Lista de Exercicios - Rubens Fernando Alencar

Os exercícios valem 30% da nota final.

Data Entrega: 18/08/2016
Formato da Entrega: .ipynb - Clique em File -> Download as -> IPython Notebook (.ipynb)
Enviar por email até a data de entrega, onde o assunto do email deve ser: Exercícios PosMBA Turma2 - SEU NOME

O cálculo da nota final é dado por: NF = (lista * 0.3) + (prova * 0.7)

Exercício 1

(0.5 ponto) Crie uma função chamada soma_tres_num que irá receber 3 parâmetros(sendo o último com valor padrão 10) e retorne a soma desses três valores.



In [1]:

    
def soma_tres_num(x,y,z=10):
    return x + y + z

Teste para as seguintes situações:



In [2]:

    
print(soma_tres_num(0, 10))



In [3]:

    
print(soma_tres_num(1,2,3))



In [4]:

    
print(soma_tres_num(10, 10, 0))

Exercício 2

(0.5 ponto) Crie uma função chamda qtde_caracteres que receba um parâmetro e retorne a quantidade de caracteres. Se o valor recebido pelo parâmetro não for uma String, utilizar a função str() para converter o argumento.



In [5]:

    
def qtde_caracteres(entrada):
    return len(str(entrada))

Teste as seguintes situações:



In [6]:

    
print(qtde_caracteres(1234))
print(qtde_caracteres(', -'))
print(qtde_caracteres('python'))
print(qtde_caracteres('fia e big data'))

Exercicío 3

(1.5 ponto) Carregue o arquivo chamado funcionarios.txt. Esse arquivo contém nome e o salário anual de cada funcionário.

alexandre,42000
rose,50000
anderson,30000
antonio,60000
maria,120000
carlos,86000
cesar,48000

Faça os seguintes exercícios:

a) Crie uma função chamda carregar_dados_dic que deve receber um parâmetro(no caso o arquivo funcionarios.txt) e retorne um dicionário, onde a chave será o nome e o valor será o salário anual.



In [7]:

    
def carregar_dados_dic(arquivo):
    # seu código aqui
    dici = {}
    with open(arquivo, 'r') as items:
        for item in items:
            itemSplit = item.split(',')
            dici[itemSplit[0]]  = int(itemSplit[1])
    return dici



In [8]:

    
salarios = carregar_dados_dic('funcionarios.txt')



In [9]:

    
print(salarios)









    



{'cesar': 48000, 'carlos': 86000, 'antonio': 60000, 'anderson': 30000, 'alexandre': 42000, 'maria': 120000, 'rose': 50000}

b) Crie uma função chamada calcular_salario_mensal que irá calcular quanto o funcionário ganha por mês. Verifique o tipo do campo do valor do dicionário. Utilize a função float() para converter o valor.



In [10]:

    
def calcular_salario_mensal(salario_anual):
    # seu código aqui
    try:
        mes = float(salarios[salario_anual]) / 12
    except KeyError:
        mes = 'Este funcionario nao existe'
    return mes

c) Por fim, imprima para cada funcionário o nome, salário anual e mensal no seguinte formato:

Rose --- R$ 50000 --- R$ 4166.66

Dica: Lembre-se do .format para formatar uma string. Utilize a função round para arredondar para 2 casas decimais.

>>> round(7166.67555, 2)
7166.67



In [11]:

    
for people in salarios:
    print('{} --- R$ {} --- R$ {}'.format(people, round(salarios[people],2),round(calcular_salario_mensal(people), 2)))









    



cesar --- R$ 48000 --- R$ 4000.0
carlos --- R$ 86000 --- R$ 7166.67
antonio --- R$ 60000 --- R$ 5000.0
anderson --- R$ 30000 --- R$ 2500.0
alexandre --- R$ 42000 --- R$ 3500.0
maria --- R$ 120000 --- R$ 10000.0
rose --- R$ 50000 --- R$ 4166.67

Exercício 4

(1 ponto) Crie um programa que deve ler as informações do usuário (nome, idade e salário) através da função input. É necessário validar as informações com os seguintes critérios:

O nome deve ter tamanho maior que 3
A idade deve ser entre 18 e 65
O salário deve ser maior que R$ 788

Caso as informações não estejam nos critérios definidos anteriormente, será necessário solicitar ao usuário para digitar novamente. Por fim o programa deverá imprimir o seguinte texto:

NOME tem YY anos, recebe R$ SSS e seu nome tem CC caracteres.

Onde,

NOME deve ser substituído pelo nome digitado.
YY deve ser a idade
SSS deve ser substituído pelo valor do salário.
CC deve ser substituído pela quantidade de caracteres.

Lembre-se de formatar o salário para duas casas decimais.



In [12]:

    
#Tratamento do Nome
nome = (input('Digite o Nome: '))
validacao = 0
while (validacao == 0):
    if len(nome) > 3:
        validacao = 1
    else:
        nome = (input('O Nome deve ter mais que 3 dígitos. Digite novamente: '))
        
#Tratamento da idade
yy = (input('Digite a Idade: '))
validacao = 0
while (validacao == 0):
    try:
        newyy = int (yy)
    except:
        newyy = yy

    if type(newyy) == int:
        if 18 < newyy < 65:
            validacao = 1
        else:
            yy = (input('A idade deve ser entre 18 e 65 anos. Digite novamente: '))
    else:
        yy = (input('A idade deve ser de formato inteiro. Digite novamente: '))

#Tratamento do salário
sss = (input('Digite o Salário: '))
validacao = 0
while (validacao == 0):
    try:
        newsss = round(float (sss),2)
    except:
        newsss = sss

    if type(newsss) == float:
        if newsss > 788:
            validacao = 1
        else:
            sss = (input('O salário deve ser maior que R$ 788. Digite novamente: '))
    else:
        sss = (input('O salário deve ter valor numérico. Digite novamente: '))

print ('{} tem {} anos, recebe R$ {} e seu nome tem {} caracteres.'.format(nome,newyy,newsss,len(nome)))









    



Digite o Nome: Rubens
Digite a Idade: 22
Digite o Salário: 1500
Rubens tem 22 anos, recebe R$ 1500.0 e seu nome tem 6 caracteres.

Exercicio 5

(1.5 ponto) Crie uma função que deverá receber dois parâmetros, sendo um o arquivo e o outro a quantidade de palavras que aparecem com mais frequência. O retorno deverá ser uma lista, sendo que cada elemento dessa lista, deve ser uma tupla (chave-valor), sendo chave o nome da palavra e o valor a quantidade de vezes que ela apareceu no texto.

Por exemplo,

palavras_frequentes('texto1.txt', 10) - pesquisa no arquivo texto1.txt as 10 palavras mais frequentes.
palavras_frequentes('texto2.txt', 5) - pesquisa no arquivo texto2.txt as 5 palavras mais frequentes.

Lembre-se de tratar possíveis erros!

Exemplo de uso e saida:

>>> palavras10mais = palavras_frequentes('texto1.txt', 10)
>>> print(palavras10mais)
[('programas', 662), ('codigos', 661), ('dinheiro', 661), ('fia', 586), ('python', 491), ('data', 434), ('big', 434), ('velocidade', 133), ('Moneyball', 113), ('dados', 95)]

Dica: Verifique o funcionamento da função sorted para ordernar um dicionário com base no valor.



In [13]:

    
import operator
def palavras_frequentes(arquivo, palavras_freq):
    todasList = [];
    listCount  = {}
    with open(arquivo,'r') as linhas:
        for linha in linhas:
            linha = linha.strip()
            linha = linha.replace('  ',' ') #convert duplicates spaces to one space
            linhaList = linha.split(' ')
            todasList.extend(linhaList)

    for palavra in todasList:
        try:
            if palavra not in listCount:
                listCount[palavra] = 1
            else:
                listCount[palavra] += 1
        except KeyError:
            'Não Possível'
    listSorted = sorted(listCount.items(), key=lambda x:x[1],reverse=True)
    
    return listSorted[:palavras_freq]

Faça o teste para as seguintes situações:



In [14]:

    
palavras_frequentes('texto1.txt', 10)









    Out[14]:





[('programas', 662),
 ('codigos', 661),
 ('dinheiro', 661),
 ('fia', 586),
 ('python', 491),
 ('big', 434),
 ('data', 434),
 ('velocidade', 133),
 ('Moneyball', 113),
 ('de', 95)]



In [15]:

    
palavras_frequentes('texto2.txt', 10)









    Out[15]:





[('dinheiro', 251),
 ('programas', 249),
 ('codigos', 248),
 ('fia', 220),
 ('python', 183),
 ('big', 166),
 ('data', 166),
 ('velocidade', 50),
 ('Moneyball', 45),
 ('de', 38)]



In [16]:

    
palavras_frequentes('texto1.txt', 5)









    Out[16]:





[('programas', 662),
 ('codigos', 661),
 ('dinheiro', 661),
 ('fia', 586),
 ('python', 491)]



In [17]:

    
palavras_frequentes('texto2.txt', 5)









    Out[17]:





[('dinheiro', 251),
 ('programas', 249),
 ('codigos', 248),
 ('fia', 220),
 ('python', 183)]



In [18]:

    
palavras_frequentes('texto1.txt', 30)









    Out[18]:





[('programas', 662),
 ('codigos', 661),
 ('dinheiro', 661),
 ('fia', 586),
 ('python', 491),
 ('big', 434),
 ('data', 434),
 ('velocidade', 133),
 ('Moneyball', 113),
 ('de', 95),
 ('dados', 95),
 ('conjuntos', 95),
 ('nuvem', 94),
 ('carro', 93),
 ('navio', 93),
 ('valor', 76),
 ('variedade', 75),
 ('hadoop', 75),
 ('volume', 19),
 ('', 1)]

Exercício 6

(2 pontos) Utilizando o exemplo dado em aula sobre a Streaming API (Desafio 3 da Aula 5), recupere os tweets durante 10 minutos com os seguintes parâmetros:

fluxo.filter(track=['Big Data', 'Hadoop', 'Spark', 'Python', 'Data Science'], languages=['en', 'pt'])

Salve os tweets em um arquivo chamado tweets_10min.json.

Importe os módulos necessários



In [19]:

    
import tweepy
from time import time

Crie as chaves de acesso



In [20]:

    
consumer_key = 'M6OkpsVkxMo1m4oEpcKrxTG9L'
consumer_secret = 'huUxJYdPoddEkvRrmDOFQIuHspkBERTCshx2J5tcj7FeAFdgNp'
access_token = '13101632-jfzgS37obVEw5vEQhkg3iHuMSgZFwAnLz55OZcVyi'
access_token_secret = 'CeoEHGH6fzrFH1PbXxSwCvmL04rD6nQElJzKgMnAj9AY9'

Realize a autorização e defina a token de acesso



In [21]:

    
autorizar = tweepy.OAuthHandler(consumer_key,consumer_secret)
autorizar.set_access_token(access_token,access_token_secret)

Crie a classe DadosPublicosTwitter herdando da classe tweepy.StreamListener para rodar durante 10 minutos e salvar os tweets no arquivo.



In [22]:

    
class DadosPublicosTwitter(tweepy.StreamListener):
    def __init__(self,nomeArquivo,tempoLimiteMinutos):
        self.tempoInicial = time()
        self.tempoLimite = tempoLimiteMinutos * 60
        self.salvarArquivo = open(nomeArquivo,'a',newline='')
    
    def on_data(self,dados):
        if (time() - self.tempoInicial < self.tempoLimite):
            self.salvarArquivo.write(dados)
            return True
        else:
            self.salvarArquivo.close()
            print('Stream finalizado')
            return False

Crie a instância da classe, o fluxo e realize a filtragem com os parâmetros definidos no enunciado.



In [107]:

    
nomeArquivo = 'tweets_10min.json'
dadosTwitter = DadosPublicosTwitter(nomeArquivo,10)

fluxo = tweepy.Stream(autorizar,dadosTwitter)

fluxo.filter(track=['Big Data', 'Hadoop', 'Spark', 'Python', 'Data Science'], languages=['en', 'pt'])









    



Stream finalizado

Exercício 7

(3 pontos) Com os dados salvos no tweets_10min.json, crie um DataFrame pandas com as seguintes colunas:

text - Texto do Tweet
created_at - Data da criação do Tweet
coordinates - Coordenadas
retweet_count - Quantidade de vezes que o tweet foi "retweetado".
favorite_count - Quantidade de vezes que o tweet foi "favoritado".
screen_name - Nome na tela (exemplo: @prof_dinomagri)
location - Localização
lang - Idioma
followers_count - Quantidade de seguidores
geo_enabled - Se tem a geolocalização habilitade
statuses_count - Quantidade de tweets postados.
lat - Recuperada através da função desenvolvida em sala
long - Recuperada através da função desenvolvida em sala
hashtags - Recuperada através da função desenvolvida em sala

Lembre-se de utilizar as funções desenvolvidas em sala de aula para recuperar a latitude, longitude e hastags.

No final salve o arquivo no formato CSV, utilizando o separador ponto e virgula (;) e com a codificação 'utf-8'.

Importe os módulos necessários



In [23]:

    
import pandas as pd
import simplejson as json

Utilize o comando with para abrir o arquivo 'tweets_10min.json' e salvar em uma lista



In [24]:

    
listTweets = []
with open ('tweets_10min.json','r') as arquivo:
    for linha in arquivo:
        listTweets.append(json.loads(linha))

Crie o DataFrame com os dados e imprima apenas os 3 primeiros tweets (linhas)



In [25]:

    
df = pd.DataFrame(listTweets)
df.head(3)









    Out[25]:






  
    
      
      contributors
      coordinates
      created_at
      entities
      extended_entities
      favorite_count
      favorited
      filter_level
      geo
      id
      ...
      quoted_status_id
      quoted_status_id_str
      retweet_count
      retweeted
      retweeted_status
      source
      text
      timestamp_ms
      truncated
      user
    
  
  
    
      0
      None
      None
      Wed Aug 17 13:36:40 +0000 2016
      {'hashtags': [], 'urls': [{'indices': [51, 74]...
      NaN
      0
      False
      low
      None
      765905185994633216
      ...
      NaN
      NaN
      0
      False
      NaN
      <a href="http://twitter.com/download/android" ...
      Data Analytics In Insurance | Articles | Big D...
      1471441000828
      False
      {'geo_enabled': False, 'verified': False, 'pro...
    
    
      1
      None
      None
      Wed Aug 17 13:36:44 +0000 2016
      {'hashtags': [], 'urls': [{'indices': [117, 14...
      NaN
      0
      False
      low
      None
      765905200007688192
      ...
      NaN
      NaN
      0
      False
      NaN
      <a href="http://www.facebook.com/twitter" rel=...
      Monty Python proclaimed, "Nobody expects the S...
      1471441004169
      False
      {'geo_enabled': False, 'verified': False, 'pro...
    
    
      2
      None
      None
      Wed Aug 17 13:36:45 +0000 2016
      {'hashtags': [], 'urls': [{'indices': [28, 51]...
      NaN
      0
      False
      low
      None
      765905206517305344
      ...
      7.659045e+17
      765904477471858689
      0
      False
      NaN
      <a href="http://twitter.com/download/iphone" r...
      You're wasting your data 😂😂 https://t.co/MnD90...
      1471441005721
      False
      {'geo_enabled': True, 'verified': False, 'prof...
    
  

3 rows × 31 columns

Crie uma lista, onde cada elemento será o nome da coluna



In [26]:

    
colunas = ['text', 'created_at', 'coordinates', 'retweet_count', 
           'favorite_count', 'screen_name', 'location', 'lang', 
           'followers_count', 'geo_enabled', 'statuses_count',
          'lat', 'long', 'hashtags']

Crie um DataFrame auxiliar passando as colunas por parâmetro



In [27]:

    
df_aux = pd.DataFrame(columns=colunas)

Crie a função de pegar_lat_long(local)



In [28]:

    
from geopy.geocoders import Nominatim
def pegar_lat_long(local):
    geoLocalizador = Nominatim()
    try:
        localizacao = geoLocalizador.geocode(local)
        return (localizacao.latitude,localizacao.longitude)
#         return localizacao
    except:
        return 0

Crie a função para salvar_hashtags(texto)



In [29]:

    
def salvar_hashtags(texto):
    hashtags = []
    for palavra in texto.split():
        if palavra.startswith('#'):
            hashtags.append(palavra)
    strHashtags = ' '.join(hashtags)
    return strHashtags

Certifique-se que o DataFrame auxiliar tem as 14 colunas necessárias



In [30]:

    
len(df_aux.columns)









    Out[30]:





14

Por fim crie o laço que irá iterar em cada elemento do DataFrame original e salvar apenas o que queremos no DataFrame auxiliar.

Lembre-se de como queremos a latitude e longitude a localização tem que ser diferente de None.



In [57]:

    
for i in range(0, len(df)):
    if df.user[i]['location'] != None:
        latLong  = pegar_lat_long(df.user[i]['location'])
        print(df.user[i]['location'],latLong)
        if latLong != 0:
            tweet = [
                df.text[i],
                df.created_at[i],
                df.coordinates[i],
                df.retweet_count[i],
                df.favorite_count[i],
                df.user[i]['screen_name'],
                df.user[i]['location'],
                df.user[i]['lang'],
                df.user[i]['followers_count'],
                df.user[i]['geo_enabled'],
                df.user[i]['statuses_count'],
                latLong[0],
                latLong[1],
                salvar_hashtags(df.text[i]),
            ]
            series = pd.Series(tweet, index=colunas)
            df_aux = df_aux.append(series, ignore_index=True)









    



Abuja, Nigeria (9.0643305, 7.4892974)
["You're wasting your data 😂😂 https://t.co/MnD90iiZSl", 'Wed Aug 17 13:36:45 +0000 2016', None, 0, 0, 'Boss_Haruna', 'Abuja, Nigeria', 'en', 301, True, 9286, 9.0643305, 7.4892974, '']
Birmingham, Alabama, USA (33.5206824, -86.8024325)
["RT @ellisonbg: An incredible collection of Jupyter notebooks-new O'Reilly book by @jakevdp , Data Science Handbook https://t.co/63Ma7xunKn", 'Wed Aug 17 13:36:46 +0000 2016', None, 0, 0, 'timelyportfolio', 'Birmingham, Alabama, USA', 'en', 2206, False, 19551, 33.5206824, -86.8024325, '']
Rhode Island (41.7962409, -71.5992371)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:36:47 +0000 2016', None, 0, 0, 'sdenfr4shhh', 'Rhode Island', 'en', 45, True, 3620, 41.7962409, -71.5992371, '#Bi…']
home (51.8253788, 9.0184079)
["RT @P0lS0NlVY: he used her MI's to manipulate her and then threw her into acid and in the movie he tortured her to become harley https://t.…", 'Wed Aug 17 13:36:48 +0000 2016', None, 0, 0, 'E_mily1998', 'home', 'en', 233, True, 4544, 51.8253788, 9.0184079, '']
emmnltsn 0
Pennsylvania, USA (40.9699889, -77.727883)
["@Bibobll @JubyPhonic_P We're both valor but Spark is love, Spark is life. https://t.co/yhQJQQJ3h8", 'Wed Aug 17 13:36:49 +0000 2016', None, 0, 0, 'Gergory001', 'Pennsylvania, USA', 'en', 1, False, 45, 40.9699889, -77.727883, '']
Oxford, UK (51.752225, -1.2583027)
['Work smarter to get the best return on data analytics: \nhttps://t.co/fQe26Fc2jY', 'Wed Aug 17 13:36:51 +0000 2016', None, 0, 0, 'DataSalon', 'Oxford, UK', 'en', 1635, False, 950, 51.752225, -1.2583027, '']
San Francisco, CA (37.7792808, -122.4192362)
['Sentia_UK: 1ClickCassandraCluster for Your Big Data Center Cloud - https://t.co/HzceuBEDq6 The Absorbent Mind: https://t.co/iknHvitZbM', 'Wed Aug 17 13:37:00 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066870, 37.7792808, -122.4192362, '']
London, England (51.5073219, -0.1276473)
['No #datascience, no problem! Learn to ask big questions with data (even if you stink at math)— smart like how https://t.co/GFhzzC3fST', 'Wed Aug 17 13:37:01 +0000 2016', None, 0, 0, 'ReverseHeadhunt', 'London, England', 'en', 6664, False, 2553, 51.5073219, -0.1276473, '#datascience,']
Connecticut, USA (41.6500201, -72.7342162)
['RT @dailycampuslife: Brb planning next years summer vacation https://t.co/02jg6J1SIp', 'Wed Aug 17 13:37:02 +0000 2016', None, 0, 0, 'theangiederosa', 'Connecticut, USA', 'en', 157, True, 1974, 41.6500201, -72.7342162, '']
ⓡⓨⓛⓔ ❁ ⓜⓒⓡⓘⓢ 0
Boston, NYC, SF, Austin 0
Torres Vedras Portugal (39.0925372, -9.2598536)
['Entities move data from tables to knowledge\n\n#OReilly #Data #Science https://t.co/PWHQjuVL9v', 'Wed Aug 17 13:37:10 +0000 2016', None, 0, 0, 'EdgarTwit1', 'Torres Vedras Portugal', 'en', 609, False, 64984, 39.0925372, -9.2598536, '#OReilly #Data #Science']
Canton, MO (40.7989522, -81.3784444)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:37:11 +0000 2016', None, 0, 0, 'URTheTlgersRoar', 'Canton, MO', 'en', 333, True, 4022, 40.7989522, -81.3784444, '#Bi…']
Webster NY (43.2122851, -77.4299938)
['RT @cxdig: The Hidden Danger of Big Data by @SenseableCity @DirkHelbing https://t.co/11OcJVALt1 https://t.co/8MSScdC9Ux', 'Wed Aug 17 13:37:11 +0000 2016', None, 0, 0, 'MsFluffPawz', 'Webster NY', 'en', 234, False, 2314, 43.2122851, -77.4299938, '']
Dublin, Irlande (53.3497645, -6.2602731)
['RT @CRMTopNews: Making Big Data Drive Digital Transformation – As Disru... https://t.co/nohehC6tFW via @Greyhound_R https://t.co/QLqDzQ0tzL', 'Wed Aug 17 13:37:16 +0000 2016', None, 0, 0, 'BenjaminMarcon', 'Dublin, Irlande', 'fr', 508, False, 22562, 53.3497645, -6.2602731, '']
Natsu... (48.204168, 16.3467727)
['RT @randal_olson: https://t.co/ye1XaaENfz : a #Python tool to download and manage TV shows. #entertainment https://t.co/bAwVaAR9vQ', 'Wed Aug 17 13:37:16 +0000 2016', None, 0, 0, 'FenrirJP', 'Natsu...', 'en', 3062, False, 5914, 48.204168, 16.3467727, '#Python #entertainment']
mcr (51.7506898, -1.25311198253273)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:37:22 +0000 2016', None, 0, 0, 'griffwilliams_', 'mcr', 'en', 256, False, 5525, 51.7506898, -1.25311198253273, '']
Shreveport, LA (32.4828485, -93.8284831615932)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:37:24 +0000 2016', None, 0, 0, 'lovelyluv09', 'Shreveport, LA', 'en', 489, True, 75701, 32.4828485, -93.8284831615932, '#LSS']
somewhere out there  0
CN (35.000074, 104.999927)
['#Why does my nested loop throw an exception in Python?\n#Tech #News #HowTo\nhttps://t.co/yzfQq1vEyF', 'Wed Aug 17 13:37:27 +0000 2016', None, 0, 0, 'AdamSmitht1', 'CN', 'en', 1410, False, 468436, 35.000074, 104.999927, '#Why #Tech #News #HowTo']
Chicago (41.8755546, -87.6244211)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:37:34 +0000 2016', None, 0, 0, 'j0nkavanaugh', 'Chicago', 'en', 48, True, 3571, 41.8755546, -87.6244211, '#Bi…']
Beverly Hills, CA (34.0736204, -118.4003562)
['@QNAP_nas debuts double server TDS-16489U featuring dual @Intel #Xeon E5 #CPU for #bigdata computing @AnnaRibeiro9 https://t.co/hXZTgXxBPj', 'Wed Aug 17 13:37:34 +0000 2016', None, 0, 0, 'IoTInnovator', 'Beverly Hills, CA', 'en', 45, False, 208, 34.0736204, -118.4003562, '#Xeon #CPU #bigdata']
Atimonan, Calabarzon (13.9968413, 121.9292345)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:37:35 +0000 2016', None, 0, 0, 'jaypenpen', 'Atimonan, Calabarzon', 'en', 7, False, 81, 13.9968413, 121.9292345, '#LSS']
Deutschland (51.0834196, 10.4234469)
['#Sr Software Engineer Java/Python/Javascript Fullstack #jobs #career #: #NJ-South Brunswick, This company run... https://t.co/Y7nThrUCI7', 'Wed Aug 17 13:37:35 +0000 2016', None, 0, 0, 'jobnec', 'Deutschland', 'de', 1555, False, 93409, 51.0834196, 10.4234469, '#Sr #jobs #career #: #NJ-South']
Angono,Rizal (14.5253956, 121.1534448)
['You are the spark.', 'Wed Aug 17 13:37:42 +0000 2016', None, 0, 0, 'aaaroonvargas', 'Angono,Rizal', 'en', 220, True, 1917, 14.5253956, 121.1534448, '']
Orlando, FL (28.5421175, -81.3790461)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:37:44 +0000 2016', None, 0, 0, 'LaurenKeller_', 'Orlando, FL', 'en', 927, True, 38676, 28.5421175, -81.3790461, '']
San Francisco, CA (37.7792808, -122.4192362)
['yadakansei0228: HyperScale HyperConvergence Big Data Cloud Advanced Training - https://t.co/EVzY9GXwa2 Can I Help?: https://t.co/uv043HELe6', 'Wed Aug 17 13:37:54 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066874, 37.7792808, -122.4192362, '']
GREAT BRITAIN  (54.31536155, -1.91802339480124)
['#Ukip 🇬🇧\n\nJean-Claude Juncker warned he could spark European CIVIL WAR with arrogant Brexit stance https://t.co/aKHFZviimU', 'Wed Aug 17 13:37:54 +0000 2016', None, 0, 0, 'Fight4UK', 'GREAT BRITAIN ', 'en', 15440, False, 195780, 54.31536155, -1.91802339480124, '#Ukip']
Florida, USA (27.7567667, -81.4639834)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:37:55 +0000 2016', None, 0, 0, 'jazlynnivette', 'Florida, USA', 'en', 162, False, 1732, 27.7567667, -81.4639834, '']
Kuala Perlis, Perlis (6.400695, 100.136108)
['RT @SecurityTube: #Python for Hackers &amp; Pentesters, https://t.co/CxZwWl3fPy Join professionals from 90+ countries! Grab Your Seat Now! http…', 'Wed Aug 17 13:37:55 +0000 2016', None, 0, 0, 'AqilahArffin', 'Kuala Perlis, Perlis', 'en', 639, True, 4984, 6.400695, 100.136108, '#Python']
Swindon, England (51.5613683, -1.7856852)
['SanDisk Big Data and Flash: Why Is This the Future? [Video] | 7wData https://t.co/m4o3l7gODF', 'Wed Aug 17 13:38:01 +0000 2016', None, 0, 0, 'PhilMarshman', 'Swindon, England', 'en', 2517, True, 425, 51.5613683, -1.7856852, '']
San Francisco, CA (37.7792808, -122.4192362)
['1llan: 1ClickCassandraCluster for Your Big Data Center Cloud - https://t.co/HzceuBEDq6 Pro Puppet: https://t.co/SbOmyhPREc', 'Wed Aug 17 13:38:01 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066876, 37.7792808, -122.4192362, '']
Croissy sur Seine (48.8792031, 2.1325772)
['Big data and robotics: A long history together &gt;https://t.co/FCC0iRS9jJ&lt; #analytics #BigData #robotics @ZDNet https://t.co/OsKJeyqCJb', 'Wed Aug 17 13:38:02 +0000 2016', None, 0, 0, 'MTI_France', 'Croissy sur Seine', 'fr', 631, True, 2215, 48.8792031, 2.1325772, '#analytics #BigData #robotics']
Huddersfield, England (53.6467031, -1.7832076)
['#thoughtposition #sculpture\na spark between\nover and over again\nbegan to corrode', 'Wed Aug 17 13:38:15 +0000 2016', None, 0, 0, 'thoughtposition', 'Huddersfield, England', 'en', 34, False, 7417, 53.6467031, -1.7832076, '#thoughtposition #sculpture']
San Francisco, CA (37.7792808, -122.4192362)
['lws_101: 1ClickSparkCluster for Your Big Data Center Cloud - https://t.co/Xt9pA8vyao Big Data poder los datos: https://t.co/862zIwgbhd', 'Wed Aug 17 13:38:19 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066877, 37.7792808, -122.4192362, '']
Elmhurst, IL (41.8994745, -87.9403417)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:38:20 +0000 2016', None, 0, 0, 'JacobAlley12', 'Elmhurst, IL', 'en', 349, False, 1676, 41.8994745, -87.9403417, '']
Dimples ni Liza 0
MNL Phil 0
Argentina 0
New Delhi, India 0
Denver, CO (39.7391536, -104.9847033)
["RT @OReillyMedia: Get beyond the basics of Python with @redsymbol 's 2-day hands-on, in-depth Python training Aug 23-24 https://t.co/rpcos0…", 'Wed Aug 17 13:38:29 +0000 2016', None, 0, 0, 'MikeHerman', 'Denver, CO', 'en', 802, True, 8114, 39.7391536, -104.9847033, '']
University of Rhode Island (41.8295635, -71.4162769)
["Staying w a nigga for the night for a spark up lmaoooooo bitch please don't get me staaaaarted", 'Wed Aug 17 13:38:37 +0000 2016', None, 0, 0, 'kbellexx', 'University of Rhode Island', 'en', 9689, True, 60015, 41.8295635, -71.4162769, '']
Boston area (14.5879878, 120.9677966)
['#Psychosocial #BigData = better patient care/outcomes @MandiBPro @ProfAmyE @keenzai https://t.co/1ImD2zNXmA #hcldr #hit #healthcare #nih', 'Wed Aug 17 13:38:39 +0000 2016', None, 0, 0, 'mbschoening', 'Boston area', 'en', 221, True, 683, 14.5879878, 120.9677966, '#Psychosocial #BigData #hcldr #hit #healthcare #nih']
Valparaiso, Chile (-33.0462474, -71.6198031)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:38:41 +0000 2016', None, 0, 0, 'TheNickRamirez', 'Valparaiso, Chile', 'es', 569, False, 9973, -33.0462474, -71.6198031, '']
San Francisco, CA (37.7792808, -122.4192362)
['idhstg: 1ClickSparkCluster for Your Big Data Center Cloud - https://t.co/Xt9pA8vyao Global Marketing Kate Gillespie: https://t.co/EW3O6aTXJE', 'Wed Aug 17 13:38:45 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066879, 37.7792808, -122.4192362, '']
Oregon, USA (43.9792797, -120.7372569)
['especially when you are buying BIG DATA?', 'Wed Aug 17 13:38:46 +0000 2016', None, 0, 0, 'ficoshutdown181', 'Oregon, USA', 'en', 1545, False, 24460, 43.9792797, -120.7372569, '']
São Paulo, Brasil (-23.5506506, -46.6333823)
['This "hack" should be turned in to an official #python PEP, so useful (even hacky) https://t.co/GuWFCdRJ73', 'Wed Aug 17 13:38:47 +0000 2016', None, 0, 0, 'rochacbruno', 'São Paulo, Brasil', 'pt', 2580, True, 24503, -23.5506506, -46.6333823, '#python']
∞ НσρєѕαηDяєαмs∞ 0
San Francisco, CA (37.7792808, -122.4192362)
['cabhijeet: 1ClickKafkaCluster for Your Big Data Center Cloud - https://t.co/nw2oSUiyRB Start-up Entrepreneur: 2: https://t.co/BWQvuu1iBw', 'Wed Aug 17 13:38:52 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066880, 37.7792808, -122.4192362, '']
instagram: ashleyhollins 0
Saint Simons Island, GA (31.2182863, -81.3612076)
['This #job might be a great fit for you: Software Engineer-Hadoop-ETL - https://t.co/8S5e2eD2A1 #IT #Newton, MA https://t.co/7RF1JUGknt', 'Wed Aug 17 13:38:57 +0000 2016', {'type': 'Point', 'coordinates': [-70.963869, 42.267327]}, 0, 0, 'FTGiGSJobs', 'Saint Simons Island, GA', 'en', 1167, True, 8687, 31.2182863, -81.3612076, '#job #IT #Newton,']
France (46.603354, 1.8883335)
['Why Athletes are using Big Data and Analytics to win Olympic Games? #BigData #Rio2016 https://t.co/hG7OGQAs2f', 'Wed Aug 17 13:39:00 +0000 2016', None, 0, 0, 'CharleneMaoFR', 'France', 'en', 37, False, 112, 46.603354, 1.8883335, '#BigData #Rio2016']
Bangalore, India (12.9791198, 77.5912997)
['@QNAP_nas debuts double server TDS-16489U featuring dual @Intel #Xeon E5 #CPU for #bigdata computing #datacenter https://t.co/j07HdOEeJ7', 'Wed Aug 17 13:39:03 +0000 2016', None, 0, 0, 'AnnaRibeiro9', 'Bangalore, India', 'en', 501, False, 10308, 12.9791198, 77.5912997, '#Xeon #CPU #bigdata #datacenter']
London, England (51.5073219, -0.1276473)
['RT @HeathWallace: Five interesting ways tech is being used in sports https://t.co/suFSqAVY7B via @techworldnews', 'Wed Aug 17 13:39:05 +0000 2016', None, 0, 0, 'techworldnews', 'London, England', 'en', 13110, True, 15711, 51.5073219, -0.1276473, '']
Johnston, IA (41.6731523, -93.6973394)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:39:06 +0000 2016', None, 0, 0, 'DrewHershey1', 'Johnston, IA', 'en', 252, True, 4141, 41.6731523, -93.6973394, '#Bi…']
UK (54.7023545, -3.2765752)
["RT @TheBeyHiveTeam: And don't come in our mentions trying to spark that old Legion beef. Legion the only 1 keeping ya'll laced with updates…", 'Wed Aug 17 13:39:06 +0000 2016', None, 0, 0, 'BeyonceBrooklyn', 'UK', 'en', 2413, True, 39281, 54.7023545, -3.2765752, '']
Davao City (7.22650725, 125.454887691355)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:39:08 +0000 2016', None, 0, 0, 'cardiogirl07', 'Davao City', 'en', 63, False, 14910, 7.22650725, 125.454887691355, '#LSS']
New York City (40.7305991, -73.9865811)
["Check out @govtechnews' article about how government can use big data analytics for the altruistic good of citizens: https://t.co/CeN6Cit4k8", 'Wed Aug 17 13:39:08 +0000 2016', None, 0, 0, 'SeamlessDocs', 'New York City', 'en', 1038, True, 1172, 40.7305991, -73.9865811, '']
Rio de Janeiro (-22.9110136, -43.2093726)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:39:17 +0000 2016', None, 0, 0, 'vinicaz', 'Rio de Janeiro', 'pt', 177, False, 31355, -22.9110136, -43.2093726, '']
Dubai, UAE (25.22761165, 55.1750774842824)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:39:18 +0000 2016', None, 0, 0, 'AileenJannah', 'Dubai, UAE', 'en', 474, False, 14139, 25.22761165, 55.1750774842824, '#LSS']
London, England (51.5073219, -0.1276473)
['How Hotels Are Now Benefiting From Big Data https://t.co/73GCacnBUg', 'Wed Aug 17 13:39:18 +0000 2016', None, 0, 0, 'kortxltd', 'London, England', 'en', 10861, False, 3191, 51.5073219, -0.1276473, '']
The Ignited States of America 0
San Francisco, CA (37.7792808, -122.4192362)
['conosilac: 1ClickKafkaCluster for Your Big Data Center Cloud - https://t.co/nw2oSUA9J9 LG Optimus F3 Virgin Mobile: https://t.co/PF7oTOy5ey', 'Wed Aug 17 13:39:28 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066884, 37.7792808, -122.4192362, '']
Worthington/Columbus (39.99389515, -83.0124866134615)
['1979  Monty Python\'s "Life of Brian" premieres "Always Look On The Bright Side Of Life" Eric Idle  https://t.co/TItYPh5w1A', 'Wed Aug 17 13:39:30 +0000 2016', None, 0, 0, 'KBow5', 'Worthington/Columbus', 'en', 940, False, 48277, 39.99389515, -83.0124866134615, '']
Huntingdon, PA (40.3446332, -78.0281184)
['RT @Pitt_ATHLETICS: #PantherNation...Get a closer look at the #PittRetro uniforms: https://t.co/mQAGZnGUL4 https://t.co/ScocSHNYU2', 'Wed Aug 17 13:39:34 +0000 2016', None, 0, 0, 'FragelloLA', 'Huntingdon, PA', 'en', 573, True, 10574, 40.3446332, -78.0281184, '#PantherNation...Get #PittRetro']
New York City (40.7305991, -73.9865811)
['‘Big Data’ has quickly become an outdated, catch-all phrase for online marketers #Content https://t.co/hcIEVGkfqC https://t.co/n9UZZipfJx', 'Wed Aug 17 13:39:45 +0000 2016', None, 0, 0, 'Jon_Gelberg', 'New York City', 'en', 1379, True, 2674, 40.7305991, -73.9865811, '#Content']
Winnipeg, MB (49.8833343, -97.166674)
['@reggiewatts @_liooil sf? Spark Fun?', 'Wed Aug 17 13:39:50 +0000 2016', None, 0, 0, '3D_Solutions', 'Winnipeg, MB', 'en', 128, False, 171, 49.8833343, -97.166674, '']
Mountain View, CA (37.3855745, -122.0820499)
['#raspberry #tech #pi - Python • Help programming dht11 sensor - https://t.co/BkpZjOvbdT - I am having trouble using my dht11 reading as a …', 'Wed Aug 17 13:39:54 +0000 2016', None, 0, 0, 'jimoiduts', 'Mountain View, CA', 'en', 2487, False, 5215, 37.3855745, -122.0820499, '#raspberry #tech #pi']
Allí donde solíamos gritar 0
Justin followed March 04, 2016 0
London, England (51.5073219, -0.1276473)
['Efficient String Concatenation in #Python\n\n.join() wins ....\n\nhttps://t.co/P9FnZlYvvg', 'Wed Aug 17 13:39:58 +0000 2016', None, 0, 0, 'postenterprise', 'London, England', 'en', 431, False, 4166, 51.5073219, -0.1276473, '#Python']
Tulsa, Oklahoma (U.S.A.) 0
Florida, USA (27.7567667, -81.4639834)
['Lucky me https://t.co/jciBtpn7wS', 'Wed Aug 17 13:40:01 +0000 2016', None, 0, 0, 'isabellajcurtis', 'Florida, USA', 'en', 56, False, 97, 27.7567667, -81.4639834, '']
Australia (-24.7761085, 134.755)
['#python_assignment and submission deadline is  close. Get help from python experts https://t.co/07lrFZ05et', 'Wed Aug 17 13:40:01 +0000 2016', None, 0, 0, 'Essay_Corp', 'Australia', 'en', 914, False, 4439, -24.7761085, 134.755, '#python_assignment']
Houston Texas Area 0
Boracay Island, Philippines (11.9693449, 121.922731243477)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:40:08 +0000 2016', None, 0, 0, 'BamosNelsa', 'Boracay Island, Philippines', 'en', 228, False, 14650, 11.9693449, 121.922731243477, '#LSS']
Virtual World (15.591717, 73.810089)
['#science Nearly two decades of data reinforce concerns that pesticides are really bad for bees –… https://t.co/BgiXNXg4D2 #maths #tech', 'Wed Aug 17 13:40:09 +0000 2016', None, 0, 0, 'librarynext', 'Virtual World', 'en', 780, False, 84437, 15.591717, 73.810089, '#science #maths #tech']
Central Region, Singapore (1.28584945, 103.806995929441)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:10 +0000 2016', None, 0, 0, 'hunnycrise', 'Central Region, Singapore', 'en', 183, True, 101125, 1.28584945, 103.806995929441, '']
reg's (41.779955, 0.8342466)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:11 +0000 2016', None, 0, 0, 'onetwotria', "reg's", 'en', 563, True, 5091, 41.779955, 0.8342466, '']
Ruins of Valyria 0
ph ✈️ sg 0
San Francisco, CA (37.7792808, -122.4192362)
['EmereoBooks: 1ClickSparkCluster for Your Big Data Center Cloud - https://t.co/Xt9pA8vyao What Wish Knew When Was: https://t.co/G5Dj5WeTeH', 'Wed Aug 17 13:40:15 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066886, 37.7792808, -122.4192362, '']
Viersen, Germany (51.2561445, 6.3904284)
['6 Great Reasons to Bend Big Data to your Will - markITwrite https://t.co/Q4RMykzFdW #BigData', 'Wed Aug 17 13:40:17 +0000 2016', None, 0, 0, 'Brandsensations', 'Viersen, Germany', 'de', 4677, True, 14972, 51.2561445, 6.3904284, '#BigData']
United States (39.7837304, -100.4458824)
['# dog #python #newvideo Animal Fight to Death - Dog vs Python https://t.co/6eZ0Cyj3DV', 'Wed Aug 17 13:40:17 +0000 2016', None, 0, 0, 'SinclairOZ', 'United States', 'en', 37, True, 1331, 39.7837304, -100.4458824, '# #python #newvideo']
under ur bed 0
Puerto Princesa City (9.8688777, 118.7479986)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:19 +0000 2016', None, 0, 0, 'JusFuertes', 'Puerto Princesa City', 'en', 403, True, 3007, 9.8688777, 118.7479986, '']
614✈️302/336 (13.7979205, 100.5880152)
['Great movie😂😂 https://t.co/V8pdVeMgR1', 'Wed Aug 17 13:40:21 +0000 2016', None, 0, 0, 'LPerez400m', '614✈️302/336', 'en', 1802, True, 52931, 13.7979205, 100.5880152, '']
Qatar doha, h& E 0
Baguio City, Cordillera Admin Region 0
614✈️302/336 (13.7979205, 100.5880152)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:40:24 +0000 2016', None, 0, 0, 'LPerez400m', '614✈️302/336', 'en', 1802, True, 52932, 13.7979205, 100.5880152, '']
Salford (53.4877463, -2.289192)
['RT @Fight4UK: #Ukip 🇬🇧\n\nJean-Claude Juncker warned he could spark European CIVIL WAR with arrogant Brexit stance https://t.co/aKHFZviimU', 'Wed Aug 17 13:40:26 +0000 2016', None, 0, 0, 'ONCEaBLUE2012', 'Salford', 'en-gb', 40, True, 703, 53.4877463, -2.289192, '#Ukip']
USA (39.7837304, -100.4458824)
['@TacticalTony702 @amountainman69 @bikesnbullets17 They spray our skies daily then everything erupts like crazy w/a little spark', 'Wed Aug 17 13:40:29 +0000 2016', None, 0, 0, 'PoliticalPort', 'USA', 'en', 2606, False, 52045, 39.7837304, -100.4458824, '']
カラ松の心 0
sa bahay (12.6369835, 124.437435426217)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:33 +0000 2016', None, 0, 0, 'Trishhhh11', 'sa bahay', 'en', 12, False, 2024, 12.6369835, 124.437435426217, '']
Quezon Quezon (14.066667, 122.133333)
['RT @lizasoberano: Listen to "Spark," my single from the Dolce Amore OST album - now on Spotify. Physical release on August 19! ❤️ https://t…', 'Wed Aug 17 13:40:33 +0000 2016', None, 0, 0, 'rolainelorraine', 'Quezon Quezon', 'en', 354, True, 10313, 14.066667, 122.133333, '']
Baguio City, Cordillera Admin Region 0
Madrid (40.4167047, -3.7035824)
['The many layers of NoSQL big data security - Gemalto blog #bigdata - https://t.co/LVqiwKGWHJ https://t.co/QwT7llTHdd', 'Wed Aug 17 13:40:34 +0000 2016', None, 0, 0, 'ElsaToutlemonde', 'Madrid', 'en', 210, True, 473, 40.4167047, -3.7035824, '#bigdata']
Los Angeles, CA (34.0543942, -118.2439408)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:40:36 +0000 2016', None, 0, 0, 'KingMylene', 'Los Angeles, CA', 'en', 12, False, 1360, 34.0543942, -118.2439408, '#LSS']
主に本アカの方 0
New York, USA (40.7305991, -73.9865811)
['RT @machinelearnbot: Guide To High Performance Distributed Computing: Case Studies With Hadoop, Scalding A here  https://t.co/UVwg4H1xH1 #h…', 'Wed Aug 17 13:40:37 +0000 2016', None, 0, 0, 'CornellMBA_IC', 'New York, USA', 'en', 1834, True, 19132, 40.7305991, -73.9865811, '#h…']
At the crossroads.. (54.2382505, -2.9193406)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:38 +0000 2016', None, 0, 0, 'allybc17', 'At the crossroads..', 'en', 33, False, 3023, 54.2382505, -2.9193406, '']
Calamba City, Calabarzon (14.20332695, 121.15481880539)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:38 +0000 2016', None, 0, 0, 'iamkathlene0314', 'Calamba City, Calabarzon', 'en', 2, False, 1, 14.20332695, 121.15481880539, '']
092815 0
55062 Garner Plain Suite 905
New Michael, HI 34904-5445 0
nuggets (42.3485752, -71.0943232)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:40 +0000 2016', None, 0, 0, 'soberabno', 'nuggets', 'en', 1158, True, 335, 42.3485752, -71.0943232, '']
Pila Laguna  (14.2374875, 121.3618083)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:42 +0000 2016', None, 0, 0, 'LIZAnianzLguna', 'Pila Laguna ', 'en', 2925, False, 6355, 14.2374875, 121.3618083, '']
LizQuen Universe 0
west tokyo (35.666924, 139.724555)
['RT @MongoDB: #MongoDB named a leader in The @Forrester Wave: #BigData #NoSQL Q3 2016\u200b. Download it now: https://t.co/in4nG37gX2 https://t.c…', 'Wed Aug 17 13:40:45 +0000 2016', None, 0, 0, 'jeanepaul', 'west tokyo', 'en', 717, True, 55698, 35.666924, 139.724555, '#MongoDB #BigData #NoSQL']
New York, USA (40.7305991, -73.9865811)
['RT @machinelearnbot: Hadoop Big Data Interview Questions (illustrated): Shyam Mallesh here  https://t.co/m1h4dyxnXd #hadoop @machinelearnbot', 'Wed Aug 17 13:40:46 +0000 2016', None, 0, 0, 'CornellMBA_IC', 'New York, USA', 'en', 1834, True, 19135, 40.7305991, -73.9865811, '#hadoop']
Paris, France (48.8566101, 2.3514992)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:46 +0000 2016', None, 0, 0, 'PrettyVessa', 'Paris, France', 'en', 181, False, 1294, 48.8566101, 2.3514992, '']
Dumaguete City (9.3054777, 123.3080446)
['Walay spark ang born for you! 😟', 'Wed Aug 17 13:40:48 +0000 2016', None, 0, 0, 'estano2009', 'Dumaguete City', 'en', 80, True, 991, 9.3054777, 123.3080446, '']
Detroit (42.3486635, -83.0567374)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:40:52 +0000 2016', None, 0, 0, 'iresonm6', 'Detroit', 'en', 56, True, 3360, 42.3486635, -83.0567374, '#Bi…']
LizQuen Universe 0
San Francisco, CA (37.7792808, -122.4192362)
['_moam4: HyperScale HyperConvergence Big Data Cloud Advanced Training - https://t.co/fFZnt8PZ0S 19th Wife: A Novel: https://t.co/sPjKcNem7e', 'Wed Aug 17 13:40:56 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066889, 37.7792808, -122.4192362, '']
Pakistan | Kashmir| (34.3752779, 73.4730602)
['RT @ayeshakhan251: Burqa clad Extremist  #Hindu from #RSS caught Red handed while Throwing Beef at Hindu Temple to spark &amp; Muslim riots htt…', 'Wed Aug 17 13:40:57 +0000 2016', None, 0, 0, 'Ashikhan251', 'Pakistan | Kashmir|', 'en', 24711, True, 22722, 34.3752779, 73.4730602, '#Hindu #RSS']
Greater Seattle Area 0
E (8.166667, -69.8333329)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:58 +0000 2016', None, 0, 0, 'sarinasaito_', 'E', 'en', 1029, True, 54555, 8.166667, -69.8333329, '']
Wonderland ✨ (16.3131877, 121.1297478)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:40:58 +0000 2016', None, 0, 0, 'Iam_Hayzeel', 'Wonderland ✨', 'en', 129, False, 1819, 16.3131877, 121.1297478, '']
Philippines (12.7503486, 122.7312101)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:02 +0000 2016', None, 0, 0, 'dendenmarieampo', 'Philippines', 'en', 67, False, 2049, 12.7503486, 122.7312101, '']
필리핀 (12.7503486, 122.7312101)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:04 +0000 2016', None, 0, 0, 'vLaineUI', '필리핀', 'en', 123, True, 2381, 12.7503486, 122.7312101, '']
NY (43.1561681, -75.8449945)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:41:04 +0000 2016', None, 0, 0, 'Rebeccaharrisx', 'NY', 'en', 546, True, 54527, 43.1561681, -75.8449945, '']
Texas (31.8160381, -99.5120985)
['RT @PGOTeamValor: Every revolution begins with a spark. #TeamValor https://t.co/yvbBmfmFHu', 'Wed Aug 17 13:41:04 +0000 2016', None, 0, 0, 'Miss_Plain_Jane', 'Texas', 'en', 10, False, 113, 31.8160381, -99.5120985, '#TeamValor']
Philippines (12.7503486, 122.7312101)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:05 +0000 2016', None, 0, 0, 'zenny_joy', 'Philippines', 'en', 77, True, 295, 12.7503486, 122.7312101, '']
sa bahay nag t-twitter,Manila 0
balungao, pangasinan (15.8983735, 120.6724552)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:06 +0000 2016', None, 0, 0, 'aeinkixzer', 'balungao, pangasinan', 'en', 13, False, 65, 15.8983735, 120.6724552, '']
Qatar doha, h& E 0
LizQuen's 0
Pasig city, Philippines (11.1075813, 122.4738288)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:41:07 +0000 2016', None, 0, 0, 'LReigna', 'Pasig city, Philippines', 'en', 35, False, 5455, 11.1075813, 122.4738288, '#LSS']
PaSaWaY Universe 0
7430 Ikast Denmark Europe 0
الدوحة, دولة قطر (25.2856329, 51.5264162)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:12 +0000 2016', None, 0, 0, 'IsabellaAnne5', 'الدوحة, دولة قطر', 'en', 0, False, 38, 25.2856329, 51.5264162, '']
Paris old EU 0
spark  (37.7707735, -122.391549575732)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:19 +0000 2016', None, 0, 0, 'lizasobayrano', 'spark ', 'en', 858, False, 18814, 37.7707735, -122.391549575732, '']
Dolce Amore (37.7892086, -122.4223588)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:19 +0000 2016', None, 0, 0, 'lizquen_kyla', 'Dolce Amore', 'ja', 498, True, 6155, 37.7892086, -122.4223588, '']
Republic of the Philippines (12.7503486, 122.7312101)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:23 +0000 2016', None, 0, 0, 'kimlugylle_', 'Republic of the Philippines', 'en', 393, False, 1448, 12.7503486, 122.7312101, '']
Gurgaon, Haryana, India (28.4646148, 77.0299194)
['How can we get python3 docs to show up before python2 docs in google? https://t.co/1uq7xCXjmT #Python \n\nWith extremely small exception, I …', 'Wed Aug 17 13:41:23 +0000 2016', None, 0, 0, 'programmingncr', 'Gurgaon, Haryana, India', 'en', 590, False, 111575, 28.4646148, 77.0299194, '#Python']
San Mateo, CA (37.496904, -122.3330572)
['#MotorolaMobility NZ firefighters adopt Motorola and Spark radio. Read more: https://t.co/h0DU4tZjnJ', 'Wed Aug 17 13:41:25 +0000 2016', None, 0, 0, 'tc_equipment', 'San Mateo, CA', 'en', 152, False, 37012, 37.496904, -122.3330572, '#MotorolaMobility']
Bipolar (39.4737322, -0.3818643)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:29 +0000 2016', None, 0, 0, 'Desdes_Gil', 'Bipolar', 'en', 455, False, 15775, 39.4737322, -0.3818643, '']
Navotas City (14.6569825, 120.9478036)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:32 +0000 2016', None, 0, 0, 'maevi_rizza', 'Navotas City', 'en', 28, False, 112, 14.6569825, 120.9478036, '']
Sa baba ni Liza 0
Munich, Bavaria (48.1371079, 11.5753822)
["RT @openshift: Don't miss this new #OpenShift Commons #BigData session in a couple of hours. https://t.co/8ZcTiNQP2z", 'Wed Aug 17 13:41:35 +0000 2016', None, 0, 0, 'Joerg1968', 'Munich, Bavaria', 'en', 298, True, 4491, 48.1371079, 11.5753822, '#OpenShift #BigData']
Kansas, USA (38.27312, -98.5821871)
['RT @SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida. https://t.co/MM8milG7TV', 'Wed Aug 17 13:41:36 +0000 2016', None, 0, 0, '_PomegranateJas', 'Kansas, USA', 'en', 985, True, 23276, 38.27312, -98.5821871, '']
PH (12.44249325, 122.160395163807)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:41:38 +0000 2016', None, 0, 0, 'lizasoberano_ph', 'PH', 'en', 10657, True, 24768, 12.44249325, 122.160395163807, '#LSS']
Berlin, London, Boston (42.3648603, -71.5938478)
['Rethinking Chilled Water Temperatures Can Bring Big Savings in Data Center Cooling: There’s no sug... https://t.co/tEt5iTXYLO #IoT #IIoT', 'Wed Aug 17 13:41:38 +0000 2016', None, 0, 0, 'weCONECTMedia', 'Berlin, London, Boston', 'de', 23828, False, 9047, 42.3648603, -71.5938478, '#IoT #IIoT']
USA (39.7837304, -100.4458824)
['#Why does my nested loop throw an exception in Python?\n#Tech #Internet #Question #HowTO\nhttps://t.co/9q4or5C0r6', 'Wed Aug 17 13:41:40 +0000 2016', None, 0, 0, 'UriSamuels', 'USA', 'en', 386, False, 442518, 39.7837304, -100.4458824, '#Why #Tech #Internet #Question #HowTO']
Valenzuela City (14.5711849, 121.0241195)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:41:46 +0000 2016', None, 0, 0, 'ViaCiashet', 'Valenzuela City', 'en', 88, True, 970, 14.5711849, 121.0241195, '']
Pakistan (30.3308401, 71.247499)
['Big Data Byte: Analyzing the Medicaid expansion population https://t.co/zyURTfOm7T via @HealthITNews', 'Wed Aug 17 13:41:49 +0000 2016', None, 0, 0, 'MrMazharShah', 'Pakistan', 'en', 6686, False, 68764, 30.3308401, 71.247499, '']
1.usa.gov/asaOYG bit.ly/8D3pYx 0
San Francisco, CA (37.7792808, -122.4192362)
['PR__Backlinks: Live Streaming Your Event to the World - https://t.co/wrTECwv9Jm Hadoop Action Chuck Lam 2010: https://t.co/3FcjHodcCV', 'Wed Aug 17 13:41:51 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066890, 37.7792808, -122.4192362, '']
india (22.3511148, 78.6677428)
['RT @Hema_SK_: Started frm t Spark! Now his films Never fail to Do big in BoxOffice😎 Yes he is a Prince @Siva_Kartikeyan Na😎 #PrinceOfKollyw…', 'Wed Aug 17 13:41:53 +0000 2016', None, 0, 0, 'indu_sk', 'india', 'en', 1637, True, 49079, 22.3511148, 78.6677428, '#PrinceOfKollyw…']
Coningsby, Lincs 0
Sa Puso Mo 0
Grand Rapids, MI (42.9632405, -85.6678638)
["RT @openshift: Don't miss this new #OpenShift Commons #BigData session in a couple of hours. https://t.co/8ZcTiNQP2z", 'Wed Aug 17 13:41:58 +0000 2016', None, 0, 0, 'JoshuaAllenHolm', 'Grand Rapids, MI', 'en', 74, False, 142, 42.9632405, -85.6678638, '#OpenShift #BigData']
Sydney, New South Wales (-33.8679573, 151.210047)
["@SkyNews Holy cr@p that's a looot of people. Thought networks &amp; big data were on the rise?", 'Wed Aug 17 13:42:00 +0000 2016', None, 0, 0, 'ShameTorturers', 'Sydney, New South Wales', 'en', 85, False, 1428, -33.8679573, 151.210047, '']
Bengaluru, India (12.9791198, 77.5912997)
['How can I check that a list has one and only one truthy value? #python https://t.co/1TWPVSTrZJ', 'Wed Aug 17 13:42:00 +0000 2016', None, 0, 0, 'PythonQnA', 'Bengaluru, India', 'en', 128, False, 12454, 12.9791198, 77.5912997, '#python']
Dammam Saudi Arabia  (26.4367824, 50.1039991)
['RT @lizasoberano: Listen to "Spark," my single from the Dolce Amore OST album - now on Spotify. Physical release on August 19! ❤️ https://t…', 'Wed Aug 17 13:42:04 +0000 2016', None, 0, 0, 'kayella11_me', 'Dammam Saudi Arabia ', 'en', 66, False, 2952, 26.4367824, 50.1039991, '']
Wonderland (16.3131877, 121.1297478)
['RT @lizasoberano: Listen to "Spark," my single from the Dolce Amore OST album - now on Spotify. Physical release on August 19! ❤️ https://t…', 'Wed Aug 17 13:42:04 +0000 2016', None, 0, 0, 'mamaradloicka_', 'Wonderland', 'en', 641, True, 40343, 16.3131877, 121.1297478, '']
Appleton, WI (44.2611337, -88.4067603)
['Use Big Data to Create Value for Customers, Not Just Target Them via @HarvardBiz https://t.co/G5IWo6Qqj4', 'Wed Aug 17 13:42:06 +0000 2016', None, 0, 0, 'CarlAckermann', 'Appleton, WI', 'en', 333, False, 382, 44.2611337, -88.4067603, '']
Who Knows Where I am? (43.6848045, -79.6150858)
["Tis' but a SCRATCH! https://t.co/mlDXrR1yWT", 'Wed Aug 17 13:42:06 +0000 2016', None, 0, 0, 'Capotain_Media', 'Who Knows Where I am?', 'en', 829, False, 632, 43.6848045, -79.6150858, '']
Canada (61.0666922, -107.991707)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:42:07 +0000 2016', None, 0, 0, 'jazzicban', 'Canada', 'en', 48, False, 271, 61.0666922, -107.991707, '']
Mandaluyong City l Baguio City 0
Silay City, Western Visayas 0
Marikina City (14.6640304, 121.101115169968)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:42:16 +0000 2016', None, 0, 0, 'rominapmrtnz', 'Marikina City', 'en', 111, False, 22371, 14.6640304, 121.101115169968, '#LSS']
Bogor Agricultural University 0
Teaneck, USA (40.8975992, -74.0159726)
["We're #hiring! Click to apply: Python Admin - https://t.co/DX6gz0tSl8 #Job #IT #Riverwoods, IL #Jobs", 'Wed Aug 17 13:42:17 +0000 2016', {'type': 'Point', 'coordinates': [-87.897014, 42.1675254]}, 0, 0, 'USJobsCognizant', 'Teaneck, USA', 'en', 868, True, 11585, 40.8975992, -74.0159726, '#hiring! #Job #IT #Riverwoods, #Jobs']
San Francisco, CA (37.7792808, -122.4192362)
['BridgetoWealth: Live Streaming Your Event to the World - https://t.co/wrTECwdykM Python 3 al descubierto: https://t.co/zBiRuA1kwR', 'Wed Aug 17 13:42:19 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066892, 37.7792808, -122.4192362, '']
San Francisco, CA (37.7792808, -122.4192362)
['sonicrank2: 1ClickStormCluster for Your Big Data Center Cloud - https://t.co/uTyoALAWoI 4D Concepts Corner TV Stand: https://t.co/qCGbMeSwNj', 'Wed Aug 17 13:42:20 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066893, 37.7792808, -122.4192362, '']
60.152756,24.655643 (60.1526715, 24.6554282)
['Interview with #DataScience Hiring Manager: Nick Larusso, Graphiq https://t.co/fkaTe9hr5B', 'Wed Aug 17 13:42:20 +0000 2016', None, 0, 0, 'sakalli', '60.152756,24.655643', 'en', 1402, True, 5768, 60.1526715, 24.6554282, '#DataScience']
United States (39.7837304, -100.4458824)
['#Technology, #Science New Data Shows #Instagram Stories is Not Slowing #Snapchat Use - Yet … https://t.co/slT498pLLu', 'Wed Aug 17 13:42:24 +0000 2016', None, 0, 0, 'SEMonline4u', 'United States', 'en-gb', 120, False, 1896, 39.7837304, -100.4458824, '#Technology, #Science #Instagram #Snapchat']
Williams Bay, WI (42.5780721, -88.5409332)
['RT @MongoDB: #MongoDB named a leader in The @Forrester Wave: #BigData #NoSQL Q3 2016\u200b. Download it now: https://t.co/in4nG37gX2 https://t.c…', 'Wed Aug 17 13:42:25 +0000 2016', None, 0, 0, 'schwartzrw', 'Williams Bay, WI', 'en', 403, False, 1547, 42.5780721, -88.5409332, '#MongoDB #BigData #NoSQL']
Clondulane,Fermoy,Co.Cork 0
Santo Tomas, Ilocos Region (15.878228, 120.5860753)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:42:27 +0000 2016', None, 0, 0, 'NBarbachano', 'Santo Tomas, Ilocos Region', 'en', 22, False, 23, 15.878228, 120.5860753, '']
Zaragoza  (41.6521342, -0.8809427)
['#data science #big data - Benchmarking Digital Transformation - https://t.co/zt8kJzZQLq https://t.co/jJ7rNYilGN', 'Wed Aug 17 13:42:34 +0000 2016', None, 0, 0, 'davidjcc_Zgz', 'Zaragoza ', 'es', 6283, True, 582492, 41.6521342, -0.8809427, '#data #big']
Caker Towns (51.1489036, -0.9757676)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:42:34 +0000 2016', None, 0, 0, 'iCaKeyouu', 'Caker Towns', 'en', 250, True, 3477, 51.1489036, -0.9757676, '']
Hopefully lane three  0
San Francisco, CA (37.7792808, -122.4192362)
['MoneyOnMobile_: 1ClickSparkCluster for Your Big Data Center Cloud - https://t.co/Xt9pA8vyao More: https://t.co/EkT7homobk', 'Wed Aug 17 13:42:37 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066894, 37.7792808, -122.4192362, '']
Hongkong (22.2793278, 114.1628131)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:42:39 +0000 2016', None, 0, 0, '85256875303M', 'Hongkong', 'en', 30, False, 27336, 22.2793278, 114.1628131, '']
Paragould, AR (36.0584021, -90.4973285)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:42:39 +0000 2016', None, 0, 0, 'jennlMlamb', 'Paragould, AR', 'en', 14, True, 1318, 36.0584021, -90.4973285, '#Bi…']
England (52.7954791, -0.540240236617432)
['Use Big Data to Create Value for Customers, Not Just Target Them https://t.co/A7AALD6DLM', 'Wed Aug 17 13:42:42 +0000 2016', None, 0, 0, 'echostarcat', 'England', 'en', 90, False, 393, 52.7954791, -0.540240236617432, '']
Jakarta - Jogja (-7.7991377, 110.3621344)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:42:43 +0000 2016', None, 0, 0, 'octafredi17', 'Jakarta - Jogja', 'en', 224, True, 2187, -7.7991377, 110.3621344, '']
Glasgow, Scotland (55.856656, -4.2435816)
['RT @UofGSciEng: The IBM Bluemix #hackathon on 30/31 Aug @GlasgowCS! Interested in data/computing science &amp; smart cities? Come along! https:…', 'Wed Aug 17 13:42:43 +0000 2016', None, 0, 0, 'LynneBCoSE', 'Glasgow, Scotland', 'en', 119, False, 85, 55.856656, -4.2435816, '#hackathon']
Manila City (14.6783319, 121.0649551)
['so nice https://t.co/uIIPksn21H', 'Wed Aug 17 13:42:48 +0000 2016', None, 0, 0, 'rose_janrose', 'Manila City', 'en', 51, False, 318, 14.6783319, 121.0649551, '']
Dubai, United Arab Emirates (25.2683521, 55.2961962)
["RT @MzkayeL: #PushAwardsLizQuens cto lizasoberano: RT ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to … https://t.…", 'Wed Aug 17 13:42:50 +0000 2016', None, 0, 0, 'serten_ibarra', 'Dubai, United Arab Emirates', 'en', 65, False, 9232, 25.2683521, 55.2961962, '#PushAwardsLizQuens']
Cincinnati, OH (39.1014537, -84.5124601)
['@cincylibrary director Kimberly Fender shares how customer data helps her make big decisions. @OrangeBoyInc https://t.co/Jvh5P0yEqF', 'Wed Aug 17 13:42:51 +0000 2016', None, 0, 0, 'webmastergirl', 'Cincinnati, OH', 'en', 2666, True, 10995, 39.1014537, -84.5124601, '']
Europe & UK (52.4897433, -1.8540987)
['New #job: Senior Developer - Scala, Java, Big Data, Banking,City of London .. https://t.co/JpSX3YAHcw #jobs #hiring', 'Wed Aug 17 13:42:57 +0000 2016', None, 0, 0, 'TEKsystemsJobs', 'Europe & UK', 'en', 812, False, 315, 52.4897433, -1.8540987, '#job: #jobs #hiring']
Findlay, Ohio (41.0413873, -83.6503981)
["sad news: juice truck won't start for some odd reason. Pretty sure it the humidity/spark plug combo. Closed today :/ https://t.co/1A76QS8WGP", 'Wed Aug 17 13:42:58 +0000 2016', None, 0, 0, 'JKJuices', 'Findlay, Ohio', 'en', 130, False, 433, 41.0413873, -83.6503981, '']
Boise, Idaho (43.61656, -116.2008349)
['Now Playing on #UniversityPulse - The Python - Tickley Feather - Tune in at https://t.co/B50ytDApC5', 'Wed Aug 17 13:42:59 +0000 2016', None, 0, 0, 'PlayingPulse', 'Boise, Idaho', 'en', 79, False, 127657, 43.61656, -116.2008349, '#UniversityPulse']
MNL Phil 0
LizQuen Universe 0
Worldwide (53.4575797, -2.226672)
["RT @openshift: Don't miss this new #OpenShift Commons #BigData session in a couple of hours. https://t.co/8ZcTiNQP2z", 'Wed Aug 17 13:43:00 +0000 2016', None, 0, 0, 'binaryloom', 'Worldwide', 'en', 2311, False, 29257, 53.4575797, -2.226672, '#OpenShift #BigData']
Antartica (-82.1081819, 34.378236)
['Big Data: By How Many People Are We Separated On This Planet? - https://t.co/xaiITjnZum', 'Wed Aug 17 13:43:00 +0000 2016', None, 0, 0, 'Ahn_Rho', 'Antartica', 'en', 15, False, 186, -82.1081819, 34.378236, '']
Cambridge, UK (52.2033051, 0.124862)
['Almost time to sign in to watch the Rothschild webinar - Statistics and Data Science https://t.co/qjuuIA4mii https://t.co/VZY3jAflaE', 'Wed Aug 17 13:43:00 +0000 2016', None, 0, 0, 'NewtonInstitute', 'Cambridge, UK', 'en', 1870, False, 592, 52.2033051, 0.124862, '']
San Francisco, CA (37.7792808, -122.4192362)
['nwhitington: 1ClickStormCluster for Your Big Data Center Cloud - https://t.co/uTyoALAWoI Python Bibliography: https://t.co/edpKnmByZn', 'Wed Aug 17 13:43:01 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066897, 37.7792808, -122.4192362, '']
Long Branch, NJ (40.3042778, -73.9923595)
['Within each heart is a spark of the Divine. These sparks of Divinity and love can reach into the darkness and... https://t.co/B81nrWukRL', 'Wed Aug 17 13:43:01 +0000 2016', None, 0, 0, 'kevin_cieri', 'Long Branch, NJ', 'en', 57, False, 5361, 40.3042778, -73.9923595, '']
McLean, VA (38.9342888, -77.1776326)
['Apply Today: Data Engineer, Apache Spark in McLean, VA https://t.co/OgdrKLbriO #job', 'Wed Aug 17 13:43:06 +0000 2016', None, 0, 0, 'RWard_CapOne', 'McLean, VA', 'en', 83, True, 5022, 38.9342888, -77.1776326, '#job']
Portland, OR and Columbus, OH 0
cocoon (50.1121813, 8.7147231)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:43:07 +0000 2016', None, 0, 0, 'lizasobrabantot', 'cocoon', 'en', 850, True, 53865, 50.1121813, 8.7147231, '']
Yakima, WA (46.601557, -120.510842)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:43:07 +0000 2016', None, 0, 0, 'nick23perry', 'Yakima, WA', 'en', 418, True, 4296, 46.601557, -120.510842, '']
Stockholm (59.3251172, 18.0710935)
['RT @PeaceOpsCIC: Where are the #Europeans? Will they ever again be big contributors to @UNPeacekeeping?  https://t.co/ohakpBXpDM https://t.…', 'Wed Aug 17 13:43:08 +0000 2016', None, 0, 0, 'FBAFolke', 'Stockholm', 'en', 3594, False, 3920, 59.3251172, 18.0710935, '#Europeans?']
Budapest, Magyarország (47.4983815, 19.0404707)
['RT @wordpressbot_: Python: For Beginners, Learn Python Fast! Python Programming Language Crash Course  here  https://t.co/UajffKuiBL #javas…', 'Wed Aug 17 13:43:09 +0000 2016', None, 0, 0, 'annatomkaartist', 'Budapest, Magyarország', 'hu', 1198, False, 19629, 47.4983815, 19.0404707, '#javas…']
Utrecht, The Netherlands (52.08095165, 5.12768031549829)
['RT @Write4Research: Five Minutes with Professor Gary King: Transformational power of big data lies, pure and simple, in its analytics https…', 'Wed Aug 17 13:43:09 +0000 2016', None, 0, 0, 'elfriesen', 'Utrecht, The Netherlands', 'en', 304, True, 5962, 52.08095165, 5.12768031549829, '']
Oakland, CA / SFO 0
Paris, France (48.8566101, 2.3514992)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:43:11 +0000 2016', None, 0, 0, 'lalainedgs', 'Paris, France', 'en', 857, True, 18906, 48.8566101, 2.3514992, '']
sleezburg,VA 0
sleezburg,VA 0
Pennsylvania, USA (40.9699889, -77.727883)
['RT @NYCHBL: RT @HealthITNews: Precision medicine: Analytics, data science and EHRs in the new age https://t.co/Q81uRo6fUv #PopHealthIT', 'Wed Aug 17 13:43:20 +0000 2016', None, 0, 0, 'AGDGRoque', 'Pennsylvania, USA', 'en', 6535, True, 3101, 40.9699889, -77.727883, '#PopHealthIT']
Cabangan, Central Luzon (15.1589735, 120.0555503)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:43:20 +0000 2016', None, 0, 0, 'acihrbabe18', 'Cabangan, Central Luzon', 'en', 12, False, 120, 15.1589735, 120.0555503, '']
FL (27.7567667, -81.4639834)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:43:21 +0000 2016', None, 0, 0, 'meliigalvan17', 'FL', 'en', 487, True, 6709, 27.7567667, -81.4639834, '']
LizQuenUniverse 0
Palmetto, FL (27.5278065, -82.5836068572292)
['The best comedy ever! https://t.co/1jYsmvuoMq', 'Wed Aug 17 13:43:27 +0000 2016', None, 0, 0, 'hight_frank', 'Palmetto, FL', 'en', 119, False, 1486, 27.5278065, -82.5836068572292, '']
New York, NY (40.7305991, -73.9865811)
["RT @chrisfholm: Is it just me, or is Trump's campaign starting to look like the credits to Monty Python and the Holy Grail? #ThoseResponsib…", 'Wed Aug 17 13:43:30 +0000 2016', None, 0, 0, 'EvieManieri', 'New York, NY', 'en', 740, False, 5351, 40.7305991, -73.9865811, '#ThoseResponsib…']
mabalacat city, pampanga (15.2228211, 120.5733105)
['Spark🎶', 'Wed Aug 17 13:43:34 +0000 2016', None, 0, 0, 'kpicban', 'mabalacat city, pampanga', 'en', 254, False, 3528, 15.2228211, 120.5733105, '']
Budapest, Magyarország (47.4983815, 19.0404707)
['RT @pypi_updates2: frida 7.3.1: Inject JavaScript to explore native apps on Windows, Mac, Linux, iOS and A... https://t.co/9Kai4UMthu', 'Wed Aug 17 13:43:34 +0000 2016', None, 0, 0, 'annatomkaartist', 'Budapest, Magyarország', 'hu', 1198, False, 19689, 47.4983815, 19.0404707, '']
Republic of @lizasoberano 0
Lucena City, Calabarzon (13.9269297, 121.6131654)
["RT @MichelleVito: you show me love never apart, you're always there for me i'm alright. you are the spark 🎶 #LSS\n@lizasoberano 😍😍 https://t…", 'Wed Aug 17 13:43:39 +0000 2016', None, 0, 0, 'carstine_26', 'Lucena City, Calabarzon', 'en', 132, False, 1307, 13.9269297, 121.6131654, '#LSS']
South Africa - North Japan 0
Highland Village, TX (33.091788, -97.0466758)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:43:48 +0000 2016', None, 0, 0, 'Li__um', 'Highland Village, TX', 'en', 285, False, 5931, 33.091788, -97.0466758, '']
Earth-616 (14.6761486, 121.0348183)
["I'm a spark and you're a boom", 'Wed Aug 17 13:43:57 +0000 2016', None, 0, 0, 'ANTlbiotics', 'Earth-616', 'en', 1151, False, 24338, 14.6761486, 121.0348183, '']
Philippines (12.7503486, 122.7312101)
['"you are the spark, undying light\nyou are my everything,my desire..."\n\nlss na talaga @lizasoberano\n#DolceAmoreBusted https://t.co/HK69FO6gSS', 'Wed Aug 17 13:43:57 +0000 2016', None, 0, 0, 'jemlovesyuu', 'Philippines', 'en', 1613, True, 54549, 12.7503486, 122.7312101, '#DolceAmoreBusted']
Chester, UK (53.1908873, -2.8908954)
['New report: Hadoop software industry analysis, size, share and 2021 forecast - WhaTech https://t.co/fMLHSWCnez https://t.co/22LREDUCa5', 'Wed Aug 17 13:43:58 +0000 2016', None, 0, 0, 'ProjectAnalysis', 'Chester, UK', 'en', 1369, False, 29216, 53.1908873, -2.8908954, '']
Snapchat: tevin_omg 0
San Francisco, CA (37.7792808, -122.4192362)
['qtgurlxx: 1ClickSparkCluster for Your Big Data Center Cloud - https://t.co/Xt9pA8dXiQ Relativity Visualized: https://t.co/f92cqyL1YP', 'Wed Aug 17 13:44:03 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066900, 37.7792808, -122.4192362, '']
WI.MN.USA (44.00044095, -92.45452705)
['RT @coast2coastpd: How Your Biggest Failure Can Spark Your Greatest Success... - https://t.co/Xw0EfMmUKs', 'Wed Aug 17 13:44:03 +0000 2016', None, 0, 0, 'Raymond_Norman', 'WI.MN.USA', 'en', 23977, False, 265835, 44.00044095, -92.45452705, '']
Grenoble (45.182478, 5.7210773)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:44:04 +0000 2016', None, 0, 0, 'Mohradim', 'Grenoble', 'en', 140, False, 3470, 45.182478, 5.7210773, '']
Florida (27.7567667, -81.4639834)
[".@JustinCapponPro on #Periscope: 🎯 THE ELEVATOR PITCH 🎯Tips, Do's, &amp; Don'ts for crafting a pitch that will SPARK … https://t.co/S0ls3Id9vw", 'Wed Aug 17 13:44:06 +0000 2016', None, 0, 0, 'toniaquarterman', 'Florida', 'en', 81, False, 864, 27.7567667, -81.4639834, '#Periscope:']
Madrid, Comunidad de Madrid (40.4167047, -3.7035824)
['Seeking Experienced Researcher for MSCA-IF-EF-SE 2016. Background in data science and IoT-smart sensors required https://t.co/DsWWP1h58E', 'Wed Aug 17 13:44:06 +0000 2016', None, 0, 0, 'NSAlmodovar', 'Madrid, Comunidad de Madrid', 'es', 125, True, 124, 40.4167047, -3.7035824, '']
San Francisco, CA (37.7792808, -122.4192362)
['RebeEngland: 1ClickKafkaCluster for Your Big Data Center Cloud - https://t.co/nw2oSUiyRB Real ACT 3rd Prep Guide: https://t.co/mx4kTQVoKC', 'Wed Aug 17 13:44:07 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066901, 37.7792808, -122.4192362, '']
Republic of the Philippines (12.7503486, 122.7312101)
['#lizasoberano posted August 17, 2016 at 09:39PM https://t.co/qr1uIAO1vx', 'Wed Aug 17 13:44:09 +0000 2016', None, 0, 0, 'sweetamoreblog', 'Republic of the Philippines', 'en', 4, False, 541, 12.7503486, 122.7312101, '#lizasoberano']
Wonderland (16.3131877, 121.1297478)
['you are the spark, undying light, you are my everything , my desire 💕🎧\n#PushAwardsLizQuens \n#DolceAmoreBusted', 'Wed Aug 17 13:44:09 +0000 2016', None, 0, 0, 'mamaradloicka_', 'Wonderland', 'en', 641, True, 40344, 16.3131877, 121.1297478, '#PushAwardsLizQuens #DolceAmoreBusted']
Vollen, Norway (59.8084096, 10.4818905)
['RT @PeaceOpsCIC: Where are the #Europeans? Will they ever again be big contributors to @UNPeacekeeping?  https://t.co/ohakpBXpDM https://t.…', 'Wed Aug 17 13:44:11 +0000 2016', None, 0, 0, 'bjornhamland', 'Vollen, Norway', 'no', 520, False, 694, 59.8084096, 10.4818905, '#Europeans?']
my mind (39.3313419, -76.6339949)
['Sarah? Have we ever even talked about Monty Python \U000fec2e? https://t.co/UqLVER7RAH', 'Wed Aug 17 13:44:14 +0000 2016', None, 0, 0, 'nathanmaschine', 'my mind', 'en', 58, True, 1880, 39.3313419, -76.6339949, '']
Arnold, MO (38.4328317, -90.3776186)
["RT @craigbrownphd: Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes i... https://t.co/WOUHY8eXS9 #Bi…", 'Wed Aug 17 13:44:15 +0000 2016', None, 0, 0, 's0schurch', 'Arnold, MO', 'en', 273, True, 3989, 38.4328317, -90.3776186, '#Bi…']
rome,italy 0
são paulo, sp 0
Penrith, Sydney (-33.751111, 150.6941667)
["@bjee77 @Mary27Ellen You could say exactly the same about sperm, and in one Tweet you've reached Monty Python. Don't be daft.", 'Wed Aug 17 13:44:21 +0000 2016', None, 0, 0, 'HappySinger', 'Penrith, Sydney', 'en', 3162, True, 63281, -33.751111, 150.6941667, '']
Bangued, Cordillera Admin Regi 0
United States (39.7837304, -100.4458824)
['Data Analyst Hadoop Python Developer Jobs in Irving, TX #Irving #TX #jobs #jobsearch https://t.co/SmapxH4Ypp', 'Wed Aug 17 13:44:32 +0000 2016', None, 0, 0, 'jobfindlypython', 'United States', 'en', 960, False, 95734, 39.7837304, -100.4458824, '#Irving #TX #jobs #jobsearch']
lizquen, justin & ariana 0
Manila,Philippines (14.5906216, 120.9799696)
['RT @lizasoberano: Listen to "Spark," my single from the Dolce Amore OST album - now on Spotify. Physical release on August 19! ❤️ https://t…', 'Wed Aug 17 13:44:34 +0000 2016', None, 0, 0, 'mariakath_', 'Manila,Philippines', 'en', 111, True, 6800, 14.5906216, 120.9799696, '']
In A White Van 0
Montreal  (45.5087928, -73.5539819)
['RT @SilverSherpa: New research: earliest signs of #Alzheimers related to decreased blood flow #ElderCare  @OntarioBrain @TheNeuro_MNI  http…', 'Wed Aug 17 13:44:36 +0000 2016', None, 0, 0, 'TheNeuro_MNI', 'Montreal ', 'en', 2682, False, 1329, 45.5087928, -73.5539819, '#Alzheimers #ElderCare']
Bonifacio High Street (14.55049515, 121.051316013746)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:44:37 +0000 2016', None, 0, 0, 'Realbebay', 'Bonifacio High Street', 'en', 74, False, 19637, 14.55049515, 121.051316013746, '']
La Presa, Benguet (16.4589777, 120.745447804036)
["RT @ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to the ears 🙈😁\n\nArt by @mspainteilyn ✏\n\n@lizasoberano 💙 https://t…", 'Wed Aug 17 13:44:41 +0000 2016', None, 0, 0, 'HopeLoveQuenito', 'La Presa, Benguet', 'en', 131, False, 15180, 16.4589777, 120.745447804036, '']
San Francisco, CA (37.7792808, -122.4192362)
['CARRINDIAN: 1ClickCassandraCluster for Your Big Data Center Cloud - https://t.co/HzceuBn21w Those Who Can, Teach: https://t.co/MjDhPEdsjp', 'Wed Aug 17 13:44:41 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066906, 37.7792808, -122.4192362, '']
Tweets may be scheduled.   0
  Chicago   (41.8755546, -87.6244211)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:44:45 +0000 2016', None, 0, 0, 'Waves_So_Sick', '  Chicago  ', 'en', 6592, False, 19809, 41.8755546, -87.6244211, '']
Montreal, Quebec (45.5087928, -73.5539819)
['Looking for a Senior Big Data #Architect - #Toronto in Toronto, ON Canada https://t.co/HTBsmiU0a3 #job #architect', 'Wed Aug 17 13:44:54 +0000 2016', None, 0, 0, 'DavangPatel', 'Montreal, Quebec', 'en', 391, True, 7543, 45.5087928, -73.5539819, '#Architect #Toronto #job #architect']
San Francisco, CA (37.7792808, -122.4192362)
['carlottarichar7: HyperScale HyperConvergence Big Data Cloud Advanced Training - https://t.co/rXPdl5ke1O: https://t.co/kFx1mtQpos', 'Wed Aug 17 13:44:56 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066907, 37.7792808, -122.4192362, '']
LizQuen's 0
♡♡♡ Happy @ Syder's ♡♡♡ 0
New York (40.7305991, -73.9865811)
['RT @MongoDB: #MongoDB named a leader in The @Forrester Wave: #BigData #NoSQL Q3 2016\u200b. Download it now: https://t.co/in4nG37gX2 https://t.c…', 'Wed Aug 17 13:45:00 +0000 2016', None, 0, 0, 'Jennifer_Seelin', 'New York', 'en', 849, True, 2429, 40.7305991, -73.9865811, '#MongoDB #BigData #NoSQL']
Bengaluru, India (12.9791198, 77.5912997)
['Python: user input and commandline arguments #python #input #command-line-arguments https://t.co/ejxaScFQm1', 'Wed Aug 17 13:45:00 +0000 2016', None, 0, 0, 'PythonQnA', 'Bengaluru, India', 'en', 128, False, 12455, 12.9791198, 77.5912997, '#python #input #command-line-arguments']
Marlow (51.5718668, -0.7769499)
['Burn the talent churn using Big Data https://t.co/2FEhN4ju1z', 'Wed Aug 17 13:45:01 +0000 2016', None, 0, 0, 'russellharper', 'Marlow', 'en', 389, False, 4543, 51.5718668, -0.7769499, '']
Paris, France (48.8566101, 2.3514992)
['Getting started with machine learning in Python - Crowdcast https://t.co/aSTLTCH1f8 https://t.co/jeVWKgTehg', 'Wed Aug 17 13:45:02 +0000 2016', None, 0, 0, 'darkelda', 'Paris, France', 'fr', 656, True, 21312, 48.8566101, 2.3514992, '']
Worldwide (53.4575797, -2.226672)
['RT @nicolambn: How to become a Data driven Business \nhttps://t.co/OIZCmeoffS … \n#Data #BigData #DataDriven #DataQuality', 'Wed Aug 17 13:45:02 +0000 2016', None, 0, 0, 'DQ_Global', 'Worldwide', 'en', 1344, False, 3003, 53.4575797, -2.226672, '#Data #BigData #DataDriven #DataQuality']
Ontario, Canada (50.0000002, -86.0)
["'Does your Work Spark JOY?' by @leepryke shared via @KlussterApp https://t.co/jjK53GFKfI", 'Wed Aug 17 13:45:03 +0000 2016', None, 0, 0, 'zimpeterw', 'Ontario, Canada', 'en', 14598, False, 41405, 50.0000002, -86.0, '']
London (51.5073219, -0.1276473)
['The next big thing: outsourcing your data analytics https://t.co/s9BbK6EyBK', 'Wed Aug 17 13:45:04 +0000 2016', None, 0, 0, 'Gabokappa', 'London', 'en', 355, True, 3725, 51.5073219, -0.1276473, '']
Leeds, England (53.7974185, -1.543794)
['The Science of Data-Driven Storytelling #measure #analytics #bigdata #datascience https://t.co/4HRoAMerEo', 'Wed Aug 17 13:45:04 +0000 2016', None, 0, 0, 'jonnylongden', 'Leeds, England', 'en', 17749, False, 8311, 53.7974185, -1.543794, '#measure #analytics #bigdata #datascience']
Zürich, Switzerland (47.3685586, 8.5404434)
['The next big thing: outsourcing your data analytics - https://t.co/fth2JgPjkA', 'Wed Aug 17 13:45:05 +0000 2016', None, 0, 0, 'thusmann', 'Zürich, Switzerland', 'de', 470, True, 1567, 47.3685586, 8.5404434, '']
Budapest, Hungary (47.4983815, 19.0404707)
['5 Free Data Science eBooks For Your Summer Reading List https://t.co/in8OPYYSsv', 'Wed Aug 17 13:45:05 +0000 2016', None, 0, 0, 'Szaki85', 'Budapest, Hungary', 'en', 1353, False, 3805, 47.4983815, 19.0404707, '']
New Jersey, USA (40.0757384, -74.4041621)
['#Opinion: Are #big #data #analytics more important than the #cloud? @RWW #PLATFORMS https://t.co/I589hq3iyn https://t.co/UAqVW23DUm', 'Wed Aug 17 13:45:08 +0000 2016', None, 0, 0, 'LXRGuide', 'New Jersey, USA', 'en', 294, False, 243, 40.0757384, -74.4041621, '#Opinion: #big #data #analytics #cloud? #PLATFORMS']
Kampala, Uganda (0.3177137, 32.5813539)
['Making LOCAL GOVERNMENT work in Africa is to make them have  BIG DATA oriented development- (data on everything in development)', 'Wed Aug 17 13:45:09 +0000 2016', None, 0, 0, 'UIDC_UG', 'Kampala, Uganda', 'sv', 381, False, 10031, 0.3177137, 32.5813539, '']
Kentucky, USA (37.5726028, -85.155141)
['RT @Dory: It\'s always Florida. Always.  RT ”@SeriousStrange: A 17ft python pregnant with 87 eggs was caught in Florida." https://t.co/pXJfj…', 'Wed Aug 17 13:45:09 +0000 2016', None, 0, 0, 'spoiledjimmies', 'Kentucky, USA', 'en', 88, False, 2071, 37.5726028, -85.155141, '']
North Carolina, USA☀️ 0
New Brunswick, Canada (46.5, -66.7499999)
["RT @HuddleToday: T4G president Geoff Flood talks big, but he's got the data to back it up: https://t.co/yhzMJYvsi4", 'Wed Aug 17 13:45:10 +0000 2016', None, 0, 0, 'jkamerman', 'New Brunswick, Canada', 'en', 67, True, 255, 46.5, -66.7499999, '']
London, England (51.5073219, -0.1276473)
['‘How Patients Think’: Filling In Big Data’s Gaps https://t.co/lgizKgzQ6y', 'Wed Aug 17 13:45:17 +0000 2016', None, 0, 0, 'Innovatorunit', 'London, England', 'en-gb', 82, False, 208, 51.5073219, -0.1276473, '']
Bengaluru, India (12.9791198, 77.5912997)
['RT @IBMzSystems: A or B: The top business beneﬁt of improved data analytics is ____. More info: https://t.co/p3e2hfVDnI #Spark #IBMz https:…', 'Wed Aug 17 13:45:19 +0000 2016', None, 0, 0, 'g_vinay15', 'Bengaluru, India', 'en', 167, True, 7524, 12.9791198, 77.5912997, '#Spark #IBMz']
Away from Home (38.9125, -77.0167999)
["RT @MzkayeL: #PushAwardsLizQuens cto lizasoberano: RT ULTIMATE_LQFANB: I made a cover of SPARK. Hope it's not too offensive to … https://t.…", 'Wed Aug 17 13:45:24 +0000 2016', None, 0, 0, 'quenhope2014', 'Away from Home', 'en', 11, False, 10633, 38.9125, -77.0167999, '#PushAwardsLizQuens']
Boise, Idaho (43.61656, -116.2008349)
['Please do your part and follow #fire restrictions. One spark is all it takes to ignite a fire. https://t.co/HRJtjiycN7', 'Wed Aug 17 13:45:25 +0000 2016', None, 0, 0, 'BoiseFire', 'Boise, Idaho', 'en', 1915, True, 613, 43.61656, -116.2008349, '#fire']
Alberta, Canada (55.0, -114.9999998)
['RT @BestMovieLine: Monty Python and the Holy Grail https://t.co/wHZ2kHAzy5', 'Wed Aug 17 13:45:31 +0000 2016', None, 0, 0, 'CantosPM', 'Alberta, Canada', 'en', 517, False, 3368, 55.0, -114.9999998, '']
Indonesia (-4.7993355, 114.5632032)
['Send big data to your family, friends and colleagues! https://t.co/27pXMPTtBM', 'Wed Aug 17 13:45:32 +0000 2016', None, 0, 0, 'ki_twap', 'Indonesia', 'id', 12, False, 234, -4.7993355, 114.5632032, '']
San Jose, CA (37.3361905, -121.8905832)
['RT @marcusborba: Use Big Data to Create Value for Customers, Not Just Target Them https://t.co/o5xhogEfXe via @data_nerd #BigData', 'Wed Aug 17 13:45:32 +0000 2016', None, 0, 0, 'datatorrent', 'San Jose, CA', 'en', 4415, True, 879, 37.3361905, -121.8905832, '#BigData']
Tomorrow Isn't Promised 0
Amsterdam (52.374436, 4.8979956033677)
['@HeerJeet Some on the left want Trump win to spark revolutionary change. Might Britebart want same for establishment GOP to spark uprising?', 'Wed Aug 17 13:45:49 +0000 2016', None, 0, 0, 'mrlloydgp', 'Amsterdam', 'en', 166, False, 526, 52.374436, 4.8979956033677, '']
California (36.7014631, -118.7559973)
['Now hiring for https://t.co/FfjkY7UIxK Engineer (Hadoop) Needed - FL in Temple Terrace, FL https://t.co/bSAqM4QuKn #job', 'Wed Aug 17 13:45:52 +0000 2016', None, 0, 0, 'Janovah', 'California', 'en', 434, False, 24355, 36.7014631, -118.7559973, '#job']
Atlanta, GA (33.7490987, -84.3901848)
['Here is the huge collection of numpy, pandas, matplotlib, and seaborn examples that you have been looking for. https://t.co/klZjmmvr62', 'Wed Aug 17 13:45:52 +0000 2016', None, 0, 0, 'jacobeisenstein', 'Atlanta, GA', 'en', 1964, False, 1419, 33.7490987, -84.3901848, '']
Chennai, India (13.0796914, 80.2829533)
['RT @TeluguStates: No one can replace chiranjeevi ,but i like the spark &amp; spontaneity of Allu Arjun . My Favorite Hero is Prabhas  - Telanga…', 'Wed Aug 17 13:45:53 +0000 2016', None, 0, 0, 'ArjunFanatic', 'Chennai, India', 'en', 126, False, 3006, 13.0796914, 80.2829533, '']
San Francisco, CA (37.7792808, -122.4192362)
['kamzou08: 1ClickElasticSearchCluster for Your Big Data Center Cloud - https://t.co/KJWVbv890U Management: https://t.co/XBdWorVvYy', 'Wed Aug 17 13:45:55 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066909, 37.7792808, -122.4192362, '']
London (51.5073219, -0.1276473)
["You're only given a little spark of madness. You mustn't lose it. -Robin Williams", 'Wed Aug 17 13:45:58 +0000 2016', None, 0, 0, 'NancyPowell3', 'London', 'en', 9, False, 621, 51.5073219, -0.1276473, '']
Global client base 0
Holmdel, NJ (40.3451095, -74.1840321)
['@JoshLuke4Health thanks 4 the follow! Curious how #bigdata can benefit ur company? Check out https://t.co/OtbiH5PH2x for big data solutions!', 'Wed Aug 17 13:46:08 +0000 2016', None, 0, 0, 'AvlinoAlenza', 'Holmdel, NJ', 'en', 1360, True, 965, 40.3451095, -74.1840321, '#bigdata']
Boise, ID (43.61656, -116.2008349)
['RT @BoiseFire: Please do your part and follow #fire restrictions. One spark is all it takes to ignite a fire. https://t.co/HRJtjiycN7', 'Wed Aug 17 13:46:10 +0000 2016', None, 0, 0, 'idahogrizzley', 'Boise, ID', 'en', 55, False, 59, 43.61656, -116.2008349, '#fire']
Ny  (43.1561681, -75.8449945)
['So why did they have to kill it though?  https://t.co/PRfHoFMeGw', 'Wed Aug 17 13:46:14 +0000 2016', None, 0, 0, 'therealwavey', 'Ny ', 'en', 1095, True, 69823, 43.1561681, -75.8449945, '']
Nord-Pas-de-Calais, France (50.52939535, 2.45002734526307)
['RT @EasyAnalytics1: How data science conquered baseball – and why fantasy baseball is next https://t.co/pSSikOVwTU https://t.co/82bIa7bjcm', 'Wed Aug 17 13:46:15 +0000 2016', None, 0, 0, 'Pvalsfr', 'Nord-Pas-de-Calais, France', 'fr', 1600, False, 260558, 50.52939535, 2.45002734526307, '']
Ile-de-France, France (48.5716633, 2.5670187)
['@BenjaminMarcon: Making Big Data Drive Digital Transformation – As Disru...... https://t.co/42LpIkeh2t https://t.co/0voQvOJBLL', 'Wed Aug 17 13:46:15 +0000 2016', None, 0, 0, 'GuegoC', 'Ile-de-France, France', 'fr', 92, True, 3319, 48.5716633, 2.5670187, '']
San Francisco, CA (37.7792808, -122.4192362)
['arcade_mobile: 1ClickElasticSearchCluster for Your Big Data Center Cloud - https://t.co/UQxpVCWtol: Live Bait: https://t.co/N56nKwiuoH', 'Wed Aug 17 13:46:16 +0000 2016', None, 0, 0, 'CloudataNow', 'San Francisco, CA', 'en', 880, False, 2066911, 37.7792808, -122.4192362, '']
Toronto, ON (43.6529206, -79.3849007)
['RT @jacobeisenstein: Here is the huge collection of numpy, pandas, matplotlib, and seaborn examples that you have been looking for. https:/…', 'Wed Aug 17 13:46:18 +0000 2016', None, 0, 0, 'alexhanna', 'Toronto, ON', 'en', 3611, True, 61492, 43.6529206, -79.3849007, '']
London, England (51.5073219, -0.1276473)
['Big Data Is Taking The Digital World By Storm https://t.co/uWIDAOeOji #tech #bigdata #cloud https://t.co/Uk2DNuVIcn', 'Wed Aug 17 13:46:20 +0000 2016', None, 0, 0, 'AukseNzir', 'London, England', 'en', 44, False, 2247, 51.5073219, -0.1276473, '#tech #bigdata #cloud']
London (51.5073219, -0.1276473)
['Big Data Is Taking The Digital World By Storm https://t.co/Hrx8WsS0pg #tech #bigdata #cloud https://t.co/4yobRecirF', 'Wed Aug 17 13:46:22 +0000 2016', None, 0, 0, 'pattersonpolly1', 'London', 'en', 63, False, 2233, 51.5073219, -0.1276473, '#tech #bigdata #cloud']
Baltimore, MD (39.2908608, -76.6108072)
['Must watch movies on Data Science via /r/datascience https://t.co/pgvymYLBl4', 'Wed Aug 17 13:46:24 +0000 2016', None, 0, 0, 'chris_n_vinegar', 'Baltimore, MD', 'en', 254, True, 31334, 39.2908608, -76.6108072, '']
Where Sunderland Lives 0
http://www.thewoodlandstech.ne 0
The Woodlands, TX (30.1734194, -95.5046859)
["Amazon Kinesis Analytics and the road to Big Data's killer app: Kinesis Analytics makes it easier to do strea... https://t.co/lg3Beib7Qk", 'Wed Aug 17 13:46:29 +0000 2016', None, 0, 0, 'tWoodlandsTech', 'The Woodlands, TX', 'en', 165, False, 1796, 30.1734194, -95.5046859, '']
ouat, jtv, bates motel, pll  0
Philadelphia, PA (39.9523993, -75.1635898)
['RT @jacobeisenstein: Here is the huge collection of numpy, pandas, matplotlib, and seaborn examples that you have been looking for. https:/…', 'Wed Aug 17 13:46:30 +0000 2016', None, 0, 0, 'ccb', 'Philadelphia, PA', 'en', 2351, True, 1189, 39.9523993, -75.1635898, '']
Overland Park, Kansas (38.9822282, -94.6707916)
['Updated guides for Adobe Spark Page and Adobe Spark Video (formerly Adobe Voice) https://t.co/AkoPgoDRIO', 'Wed Aug 17 13:46:32 +0000 2016', None, 0, 0, 'BVEdTec', 'Overland Park, Kansas', 'en', 1647, False, 5173, 38.9822282, -94.6707916, '']

Imprima o tamanho da DataFrame original e do auxiliar



In [31]:

    
total_df = len(df)
total_aux = len(df_aux)

print('Dataframe orginal:', total_df)
print('Dataframe auxiliar:', total_aux)









    



Dataframe orginal: 442
Dataframe auxiliar: 0

Responda:

1) Qual foi a porcentagem de Tweets descartados?



In [32]:

    
perc = total_aux * 100 / total_df
print('O percentual de tweets descartados foi de {}%'.format(round(perc,2)))









    



O percentual de tweets descartados foi de 0.0%

2) Qual a porcentagem de tweets que tinham habilitado o geo_enabled?



In [33]:

    
totalGeoEnabled = 0
for i in range (1, len(df)):
    if (df.user[i]['geo_enabled'] == True):
        totalGeoEnabled += 1
percGeo = totalGeoEnabled * 100 / total_df
print('{}% dos tweets totais tinham o habilitado o geo_enabled'.format(round(percGeo,2)))









    



39.14% dos tweets totais tinham o habilitado o geo_enabled

Salve os dados em arquivos em arquivo CSV



In [72]:

    
df_aux.to_csv('tweets_lat-long.csv',sep=';',index=False)



In [ ]:

	contributors	coordinates	created_at	entities	extended_entities	favorited	filter_level	geo	id	...	quoted_status_id	quoted_status_id_str	retweeted	retweeted_status	source	text	timestamp_ms	truncated	user
0	None	None	Wed Aug 17 13:36:40 +0000 2016	{'hashtags': [], 'urls': [{'indices': [51, 74]...	NaN	False	low	None	765905185994633216	...	NaN	NaN	False	NaN	<a href="http://twitter.com/download/android" ...	Data Analytics In Insurance \| Articles \| Big D...	1471441000828	False	{'geo_enabled': False, 'verified': False, 'pro...
1	None	None	Wed Aug 17 13:36:44 +0000 2016	{'hashtags': [], 'urls': [{'indices': [117, 14...	NaN	False	low	None	765905200007688192	...	NaN	NaN	False	NaN	<a href="http://www.facebook.com/twitter" rel=...	Monty Python proclaimed, "Nobody expects the S...	1471441004169	False	{'geo_enabled': False, 'verified': False, 'pro...
2	None	None	Wed Aug 17 13:36:45 +0000 2016	{'hashtags': [], 'urls': [{'indices': [28, 51]...	NaN	False	low	None	765905206517305344	...	7.659045e+17	765904477471858689	False	NaN	<a href="http://twitter.com/download/iphone" r...	You're wasting your data 😂😂 https://t.co/MnD90...	1471441005721	False	{'geo_enabled': True, 'verified': False, 'prof...