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 > 1699]
records = records[records.pub_year < 1800]


    

In [3]:

    

len(records)


    

    
Out[3]:





100042

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
    
  
  

    

      
102
      
102
      
117
      
117
      
117
      
11003743
      
historia de las guerras de los judios y de la ...
      
NaN
      
josephus, flavius
      
en la oficina de don benito cano
      
madrid
      
1791
      
NaN
      
NaN
      
historia de las guerras de los judios y de la ...
      
historia de las guerras de los judios y de la ...
      
josephus, flavius
      
madrid,spain
      
madrid
      
spain
      
historia de las guerras de los judios y de la ...
    
  

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 0x7fcd7a798b70>






    

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 0x7fcd7e7fab00>






    

In [9]:

    

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


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fcde46a5390>






    

In [10]:

    

# records[records.pub_year < 1900].sort_values('pub_year').groupby('pub_year').count()['control_number'].plot()


    

In [11]:

    

# records[records.pub_year > 1900].sort_values('pub_year').groupby('pub_year').count()['control_number'].plot()


    

In [12]:

    

top_slugs = records.groupby('slug').count()['control_number'].sort_values(inplace=False, ascending=False).ix[:11].index
top_slugs


    

    
Out[12]:





Index(['madrid,spain', 'mexico,mexico', 'barcelona,spain', 'lisbon,portugal',
       's.l.', 'valencia,spain', 'sevilla,spain', 'lima,peru',
       'salamanca,spain', 'zaragoza,spain', 'puebla,mexico'],
      dtype='object', name='slug')

In [13]:

    

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


    

In [14]:

    

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


    

In [20]:

    

top_producer_df = pd.DataFrame({
#     'madrid,spain': group_top_producers.ix['madrid,spain'],
    'mexico,mexico': group_top_producers.ix['mexico,mexico'],
#     'barcelona,spain': group_top_producers.ix['barcelona,spain'],
#     'lisbon,portugal': group_top_producers.ix['lisbon,portugal'],
#     'valencia,spain': group_top_producers.ix['valencia,spain'], 
#     'sevilla,spain': group_top_producers.ix['sevilla,spain'],
    'lima,peru': group_top_producers.ix['lima,peru'],
#     'salamanca,spain': group_top_producers.ix['salamanca,spain'],
#     'zaragoza,spain': group_top_producers.ix['zaragoza,spain'],
    'puebla,mexico': group_top_producers.ix['puebla,mexico'],
       
}).fillna(0)


    

In [21]:

    

top_producer_df.plot()


    

    
Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fce728dc0f0>






    

In [17]:

    

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


    

In [18]:

    

top_producer_df_percent = pd.DataFrame({
    'madrid,spain': top_producer_df['madrid,spain'].divide(counted_by_year.values),
    'mexico,mexico': top_producer_df['mexico,mexico'].divide(counted_by_year.values),
    'barcelona,spain': top_producer_df['barcelona,spain'].divide(counted_by_year.values),
    'lisbon,portugal': top_producer_df['lisbon,portugal'].divide(counted_by_year.values),
    'valencia,spain': top_producer_df['valencia,spain'].divide(counted_by_year.values), 
    'sevilla,spain': top_producer_df['sevilla,spain'].divide(counted_by_year.values),
    'lima,peru': top_producer_df['lima,peru'].divide(counted_by_year.values),
    'salamanca,spain': top_producer_df['salamanca,spain'].divide(counted_by_year.values),
    'zaragoza,spain': top_producer_df['zaragoza,spain'].divide(counted_by_year.values),
    'puebla,mexico': top_producer_df['puebla,mexico'].divide(counted_by_year.values),
       
})


    

In [19]:

    

top_producer_df_percent.plot.area(stacked=False)


    

    
Out[19]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fce3d6ebba8>






    

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