In [1]:

    

%matplotlib inline


    

In [2]:

    

from bigbang.archive import Archive
import bigbang.parse as parse
import bigbang.graph as graph
import bigbang.mailman as mailman
import bigbang.process as process
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
from pprint import pprint as pp
import pytz
import numpy as np
import math
import nltk
from itertools import repeat
from nltk.stem.lancaster import LancasterStemmer
st = LancasterStemmer()
from nltk.corpus import stopwords
import re
import random


    

In [3]:

    

urls = ["http://lists.ncuc.org/pipermail/ncuc-discuss/",
        "https://mm.icann.org/pipermail/cc-humanrights/"]#,
        #"http://mail.scipy.org/pipermail/scipy-dev/",
        #"http://mail.scipy.org/pipermail/scipy-user/",
        #"http://mail.scipy.org/pipermail/numpy-discussion/"]


archives= [Archive(url,archive_dir="../archives") for url in urls]


    

    




/home/gogol/Data/bigbang/bigbang/archive.py:74: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  self.data.sort(columns='Date', inplace=True)

In [4]:

    

text = list(open("test123.csv")) # This file is the target words got from other notebook (Special words analysis).
#Example one is the 224 words shared by "http://mail.scipy.org/pipermail/ipython-dev/" and 
#"http://mail.scipy.org/pipermail/ipython-user/" which have potential of idea transferring between the two lists.


    

    




---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-4-3294dd53f696> in <module>()
----> 1 text = list(open("test123.csv")) # This file is the target words got from other notebook (Special words analysis).
      2 #Example one is the 224 words shared by "http://mail.scipy.org/pipermail/ipython-dev/" and
      3 #"http://mail.scipy.org/pipermail/ipython-user/" which have potential of idea transferring between the two lists.

IOError: [Errno 2] No such file or directory: 'test123.csv'

In [5]:

    

new_text=[]
for line in text:
    line = line.strip()
    contents = line.split(",")
    new_text.append(contents)


    

In [6]:

    

new_text1 = new_text[0]


    

In [14]:

    

random_label = random.sample(range(len(new_text1)), 10) #generate 5 random sample words


    

In [15]:

    

new_text2 = []
for i in range(len(new_text1)):
    if i in random_label:
        new_text2.append(new_text1[i])


    

In [16]:

    

new_text2


    

    
Out[16]:





['desktop',
 'alex',
 'drop',
 'wxpython',
 'zero',
 'yup',
 'ps',
 'keyword',
 'pol',
 'runtim']

In [10]:

    

"""df = [0] * len(new_text2)

for a in range(len(new_text2)):
    df[a] = pd.DataFrame(columns=["MessageId","Date","From","In-Reply-To","Count"])
    for row in archives[0].data.iterrows():
        w = row[1]["Body"].replace("'", "")
        k = re.sub(r'[^\w]', ' ', w)
        k = k.lower()
        t = nltk.tokenize.word_tokenize(k)
        subdict = {}
        count = 0
        for g in t:
            try:
                word = st.stem(g)
            except:
                print g
                pass
            if word == new_text2[a]:
                count += 1
        if count == 0:
            continue
        else:
            subdict["MessageId"] = row[0]
            subdict["Date"] = row[1]["Date"]
            subdict["From"] = row[1]["From"]
            subdict["In-Reply-To"] = row[1]["In-Reply-To"]
            subdict["Count"] = count
            df[a] = df[a].append(subdict,ignore_index=True)"""


    

In [17]:

    

df = [0] * len(new_text2)
for a in range(len(new_text2)):
    df[a] = pd.DataFrame(columns=["MessageId","Date","From","In-Reply-To","Count"])
for row in archives[0].data.iterrows():
    w = row[1]["Body"].replace("'", "")
    k = re.sub(r'[^\w]', ' ', w)
    k = k.lower()
    t = nltk.tokenize.word_tokenize(k)
    count = [0] * len(new_text2)
    for g in t:
        try:
            word = st.stem(g)
        except:
            print g
            pass
        for p in range(len(new_text2)):
            if word == new_text2[p]:
                count[p] += 1
    for m in range(len(new_text2)):
        subdict = {}
        if count[m] == 0:
            continue
        else:
            subdict["MessageId"] = row[0]
            subdict["Date"] = row[1]["Date"]
            subdict["From"] = row[1]["From"]
            subdict["In-Reply-To"] = row[1]["In-Reply-To"]
            subdict["Count"] = count[m]
            df[m] = df[m].append(subdict,ignore_index=True) 
"""    if count == 0:
        continue
    else:
        subdict["MessageId"] = row[0]
        subdict["Date"] = row[1]["Date"]
        subdict["From"] = row[1]["From"]
        subdict["In-Reply-To"] = row[1]["In-Reply-To"]
        subdict["Count"] = count
        df[a] = df[a].append(subdict,ignore_index=True)"""


    

    
Out[17]:





'    if count == 0:\n        continue\n    else:\n        subdict["MessageId"] = row[0]\n        subdict["Date"] = row[1]["Date"]\n        subdict["From"] = row[1]["From"]\n        subdict["In-Reply-To"] = row[1]["In-Reply-To"]\n        subdict["Count"] = count\n        df[a] = df[a].append(subdict,ignore_index=True)'

In [18]:

    

df1 = [0] * len(new_text2)
for a in range(len(new_text2)):
    df1[a] = pd.DataFrame(columns=["MessageId","Date","From","In-Reply-To","Count"])
for row in archives[1].data.iterrows():
    w = row[1]["Body"].replace("'", "")
    k = re.sub(r'[^\w]', ' ', w)
    k = k.lower()
    t = nltk.tokenize.word_tokenize(k)
    count = [0] * len(new_text2)
    for g in t:
        try:
            word = st.stem(g)
        except:
            print g
            pass
        for p in range(len(new_text2)):
            if word == new_text2[p]:
                count[p] += 1
    for m in range(len(new_text2)):
        subdict = {}
        if count[m] == 0:
            continue
        else:
            subdict["MessageId"] = row[0]
            subdict["Date"] = row[1]["Date"]
            subdict["From"] = row[1]["From"]
            subdict["In-Reply-To"] = row[1]["In-Reply-To"]
            subdict["Count"] = count[m]
            df1[m] = df1[m].append(subdict,ignore_index=True)


    

In [11]:

    

"""df1 = [0] * len(new_text2)

for a in range(len(new_text2)):
    df1[a] = pd.DataFrame(columns=["MessageId","Date","From","In-Reply-To","Count"])
    for row in archives[1].data.iterrows():
        w = row[1]["Body"].replace("'", "")
        k = re.sub(r'[^\w]', ' ', w)
        k = k.lower()
        t = nltk.tokenize.word_tokenize(k)
        subdict = {}
        count = 0
        for g in t:
            try:
                word = st.stem(g)
            except:
                print g
                pass
            if word == new_text2[a]:
                count += 1
        if count == 0:
            continue
        else:
            subdict["MessageId"] = row[0]
            subdict["Date"] = row[1]["Date"]
            subdict["From"] = row[1]["From"]
            subdict["In-Reply-To"] = row[1]["In-Reply-To"]
            subdict["Count"] = count
            df1[a] = df1[a].append(subdict,ignore_index=True)"""


    

In [63]:

    

#First mailing list
start_date = []
end_date = []
for k in range(len(new_text2)):
    start_date.append(df[k].iloc[0]["Date"])
    end_date.append(df[k].iloc[len(df[k].index)-1]["Date"])


    

In [65]:

    

start_date1 = min(start_date)
end_date1 = max(end_date)
total_month = (end_date1.year - start_date1.year)*12 + (end_date1.month-start_date1.month+1)


    

In [22]:

    

total_month


    

    
Out[22]:





138

In [23]:

    

k_time_dict = []
for k in range(len(new_text2)):
    time_dict = {}
    for row in df[k].iterrows():
        if (row[1]['Date'].year, row[1]['Date'].month) not in time_dict:
            time_dict[(row[1]['Date'].year, row[1]['Date'].month)] = row[1]["Count"]
        else:
            time_dict[(row[1]['Date'].year, row[1]['Date'].month)] += row[1]["Count"]
    k_time_dict.append(time_dict)


    

In [102]:

    

k_month = []
temp_year = start_date1.year
temp_month = start_date1.month
for i in range(total_month):
    k_month.append((temp_year, temp_month))
    if temp_month == 12:
        temp_year += 1
        temp_month = 0
    temp_month += 1


    

In [103]:

    

k_by_month = []
for k in range(len(new_text2)):
    by_month = []
    for item in k_month:
        if item in k_time_dict[k]:
            by_month.append(k_time_dict[k][item])
        else:
            by_month.append(0)
    k_by_month.append(by_month)


    

In [104]:

    

x = range(1,total_month+1)
y0 = k_by_month[0]
plt.plot(x, y0, color='blue')
y1 = k_by_month[1]
plt.plot(x, y1, color='red')
y2 = k_by_month[2]
plt.plot(x, y2, color='green')
y3 = k_by_month[3]
plt.plot(x, y3, color='yellow')
y4 = k_by_month[4]
plt.plot(x, y4, color='purple')
new_text2[:5]


    

    
Out[104]:





['desktop', 'alex', 'drop', 'wxpython', 'zero']






    

In [105]:

    

x = range(1,total_month+1)
y0 = k_by_month[5]
plt.plot(x, y0, color='blue')
y1 = k_by_month[6]
plt.plot(x, y1, color='red')
y2 = k_by_month[7]
plt.plot(x, y2, color='green')
y3 = k_by_month[8]
plt.plot(x, y3, color='yellow')
y4 = k_by_month[9]
plt.plot(x, y4, color='purple')
new_text2[5:]


    

    
Out[105]:





['yup', 'ps', 'keyword', 'pol', 'runtim']






    

In [ ]:

    

#Example as word "desktop"


    

In [106]:

    

normalized=[0] * len(new_text2)
for i in range(len(new_text2)):
    normalized[i] = np.asarray((k_by_month[i]))/float(sum(k_by_month[i]))


    

In [107]:

    

#define any month consists of over 25% of frequencies as spike.
for a in range(len(new_text2)):
    count = 0  #number of spikes
    inf = []
    for i in range(total_month):
        if normalized[a][i] >= 0.25:
            count += 1
            inf.append(k_month[i])
    if count > 0:
        print [new_text2[a],"Spike",count,inf]


    

    




['wxpython', 'Spike', 1, [(2004, 11)]]
['runtim', 'Spike', 1, [(2009, 7)]]

In [112]:

    

#Appear after 1/3 time span
#Die out before last 1/3 time span 
timespan = k_month[int(total_month/3)]
timespan1 = k_month[int(total_month*2/3)]


    

In [116]:

    

type(timespan)


    

    
Out[116]:





tuple

In [121]:

    

for a in range(len(new_text2)):
    if start_date[a].year > timespan[0]:
        print [new_text2[a],"LateFirstAppear"]
    elif start_date[a].year == timespan[0] and start_date[a].month >=  timespan[1]:
        print [new_text2[a],"LateFirstAppear"]
    if end_date[a].year <  timespan1[0]:
        print [new_text2[a],"EarlyLastAppear"]
    elif end_date[a].year == timespan1[0] and end_date[a].month <=  timespan1[1]:
        print [new_text2[a],"EarlyLastAppear"]


    

In [61]:

    

x = range(1,total_month+1)
y4 = k_by_month[4]
plt.plot(x, y4, color='purple')


    

    
Out[61]:





[<matplotlib.lines.Line2D at 0x10cd6ba90>]






    

In [65]:

    

x = range(55,65)
y4 = k_by_month[4]
plt.plot(x, y4[54:64], color='purple')


    

    
Out[65]:





[<matplotlib.lines.Line2D at 0x10fd46f10>]






    

In [96]:

    

k_time_dict


    

    
Out[96]:





[{(2004, 5): 2,
  (2004, 11): 8,
  (2006, 1): 10,
  (2006, 5): 8,
  (2007, 4): 19,
  (2007, 5): 3,
  (2007, 10): 8,
  (2007, 12): 2,
  (2008, 4): 1,
  (2008, 8): 7,
  (2008, 9): 11,
  (2008, 10): 2,
  (2009, 1): 13,
  (2009, 2): 2,
  (2009, 4): 12,
  (2009, 6): 2,
  (2009, 7): 11,
  (2009, 8): 4,
  (2009, 11): 57,
  (2010, 1): 46,
  (2010, 4): 1,
  (2010, 8): 9,
  (2010, 10): 2,
  (2011, 2): 3,
  (2011, 4): 1,
  (2011, 6): 2,
  (2011, 7): 5,
  (2011, 10): 2,
  (2011, 11): 1,
  (2012, 1): 1,
  (2012, 2): 3,
  (2012, 9): 4,
  (2012, 12): 4,
  (2013, 1): 1,
  (2013, 2): 7,
  (2013, 4): 5,
  (2013, 5): 3,
  (2013, 7): 2,
  (2013, 9): 7,
  (2013, 11): 1,
  (2013, 12): 2,
  (2014, 1): 3,
  (2014, 2): 9,
  (2014, 3): 5,
  (2014, 5): 6,
  (2014, 6): 1,
  (2014, 8): 12,
  (2014, 9): 16},
 {(2003, 4): 4,
  (2003, 5): 2,
  (2004, 5): 2,
  (2004, 11): 10,
  (2005, 1): 4,
  (2006, 1): 2,
  (2006, 2): 2,
  (2007, 3): 14,
  (2007, 6): 1,
  (2008, 3): 1,
  (2008, 6): 9,
  (2008, 7): 4,
  (2008, 8): 1,
  (2008, 9): 7,
  (2009, 9): 2,
  (2010, 11): 11,
  (2010, 12): 3,
  (2011, 2): 4,
  (2012, 1): 3,
  (2012, 6): 1,
  (2013, 2): 1,
  (2013, 3): 1,
  (2013, 10): 1,
  (2014, 3): 12,
  (2014, 5): 1},
 {(2003, 5): 2,
  (2003, 8): 2,
  (2003, 9): 2,
  (2004, 6): 4,
  (2004, 8): 2,
  (2004, 11): 5,
  (2005, 3): 2,
  (2005, 7): 2,
  (2006, 1): 42,
  (2006, 3): 8,
  (2006, 6): 2,
  (2006, 10): 2,
  (2006, 11): 3,
  (2007, 3): 2,
  (2007, 4): 3,
  (2007, 5): 1,
  (2007, 6): 3,
  (2007, 9): 9,
  (2007, 10): 5,
  (2008, 1): 15,
  (2008, 3): 2,
  (2008, 5): 1,
  (2008, 6): 6,
  (2008, 8): 12,
  (2008, 9): 3,
  (2008, 10): 3,
  (2008, 11): 2,
  (2008, 12): 3,
  (2009, 2): 6,
  (2009, 6): 4,
  (2009, 7): 13,
  (2009, 8): 18,
  (2009, 12): 1,
  (2010, 1): 5,
  (2010, 3): 7,
  (2010, 4): 4,
  (2010, 5): 2,
  (2010, 7): 4,
  (2010, 8): 1,
  (2010, 9): 8,
  (2010, 10): 14,
  (2011, 2): 9,
  (2011, 3): 3,
  (2011, 4): 1,
  (2011, 5): 8,
  (2011, 6): 6,
  (2011, 7): 5,
  (2011, 10): 3,
  (2011, 11): 1,
  (2012, 2): 3,
  (2012, 3): 2,
  (2012, 4): 2,
  (2012, 5): 2,
  (2012, 6): 11,
  (2012, 7): 1,
  (2012, 8): 5,
  (2012, 9): 7,
  (2012, 11): 6,
  (2012, 12): 8,
  (2013, 1): 5,
  (2013, 3): 2,
  (2013, 4): 5,
  (2013, 5): 5,
  (2013, 6): 10,
  (2013, 7): 5,
  (2013, 8): 18,
  (2013, 9): 1,
  (2013, 10): 11,
  (2013, 11): 4,
  (2013, 12): 9,
  (2014, 2): 14,
  (2014, 3): 19,
  (2014, 5): 1,
  (2014, 6): 6,
  (2014, 7): 2,
  (2014, 8): 18,
  (2014, 9): 5},
 {(2003, 4): 2,
  (2004, 7): 2,
  (2004, 8): 2,
  (2004, 9): 2,
  (2004, 11): 78,
  (2005, 5): 2,
  (2005, 6): 2,
  (2005, 7): 2,
  (2005, 12): 2,
  (2006, 10): 1,
  (2006, 11): 3,
  (2007, 2): 1,
  (2007, 3): 11,
  (2007, 4): 5,
  (2007, 5): 1,
  (2007, 12): 14,
  (2008, 4): 2,
  (2008, 9): 3,
  (2008, 10): 1,
  (2009, 2): 7,
  (2009, 3): 5,
  (2009, 4): 2,
  (2009, 7): 20,
  (2010, 2): 6,
  (2010, 3): 6,
  (2010, 4): 2,
  (2010, 5): 4,
  (2010, 6): 1,
  (2010, 7): 2,
  (2011, 2): 1,
  (2011, 5): 1,
  (2011, 7): 3,
  (2011, 11): 13,
  (2012, 1): 1,
  (2012, 4): 1,
  (2013, 12): 6,
  (2014, 9): 10},
 {(2003, 4): 2,
  (2003, 9): 6,
  (2003, 10): 2,
  (2003, 12): 2,
  (2004, 8): 4,
  (2004, 11): 4,
  (2005, 3): 2,
  (2005, 4): 6,
  (2006, 1): 2,
  (2006, 4): 6,
  (2006, 5): 2,
  (2006, 12): 1,
  (2007, 1): 2,
  (2007, 3): 2,
  (2007, 4): 1,
  (2007, 8): 4,
  (2007, 11): 2,
  (2008, 2): 2,
  (2008, 5): 1,
  (2008, 6): 7,
  (2008, 7): 16,
  (2008, 9): 3,
  (2009, 3): 2,
  (2009, 8): 3,
  (2009, 12): 1,
  (2010, 2): 8,
  (2010, 3): 3,
  (2010, 4): 3,
  (2010, 7): 2,
  (2010, 9): 2,
  (2010, 10): 9,
  (2010, 12): 2,
  (2011, 2): 3,
  (2011, 4): 5,
  (2011, 5): 4,
  (2011, 6): 8,
  (2011, 7): 3,
  (2011, 11): 2,
  (2012, 2): 5,
  (2012, 5): 5,
  (2012, 6): 10,
  (2012, 8): 5,
  (2012, 10): 1,
  (2012, 12): 10,
  (2013, 2): 6,
  (2013, 3): 6,
  (2013, 5): 5,
  (2013, 9): 2,
  (2013, 10): 1,
  (2014, 1): 1,
  (2014, 8): 5},
 {(2003, 12): 2,
  (2004, 6): 2,
  (2005, 4): 6,
  (2005, 6): 2,
  (2005, 7): 8,
  (2005, 8): 4,
  (2006, 1): 6,
  (2006, 2): 4,
  (2006, 11): 1,
  (2007, 1): 1,
  (2007, 3): 2,
  (2007, 4): 7,
  (2007, 5): 4,
  (2007, 7): 3,
  (2007, 8): 2,
  (2007, 9): 3,
  (2007, 10): 3,
  (2007, 11): 1,
  (2007, 12): 5,
  (2008, 1): 1,
  (2008, 3): 1,
  (2008, 5): 1,
  (2008, 6): 7,
  (2008, 7): 2,
  (2008, 8): 3,
  (2008, 9): 6,
  (2009, 2): 2,
  (2009, 3): 4,
  (2009, 4): 6,
  (2010, 1): 1,
  (2010, 3): 2,
  (2010, 5): 1,
  (2010, 8): 5,
  (2010, 9): 3,
  (2011, 2): 3,
  (2011, 3): 1,
  (2011, 4): 5,
  (2011, 5): 1,
  (2011, 6): 4,
  (2011, 9): 1,
  (2012, 1): 2,
  (2012, 2): 4,
  (2012, 4): 1,
  (2012, 6): 14,
  (2012, 8): 3,
  (2012, 9): 3,
  (2012, 10): 1,
  (2012, 11): 4,
  (2013, 1): 1,
  (2013, 2): 2,
  (2013, 4): 6,
  (2013, 5): 2,
  (2013, 7): 6,
  (2013, 9): 3,
  (2013, 12): 4,
  (2014, 1): 2,
  (2014, 2): 2,
  (2014, 3): 1,
  (2014, 5): 3,
  (2014, 7): 1,
  (2014, 9): 1},
 {(2003, 4): 2,
  (2003, 5): 6,
  (2003, 8): 2,
  (2003, 9): 2,
  (2003, 10): 4,
  (2003, 12): 2,
  (2004, 5): 2,
  (2004, 6): 8,
  (2004, 8): 28,
  (2004, 9): 2,
  (2004, 12): 2,
  (2005, 3): 8,
  (2005, 4): 12,
  (2005, 5): 12,
  (2005, 6): 2,
  (2005, 7): 4,
  (2005, 8): 2,
  (2005, 9): 6,
  (2005, 10): 4,
  (2005, 11): 2,
  (2006, 1): 6,
  (2006, 2): 4,
  (2007, 4): 2,
  (2007, 5): 1,
  (2007, 7): 1,
  (2007, 8): 1,
  (2007, 9): 1,
  (2007, 11): 1,
  (2007, 12): 1,
  (2008, 1): 1,
  (2008, 4): 1,
  (2008, 5): 8,
  (2008, 6): 4,
  (2008, 7): 3,
  (2008, 8): 2,
  (2008, 9): 4,
  (2009, 3): 1,
  (2009, 4): 3,
  (2009, 7): 2,
  (2009, 8): 6,
  (2009, 9): 3,
  (2009, 10): 1,
  (2010, 1): 2,
  (2010, 2): 2,
  (2010, 3): 5,
  (2010, 4): 2,
  (2010, 5): 1,
  (2010, 6): 2,
  (2010, 7): 4,
  (2010, 8): 10,
  (2010, 9): 5,
  (2010, 10): 6,
  (2010, 11): 2,
  (2011, 1): 6,
  (2011, 3): 1,
  (2011, 4): 2,
  (2011, 6): 2,
  (2011, 7): 5,
  (2011, 9): 3,
  (2011, 11): 5,
  (2011, 12): 1,
  (2012, 1): 2,
  (2012, 2): 1,
  (2012, 4): 5,
  (2012, 5): 4,
  (2012, 6): 2,
  (2012, 7): 8,
  (2012, 8): 2,
  (2012, 10): 1,
  (2012, 11): 4,
  (2012, 12): 2,
  (2013, 2): 2,
  (2013, 3): 2,
  (2013, 7): 9,
  (2013, 8): 4,
  (2013, 9): 1,
  (2013, 10): 11,
  (2013, 12): 2,
  (2014, 2): 3,
  (2014, 3): 2,
  (2014, 4): 1,
  (2014, 5): 4,
  (2014, 7): 3,
  (2014, 8): 11},
 {(2003, 5): 2,
  (2004, 6): 4,
  (2004, 11): 2,
  (2005, 1): 3,
  (2005, 3): 4,
  (2005, 4): 4,
  (2005, 9): 2,
  (2005, 12): 12,
  (2006, 1): 8,
  (2006, 10): 2,
  (2007, 2): 6,
  (2007, 3): 6,
  (2007, 5): 5,
  (2007, 9): 1,
  (2008, 2): 14,
  (2008, 5): 3,
  (2008, 6): 3,
  (2008, 7): 2,
  (2008, 9): 4,
  (2009, 2): 2,
  (2009, 9): 2,
  (2009, 12): 1,
  (2010, 5): 6,
  (2010, 7): 3,
  (2011, 4): 3,
  (2011, 6): 2,
  (2011, 10): 3,
  (2012, 2): 3,
  (2012, 5): 5,
  (2012, 6): 3,
  (2012, 7): 2,
  (2012, 9): 3,
  (2012, 11): 9,
  (2013, 4): 12,
  (2013, 5): 6,
  (2013, 6): 4,
  (2013, 9): 11,
  (2013, 12): 7,
  (2014, 1): 8,
  (2014, 2): 23,
  (2014, 5): 13},
 {(2003, 4): 2,
  (2004, 6): 2,
  (2004, 9): 8,
  (2006, 12): 5,
  (2007, 4): 7,
  (2007, 8): 1,
  (2007, 12): 1,
  (2008, 1): 8,
  (2008, 2): 6,
  (2008, 3): 19,
  (2008, 4): 2,
  (2008, 5): 2,
  (2008, 6): 2,
  (2008, 7): 1,
  (2008, 8): 2,
  (2008, 9): 4,
  (2008, 11): 2,
  (2009, 2): 4,
  (2009, 3): 2,
  (2009, 7): 29,
  (2010, 5): 3,
  (2010, 6): 5,
  (2010, 7): 17,
  (2010, 8): 17,
  (2010, 9): 13,
  (2010, 10): 8,
  (2011, 4): 1,
  (2011, 5): 3,
  (2011, 6): 1,
  (2012, 1): 8,
  (2012, 2): 6,
  (2012, 4): 4,
  (2012, 5): 1,
  (2012, 6): 6,
  (2012, 8): 2,
  (2012, 10): 7,
  (2012, 12): 3,
  (2013, 2): 2,
  (2013, 5): 7,
  (2013, 6): 4,
  (2013, 7): 12,
  (2013, 8): 10,
  (2013, 9): 13,
  (2013, 10): 3,
  (2014, 1): 2,
  (2014, 2): 6,
  (2014, 3): 8,
  (2014, 4): 9,
  (2014, 5): 3,
  (2014, 6): 5},
 {(2003, 5): 12,
  (2004, 6): 4,
  (2004, 7): 2,
  (2004, 8): 4,
  (2004, 11): 1,
  (2005, 3): 4,
  (2005, 4): 2,
  (2005, 5): 8,
  (2005, 7): 4,
  (2005, 12): 14,
  (2006, 5): 4,
  (2007, 5): 3,
  (2007, 8): 2,
  (2008, 2): 2,
  (2008, 3): 4,
  (2008, 4): 2,
  (2008, 5): 1,
  (2008, 8): 2,
  (2008, 10): 1,
  (2008, 12): 2,
  (2009, 6): 6,
  (2009, 7): 89,
  (2009, 8): 6,
  (2009, 9): 6,
  (2009, 10): 3,
  (2010, 7): 2,
  (2010, 10): 6,
  (2011, 4): 12,
  (2011, 5): 6,
  (2011, 6): 5,
  (2011, 7): 9,
  (2011, 11): 3,
  (2011, 12): 2,
  (2012, 2): 3,
  (2012, 6): 7,
  (2012, 9): 4,
  (2012, 10): 2,
  (2012, 11): 1,
  (2012, 12): 7,
  (2013, 1): 6,
  (2013, 2): 4,
  (2013, 7): 1,
  (2013, 8): 1,
  (2013, 9): 6,
  (2013, 12): 1,
  (2014, 1): 1,
  (2014, 3): 3,
  (2014, 4): 1,
  (2014, 5): 3,
  (2014, 7): 3,
  (2014, 8): 1,
  (2014, 9): 3}]

In [ ]: