    'madrid,spain': group_top_producers.ix['madrid,spain'],
    'barcelona,spain': group_top_producers.ix['barcelona,spain'],
    'mexico,mexico': group_top_producers.ix['mexico,mexico'],
    'buenos aires,argentina': group_top_producers.ix['buenos aires,argentina'],
    'santiago,chile': group_top_producers.ix['santiago,chile'],
    'rio de janeiro,brazil': group_top_producers.ix['rio de janeiro,brazil'],
    'sao paulo,brazil': group_top_producers.ix['sao paulo,brazil'],
    'bogota,colombia': group_top_producers.ix['bogota,colombia'],
    'lisbon,portugal': group_top_producers.ix['lisbon,portugal'],
    'caracas,venezuela': group_top_producers.ix['caracas,venezuela'],
})


    

In [13]:

    

top_producer_df.plot()


    

    
Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fdf6139b240>






    

In [14]:

    

counted_by_year = records.sort_values('pub_year').groupby('pub_year').count()['control_number']


    

In [15]:

    

top_producer_df_percent = pd.DataFrame({
    'madrid,spain': top_producer_df['madrid,spain'].divide(counted_by_year.values),
    'barcelona,spain': top_producer_df['barcelona,spain'].divide(counted_by_year.values),
    'mexico,mexico': top_producer_df['mexico,mexico'].divide(counted_by_year.values),
    'buenos aires,argentina': top_producer_df['buenos aires,argentina'].divide(counted_by_year.values),
    'santiago,chile': top_producer_df['santiago,chile'].divide(counted_by_year.values),
#     'rio de janeiro,brazil': top_producer_df['rio de janeiro,brazil'].divide(counted_by_year.values),
#     'sao paulo,brazil': top_producer_df['sao paulo,brazil'].divide(counted_by_year.values),
#     'bogota,colombia': top_producer_df['bogota,colombia'].divide(counted_by_year.values),
#     'lisbon,portugal': top_producer_df['lisbon,portugal'].divide(counted_by_year.values),
#     'caracas,venezuela': top_producer_df['caracas,venezuela'].divide(counted_by_year.values),
})


    

In [16]:

    

top_producer_df_percent.plot.area(stacked=False)


    

    
Out[16]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fdf212dcba8>






    

In [ ]: