In this Notebook, the statistics of the library accounts is being summarized. The libTwitterStats-function takes as arguments
The file will be saved as NatBib_libTwitterStats_2014-04-09.csv etc.
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import csv
def impCSV(input_file):
'''
input_file = csv with keys: "URL", "Twitter"
output = list of dictionaries
'''
f = open(input_file, 'r')
d = csv.DictReader(f)
LoD = [] # list of dictionaries
for row in d:
LoD.append(row)
f.close()
return LoD
def exp2CSV(listOfDict, filename):
'''
arguments = list of dictionaries, filename
output = saves file to cwd (current working directory)
'''
keyz = listOfDict[0].keys()
f = open(filename,'w')
dict_writer = csv.DictWriter(f,keyz)
dict_writer.writer.writerow(keyz)
dict_writer.writerows(listOfDict)
f.close()
In [11]:
def libTwitterStats(basicStatsFile, NetWork_datestamp, Friends_datestamp, timelineStats_datestamp):
# 1. NatBib_BasicStats_2014-03-24.csv
# all keys
filename = basicStatsFile
datestampNW = NetWork_datestamp # datestamp of Networkfile
datestampFriends = Friends_datestamp
datestampTLS = timelineStats_datestamp
LoD = []
bs = impCSV(filename)
print 'Number of libraries:', len(bs)
# 2. open corresponding Network file for each library
for e in bs:
sn = e['screen_name']
nwfn = sn + '_NetWork_' + datestampNW + '.csv'
tweets = int(e['statuses_count'])
nwdic = impCSV(nwfn)[0]
nwdic.pop('screen_name', None)
nwdic.pop('libLocation', None)
nwdic.pop('followBack', None)
nwdic.pop('followers_ids', None)
nwdic.pop('friends_ids', None)
e.update(nwdic) # update the dictionary e
# 3. friends file offnen
friendsFileName = sn + '_Friends_' + datestampFriends + '.csv'
try:
friends = impCSV(friendsFileName)
friendsDic = {}
from collections import Counter
LoCluster = []
LoLocation = []
for i in friends:
LoCluster.append(i['cluster'])
LoLocation.append(i['friends_location'])
cluster = Counter(LoCluster)
location = Counter(LoLocation)
except:
friendsDic = {'friend_librarian': 'NA', 'friend_library': 'NA', 'friend_publisher': 'NA',
'friend_varia': 'NA', 'friend_without_description': 'NA', 'friend_local': 'NA',
'friend_without_place': 'NA', 'friend_other_place': 'NA'}
friendsDic['friend_librarian'] = cluster['librarian']
friendsDic['friend_library'] = cluster['Bib']
friendsDic['friend_publisher'] = cluster['publisher']
friendsDic['friend_varia'] = cluster['varia']
friendsDic['friend_without_description'] = cluster['--']
friendsDic['friend_local'] = location['local']
friendsDic['friend_without_place'] = location['--']
friendsDic['friend_other_place'] = len(friends) - (friendsDic['friend_local'] + friendsDic['friend_without_place'])
e.update(friendsDic) # update the dictionary e
# 4. follower file offnen
followersFileName = sn + '_Followers_' + datestampFriends + '.csv'
followers = impCSV(followersFileName)
dFol = {}
from collections import Counter
LoClusterFol = []
LoLocationFol = []
for i in followers:
LoClusterFol.append(i['cluster'])
LoLocationFol.append(i['followers_location'])
clusterFol = Counter(LoClusterFol)
locationFol = Counter(LoLocationFol)
dFol['follower_librarian'] = clusterFol['librarian']
dFol['follower_library'] = clusterFol['Bib']
dFol['follower_publisher'] = clusterFol['publisher']
dFol['follower_varia'] = clusterFol['varia']
dFol['follower_without_description'] = clusterFol['--']
dFol['follower_local'] = locationFol['local']
dFol['follower_without_place'] = locationFol['--']
dFol['follower_other_place'] = len(followers) - (dFol['follower_local'] + dFol['follower_without_place'])
e.update(dFol) # update the dictionary e
# 5. open UniBib_timelineStats_2014-04-02.csv
tlfilename = filename[:-26] + '_timelineStats_' + datestampTLS + '.csv'
tlstats = impCSV(tlfilename)
dTLS = {}
#tweets = 0
isGenuine = 0
hasMedia = 0
hasURL = 0
hasMention = 0
hasHash = 0
hasEntity = 0
resFac = 0
isFav = 0
isRT = 0
isReply = 0
repNoOrph = 0
hrs2a = 0
isAuto = 0
for ix in tlstats:
if ix['screen_name'].lower() == sn.lower():
#tweets += 1
if int(ix['genuine_tweet']) == 1: # all the following stats shall only be calculated
isGenuine += 1 # for the genuine tweets of the library
hasMedia += int(ix['has_media'])
hasURL += int(ix['has_url'])
hasMention += int(ix['has_mention'])
hasHash += int(ix['has_hashtag'])
if int(ix['has_media']) + int(ix['has_url']) + int(ix['has_mention']) + int(ix['has_hashtag']) > 0:
hasEntity += 1
resFac += float(ix['resonance_Factor'])
if int(ix['favorite_count']) > 0:
isFav += 1
if int(ix['rt_count']) > 0:
isRT += 1
if int(ix['is_reply']) == 1:
isReply += 1
if int(ix['orphan']) == 0:
repNoOrph += 1 # to calculate average of reply time, the
hrs2a += float(ix['hours_to_answer']) # orphaned replies can't be taken into account
if int(ix['auto_tweet']) == 1:
isAuto += 1
if repNoOrph == 0:
dTLS['avg_hours_to_answer'] = 0
else:
dTLS['avg_hours_to_answer'] = round(float(hrs2a)/repNoOrph,2)
if isGenuine > 0:
dTLS['genuine_tweet_ratio'] = round(float(isGenuine)/tweets,2)
dTLS['avg_has_media'] = round(float(hasMedia)/isGenuine,2)
dTLS['avg_has_url'] = round(float(hasURL)/isGenuine,2)
dTLS['avg_has_mention'] = round(float(hasMention)/isGenuine,2)
dTLS['avg_has_hashtag'] = round(float(hasHash)/isGenuine,2)
dTLS['avg_has_entity'] = round(float(hasEntity)/isGenuine,2)
dTLS['avg_resonanceFactor'] = round(float(resFac)/isGenuine,2)
dTLS['avg_Favs'] = round(float(isFav)/isGenuine,2)
dTLS['avg_RTs'] = round(float(isRT)/isGenuine,2)
dTLS['replies_ratio'] = round(float(isReply)/isGenuine,2)
dTLS['auto_tweet_ratio'] = round(float(isAuto)/tweets,2)
else:
dTLS['genuine_tweet_ratio'] = 0.0
dTLS['avg_has_media'] = 'NA'
dTLS['avg_has_url'] = 'NA'
dTLS['avg_has_mention'] = 'NA'
dTLS['avg_has_hashtag'] = 'NA'
dTLS['avg_has_entity'] = 'NA'
dTLS['avg_resonanceFactor'] = 'NA'
dTLS['avg_Favs'] = 'NA'
dTLS['avg_RTs'] = 'NA'
dTLS['replies_ratio'] = 'NA'
dTLS['auto_tweet_ratio'] = 'NA'
e.update(dTLS) # update the dictionary e
# append the List of Dictionaries with the new dictionary
LoD.append(e)
import datetime
datestamp = datetime.datetime.now().strftime('%Y-%m-%d')
libTwitterStatsFilename = filename[:-26] + '_libTwitterStats_' + datestamp + '.csv'
exp2CSV(LoD, libTwitterStatsFilename)
print "The summary was saved as " + libTwitterStatsFilename + "."
return LoD
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NatBib = libTwitterStats('NatBib_BasicStats_2014-04-07.csv', '2014-04-06','2014-04-07','2014-04-09')
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UniBib = libTwitterStats('UniBib_BasicStats_2014-04-07.csv', '2014-04-06','2014-04-06','2014-04-09')
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OeBib = libTwitterStats('OeBib_BasicStats_2014-04-07.csv', '2014-04-06','2014-04-06','2014-04-09')
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