#Import libraries

import pandas as pd
import requests
import tweepy
from tweepy import OAuthHandler
from tweepy import API
from tweepy import Cursor
import time
import datetime as dt
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

#data gathering section

Do the following activities: 1.Read from archive file¶

2.Read from TSV file with URL 3.Read from twitter via Twitter API

#Read CSV file into a dataframe using pandas read-csv function.
archive_df = pd.read_csv('twitter-archive-enhanced.csv')

#Read TSV file from a URL using requests function.
url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
r = requests.get(url, allow_redirects=True)
open('image_predictions.tsv', 'wb').write(r.content)

# read tsv file to dataframe
images_df = pd.read_csv('image-predictions.tsv', sep = '\t', encoding = 'utf-8')

#Read data from twitter using twitter API

consumer_key = '**' # fetched from twitter dev profile.
consumer_secret = '**' #fetched from twittter dev profile.
access_token = '**' #fetched from twitter dev profile.
access_token_secret = '**' #fetched from twitter dev profile.

#tweepy function for api access
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)


api = tweepy.API(auth, 
                 parser = tweepy.parsers.JSONParser(), wait_on_rate_limit = True, wait_on_rate_limit_notify = True)

# declare dictionary to hold twitter data
tweet_df_list = []


# Get the tweet details for every tweet id from archive dataframe 
for tweet_id in archive_df['tweet_id']:
    try:
    
        page = api.get_status(tweet_id, tweet_mode = 'extended')
        favorites = page['favorite_count'] 
        retweets = page['retweet_count'] 
        user_followers = page['user']['followers_count'] 
        user_favourites = page['user']['favourites_count'] 
        date_time = page['created_at'] 
        
        tweet_df_list.append({'tweet_id': int(tweet_id),
                        'favorites': int(favorites),
                        'retweets': int(retweets),
                        'user_followers': int(user_followers),
                        'user_favourites': int(user_favourites),
                        'date_time': pd.to_datetime(date_time)})
        # Catch the exceptions of the TweepError
    except Exception as e:
        print(str(tweet_id)+ " _ " + str(e))
        
    
    # convert dictionary to dataframe
tweet_df = pd.DataFrame(tweet_df_list, columns = ['tweet_id', 'favorites', 'retweets',
                                               'user_followers', 'user_favourites', 'date_time'])

# Save the dataFrame in file
tweet_df.to_csv('tweet_list.txt', encoding = 'utf-8', index=False)

888202515573088257 _ [{'code': 144, 'message': 'No status found with that ID.'}]
873697596434513921 _ [{'code': 144, 'message': 'No status found with that ID.'}]
869988702071779329 _ [{'code': 144, 'message': 'No status found with that ID.'}]
866816280283807744 _ [{'code': 144, 'message': 'No status found with that ID.'}]
861769973181624320 _ [{'code': 144, 'message': 'No status found with that ID.'}]
845459076796616705 _ [{'code': 144, 'message': 'No status found with that ID.'}]
842892208864923648 _ [{'code': 144, 'message': 'No status found with that ID.'}]
837012587749474308 _ [{'code': 144, 'message': 'No status found with that ID.'}]
827228250799742977 _ [{'code': 144, 'message': 'No status found with that ID.'}]
802247111496568832 _ [{'code': 144, 'message': 'No status found with that ID.'}]
775096608509886464 _ [{'code': 144, 'message': 'No status found with that ID.'}]
Rate limit reached. Sleeping for: 732
754011816964026368 _ [{'code': 144, 'message': 'No status found with that ID.'}]
Rate limit reached. Sleeping for: 731

# Read the tweet list file into a dataframe
tweet_df = pd.read_csv('tweet_list.txt', encoding = 'utf-8')

data gathering section ends...

data assessment section begins..

#Display archive dataframe for eye-ball check

archive_df

#Display image dataframe for eye-ball check
images_df

#Display  tweet dataframe for eye-ball check
tweet_df

#random check on all 3 data sources...
# Archive dataframe
print(archive_df['text'][100])
print(archive_df['expanded_urls'][200])

# image dataframe
print(images_df['p1'][50])
print(images_df['p2'][150])

# tweet data dataframe
print(tweet_df['tweet_id'][50])
print(tweet_df['date_time'][150])

Here are my favorite #dogsatpollingstations 
Most voted for a more consistent walking schedule and to increase daily pats tenfold. All 13/10 https://t.co/17FVMl4VZ5
https://twitter.com/dog_rates/status/854010172552949760/photo/1,https://twitter.com/dog_rates/status/854010172552949760/photo/1
triceratops
pug
882268110199369728
2017-05-10 00:08:34

# Assess archive data programatically
archive_df.info()
archive_df.describe()
archive_df['rating_numerator'].value_counts()
archive_df['rating_denominator'].value_counts()
archive_df['name'].value_counts()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
tweet_id                      2356 non-null int64
in_reply_to_status_id         78 non-null float64
in_reply_to_user_id           78 non-null float64
timestamp                     2356 non-null object
source                        2356 non-null object
text                          2356 non-null object
retweeted_status_id           181 non-null float64
retweeted_status_user_id      181 non-null float64
retweeted_status_timestamp    181 non-null object
expanded_urls                 2297 non-null object
rating_numerator              2356 non-null int64
rating_denominator            2356 non-null int64
name                          2356 non-null object
doggo                         2356 non-null object
floofer                       2356 non-null object
pupper                        2356 non-null object
puppo                         2356 non-null object
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

None              745
a                  55
Charlie            12
Cooper             11
Lucy               11
Oliver             11
Tucker             10
Lola               10
Penny              10
Winston             9
Bo                  9
Sadie               8
the                 8
Bailey              7
Toby                7
Daisy               7
an                  7
Buddy               7
Leo                 6
Jax                 6
Rusty               6
Milo                6
Jack                6
Dave                6
Oscar               6
Koda                6
Bella               6
Scout               6
Stanley             6
Larry               5
                 ... 
Wiggles             1
Comet               1
Monkey              1
Millie              1
Shikha              1
Linus               1
Schnitzel           1
Burt                1
Willy               1
Napolean            1
Ronduh              1
Miguel              1
Karma               1
Skittles            1
Jerome              1
Jennifur            1
Flash               1
Danny               1
Ralphson            1
Alejandro           1
Jomathan            1
Fiji                1
Livvie              1
Kaiya               1
Michelangelope      1
Cedrick             1
his                 1
Ed                  1
Brandi              1
Rumble              1
Name: name, Length: 957, dtype: int64

# Assess image data programatically
images_df.info()
images_df.describe()
images_df['tweet_id'].value_counts()
images_df['jpg_url'].value_counts()
images_df['p1'].value_counts()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
tweet_id    2075 non-null int64
jpg_url     2075 non-null object
img_num     2075 non-null int64
p1          2075 non-null object
p1_conf     2075 non-null float64
p1_dog      2075 non-null bool
p2          2075 non-null object
p2_conf     2075 non-null float64
p2_dog      2075 non-null bool
p3          2075 non-null object
p3_conf     2075 non-null float64
p3_dog      2075 non-null bool
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB

golden_retriever             150
Labrador_retriever           100
Pembroke                      89
Chihuahua                     83
pug                           57
chow                          44
Samoyed                       43
toy_poodle                    39
Pomeranian                    38
malamute                      30
cocker_spaniel                30
French_bulldog                26
Chesapeake_Bay_retriever      23
miniature_pinscher            23
seat_belt                     22
Staffordshire_bullterrier     20
German_shepherd               20
Siberian_husky                20
Cardigan                      19
web_site                      19
Eskimo_dog                    18
teddy                         18
Maltese_dog                   18
Shetland_sheepdog             18
beagle                        18
Rottweiler                    17
Shih-Tzu                      17
Lakeland_terrier              17
kuvasz                        16
Italian_greyhound             16
                            ... 
alp                            1
lacewing                       1
water_bottle                   1
flamingo                       1
piggy_bank                     1
wild_boar                      1
lorikeet                       1
boathouse                      1
stove                          1
ibex                           1
hummingbird                    1
four-poster                    1
Scotch_terrier                 1
hotdog                         1
prayer_rug                     1
rapeseed                       1
guenon                         1
electric_fan                   1
hammer                         1
teapot                         1
military_uniform               1
scorpion                       1
African_crocodile              1
sea_urchin                     1
pitcher                        1
lynx                           1
envelope                       1
banana                         1
standard_schnauzer             1
ice_lolly                      1
Name: p1, Length: 378, dtype: int64

# Assess tweet data programatically
tweet_df.info()
tweet_df.describe()
tweet_df['tweet_id'].value_counts()
tweet_df['user_followers'].value_counts()
tweet_df['user_favourites'].value_counts()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2344 entries, 0 to 2343
Data columns (total 6 columns):
tweet_id           2344 non-null int64
favorites          2344 non-null int64
retweets           2344 non-null int64
user_followers     2344 non-null int64
user_favourites    2344 non-null int64
date_time          2344 non-null object
dtypes: int64(5), object(1)
memory usage: 110.0+ KB

134700    2326
134701      18
Name: user_favourites, dtype: int64

Assessment section ends..

Cleaning section begins

Create copies of each dataframes before actual cleaning process.

archive_df_copy = archive_df.copy()
images_df_copy = images_df.copy()
tweet_df_copy = tweet_df.copy()

#verify copy
archive_df_copy.head()
images_df_copy.head()
tweet_df_copy.head()

data copying ends....

Define:¶

Merge 3 data sources into a single dataframe and then save it to a CSV file. Use tweet_id as key to merge. This is a tidyness issue.

#Code

df_master = pd.merge(archive_df, images_df, how = 'inner', on = ['tweet_id'] )
df_master = pd.merge(df_master, tweet_df, how = 'inner', on = ['tweet_id'])
df_master.to_csv('df_master.csv', encoding = 'utf-8')

#Test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2068 entries, 0 to 2067
Data columns (total 33 columns):
tweet_id                      2068 non-null int64
in_reply_to_status_id         23 non-null float64
in_reply_to_user_id           23 non-null float64
timestamp                     2068 non-null object
source                        2068 non-null object
text                          2068 non-null object
retweeted_status_id           75 non-null float64
retweeted_status_user_id      75 non-null float64
retweeted_status_timestamp    75 non-null object
expanded_urls                 2068 non-null object
rating_numerator              2068 non-null int64
rating_denominator            2068 non-null int64
name                          2068 non-null object
doggo                         2068 non-null object
floofer                       2068 non-null object
pupper                        2068 non-null object
puppo                         2068 non-null object
jpg_url                       2068 non-null object
img_num                       2068 non-null int64
p1                            2068 non-null object
p1_conf                       2068 non-null float64
p1_dog                        2068 non-null bool
p2                            2068 non-null object
p2_conf                       2068 non-null float64
p2_dog                        2068 non-null bool
p3                            2068 non-null object
p3_conf                       2068 non-null float64
p3_dog                        2068 non-null bool
favorites                     2068 non-null int64
retweets                      2068 non-null int64
user_followers                2068 non-null int64
user_favourites               2068 non-null int64
date_time                     2068 non-null object
dtypes: bool(3), float64(7), int64(8), object(15)
memory usage: 506.9+ KB

#Test
#count before deletion
len(df_master)

2068

Define¶

Delete Duplicate rows.

#Code
df_master = df_master.drop_duplicates()

#Test
#check for duplicates after deleting duplicates.. A value of zero rows suggests no duplicates any more..
df_master[df_master.duplicated(keep=False)]

Define¶

Delete retweet rows.. Delete rows where retweeted_status_id, retweeted_status_user_id are not NaN

#Code
some_values = ['NaN']

df_master = df_master.loc[df_master['retweeted_status_id'].isin(some_values)]
df_master = df_master.loc[df_master['retweeted_status_user_id'].isin(some_values)]

#test
#check whether retweet rows filtered out
df_master

Define¶

quality issue #3 Delete useless columns as these contains most of the values as NaN

#code
df_master = df_master.drop('in_reply_to_status_id', 1)

df_master = df_master.drop('in_reply_to_user_id', 1)

df_master = df_master.drop('retweeted_status_id', 1)

df_master = df_master.drop('retweeted_status_timestamp', 1)

#Test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 29 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    0 non-null float64
expanded_urls               1993 non-null object
rating_numerator            1993 non-null int64
rating_denominator          1993 non-null int64
name                        1993 non-null object
doggo                       1993 non-null object
floofer                     1993 non-null object
pupper                      1993 non-null object
puppo                       1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
p1                          1993 non-null object
p1_conf                     1993 non-null float64
p1_dog                      1993 non-null bool
p2                          1993 non-null object
p2_conf                     1993 non-null float64
p2_dog                      1993 non-null bool
p3                          1993 non-null object
p3_conf                     1993 non-null float64
p3_dog                      1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
date_time                   1993 non-null object
dtypes: bool(3), float64(4), int64(8), object(14)
memory usage: 426.2+ KB

Define¶

Data quality issue#4 Delete date_time as it's duplicate , same value coming from 2 data sources.

#code
df_master = df_master.drop('date_time', 1)

#test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 28 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    0 non-null float64
expanded_urls               1993 non-null object
rating_numerator            1993 non-null int64
rating_denominator          1993 non-null int64
name                        1993 non-null object
doggo                       1993 non-null object
floofer                     1993 non-null object
pupper                      1993 non-null object
puppo                       1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
p1                          1993 non-null object
p1_conf                     1993 non-null float64
p1_dog                      1993 non-null bool
p2                          1993 non-null object
p2_conf                     1993 non-null float64
p2_dog                      1993 non-null bool
p3                          1993 non-null object
p3_conf                     1993 non-null float64
p3_dog                      1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dtypes: bool(3), float64(4), int64(8), object(13)
memory usage: 410.7+ KB

define¶

data quality issue #5¶

for all the denominator < 10, replace with value 10 as denominator should not be less than 10

#code
mask = df_master.rating_denominator < 10
column_name = 'rating_denominator'
df_master.loc[mask, column_name] = 10

#Test
#a value of zero shows , cleaning step is successful.
df_master.loc[df_master['rating_denominator'] < 10]

define¶

data quality issue #6¶

In name field , a number of values are invalid. A proper dog name is in uppercase. Filter out all lowercase names.

#Code
mask = (df_master.name.str.islower() == True)
column_name = 'name'
df_master.loc[mask, column_name] = 'None'

#test
#eye ball check to confirm that invalid values such as   'a' ,'an' , 'the' are replaced with 'None'
df_master

Define¶

data quality issue #7¶

change data type of retweeted_id from float to object type. Keep tweet_id as int to facilitate visualization on it as plot axis.¶

#code
df_master['retweeted_status_user_id'] = df_master['retweeted_status_user_id'].astype(str)

#Test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 28 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    1993 non-null object
expanded_urls               1993 non-null object
rating_numerator            1993 non-null int64
rating_denominator          1993 non-null int64
name                        1993 non-null object
doggo                       1993 non-null object
floofer                     1993 non-null object
pupper                      1993 non-null object
puppo                       1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
p1                          1993 non-null object
p1_conf                     1993 non-null float64
p1_dog                      1993 non-null bool
p2                          1993 non-null object
p2_conf                     1993 non-null float64
p2_dog                      1993 non-null bool
p3                          1993 non-null object
p3_conf                     1993 non-null float64
p3_dog                      1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dtypes: bool(3), float64(3), int64(8), object(14)
memory usage: 410.7+ KB

define¶

quality issue #8¶

rename columns to give them a sensible names.

#Code
df_master = df_master.rename(columns = {'p1': 'category1', 'p2': 'category2', 'p3': 'category3', 
                                        'p1_conf': 'cat1_conf', 'p2_conf': 'cat2_conf', 
                                        'p3_conf': 'cat3_conf', 'p1_dog': 'cat1_dog',
                                        'p2_dog': 'cat2_dog', 'p3_dog': 'cat3_dog'})

#Test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 28 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    1993 non-null object
expanded_urls               1993 non-null object
rating_numerator            1993 non-null int64
rating_denominator          1993 non-null int64
name                        1993 non-null object
doggo                       1993 non-null object
floofer                     1993 non-null object
pupper                      1993 non-null object
puppo                       1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
category1                   1993 non-null object
cat1_conf                   1993 non-null float64
cat1_dog                    1993 non-null bool
category2                   1993 non-null object
cat2_conf                   1993 non-null float64
cat2_dog                    1993 non-null bool
category3                   1993 non-null object
cat3_conf                   1993 non-null float64
cat3_dog                    1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dtypes: bool(3), float64(3), int64(8), object(14)
memory usage: 410.7+ KB

define¶

quality issue#9¶

As denominator is always 10 ,there is not point in keeping this column. We can rename numerator column insteasd and rename it to rating_out_of_10

#code

df_master = df_master.drop('rating_denominator', 1)

df_master = df_master.rename(columns = {'rating_numerator': 'rating_out_of_10'})

#test
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 27 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    1993 non-null object
expanded_urls               1993 non-null object
rating_out_of_10            1993 non-null int64
name                        1993 non-null object
doggo                       1993 non-null object
floofer                     1993 non-null object
pupper                      1993 non-null object
puppo                       1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
category1                   1993 non-null object
cat1_conf                   1993 non-null float64
cat1_dog                    1993 non-null bool
category2                   1993 non-null object
cat2_conf                   1993 non-null float64
cat2_dog                    1993 non-null bool
category3                   1993 non-null object
cat3_conf                   1993 non-null float64
cat3_dog                    1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dtypes: bool(3), float64(3), int64(7), object(14)
memory usage: 395.1+ KB

Define¶

tidyness issue #2¶

doggo, floffer, pupper, puppo are nothing but dog stages. Merge these columns into a new column dog_stage.

#code

mask = (df_master.doggo == 'None')
column_name = 'doggo'
df_master.loc[mask, column_name] = ' '

mask = (df_master.floofer == 'None')
column_name = 'floofer'
df_master.loc[mask, column_name] = ' '

mask = (df_master.pupper == 'None')
column_name = 'pupper'
df_master.loc[mask, column_name] = ' '

mask = (df_master.puppo == 'None')
column_name = 'puppo'
df_master.loc[mask, column_name] = ' '


#merge these columns and create a column named dog_stage
df_master['dog_stage'] = df_master['doggo'] + df_master['floofer'] + df_master['pupper'] + df_master['puppo']

#delete redundant columns doggo, floofer, pupper, and puppo
df_master = df_master.drop('doggo', 1)
df_master = df_master.drop('floofer', 1)
df_master = df_master.drop('pupper', 1)
df_master = df_master.drop('puppo', 1)

#test 
#check dataframe info for above changes...
df_master.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1993 entries, 0 to 2067
Data columns (total 24 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    1993 non-null object
expanded_urls               1993 non-null object
rating_out_of_10            1993 non-null int64
name                        1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
category1                   1993 non-null object
cat1_conf                   1993 non-null float64
cat1_dog                    1993 non-null bool
category2                   1993 non-null object
cat2_conf                   1993 non-null float64
cat2_dog                    1993 non-null bool
category3                   1993 non-null object
cat3_conf                   1993 non-null float64
cat3_dog                    1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dog_stage                   1993 non-null object
dtypes: bool(3), float64(3), int64(7), object(11)
memory usage: 348.4+ KB

# Test
list(df_master)

['tweet_id',
 'timestamp',
 'source',
 'text',
 'retweeted_status_user_id',
 'expanded_urls',
 'rating_out_of_10',
 'name',
 'jpg_url',
 'img_num',
 'category1',
 'cat1_conf',
 'cat1_dog',
 'category2',
 'cat2_conf',
 'cat2_dog',
 'category3',
 'cat3_conf',
 'cat3_dog',
 'favorites',
 'retweets',
 'user_followers',
 'user_favourites',
 'dog_stage']

# Store the clean DataFrame in a CSV file
df_master.to_csv('twitter_archive_master.csv', index=False, encoding = 'utf-8')

df_master = pd.read_csv('twitter_archive_master.csv')
df_master.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1993 entries, 0 to 1992
Data columns (total 24 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    0 non-null float64
expanded_urls               1993 non-null object
rating_out_of_10            1993 non-null int64
name                        1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
category1                   1993 non-null object
cat1_conf                   1993 non-null float64
cat1_dog                    1993 non-null bool
category2                   1993 non-null object
cat2_conf                   1993 non-null float64
cat2_dog                    1993 non-null bool
category3                   1993 non-null object
cat3_conf                   1993 non-null float64
cat3_dog                    1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dog_stage                   1993 non-null object
dtypes: bool(3), float64(4), int64(7), object(10)
memory usage: 332.9+ KB

Data cleaing Section ends here.....

visualization section begins here....

# Import the clean dataset into dataframe
df_master = pd.read_csv('twitter_archive_master.csv')
df_master.info()
df_master

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1993 entries, 0 to 1992
Data columns (total 24 columns):
tweet_id                    1993 non-null int64
timestamp                   1993 non-null object
source                      1993 non-null object
text                        1993 non-null object
retweeted_status_user_id    0 non-null float64
expanded_urls               1993 non-null object
rating_out_of_10            1993 non-null int64
name                        1993 non-null object
jpg_url                     1993 non-null object
img_num                     1993 non-null int64
category1                   1993 non-null object
cat1_conf                   1993 non-null float64
cat1_dog                    1993 non-null bool
category2                   1993 non-null object
cat2_conf                   1993 non-null float64
cat2_dog                    1993 non-null bool
category3                   1993 non-null object
cat3_conf                   1993 non-null float64
cat3_dog                    1993 non-null bool
favorites                   1993 non-null int64
retweets                    1993 non-null int64
user_followers              1993 non-null int64
user_favourites             1993 non-null int64
dog_stage                   1993 non-null object
dtypes: bool(3), float64(4), int64(7), object(10)
memory usage: 332.9+ KB

#drwaing a scatter plot with axes as tweet_id and No. of retweets.
df_master.plot(kind = 'scatter', x = 'tweet_id', y = 'retweets', alpha = 0.5, color = 'red')
plt.xlabel('tweet_id')
plt.ylabel('retweets')
plt.title('Tweet Id vs Retweet Scatter plot')

Text(0.5,1,'Tweet Id vs Retweet Scatter plot')

#drawing a scatter plot with tweet_id and no. of user-favorites as axes.
df_master.plot(kind = 'scatter', x = 'tweet_id', y = 'user_favourites', alpha = 1, color = 'red')
plt.xlabel('tweet_id')
plt.ylabel('user_favourites')
plt.title('Tweet and user favorites Scatter plot')

Text(0.5,1,'Tweet and user favorites Scatter plot')

#draw bar chart for 5 most favourite tweets
test = df_master.sort_values(['favorites'], ascending=False)
test1 = test._slice(slice(0, 5))

test1.plot(x='tweet_id', y='favorites', kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x7f4fb912def0>

#top 10 most retweeted tweets
#draw bar chart for 5 most favourite tweets
test = df_master.sort_values(['retweets'], ascending=False)
test1 = test._slice(slice(0, 5))

test1.plot(x='tweet_id', y='retweets', kind='bar')

<matplotlib.axes._subplots.AxesSubplot at 0x7f4fb9137b38>

#Display top 5 dog breeds
df_master['category2'].value_counts().head(5)

Labrador_retriever          96
golden_retriever            82
Cardigan                    72
Chihuahua                   43
Chesapeake_Bay_retriever    40
Name: category2, dtype: int64

#take top 5 breeds and name all other breeds as 'Others' and then draw a pie chart on breed breakup
df1 = df_master.copy()
df1.loc[(df1['category2'] != 'Labrador_retriever') & (df1['category2'] != 'golden_retriever')
         & (df1['category2'] != 'Cardigan')
         & (df1['category2'] != 'Chihuahua')
         & (df1['category2'] != 'Pomeranian')
            , 'category2'] = 'Others'

df1

# plot chart for dog breed type distribution
# 
df1[df1['category2'].notnull()]['category2'].value_counts().plot(kind = 'pie', autopct='%1.1f%%')
plt.title('Dog breed distribution')

Text(0.5,1,'Dog breed distribution')

# demonstration of using seaborn:plot scatter plot for retweets

sns.lmplot('tweet_id', 'retweets', data=df_master, fit_reg=False)

<seaborn.axisgrid.FacetGrid at 0x7f4fbb0eb668>

**visualization section ends here....

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True
5	666050758794694657	https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg	1	Bernese_mountain_dog	0.651137	True	English_springer	0.263788	True	Greater_Swiss_Mountain_dog	0.016199	True
6	666051853826850816	https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg	1	box_turtle	0.933012	False	mud_turtle	0.045885	False	terrapin	0.017885	False
7	666055525042405380	https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg	1	chow	0.692517	True	Tibetan_mastiff	0.058279	True	fur_coat	0.054449	False
8	666057090499244032	https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg	1	shopping_cart	0.962465	False	shopping_basket	0.014594	False	golden_retriever	0.007959	True
9	666058600524156928	https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg	1	miniature_poodle	0.201493	True	komondor	0.192305	True	soft-coated_wheaten_terrier	0.082086	True
10	666063827256086533	https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg	1	golden_retriever	0.775930	True	Tibetan_mastiff	0.093718	True	Labrador_retriever	0.072427	True
11	666071193221509120	https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg	1	Gordon_setter	0.503672	True	Yorkshire_terrier	0.174201	True	Pekinese	0.109454	True
12	666073100786774016	https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg	1	Walker_hound	0.260857	True	English_foxhound	0.175382	True	Ibizan_hound	0.097471	True
13	666082916733198337	https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg	1	pug	0.489814	True	bull_mastiff	0.404722	True	French_bulldog	0.048960	True
14	666094000022159362	https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg	1	bloodhound	0.195217	True	German_shepherd	0.078260	True	malinois	0.075628	True
15	666099513787052032	https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg	1	Lhasa	0.582330	True	Shih-Tzu	0.166192	True	Dandie_Dinmont	0.089688	True
16	666102155909144576	https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg	1	English_setter	0.298617	True	Newfoundland	0.149842	True	borzoi	0.133649	True
17	666104133288665088	https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg	1	hen	0.965932	False	cock	0.033919	False	partridge	0.000052	False
18	666268910803644416	https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg	1	desktop_computer	0.086502	False	desk	0.085547	False	bookcase	0.079480	False
19	666273097616637952	https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg	1	Italian_greyhound	0.176053	True	toy_terrier	0.111884	True	basenji	0.111152	True
20	666287406224695296	https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg	1	Maltese_dog	0.857531	True	toy_poodle	0.063064	True	miniature_poodle	0.025581	True
21	666293911632134144	https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg	1	three-toed_sloth	0.914671	False	otter	0.015250	False	great_grey_owl	0.013207	False
22	666337882303524864	https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg	1	ox	0.416669	False	Newfoundland	0.278407	True	groenendael	0.102643	True
23	666345417576210432	https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg	1	golden_retriever	0.858744	True	Chesapeake_Bay_retriever	0.054787	True	Labrador_retriever	0.014241	True
24	666353288456101888	https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg	1	malamute	0.336874	True	Siberian_husky	0.147655	True	Eskimo_dog	0.093412	True
25	666362758909284353	https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg	1	guinea_pig	0.996496	False	skunk	0.002402	False	hamster	0.000461	False
26	666373753744588802	https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg	1	soft-coated_wheaten_terrier	0.326467	True	Afghan_hound	0.259551	True	briard	0.206803	True
27	666396247373291520	https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg	1	Chihuahua	0.978108	True	toy_terrier	0.009397	True	papillon	0.004577	True
28	666407126856765440	https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg	1	black-and-tan_coonhound	0.529139	True	bloodhound	0.244220	True	flat-coated_retriever	0.173810	True
29	666411507551481857	https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg	1	coho	0.404640	False	barracouta	0.271485	False	gar	0.189945	False
...	...	...	...	...	...	...	...	...	...	...	...	...
2045	886366144734445568	https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpg	1	French_bulldog	0.999201	True	Chihuahua	0.000361	True	Boston_bull	0.000076	True
2046	886680336477933568	https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg	1	convertible	0.738995	False	sports_car	0.139952	False	car_wheel	0.044173	False
2047	886736880519319552	https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpg	1	kuvasz	0.309706	True	Great_Pyrenees	0.186136	True	Dandie_Dinmont	0.086346	True
2048	886983233522544640	https://pbs.twimg.com/media/DE8yicJW0AAAvBJ.jpg	2	Chihuahua	0.793469	True	toy_terrier	0.143528	True	can_opener	0.032253	False
2049	887101392804085760	https://pbs.twimg.com/media/DE-eAq6UwAA-jaE.jpg	1	Samoyed	0.733942	True	Eskimo_dog	0.035029	True	Staffordshire_bullterrier	0.029705	True
2050	887343217045368832	https://pbs.twimg.com/ext_tw_video_thumb/88734...	1	Mexican_hairless	0.330741	True	sea_lion	0.275645	False	Weimaraner	0.134203	True
2051	887473957103951883	https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg	2	Pembroke	0.809197	True	Rhodesian_ridgeback	0.054950	True	beagle	0.038915	True
2052	887517139158093824	https://pbs.twimg.com/ext_tw_video_thumb/88751...	1	limousine	0.130432	False	tow_truck	0.029175	False	shopping_cart	0.026321	False
2053	887705289381826560	https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpg	1	basset	0.821664	True	redbone	0.087582	True	Weimaraner	0.026236	True
2054	888078434458587136	https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpg	1	French_bulldog	0.995026	True	pug	0.000932	True	bull_mastiff	0.000903	True
2055	888202515573088257	https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg	2	Pembroke	0.809197	True	Rhodesian_ridgeback	0.054950	True	beagle	0.038915	True
2056	888554962724278272	https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg	3	Siberian_husky	0.700377	True	Eskimo_dog	0.166511	True	malamute	0.111411	True
2057	888804989199671297	https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg	1	golden_retriever	0.469760	True	Labrador_retriever	0.184172	True	English_setter	0.073482	True
2058	888917238123831296	https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg	1	golden_retriever	0.714719	True	Tibetan_mastiff	0.120184	True	Labrador_retriever	0.105506	True
2059	889278841981685760	https://pbs.twimg.com/ext_tw_video_thumb/88927...	1	whippet	0.626152	True	borzoi	0.194742	True	Saluki	0.027351	True
2060	889531135344209921	https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg	1	golden_retriever	0.953442	True	Labrador_retriever	0.013834	True	redbone	0.007958	True
2061	889638837579907072	https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg	1	French_bulldog	0.991650	True	boxer	0.002129	True	Staffordshire_bullterrier	0.001498	True
2062	889665388333682689	https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg	1	Pembroke	0.966327	True	Cardigan	0.027356	True	basenji	0.004633	True
2063	889880896479866881	https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpg	1	French_bulldog	0.377417	True	Labrador_retriever	0.151317	True	muzzle	0.082981	False
2064	890006608113172480	https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg	1	Samoyed	0.957979	True	Pomeranian	0.013884	True	chow	0.008167	True
2065	890240255349198849	https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg	1	Pembroke	0.511319	True	Cardigan	0.451038	True	Chihuahua	0.029248	True
2066	890609185150312448	https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg	1	Irish_terrier	0.487574	True	Irish_setter	0.193054	True	Chesapeake_Bay_retriever	0.118184	True
2067	890729181411237888	https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg	2	Pomeranian	0.566142	True	Eskimo_dog	0.178406	True	Pembroke	0.076507	True
2068	890971913173991426	https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg	1	Appenzeller	0.341703	True	Border_collie	0.199287	True	ice_lolly	0.193548	False
2069	891087950875897856	https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg	1	Chesapeake_Bay_retriever	0.425595	True	Irish_terrier	0.116317	True	Indian_elephant	0.076902	False
2070	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True
2071	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False
2072	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True
2073	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True
2074	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False

	tweet_id	favorites	retweets	user_followers	user_favourites	date_time
0	892420643555336193	38680	8559	6997101	134700	2017-08-01 16:23:56
1	892177421306343426	33156	6291	6997101	134700	2017-08-01 00:17:27
2	891815181378084864	24958	4170	6997101	134700	2017-07-31 00:18:03
3	891689557279858688	42064	8687	6997101	134700	2017-07-30 15:58:51
4	891327558926688256	40212	9447	6997101	134700	2017-07-29 16:00:24
5	891087950875897856	20163	3127	6997101	134700	2017-07-29 00:08:17
6	890971913173991426	11816	2082	6997101	134700	2017-07-28 16:27:12
7	890729181411237888	65338	18971	6997101	134700	2017-07-28 00:22:40
8	890609185150312448	27713	4279	6997101	134700	2017-07-27 16:25:51
9	890240255349198849	31857	7448	6997101	134700	2017-07-26 15:59:51
10	890006608113172480	30583	7363	6997101	134700	2017-07-26 00:31:25
11	889880896479866881	27719	4989	6997101	134700	2017-07-25 16:11:53
12	889665388333682689	48003	10101	6997101	134700	2017-07-25 01:55:32
13	889638837579907072	27103	4564	6997101	134700	2017-07-25 00:10:02
14	889531135344209921	15052	2244	6997101	134700	2017-07-24 17:02:04
15	889278841981685760	25236	5446	6997101	134700	2017-07-24 00:19:32
16	888917238123831296	28996	4517	6997101	134700	2017-07-23 00:22:39
17	888804989199671297	25527	4363	6997101	134700	2017-07-22 16:56:37
18	888554962724278272	19850	3596	6997101	134700	2017-07-22 00:23:06
19	888078434458587136	21702	3511	6997101	134700	2017-07-20 16:49:33
20	887705289381826560	30108	5414	6997101	134700	2017-07-19 16:06:48
21	887517139158093824	46119	11713	6997101	134700	2017-07-19 03:39:09
22	887473957103951883	68935	18305	6997101	134700	2017-07-19 00:47:34
23	887343217045368832	33582	10446	6997101	134700	2017-07-18 16:08:03
24	887101392804085760	30451	5984	6997101	134700	2017-07-18 00:07:08
25	886983233522544640	35058	7806	6997101	134700	2017-07-17 16:17:36
26	886736880519319552	12037	3311	6997101	134700	2017-07-16 23:58:41
27	886680336477933568	22362	4486	6997101	134700	2017-07-16 20:14:00
28	886366144734445568	21142	3208	6997102	134700	2017-07-15 23:25:31
29	886267009285017600	116	4	6997102	134700	2017-07-15 16:51:35
...	...	...	...	...	...	...
2314	666411507551481857	447	328	6997189	134700	2015-11-17 00:24:19
2315	666407126856765440	110	41	6997189	134700	2015-11-17 00:06:54
2316	666396247373291520	167	86	6997189	134700	2015-11-16 23:23:41
2317	666373753744588802	190	93	6997189	134700	2015-11-16 21:54:18
2318	666362758909284353	778	574	6997189	134700	2015-11-16 21:10:36
2319	666353288456101888	221	73	6997190	134700	2015-11-16 20:32:58
2320	666345417576210432	299	139	6997190	134700	2015-11-16 20:01:42
2321	666337882303524864	198	92	6997190	134700	2015-11-16 19:31:45
2322	666293911632134144	509	357	6997190	134700	2015-11-16 16:37:02
2323	666287406224695296	149	66	6997190	134700	2015-11-16 16:11:11
2324	666273097616637952	176	76	6997190	134700	2015-11-16 15:14:19
2325	666268910803644416	104	35	6997190	134700	2015-11-16 14:57:41
2326	666104133288665088	14346	6634	6997190	134700	2015-11-16 04:02:55
2327	666102155909144576	80	13	6997190	134700	2015-11-16 03:55:04
2328	666099513787052032	156	68	6997190	134700	2015-11-16 03:44:34
2329	666094000022159362	164	74	6997190	134700	2015-11-16 03:22:39
2330	666082916733198337	119	45	6997190	134700	2015-11-16 02:38:37
2331	666073100786774016	322	164	6997190	134700	2015-11-16 01:59:36
2332	666071193221509120	148	62	6997190	134700	2015-11-16 01:52:02
2333	666063827256086533	476	219	6997190	134700	2015-11-16 01:22:45
2334	666058600524156928	112	57	6997190	134700	2015-11-16 01:01:59
2335	666057090499244032	298	142	6997190	134700	2015-11-16 00:55:59
2336	666055525042405380	434	252	6997190	134700	2015-11-16 00:49:46
2337	666051853826850816	1223	853	6997190	134700	2015-11-16 00:35:11
2338	666050758794694657	132	58	6997190	134700	2015-11-16 00:30:50
2339	666049248165822465	109	41	6997190	134700	2015-11-16 00:24:50
2340	666044226329800704	299	141	6997190	134700	2015-11-16 00:04:52
2341	666033412701032449	125	44	6997190	134700	2015-11-15 23:21:54
2342	666029285002620928	129	47	6997190	134700	2015-11-15 23:05:30
2343	666020888022790149	2560	517	6997190	134700	2015-11-15 22:32:08

	tweet_id	favorites	retweets	user_followers	user_favourites	date_time
0	892420643555336193	38680	8559	6997101	134700	2017-08-01 16:23:56
1	892177421306343426	33156	6291	6997101	134700	2017-08-01 00:17:27
2	891815181378084864	24958	4170	6997101	134700	2017-07-31 00:18:03
3	891689557279858688	42064	8687	6997101	134700	2017-07-30 15:58:51
4	891327558926688256	40212	9447	6997101	134700	2017-07-29 16:00:24

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	...	p2_conf	p2_dog	p3	p3_conf	p3_dog	favorites	retweets	user_followers	user_favourites	date_time
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643...	...	0.085851	False	banana	0.076110	False	38680	8559	6997101	134700	2017-08-01 16:23:56
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421...	...	0.090647	True	papillon	0.068957	True	33156	6291	6997101	134700	2017-08-01 00:17:27
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181...	...	0.078253	True	kelpie	0.031379	True	24958	4170	6997101	134700	2017-07-31 00:18:03
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557...	...	0.168086	True	spatula	0.040836	False	42064	8687	6997101	134700	2017-07-30 15:58:51
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558...	...	0.225770	True	German_short-haired_pointer	0.175219	True	40212	9447	6997101	134700	2017-07-29 16:00:24
5	891087950875897856	NaN	NaN	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891087950...	...	0.116317	True	Indian_elephant	0.076902	False	20163	3127	6997101	134700	2017-07-29 00:08:17
6	890971913173991426	NaN	NaN	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	NaN	NaN	NaN	https://gofundme.com/ydvmve-surgery-for-jax,ht...	...	0.199287	True	ice_lolly	0.193548	False	11816	2082	6997101	134700	2017-07-28 16:27:12
7	890729181411237888	NaN	NaN	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/890729181...	...	0.178406	True	Pembroke	0.076507	True	65338	18971	6997101	134700	2017-07-28 00:22:40
8	890609185150312448	NaN	NaN	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/890609185...	...	0.193054	True	Chesapeake_Bay_retriever	0.118184	True	27713	4279	6997101	134700	2017-07-27 16:25:51
9	890240255349198849	NaN	NaN	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/890240255...	...	0.451038	True	Chihuahua	0.029248	True	31857	7448	6997101	134700	2017-07-26 15:59:51
10	890006608113172480	NaN	NaN	2017-07-26 00:31:25 +0000	<a href="http://twitter.com/download/iphone" r...	This is Koda. He is a South Australian decksha...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/890006608...	...	0.013884	True	chow	0.008167	True	30583	7363	6997101	134700	2017-07-26 00:31:25
11	889880896479866881	NaN	NaN	2017-07-25 16:11:53 +0000	<a href="http://twitter.com/download/iphone" r...	This is Bruno. He is a service shark. Only get...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/889880896...	...	0.151317	True	muzzle	0.082981	False	27719	4989	6997101	134700	2017-07-25 16:11:53
12	889665388333682689	NaN	NaN	2017-07-25 01:55:32 +0000	<a href="http://twitter.com/download/iphone" r...	Here's a puppo that seems to be on the fence a...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/889665388...	...	0.027356	True	basenji	0.004633	True	48003	10101	6997101	134700	2017-07-25 01:55:32
13	889638837579907072	NaN	NaN	2017-07-25 00:10:02 +0000	<a href="http://twitter.com/download/iphone" r...	This is Ted. He does his best. Sometimes that'...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/889638837...	...	0.002129	True	Staffordshire_bullterrier	0.001498	True	27103	4564	6997101	134700	2017-07-25 00:10:02
14	889531135344209921	NaN	NaN	2017-07-24 17:02:04 +0000	<a href="http://twitter.com/download/iphone" r...	This is Stuart. He's sporting his favorite fan...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/889531135...	...	0.013834	True	redbone	0.007958	True	15052	2244	6997101	134700	2017-07-24 17:02:04
15	889278841981685760	NaN	NaN	2017-07-24 00:19:32 +0000	<a href="http://twitter.com/download/iphone" r...	This is Oliver. You're witnessing one of his m...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/889278841...	...	0.194742	True	Saluki	0.027351	True	25236	5446	6997101	134700	2017-07-24 00:19:32
16	888917238123831296	NaN	NaN	2017-07-23 00:22:39 +0000	<a href="http://twitter.com/download/iphone" r...	This is Jim. He found a fren. Taught him how t...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/888917238...	...	0.120184	True	Labrador_retriever	0.105506	True	28996	4517	6997101	134700	2017-07-23 00:22:39
17	888804989199671297	NaN	NaN	2017-07-22 16:56:37 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zeke. He has a new stick. Very proud o...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/888804989...	...	0.184172	True	English_setter	0.073482	True	25527	4363	6997101	134700	2017-07-22 16:56:37
18	888554962724278272	NaN	NaN	2017-07-22 00:23:06 +0000	<a href="http://twitter.com/download/iphone" r...	This is Ralphus. He's powering up. Attempting ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/888554962...	...	0.166511	True	malamute	0.111411	True	19850	3596	6997101	134700	2017-07-22 00:23:06
19	888078434458587136	NaN	NaN	2017-07-20 16:49:33 +0000	<a href="http://twitter.com/download/iphone" r...	This is Gerald. He was just told he didn't get...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/888078434...	...	0.000932	True	bull_mastiff	0.000903	True	21702	3511	6997101	134700	2017-07-20 16:49:33
20	887705289381826560	NaN	NaN	2017-07-19 16:06:48 +0000	<a href="http://twitter.com/download/iphone" r...	This is Jeffrey. He has a monopoly on the pool...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/887705289...	...	0.087582	True	Weimaraner	0.026236	True	30108	5414	6997101	134700	2017-07-19 16:06:48
21	887517139158093824	NaN	NaN	2017-07-19 03:39:09 +0000	<a href="http://twitter.com/download/iphone" r...	I've yet to rate a Venezuelan Hover Wiener. Th...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/887517139...	...	0.029175	False	shopping_cart	0.026321	False	46119	11713	6997101	134700	2017-07-19 03:39:09
22	887473957103951883	NaN	NaN	2017-07-19 00:47:34 +0000	<a href="http://twitter.com/download/iphone" r...	This is Canela. She attempted some fancy porch...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/887473957...	...	0.054950	True	beagle	0.038915	True	68935	18305	6997101	134700	2017-07-19 00:47:34
23	887343217045368832	NaN	NaN	2017-07-18 16:08:03 +0000	<a href="http://twitter.com/download/iphone" r...	You may not have known you needed to see this ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/887343217...	...	0.275645	False	Weimaraner	0.134203	True	33582	10446	6997101	134700	2017-07-18 16:08:03
24	887101392804085760	NaN	NaN	2017-07-18 00:07:08 +0000	<a href="http://twitter.com/download/iphone" r...	This... is a Jubilant Antarctic House Bear. We...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/887101392...	...	0.035029	True	Staffordshire_bullterrier	0.029705	True	30451	5984	6997101	134700	2017-07-18 00:07:08
25	886983233522544640	NaN	NaN	2017-07-17 16:17:36 +0000	<a href="http://twitter.com/download/iphone" r...	This is Maya. She's very shy. Rarely leaves he...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/886983233...	...	0.143528	True	can_opener	0.032253	False	35058	7806	6997101	134700	2017-07-17 16:17:36
26	886736880519319552	NaN	NaN	2017-07-16 23:58:41 +0000	<a href="http://twitter.com/download/iphone" r...	This is Mingus. He's a wonderful father to his...	NaN	NaN	NaN	https://www.gofundme.com/mingusneedsus,https:/...	...	0.186136	True	Dandie_Dinmont	0.086346	True	12037	3311	6997101	134700	2017-07-16 23:58:41
27	886680336477933568	NaN	NaN	2017-07-16 20:14:00 +0000	<a href="http://twitter.com/download/iphone" r...	This is Derek. He's late for a dog meeting. 13...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/886680336...	...	0.139952	False	car_wheel	0.044173	False	22362	4486	6997101	134700	2017-07-16 20:14:00
28	886366144734445568	NaN	NaN	2017-07-15 23:25:31 +0000	<a href="http://twitter.com/download/iphone" r...	This is Roscoe. Another pupper fallen victim t...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/886366144...	...	0.000361	True	Boston_bull	0.000076	True	21142	3208	6997102	134700	2017-07-15 23:25:31
29	886258384151887873	NaN	NaN	2017-07-15 16:17:19 +0000	<a href="http://twitter.com/download/iphone" r...	This is Waffles. His doggles are pupside down....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/886258384...	...	0.025286	False	Siamese_cat	0.002849	False	27905	6316	6997102	134700	2017-07-15 16:17:19
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2038	666411507551481857	NaN	NaN	2015-11-17 00:24:19 +0000	<a href="http://twitter.com/download/iphone" r...	This is quite the dog. Gets really excited whe...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666411507...	...	0.271485	False	gar	0.189945	False	447	328	6997189	134700	2015-11-17 00:24:19
2039	666407126856765440	NaN	NaN	2015-11-17 00:06:54 +0000	<a href="http://twitter.com/download/iphone" r...	This is a southern Vesuvius bumblegruff. Can d...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666407126...	...	0.244220	True	flat-coated_retriever	0.173810	True	110	41	6997189	134700	2015-11-17 00:06:54
2040	666396247373291520	NaN	NaN	2015-11-16 23:23:41 +0000	<a href="http://twitter.com/download/iphone" r...	Oh goodness. A super rare northeast Qdoba kang...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666396247...	...	0.009397	True	papillon	0.004577	True	167	86	6997189	134700	2015-11-16 23:23:41
2041	666373753744588802	NaN	NaN	2015-11-16 21:54:18 +0000	<a href="http://twitter.com/download/iphone" r...	Those are sunglasses and a jean jacket. 11/10 ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666373753...	...	0.259551	True	briard	0.206803	True	190	93	6997189	134700	2015-11-16 21:54:18
2042	666362758909284353	NaN	NaN	2015-11-16 21:10:36 +0000	<a href="http://twitter.com/download/iphone" r...	Unique dog here. Very small. Lives in containe...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666362758...	...	0.002402	False	hamster	0.000461	False	778	574	6997189	134700	2015-11-16 21:10:36
2043	666353288456101888	NaN	NaN	2015-11-16 20:32:58 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a mixed Asiago from the Galápagos...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666353288...	...	0.147655	True	Eskimo_dog	0.093412	True	221	73	6997190	134700	2015-11-16 20:32:58
2044	666345417576210432	NaN	NaN	2015-11-16 20:01:42 +0000	<a href="http://twitter.com/download/iphone" r...	Look at this jokester thinking seat belt laws ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666345417...	...	0.054787	True	Labrador_retriever	0.014241	True	299	139	6997190	134700	2015-11-16 20:01:42
2045	666337882303524864	NaN	NaN	2015-11-16 19:31:45 +0000	<a href="http://twitter.com/download/iphone" r...	This is an extremely rare horned Parthenon. No...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666337882...	...	0.278407	True	groenendael	0.102643	True	198	92	6997190	134700	2015-11-16 19:31:45
2046	666293911632134144	NaN	NaN	2015-11-16 16:37:02 +0000	<a href="http://twitter.com/download/iphone" r...	This is a funny dog. Weird toes. Won't come do...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666293911...	...	0.015250	False	great_grey_owl	0.013207	False	509	357	6997190	134700	2015-11-16 16:37:02
2047	666287406224695296	NaN	NaN	2015-11-16 16:11:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is an Albanian 3 1/2 legged Episcopalian...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666287406...	...	0.063064	True	miniature_poodle	0.025581	True	149	66	6997190	134700	2015-11-16 16:11:11
2048	666273097616637952	NaN	NaN	2015-11-16 15:14:19 +0000	<a href="http://twitter.com/download/iphone" r...	Can take selfies 11/10 https://t.co/ws2AMaNwPW	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666273097...	...	0.111884	True	basenji	0.111152	True	176	76	6997190	134700	2015-11-16 15:14:19
2049	666268910803644416	NaN	NaN	2015-11-16 14:57:41 +0000	<a href="http://twitter.com/download/iphone" r...	Very concerned about fellow dog trapped in com...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666268910...	...	0.085547	False	bookcase	0.079480	False	104	35	6997190	134700	2015-11-16 14:57:41
2050	666104133288665088	NaN	NaN	2015-11-16 04:02:55 +0000	<a href="http://twitter.com/download/iphone" r...	Not familiar with this breed. No tail (weird)....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666104133...	...	0.033919	False	partridge	0.000052	False	14346	6634	6997190	134700	2015-11-16 04:02:55
2051	666102155909144576	NaN	NaN	2015-11-16 03:55:04 +0000	<a href="http://twitter.com/download/iphone" r...	Oh my. Here you are seeing an Adobe Setter giv...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666102155...	...	0.149842	True	borzoi	0.133649	True	80	13	6997190	134700	2015-11-16 03:55:04
2052	666099513787052032	NaN	NaN	2015-11-16 03:44:34 +0000	<a href="http://twitter.com/download/iphone" r...	Can stand on stump for what seems like a while...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666099513...	...	0.166192	True	Dandie_Dinmont	0.089688	True	156	68	6997190	134700	2015-11-16 03:44:34
2053	666094000022159362	NaN	NaN	2015-11-16 03:22:39 +0000	<a href="http://twitter.com/download/iphone" r...	This appears to be a Mongolian Presbyterian mi...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666094000...	...	0.078260	True	malinois	0.075628	True	164	74	6997190	134700	2015-11-16 03:22:39
2054	666082916733198337	NaN	NaN	2015-11-16 02:38:37 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a well-established sunblockerspan...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666082916...	...	0.404722	True	French_bulldog	0.048960	True	119	45	6997190	134700	2015-11-16 02:38:37
2055	666073100786774016	NaN	NaN	2015-11-16 01:59:36 +0000	<a href="http://twitter.com/download/iphone" r...	Let's hope this flight isn't Malaysian (lol). ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666073100...	...	0.175382	True	Ibizan_hound	0.097471	True	322	164	6997190	134700	2015-11-16 01:59:36
2056	666071193221509120	NaN	NaN	2015-11-16 01:52:02 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a northern speckled Rhododendron....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666071193...	...	0.174201	True	Pekinese	0.109454	True	148	62	6997190	134700	2015-11-16 01:52:02
2057	666063827256086533	NaN	NaN	2015-11-16 01:22:45 +0000	<a href="http://twitter.com/download/iphone" r...	This is the happiest dog you will ever see. Ve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666063827...	...	0.093718	True	Labrador_retriever	0.072427	True	476	219	6997190	134700	2015-11-16 01:22:45
2058	666058600524156928	NaN	NaN	2015-11-16 01:01:59 +0000	<a href="http://twitter.com/download/iphone" r...	Here is the Rand Paul of retrievers folks! He'...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666058600...	...	0.192305	True	soft-coated_wheaten_terrier	0.082086	True	112	57	6997190	134700	2015-11-16 01:01:59
2059	666057090499244032	NaN	NaN	2015-11-16 00:55:59 +0000	<a href="http://twitter.com/download/iphone" r...	My oh my. This is a rare blond Canadian terrie...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666057090...	...	0.014594	False	golden_retriever	0.007959	True	298	142	6997190	134700	2015-11-16 00:55:59
2060	666055525042405380	NaN	NaN	2015-11-16 00:49:46 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a Siberian heavily armored polar bear ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666055525...	...	0.058279	True	fur_coat	0.054449	False	434	252	6997190	134700	2015-11-16 00:49:46
2061	666051853826850816	NaN	NaN	2015-11-16 00:35:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is an odd dog. Hard on the outside but lo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666051853...	...	0.045885	False	terrapin	0.017885	False	1223	853	6997190	134700	2015-11-16 00:35:11
2062	666050758794694657	NaN	NaN	2015-11-16 00:30:50 +0000	<a href="http://twitter.com/download/iphone" r...	This is a truly beautiful English Wilson Staff...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666050758...	...	0.263788	True	Greater_Swiss_Mountain_dog	0.016199	True	132	58	6997190	134700	2015-11-16 00:30:50
2063	666049248165822465	NaN	NaN	2015-11-16 00:24:50 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a 1949 1st generation vulpix. Enj...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666049248...	...	0.243682	True	Doberman	0.154629	True	109	41	6997190	134700	2015-11-16 00:24:50
2064	666044226329800704	NaN	NaN	2015-11-16 00:04:52 +0000	<a href="http://twitter.com/download/iphone" r...	This is a purebred Piers Morgan. Loves to Netf...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666044226...	...	0.360687	True	miniature_pinscher	0.222752	True	299	141	6997190	134700	2015-11-16 00:04:52
2065	666033412701032449	NaN	NaN	2015-11-15 23:21:54 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a very happy pup. Big fan of well-main...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666033412...	...	0.138584	True	bloodhound	0.116197	True	125	44	6997190	134700	2015-11-15 23:21:54
2066	666029285002620928	NaN	NaN	2015-11-15 23:05:30 +0000	<a href="http://twitter.com/download/iphone" r...	This is a western brown Mitsubishi terrier. Up...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666029285...	...	0.074192	True	Rhodesian_ridgeback	0.072010	True	129	47	6997190	134700	2015-11-15 23:05:30
2067	666020888022790149	NaN	NaN	2015-11-15 22:32:08 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a Japanese Irish Setter. Lost eye...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666020888...	...	0.156665	True	Shetland_sheepdog	0.061428	True	2560	517	6997190	134700	2015-11-15 22:32:08

	tweet_id	timestamp	source	text	retweeted_status_user_id	expanded_urls	rating_numerator	rating_denominator	name	doggo	...	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog	favorites	retweets	user_followers	user_favourites
0	892420643555336193	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	None	...	bagel	0.085851	False	banana	0.076110	False	38680	8559	6997101	134700
1	892177421306343426	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	None	...	Pekinese	0.090647	True	papillon	0.068957	True	33156	6291	6997101	134700
2	891815181378084864	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	None	...	malamute	0.078253	True	kelpie	0.031379	True	24958	4170	6997101	134700
3	891689557279858688	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	NaN	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	None	...	Labrador_retriever	0.168086	True	spatula	0.040836	False	42064	8687	6997101	134700
4	891327558926688256	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	NaN	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	None	...	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True	40212	9447	6997101	134700
5	891087950875897856	2017-07-29 00:08:17 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a majestic great white breaching ...	NaN	https://twitter.com/dog_rates/status/891087950...	13	10	None	None	...	Irish_terrier	0.116317	True	Indian_elephant	0.076902	False	20163	3127	6997101	134700
6	890971913173991426	2017-07-28 16:27:12 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Jax. He enjoys ice cream so much he gets ...	NaN	https://gofundme.com/ydvmve-surgery-for-jax,ht...	13	10	Jax	None	...	Border_collie	0.199287	True	ice_lolly	0.193548	False	11816	2082	6997101	134700
7	890729181411237888	2017-07-28 00:22:40 +0000	<a href="http://twitter.com/download/iphone" r...	When you watch your owner call another dog a g...	NaN	https://twitter.com/dog_rates/status/890729181...	13	10	None	None	...	Eskimo_dog	0.178406	True	Pembroke	0.076507	True	65338	18971	6997101	134700
8	890609185150312448	2017-07-27 16:25:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zoey. She doesn't want to be one of th...	NaN	https://twitter.com/dog_rates/status/890609185...	13	10	Zoey	None	...	Irish_setter	0.193054	True	Chesapeake_Bay_retriever	0.118184	True	27713	4279	6997101	134700
9	890240255349198849	2017-07-26 15:59:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Cassie. She is a college pup. Studying...	NaN	https://twitter.com/dog_rates/status/890240255...	14	10	Cassie	doggo	...	Cardigan	0.451038	True	Chihuahua	0.029248	True	31857	7448	6997101	134700
10	890006608113172480	2017-07-26 00:31:25 +0000	<a href="http://twitter.com/download/iphone" r...	This is Koda. He is a South Australian decksha...	NaN	https://twitter.com/dog_rates/status/890006608...	13	10	Koda	None	...	Pomeranian	0.013884	True	chow	0.008167	True	30583	7363	6997101	134700
11	889880896479866881	2017-07-25 16:11:53 +0000	<a href="http://twitter.com/download/iphone" r...	This is Bruno. He is a service shark. Only get...	NaN	https://twitter.com/dog_rates/status/889880896...	13	10	Bruno	None	...	Labrador_retriever	0.151317	True	muzzle	0.082981	False	27719	4989	6997101	134700
12	889665388333682689	2017-07-25 01:55:32 +0000	<a href="http://twitter.com/download/iphone" r...	Here's a puppo that seems to be on the fence a...	NaN	https://twitter.com/dog_rates/status/889665388...	13	10	None	None	...	Cardigan	0.027356	True	basenji	0.004633	True	48003	10101	6997101	134700
13	889638837579907072	2017-07-25 00:10:02 +0000	<a href="http://twitter.com/download/iphone" r...	This is Ted. He does his best. Sometimes that'...	NaN	https://twitter.com/dog_rates/status/889638837...	12	10	Ted	None	...	boxer	0.002129	True	Staffordshire_bullterrier	0.001498	True	27103	4564	6997101	134700
14	889531135344209921	2017-07-24 17:02:04 +0000	<a href="http://twitter.com/download/iphone" r...	This is Stuart. He's sporting his favorite fan...	NaN	https://twitter.com/dog_rates/status/889531135...	13	10	Stuart	None	...	Labrador_retriever	0.013834	True	redbone	0.007958	True	15052	2244	6997101	134700
15	889278841981685760	2017-07-24 00:19:32 +0000	<a href="http://twitter.com/download/iphone" r...	This is Oliver. You're witnessing one of his m...	NaN	https://twitter.com/dog_rates/status/889278841...	13	10	Oliver	None	...	borzoi	0.194742	True	Saluki	0.027351	True	25236	5446	6997101	134700
16	888917238123831296	2017-07-23 00:22:39 +0000	<a href="http://twitter.com/download/iphone" r...	This is Jim. He found a fren. Taught him how t...	NaN	https://twitter.com/dog_rates/status/888917238...	12	10	Jim	None	...	Tibetan_mastiff	0.120184	True	Labrador_retriever	0.105506	True	28996	4517	6997101	134700
17	888804989199671297	2017-07-22 16:56:37 +0000	<a href="http://twitter.com/download/iphone" r...	This is Zeke. He has a new stick. Very proud o...	NaN	https://twitter.com/dog_rates/status/888804989...	13	10	Zeke	None	...	Labrador_retriever	0.184172	True	English_setter	0.073482	True	25527	4363	6997101	134700
18	888554962724278272	2017-07-22 00:23:06 +0000	<a href="http://twitter.com/download/iphone" r...	This is Ralphus. He's powering up. Attempting ...	NaN	https://twitter.com/dog_rates/status/888554962...	13	10	Ralphus	None	...	Eskimo_dog	0.166511	True	malamute	0.111411	True	19850	3596	6997101	134700
19	888078434458587136	2017-07-20 16:49:33 +0000	<a href="http://twitter.com/download/iphone" r...	This is Gerald. He was just told he didn't get...	NaN	https://twitter.com/dog_rates/status/888078434...	12	10	Gerald	None	...	pug	0.000932	True	bull_mastiff	0.000903	True	21702	3511	6997101	134700
20	887705289381826560	2017-07-19 16:06:48 +0000	<a href="http://twitter.com/download/iphone" r...	This is Jeffrey. He has a monopoly on the pool...	NaN	https://twitter.com/dog_rates/status/887705289...	13	10	Jeffrey	None	...	redbone	0.087582	True	Weimaraner	0.026236	True	30108	5414	6997101	134700
21	887517139158093824	2017-07-19 03:39:09 +0000	<a href="http://twitter.com/download/iphone" r...	I've yet to rate a Venezuelan Hover Wiener. Th...	NaN	https://twitter.com/dog_rates/status/887517139...	14	10	None	None	...	tow_truck	0.029175	False	shopping_cart	0.026321	False	46119	11713	6997101	134700
22	887473957103951883	2017-07-19 00:47:34 +0000	<a href="http://twitter.com/download/iphone" r...	This is Canela. She attempted some fancy porch...	NaN	https://twitter.com/dog_rates/status/887473957...	13	10	Canela	None	...	Rhodesian_ridgeback	0.054950	True	beagle	0.038915	True	68935	18305	6997101	134700
23	887343217045368832	2017-07-18 16:08:03 +0000	<a href="http://twitter.com/download/iphone" r...	You may not have known you needed to see this ...	NaN	https://twitter.com/dog_rates/status/887343217...	13	10	None	None	...	sea_lion	0.275645	False	Weimaraner	0.134203	True	33582	10446	6997101	134700
24	887101392804085760	2017-07-18 00:07:08 +0000	<a href="http://twitter.com/download/iphone" r...	This... is a Jubilant Antarctic House Bear. We...	NaN	https://twitter.com/dog_rates/status/887101392...	12	10	None	None	...	Eskimo_dog	0.035029	True	Staffordshire_bullterrier	0.029705	True	30451	5984	6997101	134700
25	886983233522544640	2017-07-17 16:17:36 +0000	<a href="http://twitter.com/download/iphone" r...	This is Maya. She's very shy. Rarely leaves he...	NaN	https://twitter.com/dog_rates/status/886983233...	13	10	Maya	None	...	toy_terrier	0.143528	True	can_opener	0.032253	False	35058	7806	6997101	134700
26	886736880519319552	2017-07-16 23:58:41 +0000	<a href="http://twitter.com/download/iphone" r...	This is Mingus. He's a wonderful father to his...	NaN	https://www.gofundme.com/mingusneedsus,https:/...	13	10	Mingus	None	...	Great_Pyrenees	0.186136	True	Dandie_Dinmont	0.086346	True	12037	3311	6997101	134700
27	886680336477933568	2017-07-16 20:14:00 +0000	<a href="http://twitter.com/download/iphone" r...	This is Derek. He's late for a dog meeting. 13...	NaN	https://twitter.com/dog_rates/status/886680336...	13	10	Derek	None	...	sports_car	0.139952	False	car_wheel	0.044173	False	22362	4486	6997101	134700
28	886366144734445568	2017-07-15 23:25:31 +0000	<a href="http://twitter.com/download/iphone" r...	This is Roscoe. Another pupper fallen victim t...	NaN	https://twitter.com/dog_rates/status/886366144...	12	10	Roscoe	None	...	Chihuahua	0.000361	True	Boston_bull	0.000076	True	21142	3208	6997102	134700
29	886258384151887873	2017-07-15 16:17:19 +0000	<a href="http://twitter.com/download/iphone" r...	This is Waffles. His doggles are pupside down....	NaN	https://twitter.com/dog_rates/status/886258384...	13	10	Waffles	None	...	shower_cap	0.025286	False	Siamese_cat	0.002849	False	27905	6316	6997102	134700
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2038	666411507551481857	2015-11-17 00:24:19 +0000	<a href="http://twitter.com/download/iphone" r...	This is quite the dog. Gets really excited whe...	NaN	https://twitter.com/dog_rates/status/666411507...	2	10	None	None	...	barracouta	0.271485	False	gar	0.189945	False	447	328	6997189	134700
2039	666407126856765440	2015-11-17 00:06:54 +0000	<a href="http://twitter.com/download/iphone" r...	This is a southern Vesuvius bumblegruff. Can d...	NaN	https://twitter.com/dog_rates/status/666407126...	7	10	None	None	...	bloodhound	0.244220	True	flat-coated_retriever	0.173810	True	110	41	6997189	134700
2040	666396247373291520	2015-11-16 23:23:41 +0000	<a href="http://twitter.com/download/iphone" r...	Oh goodness. A super rare northeast Qdoba kang...	NaN	https://twitter.com/dog_rates/status/666396247...	9	10	None	None	...	toy_terrier	0.009397	True	papillon	0.004577	True	167	86	6997189	134700
2041	666373753744588802	2015-11-16 21:54:18 +0000	<a href="http://twitter.com/download/iphone" r...	Those are sunglasses and a jean jacket. 11/10 ...	NaN	https://twitter.com/dog_rates/status/666373753...	11	10	None	None	...	Afghan_hound	0.259551	True	briard	0.206803	True	190	93	6997189	134700
2042	666362758909284353	2015-11-16 21:10:36 +0000	<a href="http://twitter.com/download/iphone" r...	Unique dog here. Very small. Lives in containe...	NaN	https://twitter.com/dog_rates/status/666362758...	6	10	None	None	...	skunk	0.002402	False	hamster	0.000461	False	778	574	6997189	134700
2043	666353288456101888	2015-11-16 20:32:58 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a mixed Asiago from the Galápagos...	NaN	https://twitter.com/dog_rates/status/666353288...	8	10	None	None	...	Siberian_husky	0.147655	True	Eskimo_dog	0.093412	True	221	73	6997190	134700
2044	666345417576210432	2015-11-16 20:01:42 +0000	<a href="http://twitter.com/download/iphone" r...	Look at this jokester thinking seat belt laws ...	NaN	https://twitter.com/dog_rates/status/666345417...	10	10	None	None	...	Chesapeake_Bay_retriever	0.054787	True	Labrador_retriever	0.014241	True	299	139	6997190	134700
2045	666337882303524864	2015-11-16 19:31:45 +0000	<a href="http://twitter.com/download/iphone" r...	This is an extremely rare horned Parthenon. No...	NaN	https://twitter.com/dog_rates/status/666337882...	9	10	None	None	...	Newfoundland	0.278407	True	groenendael	0.102643	True	198	92	6997190	134700
2046	666293911632134144	2015-11-16 16:37:02 +0000	<a href="http://twitter.com/download/iphone" r...	This is a funny dog. Weird toes. Won't come do...	NaN	https://twitter.com/dog_rates/status/666293911...	3	10	None	None	...	otter	0.015250	False	great_grey_owl	0.013207	False	509	357	6997190	134700
2047	666287406224695296	2015-11-16 16:11:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is an Albanian 3 1/2 legged Episcopalian...	NaN	https://twitter.com/dog_rates/status/666287406...	1	10	None	None	...	toy_poodle	0.063064	True	miniature_poodle	0.025581	True	149	66	6997190	134700
2048	666273097616637952	2015-11-16 15:14:19 +0000	<a href="http://twitter.com/download/iphone" r...	Can take selfies 11/10 https://t.co/ws2AMaNwPW	NaN	https://twitter.com/dog_rates/status/666273097...	11	10	None	None	...	toy_terrier	0.111884	True	basenji	0.111152	True	176	76	6997190	134700
2049	666268910803644416	2015-11-16 14:57:41 +0000	<a href="http://twitter.com/download/iphone" r...	Very concerned about fellow dog trapped in com...	NaN	https://twitter.com/dog_rates/status/666268910...	10	10	None	None	...	desk	0.085547	False	bookcase	0.079480	False	104	35	6997190	134700
2050	666104133288665088	2015-11-16 04:02:55 +0000	<a href="http://twitter.com/download/iphone" r...	Not familiar with this breed. No tail (weird)....	NaN	https://twitter.com/dog_rates/status/666104133...	1	10	None	None	...	cock	0.033919	False	partridge	0.000052	False	14346	6634	6997190	134700
2051	666102155909144576	2015-11-16 03:55:04 +0000	<a href="http://twitter.com/download/iphone" r...	Oh my. Here you are seeing an Adobe Setter giv...	NaN	https://twitter.com/dog_rates/status/666102155...	11	10	None	None	...	Newfoundland	0.149842	True	borzoi	0.133649	True	80	13	6997190	134700
2052	666099513787052032	2015-11-16 03:44:34 +0000	<a href="http://twitter.com/download/iphone" r...	Can stand on stump for what seems like a while...	NaN	https://twitter.com/dog_rates/status/666099513...	8	10	None	None	...	Shih-Tzu	0.166192	True	Dandie_Dinmont	0.089688	True	156	68	6997190	134700
2053	666094000022159362	2015-11-16 03:22:39 +0000	<a href="http://twitter.com/download/iphone" r...	This appears to be a Mongolian Presbyterian mi...	NaN	https://twitter.com/dog_rates/status/666094000...	9	10	None	None	...	German_shepherd	0.078260	True	malinois	0.075628	True	164	74	6997190	134700
2054	666082916733198337	2015-11-16 02:38:37 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a well-established sunblockerspan...	NaN	https://twitter.com/dog_rates/status/666082916...	6	10	None	None	...	bull_mastiff	0.404722	True	French_bulldog	0.048960	True	119	45	6997190	134700
2055	666073100786774016	2015-11-16 01:59:36 +0000	<a href="http://twitter.com/download/iphone" r...	Let's hope this flight isn't Malaysian (lol). ...	NaN	https://twitter.com/dog_rates/status/666073100...	10	10	None	None	...	English_foxhound	0.175382	True	Ibizan_hound	0.097471	True	322	164	6997190	134700
2056	666071193221509120	2015-11-16 01:52:02 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a northern speckled Rhododendron....	NaN	https://twitter.com/dog_rates/status/666071193...	9	10	None	None	...	Yorkshire_terrier	0.174201	True	Pekinese	0.109454	True	148	62	6997190	134700
2057	666063827256086533	2015-11-16 01:22:45 +0000	<a href="http://twitter.com/download/iphone" r...	This is the happiest dog you will ever see. Ve...	NaN	https://twitter.com/dog_rates/status/666063827...	10	10	None	None	...	Tibetan_mastiff	0.093718	True	Labrador_retriever	0.072427	True	476	219	6997190	134700
2058	666058600524156928	2015-11-16 01:01:59 +0000	<a href="http://twitter.com/download/iphone" r...	Here is the Rand Paul of retrievers folks! He'...	NaN	https://twitter.com/dog_rates/status/666058600...	8	10	None	None	...	komondor	0.192305	True	soft-coated_wheaten_terrier	0.082086	True	112	57	6997190	134700
2059	666057090499244032	2015-11-16 00:55:59 +0000	<a href="http://twitter.com/download/iphone" r...	My oh my. This is a rare blond Canadian terrie...	NaN	https://twitter.com/dog_rates/status/666057090...	9	10	None	None	...	shopping_basket	0.014594	False	golden_retriever	0.007959	True	298	142	6997190	134700
2060	666055525042405380	2015-11-16 00:49:46 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a Siberian heavily armored polar bear ...	NaN	https://twitter.com/dog_rates/status/666055525...	10	10	None	None	...	Tibetan_mastiff	0.058279	True	fur_coat	0.054449	False	434	252	6997190	134700
2061	666051853826850816	2015-11-16 00:35:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is an odd dog. Hard on the outside but lo...	NaN	https://twitter.com/dog_rates/status/666051853...	2	10	None	None	...	mud_turtle	0.045885	False	terrapin	0.017885	False	1223	853	6997190	134700
2062	666050758794694657	2015-11-16 00:30:50 +0000	<a href="http://twitter.com/download/iphone" r...	This is a truly beautiful English Wilson Staff...	NaN	https://twitter.com/dog_rates/status/666050758...	10	10	None	None	...	English_springer	0.263788	True	Greater_Swiss_Mountain_dog	0.016199	True	132	58	6997190	134700
2063	666049248165822465	2015-11-16 00:24:50 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a 1949 1st generation vulpix. Enj...	NaN	https://twitter.com/dog_rates/status/666049248...	5	10	None	None	...	Rottweiler	0.243682	True	Doberman	0.154629	True	109	41	6997190	134700
2064	666044226329800704	2015-11-16 00:04:52 +0000	<a href="http://twitter.com/download/iphone" r...	This is a purebred Piers Morgan. Loves to Netf...	NaN	https://twitter.com/dog_rates/status/666044226...	6	10	None	None	...	redbone	0.360687	True	miniature_pinscher	0.222752	True	299	141	6997190	134700
2065	666033412701032449	2015-11-15 23:21:54 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a very happy pup. Big fan of well-main...	NaN	https://twitter.com/dog_rates/status/666033412...	9	10	None	None	...	malinois	0.138584	True	bloodhound	0.116197	True	125	44	6997190	134700
2066	666029285002620928	2015-11-15 23:05:30 +0000	<a href="http://twitter.com/download/iphone" r...	This is a western brown Mitsubishi terrier. Up...	NaN	https://twitter.com/dog_rates/status/666029285...	7	10	None	None	...	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True	129	47	6997190	134700
2067	666020888022790149	2015-11-15 22:32:08 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a Japanese Irish Setter. Lost eye...	NaN	https://twitter.com/dog_rates/status/666020888...	8	10	None	None	...	collie	0.156665	True	Shetland_sheepdog	0.061428	True	2560	517	6997190	134700

Basics of Data Science

Search This Blog

WeRateDogs Complete Project

Do the following activities: 1.Read from archive file¶

Define:¶

Define¶

Define¶

Define¶

Define¶

define¶

data quality issue #5¶

define¶

data quality issue #6¶

Define¶

data quality issue #7¶

change data type of retweeted_id from float to object type. Keep tweet_id as int to facilitate visualization on it as plot axis.¶

define¶

quality issue #8¶

define¶

quality issue#9¶

Define¶

tidyness issue #2¶

Comments

Post a Comment

Popular posts from this blog

The journey of a thousand miles..

Pandas cheat sheet