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 > 1599]
records = records[records.pub_year < 1700]


    

In [3]:

    

len(records)


    

    
Out[3]:





56461

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
    
  
  

    

      
169
      
169
      
190
      
190
      
190
      
11508275
      
viage de la serenissima reyna dona maria ana d...
      
NaN
      
mascarenhas, jeronymo
      
por d. diaz de la carrera
      
madrid
      
1650
      
NaN
      
NaN
      
viage de la serenissima reyna dona maria ana d...
      
viage de la serenissima reyna dona maria ana d...
      
mascarenhas, jeronymo
      
madrid,spain
      
madrid
      
spain
      
viage de la serenissima reyna dona maria ana d...
    
  

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 0x7feb50aca588>






    

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 0x7feb5b989978>






    

In [8]:

    

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


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0x7feb50acd518>






    

In [9]:

    

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


    

In [10]:

    

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


    

In [19]:

    

top_slugs = records.groupby('slug').count()['control_number'].sort_values(inplace=False, ascending=False).ix[:12].index


    

In [20]:

    

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


    

In [21]:

    

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


    

In [28]:

    

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


    

In [29]:

    

top_producer_df.plot()


    

    
Out[29]:





<matplotlib.axes._subplots.AxesSubplot at 0x7febc45254e0>






    

In [25]:

    

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


    

In [26]:

    

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),
    'lisbon,portugal': top_producer_df['lisbon,portugal'].divide(counted_by_year.values),
    'sevilla,spain': top_producer_df['sevilla,spain'].divide(counted_by_year.values),
    'zaragoza,spain': top_producer_df['zaragoza,spain'].divide(counted_by_year.values),
#     'valencia,spain': top_producer_df['valencia,spain'].divide(counted_by_year.values),
#     'mexico,mexico': top_producer_df['mexico,mexico'].divide(counted_by_year.values),
#     'valladolid,spain': top_producer_df['valladolid,spain'].divide(counted_by_year.values),
#     'salamanca,spain': top_producer_df['salamanca,spain'].divide(counted_by_year.values),
#     'granada,spain': top_producer_df['granada,spain'].divide(counted_by_year.values),    
})


    

In [27]:

    

top_producer_df_percent.plot.area(stacked=False)


    

    
Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0x7febe3d09470>






    

In [41]:

    

america = records[records.slug.isin(['lima,peru', 'mexico,mexico'])].\
sort_values('pub_year').groupby(['slug', 'pub_year']).count()['control_number']


    

In [45]:

    

pd.DataFrame({'lima': america.ix['lima,peru'], 'mexico': america.ix['mexico,mexico']}).fillna(0).plot()


    

    
Out[45]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fec3c851d68>






    

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