Analyzing VC funding using Cruchbase API¶

by André Ríos (datablast.wordpress.com)¶

Starting MongoDB service over Ubuntu 12.04.4 LTS

sudo mongod --dbpath /var/lib/mongodb --journal

In [170]:

import matplotlib
import json
import requests
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import pymongo
import networkx as nx
import matplotlib.pyplot as plt
from IPython.display import display
import collections
from IPython.display import HTML

Connecting MongoDB collection called 'cities'

c_barcelona(companies)
f_barcelona(funding)
i_barcelona(investors)

In [171]:

conn = pymongo.Connection("localhost", 27017)
db = conn.cities
c_barcelona = db.c_barcelona
f_barcelona = db.f_barcelona
i_barcelona = db.i_barcelona

Companies in Barcelona: Create collection c_barcelona¶

Searching companies at Barcelona and save on BBDD

location_uuid for Barcelona: eead2c0cb178ad334e6d6c813c955e99

Other Cities:

London: aad17950576b8c448fd4f44dbeb59220

Paris: 67af74be0bfa027795c13cb5853c8bcc

Rome: c5a012b1f2f9a3b5c66aee6be778eda2

Berlin: e61662e5c5c2c4d92a31d0e6d6453375

cs=requests.get('http://api.crunchbase.com/v/2/organizations?organization_types=company&location_uuids=eead2c0cb178ad334e6d6c813c955e99&user_key=< enter your key >&page=1').json()

np=cs['data']['paging']['number_of_pages']

ht = 'http://api.crunchbase.com/v/2/organizations?organization_types=company&location_uuids=eead2c0cb178ad334e6d6c813c955e99&user_key=< enter your key >&page='

lc=[]

for i in range(np):

s=str(i+1)

ht_i = ht + s

cs = requests.get(ht_i).json()

ni=len(cs['data']['items'])

for j in range(ni):

lc.append(cs['data']['items'][j]['path'])

List of organization in the city: organization/< name of organization>

url_list=[]

url_1='http://api.crunchbase.com/v/2/'

for i in range(len(lc)):

rl_list.append(url_1 + str(lc[i]) + '?user_key=< your key >')

Save json file in MongoDB: Companies in Barcelona

for i in range(len(url_list)):

rs = requests.get(url_list[i]).json()

c_barcelona.insert(rs)

Erase some element with errors.

db.c_barcelona.remove({'data': {u'response': False, u'error': {u'message': u'Node 4035881 not found', u'code': 500}}})

In [172]:

n=c_barcelona.count()
df1 = DataFrame(columns=['uuid','name','role'], index=list(range(0,n)))
for i in range(n):
 df1.loc[i] = Series({'uuid': c_barcelona.find()[i]['data']['uuid'],'name':c_barcelona.find()[i]['data']['properties']['permalink']
 ,'role':c_barcelona.find()[i]['data']['properties']['primary_role']})
 
df1=df1.drop_duplicates()

In [119]:

df1.head()

Out[119]:

	uuid	name	role
0	d0647cecacb39a23bc4fd3fad90ecb4e	strands	company
1	a931b34d31f31d171f3d359875fc02c2	gsi-commerce	company
2	31af5340f371d4f618156ca86a3a28fd	akamon-entertainment	company
3	476cdc24e4c314c49b9297032674e2da	veeva	company
4	78c70050042ba916b8121ab481207ea0	3scale	company

In [96]:

dfx=df1[['name','role']]
h = HTML(dfx[0:7].to_html(index=False))
my_file = open('role_company.html', 'w')
my_file.write(h.data)
my_file.close()

In [173]:

n=c_barcelona.count()
f=0
df2 = DataFrame(columns=['uuid','name','category'], index=list(range(0,10000)))
for i in range(n):
 if (c_barcelona.find()[i]['data']['relationships']).get('categories','empty') != 'empty':
 ni=len(c_barcelona.find()[i]['data']['relationships']['categories']['items'])
 for j in range(ni):
 df2.loc[f]= Series({'uuid':c_barcelona.find()[i]['data']['uuid'],'name':c_barcelona.find()[i]['data']['properties']['permalink'],
 'category':c_barcelona.find()[i]['data']['relationships']['categories']['items'][j]['name']}) 
 f=f+1
 
df2=df2.drop_duplicates()
df2=df2.dropna(thresh=1)

In [121]:

df2.head(n=10)

Out[121]:

	uuid	name	category
0	d0647cecacb39a23bc4fd3fad90ecb4e	strands	Artificial Intelligence
1	d0647cecacb39a23bc4fd3fad90ecb4e	strands	Reviews and Recommendations
2	d0647cecacb39a23bc4fd3fad90ecb4e	strands	Software
3	d0647cecacb39a23bc4fd3fad90ecb4e	strands	Personal Finance
4	d0647cecacb39a23bc4fd3fad90ecb4e	strands	E-Commerce
5	d0647cecacb39a23bc4fd3fad90ecb4e	strands	Personalization
6	a931b34d31f31d171f3d359875fc02c2	gsi-commerce	Enterprise Software
7	31af5340f371d4f618156ca86a3a28fd	akamon-entertainment	Games
8	476cdc24e4c314c49b9297032674e2da	veeva	Content
9	476cdc24e4c314c49b9297032674e2da	veeva	Software

In [78]:

dfx=df2[['name','category']]
h = HTML(dfx[0:7].to_html(index=False))
my_file = open('cat_company.html', 'w')
my_file.write(h.data)
my_file.close()

In [174]:

f=0
df3 = DataFrame(columns=['uuid','name','path'], index=list(range(0,10000)))
for i in range(n):
 if (c_barcelona.find()[i]['data']['relationships']).get('funding_rounds','empty') != 'empty':
 ni=len(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'])
 for j in range(ni):
 df3.loc[f]= Series({'uuid':c_barcelona.find()[i]['data']['uuid'],
 'name':c_barcelona.find()[i]['data']['properties']['permalink'],
 'path':c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'][j]['path']}) 
 f=f+1
 
df3['uuid_fund'] = df3['path'].map(lambda x: str(x).replace("funding-round/",""))
df3=df3.drop_duplicates()

In [123]:

df3.head()

Out[123]:

	uuid	name	path	uuid_fund
0	d0647cecacb39a23bc4fd3fad90ecb4e	strands	funding-round/eb5cbd96fd6c6176199f993a589a4da2	eb5cbd96fd6c6176199f993a589a4da2
1	d0647cecacb39a23bc4fd3fad90ecb4e	strands	funding-round/9f1a9a6a5326712b265aa70e78a06aa9	9f1a9a6a5326712b265aa70e78a06aa9
2	d0647cecacb39a23bc4fd3fad90ecb4e	strands	funding-round/eef833bbeabcfeb4c866074eda879500	eef833bbeabcfeb4c866074eda879500
3	31af5340f371d4f618156ca86a3a28fd	akamon-entertainment	funding-round/639a184018b1b35cad644c3d11e086c2	639a184018b1b35cad644c3d11e086c2
4	31af5340f371d4f618156ca86a3a28fd	akamon-entertainment	funding-round/32705565ab649c7d969fd42f1d63e3d2	32705565ab649c7d969fd42f1d63e3d2

Funding links: Create collection f_barcelona¶

n=c_barcelona.count()

pl=[]

for i in range(n):

if (c_barcelona.find()[i]['data']['relationships']).get('funding_rounds','empty') != 'empty':

ni=len(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'])

for j in range(ni):

pl.append(c_barcelona.find()[i]['data']['relationships']['funding_rounds']['items'][j]['path'])

url_list=[]

url_1='http://api.crunchbase.com/v/2/'

for i in range(len(pl)):

url_list.append(url_1 + str(pl[i]) + '?user_key=< your key>')

for i in range(len(url_list)):

try:

rs = requests.get(url_list[i]).json()

except:

print 'Error'

f_barcelona.insert(rs)

Erase some elements with errors.

db.f_barcelona.remove({'data': {u'response': False, u'error': {u'message': u'wrong argument type nil (expected String)', u'code': 500}}})

In [175]:

df4 = DataFrame(columns=['uuid_fund','type','money','currency','year','path_inv'], index=list(range(0,10000)))
nf=f_barcelona.count()
f=0
for i in range(nf):
 if (f_barcelona.find()[i]['data']['relationships']).get('investments','empty') != 'empty':
 ni=len(f_barcelona.find()[i]['data']['relationships']['investments']['items'])
 a1=f_barcelona.find()[i]['data']['uuid']
 a2=f_barcelona.find()[i]['data']['properties']['funding_type']
 if (f_barcelona.find()[i]['data']['properties']).get('money_raised','empty') != 'empty':
 a3=f_barcelona.find()[i]['data']['properties']['money_raised']
 else:
 a3=0
 a4=f_barcelona.find()[i]['data']['properties']['money_raised_currency_code']
 a5=f_barcelona.find()[i]['data']['properties']['announced_on_year']
 for j in range(ni):
 try:
 i
 df4.loc[f]= Series({'uuid_fund':a1,'type':a2,'money':a3/ni,'currency':a4,'year':a5,
 'path_inv':f_barcelona.find()[i]['data']['relationships']['investments']['items'][j]['investor']['path']})
 f=f+1
 except:
 print 'Companies with errors'

#Total funding in each round is divided by the number of the invertors. 
df4['name'] = df4['path_inv'].map(lambda x: str(x).replace("organization/",""))
df4['name'] = df4['name'].map(lambda x: str(x).replace("person/",""))
df4=df4.drop_duplicates()
 

Companies with errors
Companies with errors
Companies with errors
Companies with errors
Companies with errors
Companies with errors
Companies with errors
Companies with errors

Investor Links: Create collection i_barcelona¶

li=[]

nf=f_barcelona.count()

for i in range(nf):

if (f_barcelona.find()[i]['data']['relationships']).get('investments','empty') != 'empty':

ni=len(f_barcelona.find()[i]['data']['relationships']['investments']['items'])

for j in range(ni):

try:

li.append(f_barcelona.find()[i]['data']['relationships']['investments']['items'][j]['investor']['path'])

except:

print 'Error'

Remove duplicate elements

a=set(li)

Searching investor uuids

url_list=[]

url_1='http://api.crunchbase.com/v/2/'

for i in range(len(list(set(li)))):

url_list.append(url_1 + str(b[i]) + '?user_key=< your key>')

nl=len(url_list)

for i in range(nl):

try:

rs = requests.get(url_list[i]).json()

except:

print 'Error'

i_barcelona.insert(rs)

In [125]:

df4.head()

Out[125]:

	uuid_fund	type	money	currency	year	path_inv	name
0	421e422d65b93b54d59ba19a465292d9	venture	1250000	EUR	2006	organization/active-venture-partners	active-venture-partners
1	421e422d65b93b54d59ba19a465292d9	venture	1250000	EUR	2006	organization/adara-venture-partners	adara-venture-partners
2	7a17150c3f14d66358956ac66fe2ea80	angel	73333	EUR	2012	organization/cabiedes-partners	cabiedes-partners
3	7a17150c3f14d66358956ac66fe2ea80	angel	73333	EUR	2012	person/francois-derbaix	francois-derbaix
4	7a17150c3f14d66358956ac66fe2ea80	angel	73333	EUR	2012	person/inaki-berenguer	inaki-berenguer

In [176]:

ni=i_barcelona.count()
df5 = DataFrame(columns=['uuid','name','role'], index=list(range(0,ni)))
for i in range(ni):
 df5.loc[i] = Series({'uuid': i_barcelona.find()[i]['data']['uuid'],
 'name':i_barcelona.find()[i]['data']['properties']['permalink'], 'role':'investor'}) 
 
df5=df5.drop_duplicates()
 

In [127]:

df5.head()

Out[127]:

	uuid	name	role
0	3eed4fa46c75fb697f5e17db8092b73b	viewpoint-capital-partners	investor
1	56e8dbb001cdbdc3db01d38b3f8ce7d4	bullnet-gestion	investor
2	025c7a60c56a47b0ac783b178661ddb7	pond-ventures	investor
3	e02e9ffcc387327b2339a3f721dbd058	bbva	investor
4	af37951e36a3ecaa003526db8f967832	costanoa-venture-capital	investor

In [81]:

dfx=df5[['name','role']]
h = HTML(dfx[0:7].to_html(index=False))
my_file = open('role_investor.html', 'w')
my_file.write(h.data)
my_file.close()

Creating relationship table between companies and investors¶

In [177]:

dfa=df3[['name','uuid_fund']]
dfb=df4[['name','uuid_fund','type','money','currency','year']]
dfc=dfa.merge(dfb, on='uuid_fund')
dfc.columns = ['company', 'link','investor','type','money','currency','year']
dfc=dfc[['company', 'investor','type','money','currency','year']]

In [129]:

dfc.head()

Out[129]:

	company	investor	type	money	currency	year
0	strands	bbva	venture	24000000	USD	2007
1	strands	antonio-asensio	venture	25000000	USD	2007
2	strands	debaeque	venture	3000000	USD	2006
3	strands	sequel	venture	3000000	USD	2006
4	akamon-entertainment	bonsai-capital	venture	700000	EUR	2013

Convert currency to EUR¶

In [178]:

from money import Money
dfc.money[dfc.currency == 'USD']=dfc.money[dfc.currency == 'USD']*0.79
dfc.currency=dfc.currency.replace('USD','EUR')
dfc['money'] = dfc['money'].map(lambda x: Money(x,'EUR'))

In [131]:

dfc.head()

Out[131]:

	company	investor	type	money	currency	year
0	strands	bbva	venture	EUR 18,960,000.00	EUR	2007
1	strands	antonio-asensio	venture	EUR 19,750,000.00	EUR	2007
2	strands	debaeque	venture	EUR 2,370,000.00	EUR	2006
3	strands	sequel	venture	EUR 2,370,000.00	EUR	2006
4	akamon-entertainment	bonsai-capital	venture	EUR 700,000.00	EUR	2013

In [132]:

dfx=dfc[['company','investor','type','money','year']]
h = HTML(dfx[0:8].to_html(index=False))
my_file = open('relation.html', 'w')
my_file.write(h.data)
my_file.close()

Analysis¶

Top 10 companies receiving VC funding:¶

In [382]:

h1=dfc[['company','money']].groupby('company').sum().reset_index()
h2=h1.sort(['money'],ascending=False)

In [134]:

h2.head(n=10)

Out[134]:

	company	money
95	privalia	EUR 395,668,000.00
30	desigual	EUR 285,000,000.00
105	scytl	EUR 89,427,998.42
117	strands	EUR 43,450,000.00
46	groupalia	EUR 35,899,996.00
112	social-point	EUR 34,845,998.42
8	arcplan-information-services-ag	EUR 26,907,400.00
82	ntr-global	EUR 26,860,000.00
43	gigle-semiconductor	EUR 24,489,998.42
116	stat-diagnostica	EUR 24,100,000.00

In [83]:

a = HTML(h2[0:9].to_html(index=False))
my_file = open('top10_company.html', 'w')
my_file.write(a.data)
my_file.close()

Top 10 investors¶

In [180]:

g1=dfc[['investor','money']].groupby('investor').sum().reset_index()
g2=g1.sort(['money'],ascending=False)

In [136]:

g2.head(n=10)

Out[136]:

	investor	money
62	eurazeo	EUR 285,000,000.00
31	cabiedes-partners	EUR 203,989,256.00
85	index-ventures	EUR 66,547,928.00
71	general-atlantic	EUR 66,547,928.00
180	vulcan-capital	EUR 31,600,000.00
88	insight-venture-partners	EUR 29,022,928.00
161	sofina	EUR 25,000,000.00
81	highland-capital-partners	EUR 24,371,500.00
126	nauta-capital	EUR 23,557,527.14
175	venture-investors	EUR 22,100,000.00

In [84]:

a = HTML(g2[0:9].to_html(index=False))
my_file = open('top10_investor.html', 'w')
my_file.write(a.data)
my_file.close()

Year vs Inversion¶

In [137]:

l1=dfc[['year','money']].groupby('year').sum().reset_index()
l2=l1.sort(['money'],ascending=False)

In [138]:

l2.head()

Out[138]:

	year	money
9	2014	4.057950e+08
6	2011	3.520405e+08
5	2010	1.015254e+08
8	2013	9.381874e+07
2	2007	7.674140e+07

In [320]:

l2.shape

Out[320]:

(10, 2)

In [85]:

a = HTML(l2[0:9].to_html(index=False))
my_file = open('top10_year.html', 'w')
my_file.write(a.data)
my_file.close()

In [321]:

l2.to_csv('fund_year.csv', sep=',', encoding='utf-8',index=False)

Categories¶

In [139]:

co=collections.Counter(df2['category'])

In [140]:

co1 = DataFrame(co.items(), columns=['Category', 'Number of Companies'])
co2=co1.sort(['Number of Companies'],ascending=False)

In [141]:

co2.head(n=10)
# Obs: each company can belong to more than one category.

Out[141]:

	Category	Number of Companies
183	E-Commerce	93
234	Software	90
87	Mobile	86
152	Games	59
270	Internet	45
138	Enterprise Software	45
210	Curated Web	44
124	Apps	41
44	Technology	41
153	Advertising	30

In [410]:

co2[co2.Category =='Startups']

Out[410]:

	Category	Number of Companies
323	Startups	15

In [408]:

a = HTML(co2[0:9].to_html(index=False))
my_file = open('top10_category.html', 'w')
my_file.write(a.data)
my_file.close()

Graph Analysis¶

Relationships (Company - Investor) as Undirected Graph¶

In [144]:

x1=list(dfc['company'])
x2=list(dfc['investor'])
y=list(dfc[['company','investor']].itertuples(index=False))
G=nx.Graph()
G.add_edges_from(y)
pos=nx.spring_layout(G)
nx.draw_networkx_nodes(G,pos,nodelist=x1,node_color='b')
nx.draw_networkx_nodes(G,pos,nodelist=x2,node_color='r')
nx.draw_networkx_edges(G,pos)
nx.draw(G)

In [25]:

h=sorted(nx.connected_components(G),key=len,reverse=True)

In [26]:

H=G.subgraph(h[1])

In [27]:

nx.draw(H, with_labels=True)

In [28]:

H=G.subgraph(h[2])

In [29]:

nx.draw(H,with_labels=True)

Relationships (Company - Investor) as D irected Graph¶

In [423]:

x1=list(dfc['company'])
x2=list(dfc['investor'])
y=list(dfc[['investor','company']].itertuples(index=False))
DG=nx.DiGraph()
DG.add_edges_from(y)
pos=nx.spring_layout(DG)
nx.draw_networkx_nodes(DG,pos,nodelist=x1,node_size=50,node_color= 'g')
nx.draw_networkx_nodes(DG,pos,nodelist=x2,node_size=80, node_color= 'b')
nx.draw_networkx_edges(DG,pos)
plt.axis('off')
plt.legend(["Company", "Investor"])
plt.savefig("direct.png",dpi=1000)
#nx.draw(G)

In [158]:

h=sorted(nx.connected_components(G),key=len,reverse=True)
#connectec components only work over undirected graphs
H=DG.subgraph(h[1])
nx.draw(H,with_labels=True)
plt.axis('off')
plt.savefig("direct_case.png",dpi=1000)

In [91]:

#The out-degree centrality for a node v is the fraction of nodes its outgoing edges are connected to.
co3=nx.out_degree_centrality(DG)
co4=DataFrame(co3.items(), columns=['investor','Degree Centrality'])
co5=co4.sort(['Degree Centrality'],ascending=False)

In [92]:

co5.head(n=10)

Out[92]:

	investor	Degree Centrality
130	caixa-capital	0.057402
78	cabiedes-partners	0.042296
183	wayra	0.039275
311	inveready-technology-investment-group	0.033233
179	nauta-capital	0.027190
190	highgrowth	0.027190
35	active-venture-partners	0.021148
16	kibo-ventures	0.018127
235	debaeque	0.015106
158	la-caixa	0.012085

In [93]:

a = HTML(co5[0:9].to_html(index=False))
my_file = open('d_central_inv.html', 'w')
my_file.write(a.data)
my_file.close()

In [192]:

#The in-degree centrality for a node v is the fraction of nodes its incoming edges are connected to.
co3=nx.in_degree_centrality(DG)
co4=DataFrame(co3.items(), columns=['company','Degree_Centrality'])
co5=co4.sort(['Degree_Centrality'],ascending=False)

In [193]:

co5.head(n=10)

Out[193]:

	company	Degree_Centrality
93	geenapp-internet	0.030211
160	era-biotech	0.027190
63	groupalia	0.024169
178	scytl	0.021148
28	tiendeo	0.021148
218	nubelo	0.018127
116	privalia	0.018127
270	infantium	0.018127
51	uvinum	0.018127
43	bidaway-com	0.015106

In [220]:

a=co5[co5.Degree_Centrality != 0]
b=a.merge(h2, on='company')
x=list(b['Degree_Centrality'])
y=list(b['money'].astype(float))
plt.scatter(x,y)
plt.xlabel('Degree Centrality')
plt.ylabel('Total Fundraised (EUR)')
plt.title(r'Degree Centrality vs Total Fundraised')
plt.show()

In [95]:

a = HTML(co5[0:9].to_html(index=False))
my_file = open('d_central_comp.html', 'w')
my_file.write(a.data)
my_file.close()

Save DataFrames¶

In [185]:

dfc.to_csv('barcelona_link.csv', sep=',', encoding='utf-8',index=False)

dfd=df1.append(df5)
dfd=dfd[['name', 'role']]
dfd.to_csv('barcelona_role.csv', sep=',', encoding='utf-8',index=False)

dfe=df2[['name','category']]
dfe.to_csv('barcelona_cat.csv', sep=',', encoding='utf-8',index=False)

In [223]:

b.money=b.money.astype(float)
b.head()

Out[223]:

	company	Degree_Centrality	money
0	geenapp-internet	0.030211	103000.00
1	era-biotech	0.027190	10469198.42
2	groupalia	0.024169	35899996.00
3	scytl	0.021148	89427998.42
4	tiendeo	0.021148	834995.00

In [224]:

b.to_csv('deg_central.csv', sep=',', encoding='utf-8',index=False)

Normalization link weight to apply PageRank Algorithm¶

In [265]:

dfc['money']=dfc['money'].astype(float)
dfc['money'] = (dfc['money'] - dfc['money'].min()) / (dfc['money'].max() - dfc['money'].min())
y=list(dfc[['investor','company','money']].itertuples(index=False))
x1=list(dfc['company'])
x2=list(dfc['investor'])
dfc.money[dfc.money == 0]=0.00000000000001

In [255]:

DG1=nx.DiGraph()
DG1.add_weighted_edges_from(y)
pos=nx.spring_layout(DG1)
nx.draw_networkx_nodes(DG1,pos,nodelist=x1,node_size=50,node_color= 'g')
nx.draw_networkx_nodes(DG1,pos,nodelist=x2,node_size=80, node_color= 'b')
nx.draw_networkx_edges(DG1,pos)

Out[255]:

<matplotlib.collections.LineCollection at 0xad2c36c>

In [309]:

pr=nx.pagerank(DG1)

In [310]:

m=list(set(x2))
for i in range(len(m)):
 if m[i] not in x1:
 del pr[m[i]]

In [315]:

co6=DataFrame(pr.items(), columns=['company','pagerank'])
b=co6.merge(h2, on='company')
x=list(b['pagerank'])
y=list(b['money'].astype(float))
plt.scatter(x,y)
plt.xlabel('PageRank')
plt.ylabel('Total Fundraised (EUR)')
plt.title(r'PageRank vs Total Fundraised')
plt.show()

In [317]:

b.money=b.money.astype(float)

Out[317]:

	company	pagerank	money
0	akamon-entertainment	0.006464	2800000
1	ebdsoft	0.002360	3594500
2	compring	0.005577	300000
3	bettymovil	0.002279	1580000
4	tiendeo	0.009005	834995

In [318]:

b.to_csv('pagerank.csv', sep=',', encoding='utf-8',index=False)

Category Score¶

In [383]:

dcat=df2[['name','category']]
m=list(h2.company)

In [384]:

dcat['name']=dcat['name'].apply(lambda x: x if x in m else 'false')
dc=dcat[dcat.name != 'false']

In [385]:

h2['money']=h2['money'].astype(float)
h2['money'] = (h2['money'] - h2['money'].min()) / (h2['money'].max() - h2['money'].min())

In [387]:

rt=dict(zip(h2.company, h2.money))

In [388]:

dc['score']=dc['name'].apply(lambda x: rt[x])

In [389]:

dc.head(20)

Out[389]:

	name	category	score
0	strands	Artificial Intelligence	0.109814
1	strands	Reviews and Recommendations	0.109814
2	strands	Software	0.109814
3	strands	Personal Finance	0.109814
4	strands	E-Commerce	0.109814
5	strands	Personalization	0.109814
7	akamon-entertainment	Games	0.007077
8	veeva	Content	0.007986
9	veeva	Software	0.007986
10	veeva	CRM	0.007986
11	veeva	Life Sciences	0.007986
12	veeva	Enterprise Software	0.007986
13	3scale	Payments	0.009983
14	3scale	Software	0.009983
15	3scale	Infrastructure	0.009983
16	3scale	Developer APIs	0.009983
17	3scale	Services	0.009983
18	3scale	Enterprise Software	0.009983
19	trovit	Employment	0.003255
20	trovit	Cars	0.003255

In [390]:

dc1=dc[['category','score']]
dl1=dc1[['category','score']].groupby('category').sum().reset_index()
dl2=dl1.sort(['score'],ascending=False)

In [393]:

dl2.head(20)

Out[393]:

	category	score
61	Fashion	1.721666
48	E-Commerce	1.152117
124	Retail	1.095642
41	Design	0.720301
115	Printing	0.720301
145	Software	0.581629
50	Electronics	0.226776
86	Law Enforcement	0.226018
18	Biotechnology	0.191800
55	Enterprise Software	0.175419
125	Reviews and Recommendations	0.120252
9	Artificial Intelligence	0.109981
66	Games	0.109883
110	Personalization	0.109814
109	Personal Finance	0.109814
140	Social Media	0.101359
96	Mobile	0.097313
114	Price Comparison	0.090733
59	Facebook Applications	0.088069
139	Social Games	0.088069

In [397]:

dl2['pct']=dl2['score']*100/(dl2.score.sum())
dl2.head()

Out[397]:

	category	score	pct
61	Fashion	1.721666	18.711779
48	E-Commerce	1.152117	12.521690
124	Retail	1.095642	11.907894
41	Design	0.720301	7.828530
115	Printing	0.720301	7.828530

In [398]:

dl2.to_csv('cat_score.csv', sep=',', encoding='utf-8',index=False)

In [398]:

In [ ]: