we_rate_dogs

Project: Wrangling and Analyze Data: WeRateDogs¶

By: Bethany Goss¶

Table of Contents¶

Data Gathering
Assessing Data
Cleaning Data
Analyzing and Visualizing Data

In [1]:

#importing packages
import pandas as pd
import numpy as np
import requests
import tweepy
import configparser
import json
from timeit import default_timer as timer
import matplotlib.pyplot as plt
from PIL import Image
from io import BytesIO
import seaborn as sns

Data Gathering¶

In the cell below, gather all three pieces of data for this project and load them in the notebook. Note: the methods required to gather each data are different.

Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)

In [2]:

ratingDogsDF=pd.read_csv("twitter_archive_enhanced.csv")
#ratingDogsDF[ratingDogsDF.pupper!="None"]

Use the Requests library to download the tweet image prediction (image_predictions.tsv)

In [3]:

imageTSV=requests.get('https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv')
with open('image_predictions.tsv', 'w') as file: 
    file.write(imageTSV.text)

In [4]:

image_predict=pd.read_csv('image_predictions.tsv', sep='\t')

Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)

In [5]:

config= configparser.ConfigParser()
config.read('config.ini')
api_key = config['twitter']['api_key']
api_key_secret = config['twitter']['api_key_secret']
access_token = config['twitter']['access_token']
access_secret = config['twitter']['access_secret']

auth = tweepy.OAuthHandler(api_key, api_key_secret)
auth.set_access_token(access_token, access_secret)

api = tweepy.API(auth, wait_on_rate_limit=True)

In [6]:

#Query Twitter API for each tweet in the Twitter archive and save JSON in a text file
#Tweet IDs for which to gather additional data via Twitter's API
tweet_ids = ratingDogsDF.tweet_id.values
len(tweet_ids)

# Query Twitter's API for JSON data for each tweet ID in the Twitter archive
count = 0
retweet_favorite_count=[]
fails_dict = {}
start = timer()
# Save each tweet's returned JSON as a new line in a .txt file
with open('tweet_json.txt', 'w') as outfile:
# This loop will likely take 20-30 minutes to run because of Twitter's rate limit
    for tweet_id in tweet_ids:
        count += 1
        print(str(count) + ": " + str(tweet_id))
        try:
            tweet = api.get_status(tweet_id, tweet_mode='extended')
            print("Success")
            json.dump(tweet._json, outfile)
            outfile.write('\n')
        except tweepy.errors.TweepyException as e:
            print("Fail")
            fails_dict[tweet_id] = e
            pass
end = timer()
print(end - start)
print(fails_dict)

1: 892420643555336193
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Rate limit reached. Sleeping for: 718

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Assessing Data¶

In this section, detect and document at least eight (8) quality issues and two (2) tidiness issue.

In [7]:

ratingDogsDF.tweet_id.duplicated().value_counts() #checking to see if there are any duplicated tweets
ratingDogsDF[ratingDogsDF.rating_denominator.isna()==True] #checking to make sure there is a denominator

Out[7]:

	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	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo

In [8]:

image_predict.jpg_url[image_predict.jpg_url.str.endswith('.jpg')== False] #checking to make sure the syntax for the urls is consistent

Out[8]:

320    https://pbs.twimg.com/tweet_video_thumb/CVKtH-...
815    https://pbs.twimg.com/tweet_video_thumb/CZ0mhd...
Name: jpg_url, dtype: object

In [9]:

#creating a dataframe by importing the file created from the api data
tweet_df = pd.DataFrame(columns=['tweet_id', 'retweet_count', 'favorite_count'])
with open('tweet_json.txt') as f:
    for line in f:
        status  = json.loads(line)
        tweet_id = status['id_str']
        retweet_count = status['retweet_count']
        favorite_count = status['favorite_count']
        tweet_df = tweet_df.append(pd.DataFrame([[tweet_id, retweet_count, favorite_count]],
                                        columns=['tweet_id', 'retweet_count', 'favorite_count']))
tweet_df = tweet_df.reset_index(drop=True)

In [10]:

#checking to make sure the new dataframe was made correctly. 
ratingDogsDF.head()
ratingDogsDF.info()#2328

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   tweet_id                    2356 non-null   int64  
 1   in_reply_to_status_id       78 non-null     float64
 2   in_reply_to_user_id         78 non-null     float64
 3   timestamp                   2356 non-null   object 
 4   source                      2356 non-null   object 
 5   text                        2356 non-null   object 
 6   retweeted_status_id         181 non-null    float64
 7   retweeted_status_user_id    181 non-null    float64
 8   retweeted_status_timestamp  181 non-null    object 
 9   expanded_urls               2297 non-null   object 
 10  rating_numerator            2356 non-null   int64  
 11  rating_denominator          2356 non-null   int64  
 12  name                        2356 non-null   object 
 13  doggo                       2356 non-null   object 
 14  floofer                     2356 non-null   object 
 15  pupper                      2356 non-null   object 
 16  puppo                       2356 non-null   object 
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB

Quality issues¶

For ratingDogsDf, Some of the 'name' objects aren't actually names
For image_predict, there are inconsistent or non-existent dog breeds in p1, p2 and p3
For ratingDogsDF, the rating_numerator and rating_denominator columns should be a float.
For image_predict, two of the jpg_urls are incomplete (endswith (...))
For ratingDogsDF, retweeted_status.timestamp and timestamp are objects and should not be
For ratingDogsDF, retweets and replies are not necessary for our examination.
The remaining IDs should all be qualitative
For ratingDogsDF, we will get rid of "None" values in the doggo - puppo columns and replace with null, then melt the columns together.

Tidiness issues¶

There are a lot of null values in rd_df columns doggo, floofer, puppo, and pupper.
Merge data frames on shared tweet_ids.

Cleaning Data¶

In this section, clean all of the issues documented while assessing.

In [11]:

# Make copies of original pieces of data
rd_df=ratingDogsDF.copy()
ip_df=image_predict.copy()
tt_df=tweet_df.copy()

Issue #1: Some of the 'name' objects aren't actually names in rd_df.¶

Define: Replace values where text is not a name.¶

Code¶

In [12]:

#creating a list of words that need to go then replacing them with None values. 
not_dog_names=["", 'a','an','the', 'very', 'unacceptable', 'this', 'such', 'quite', 'one', 'old', 'officially', 'not', 'my', 'mad', 'light', 'life', 'just', 'space', 'infuriating', 'incredible', 'incredibly', 'his', 'getting', 'by', 'all', 'actually']
rd_df.name.replace(not_dog_names, None, inplace=True)

In [13]:

#checking to see if I've missed any
list(rd_df.name.sort_values())[:20]

Out[13]:

['Abby',
 'Abby',
 'Ace',
 'Acro',
 'Adele',
 'Aiden',
 'Aja',
 'Akumi',
 'Al',
 'Albert',
 'Albert',
 'Albert',
 'Albus',
 'Albus',
 'Aldrick',
 'Alejandro',
 'Alexander',
 'Alexanderson',
 'Alf',
 'Alfie']

Test¶

In [14]:

#final test to confirm replace worked
list(dict(rd_df.name.value_counts()))[:20]

Out[14]:

['None',
 'Oliver',
 'Charlie',
 'Cooper',
 'Lucy',
 'Lola',
 'Tucker',
 'Penny',
 'Winston',
 'Bo',
 'Sadie',
 'Daisy',
 'Toby',
 'Stanley',
 'Buddy',
 'Dave',
 'Bailey',
 'Koda',
 'Louis',
 'Milo']

Issue #2: There are inconsistent or non-existent dog breeds in p1-p3 in ip_df¶

Define: In ip_df there are dog breeds that don't exist or are in an inconsistent format. We will dismiss these values¶

Code¶

In [15]:

#finding words that are not dogbreeds 
p1_dict=dict(ip_df.p1.value_counts())
p2_dict=dict(ip_df.p2.value_counts())
p3_dict=dict(ip_df.p3.value_counts())

In [16]:

#the document of not dogbreeds from the above dictionaries
notdogbreeds=pd.read_csv("not_dog_breeds.csv")
not_dog_breeds=list(notdogbreeds)

In [17]:

#put all the key of the above dictionary in a list
first_list=[]
for i in not_dog_breeds:
    first_list.append(i)
remove_from_df=[]
for i in first_list:
    if i not in remove_from_df:
        remove_from_df.append(i)

In [18]:

#replacing the not dog breeds with a None value
ip_df.p1.replace(remove_from_df, None, inplace=True)
ip_df.p2.replace(remove_from_df, None, inplace=True)
ip_df.p3.replace(remove_from_df, None, inplace= True)

In [19]:

#using regex to find all "-" and replacing it with "_" to ensure consistent syntax
ip_df.p1.replace("-", "_", inplace=True, regex=True) 
ip_df.p2.replace("-", "_", inplace=True, regex=True)
ip_df.p3.replace("-", "_", inplace=True, regex=True)

In [20]:

#also making a consistent syntax
ip_df.p1=ip_df.p1.str.capitalize()
ip_df.p2=ip_df.p2.str.capitalize()
ip_df.p3=ip_df.p3.str.capitalize()

Test¶

In [21]:

#looking over the new dictionarys to make sure all keys are capitalized and only have "_" between words
p1_dict=dict(ip_df.p1.value_counts())
p2_dict=dict(ip_df.p2.value_counts())
p3_dict=dict(ip_df.p3.value_counts())
list(p1_dict), list(p2_dict), list(p3_dict)

Out[21]:

(['Golden_retriever',
  'Labrador_retriever',
  'Pembroke',
  'Chihuahua',
  'Pug',
  'Chow',
  'Samoyed',
  'Toy_poodle',
  'Pomeranian',
  'Cocker_spaniel',
  'Malamute',
  'French_bulldog',
  'Chesapeake_bay_retriever',
  'Miniature_pinscher',
  'Seat_belt',
  'German_shepherd',
  'Staffordshire_bullterrier',
  'Siberian_husky',
  'Web_site',
  'Teddy',
  'Cardigan',
  'Eskimo_dog',
  'Maltese_dog',
  'Shetland_sheepdog',
  'Beagle',
  'Rottweiler',
  'Shih_tzu',
  'Lakeland_terrier',
  'Italian_greyhound',
  'Kuvasz',
  'Great_pyrenees',
  'West_highland_white_terrier',
  'Vizsla',
  'Pekinese',
  'American_staffordshire_terrier',
  'Dalmatian',
  'Basset',
  'Airedale',
  'Border_collie',
  'Old_english_sheepdog',
  'Soft_coated_wheaten_terrier',
  'Schipperke',
  'Blenheim_spaniel',
  'English_springer',
  'Boxer',
  'Collie',
  'Bernese_mountain_dog',
  'Doberman',
  'English_setter',
  'Dingo',
  'Whippet',
  'Borzoi',
  'Tennis_ball',
  'Malinois',
  'Great_dane',
  'Boston_bull',
  'Yorkshire_terrier',
  'Standard_poodle',
  'Norwegian_elkhound',
  'Papillon',
  'Miniature_poodle',
  'Doormat',
  'Flat_coated_retriever',
  'Basenji',
  'German_short_haired_pointer',
  'Tub',
  'Swing',
  'Siamese_cat',
  'Norfolk_terrier',
  'Brittany_spaniel',
  'Bath_towel',
  'Hamster',
  'Border_terrier',
  'Bloodhound',
  'Home_theater',
  'Saint_bernard',
  'Irish_setter',
  'Ice_bear',
  'Llama',
  'Car_mirror',
  'Dandie_dinmont',
  'Redbone',
  'Irish_terrier',
  'Ox',
  'Porcupine',
  'Lhasa',
  'Afghan_hound',
  'Walker_hound',
  'Shopping_cart',
  'Newfoundland',
  'Hippopotamus',
  'Tibetan_mastiff',
  'Bedlington_terrier',
  'Minivan',
  'Bull_mastiff',
  'Bathtub',
  'Mexican_hairless',
  'Patio',
  'Guinea_pig',
  'Gordon_setter',
  'Brown_bear',
  'Bow_tie',
  'Rhodesian_ridgeback',
  'Keeshond',
  'Hog',
  'Wombat',
  'Norwich_terrier',
  'Goose',
  'Saluki',
  'Bluetick',
  'Weimaraner',
  'Arctic_fox',
  'Jigsaw_puzzle',
  'Dishwasher',
  'Miniature_schnauzer',
  'Barrow',
  'Tibetan_terrier',
  'Irish_water_spaniel',
  'Cash_machine',
  'Balloon',
  'Scottish_deerhound',
  'Wood_rabbit',
  'Space_heater',
  'Cowboy_hat',
  'Toilet_tissue',
  'Mousetrap',
  'Ram',
  'Briard',
  'Greater_swiss_mountain_dog',
  'Leonberg',
  'Stone_wall',
  'Sea_lion',
  'White_wolf',
  'Muzzle',
  'Motor_scooter',
  'Refrigerator',
  'Welsh_springer_spaniel',
  'Curly_coated_retriever',
  'Cairn',
  'Comic_book',
  'Arabian_camel',
  'Brabancon_griffon',
  'Ski_mask',
  'Giant_schnauzer',
  'Ibizan_hound',
  'Komondor',
  'Christmas_stocking',
  'Washbasin',
  'Vacuum',
  'Seashore',
  'Triceratops',
  'Common_iguana',
  'Sorrel',
  'Prison',
  "Jack_o'_lantern",
  'Window_shade',
  'Angora',
  'Wallaby',
  'Dogsled',
  'Toyshop',
  'Jellyfish',
  'Shower_curtain',
  'Laptop',
  'Hyena',
  'Frilled_lizard',
  'Bustard',
  'Lakeside',
  'Paddle',
  'Leatherback_turtle',
  'Koala',
  'Acorn_squash',
  'Snorkel',
  'Birdhouse',
  'Street_sign',
  'Wire_haired_fox_terrier',
  'Wool',
  'Paper_towel',
  'Upright',
  'Dough',
  'Bubble',
  'Axolotl',
  'Tusker',
  'Weasel',
  'Gas_pump',
  'Appenzeller',
  'Badger',
  'Feather_boa',
  'Loafer',
  'Hermit_crab',
  'Toy_terrier',
  'Australian_terrier',
  'Chimpanzee',
  'Geyser',
  'Sussex_spaniel',
  'Tabby',
  'Ostrich',
  'Meerkat',
  'Gondola',
  'Black_and_tan_coonhound',
  'Snail',
  'Box_turtle',
  'Convertible',
  'Otter',
  'Egyptian_cat',
  'Hammer',
  'Studio_couch',
  'Syringe',
  'Tailed_frog',
  'Cup',
  'Envelope',
  'Limousine',
  'Polecat',
  'Hand_blower',
  'Marmot',
  'Four_poster',
  'Wild_boar',
  'Tricycle',
  'Revolver',
  'Pole',
  'Agama',
  'Handkerchief',
  'Bonnet',
  'Alp',
  'Bib',
  'Espresso',
  'Timber_wolf',
  'Basketball',
  'Nail',
  'Cliff',
  'Sulphur_crested_cockatoo',
  'Lawn_mower',
  'Mortarboard',
  'Silky_terrier',
  'Leopard',
  'Giant_panda',
  'Long_horned_beetle',
  'Clumber',
  'Wooden_spoon',
  'Jersey',
  'Sundial',
  'Cowboy_boot',
  'Padlock',
  'School_bus',
  'Pool_table',
  'Quilt',
  'Beach_wagon',
  'Remote_control',
  'Bakery',
  'Restaurant',
  'Loupe',
  'Pedestal',
  'Shield',
  'Hummingbird',
  'Madagascar_cat',
  'Maillot',
  'Sunglasses',
  'Lion',
  'Rapeseed',
  'Traffic_light',
  'Coil',
  'Binoculars',
  'Tiger_shark',
  'Grille',
  'Barbell',
  'Pencil_box',
  'Fountain',
  'American_black_bear',
  'Rotisserie',
  'Conch',
  'Coffee_mug',
  'Skunk',
  'Washer',
  'Bookshop',
  'Radio_telescope',
  'Cougar',
  'African_grey',
  'Teapot',
  'Coral_reef',
  'Bookcase',
  'Leaf_beetle',
  'Grey_fox',
  'Microphone',
  'Platypus',
  'Ping_pong_ball',
  'Sea_urchin',
  'Peacock',
  'Banana',
  'Dhole',
  'Lacewing',
  'Scorpion',
  'Flamingo',
  'Pitcher',
  'Microwave',
  'African_hunting_dog',
  'Picket_fence',
  'Zebra',
  'Park_bench',
  'Prayer_rug',
  'China_cabinet',
  'Bee_eater',
  'Carton',
  'Killer_whale',
  'Mud_turtle',
  'Boathouse',
  'Pot',
  'Piggy_bank',
  'Desktop_computer',
  'Three_toed_sloth',
  'Coho',
  'Swab',
  'Hay',
  'Hare',
  'Electric_fan',
  'Ibex',
  'Fire_engine',
  'Lorikeet',
  'Trombone',
  'Canoe',
  'King_penguin',
  'Shopping_basket',
  'Bearskin',
  'Crash_helmet',
  'Lynx',
  'Cuirass',
  'Candle',
  'Terrapin',
  'Starfish',
  'Beaver',
  'Damselfly',
  'Entlebucher',
  'Earthstar',
  'Pillow',
  'Carousel',
  'Bald_eagle',
  'Cheetah',
  'Minibus',
  'Clog',
  'Sliding_door',
  'Cheeseburger',
  'Sandbar',
  'Fiddler_crab',
  'Crane',
  'Scotch_terrier',
  'Snowmobile',
  'Bighorn',
  'Bison',
  'Ice_lolly',
  'Dining_table',
  'Groenendael',
  'Mailbox',
  'Panpipe',
  'Harp',
  'Maze',
  'Water_bottle',
  'Suit',
  'Toilet_seat',
  'Robin',
  'Slug',
  'Soccer_ball',
  'African_crocodile',
  'Tick',
  'Ocarina',
  'Bow',
  'Stove',
  'Standard_schnauzer',
  'Walking_stick',
  'Book_jacket',
  'Rain_barrel',
  'Black_footed_ferret',
  'Guenon',
  'Japanese_spaniel',
  'Water_buffalo',
  'Orange'],
 ['Labrador_retriever',
  'Golden_retriever',
  'Cardigan',
  'Chihuahua',
  'French_bulldog',
  'Pomeranian',
  'Chesapeake_bay_retriever',
  'Toy_poodle',
  'Siberian_husky',
  'Cocker_spaniel',
  'Miniature_poodle',
  'Beagle',
  'Pembroke',
  'Collie',
  'Eskimo_dog',
  'Kuvasz',
  'Italian_greyhound',
  'American_staffordshire_terrier',
  'Pekinese',
  'Chow',
  'Malinois',
  'Toy_terrier',
  'Samoyed',
  'Miniature_pinscher',
  'Norwegian_elkhound',
  'Boston_bull',
  'Staffordshire_bullterrier',
  'Pug',
  'Irish_terrier',
  'Shih_tzu',
  'Tibetan_mastiff',
  'Basset',
  'Whippet',
  'Great_pyrenees',
  'German_shepherd',
  'Bull_mastiff',
  'West_highland_white_terrier',
  'Boxer',
  'Malamute',
  'Shetland_sheepdog',
  'Redbone',
  'Black_and_tan_coonhound',
  'Border_collie',
  'Lhasa',
  'Doormat',
  'Soft_coated_wheaten_terrier',
  'Seat_belt',
  'Irish_setter',
  'Borzoi',
  'Teddy',
  'English_springer',
  'Brabancon_griffon',
  'Groenendael',
  'Siamese_cat',
  'Maltese_dog',
  'Lakeland_terrier',
  'Welsh_springer_spaniel',
  'Brittany_spaniel',
  'Ice_bear',
  'Great_dane',
  'Standard_poodle',
  'Tibetan_terrier',
  'Schipperke',
  'Rhodesian_ridgeback',
  'Newfoundland',
  'Papillon',
  'Rottweiler',
  'German_short_haired_pointer',
  'English_setter',
  'Ibizan_hound',
  'Vizsla',
  'Bath_towel',
  'Flat_coated_retriever',
  'Airedale',
  'Basenji',
  'Doberman',
  'Bathtub',
  'Bluetick',
  'Hamster',
  'Silky_terrier',
  'Keeshond',
  'Old_english_sheepdog',
  'Saint_bernard',
  'Llama',
  'Bloodhound',
  'Norfolk_terrier',
  'Walker_hound',
  'Greater_swiss_mountain_dog',
  'Sussex_spaniel',
  'Guinea_pig',
  'Afghan_hound',
  'Bedlington_terrier',
  'Norwich_terrier',
  'Leonberg',
  'Yorkshire_terrier',
  'Tub',
  'Sunglasses',
  'Saluki',
  'Badger',
  'Window_screen',
  'Studio_couch',
  'Toilet_seat',
  'Muzzle',
  'Border_terrier',
  'Skunk',
  'Mink',
  'Briard',
  'Blenheim_spaniel',
  'Cairn',
  'Otterhound',
  'Mexican_hairless',
  'Koala',
  'Angora',
  'Dalmatian',
  'Dandie_dinmont',
  'Arctic_fox',
  'Arabian_camel',
  'Ram',
  'Weasel',
  'Hog',
  'Dishwasher',
  'Sea_lion',
  'Wire_haired_fox_terrier',
  'American_black_bear',
  'Christmas_stocking',
  'Dingo',
  'Paddle',
  'Swab',
  'Beach_wagon',
  'Feather_boa',
  'Cash_machine',
  'Meerkat',
  'Monitor',
  'Envelope',
  'Appenzeller',
  'Tennis_ball',
  'Miniature_schnauzer',
  'Lakeside',
  'Weimaraner',
  'Black_footed_ferret',
  'Indian_elephant',
  'Carton',
  'Irish_wolfhound',
  'Oscilloscope',
  'Fur_coat',
  'Shopping_basket',
  'Fountain',
  'Macaque',
  'Bakery',
  'Rhinoceros_beetle',
  'Quill',
  'Barrel',
  'American_alligator',
  'Bow_tie',
  'Beaver',
  'English_foxhound',
  'Japanese_spaniel',
  'Swing',
  'Snowmobile',
  'Jaguar',
  'Cup',
  'Palace',
  'House_finch',
  'Harvester',
  'Binoculars',
  'Black_widow',
  'Jean',
  'Bathing_cap',
  'Wallet',
  'Volcano',
  'Curly_coated_retriever',
  'Marmot',
  'Great_white_shark',
  'Punching_bag',
  'Prison',
  'Gibbon',
  'Giant_panda',
  'Quilt',
  'Sleeping_bag',
  'Crossword_puzzle',
  'Toyshop',
  'Scotch_terrier',
  'Mongoose',
  'Goldfish',
  'Sports_car',
  'Four_poster',
  'Loggerhead',
  'Radiator',
  'Cloak',
  'Warthog',
  'Dhole',
  'Wig',
  'Australian_terrier',
  'Prayer_rug',
  'Terrapin',
  'Giant_schnauzer',
  'Persian_cat',
  'Sealyham_terrier',
  'Frilled_lizard',
  'Ox',
  'Bighorn',
  'Hare',
  'Plow',
  'Tow_truck',
  'Slug',
  'Pillow',
  'Gorilla',
  'Minivan',
  'Entertainment_center',
  'Rotisserie',
  'Orange',
  'Mask',
  'Tailed_frog',
  'Tabby',
  'Cock',
  'Corn',
  'Bison',
  'Chain_saw',
  'Lesser_panda',
  'African_hunting_dog',
  'Shovel',
  'Stingray',
  'Bow',
  'Crutch',
  'Pelican',
  'Hamper',
  'Printer',
  'Cornet',
  'Laptop',
  'Cowboy_boot',
  'Barbershop',
  'Sunglass',
  'Dugong',
  'Toaster',
  'Screw',
  'Minibus',
  'Hatchet',
  'Window_shade',
  'Lawn_mower',
  'Washbasin',
  'Sock',
  'Patio',
  'China_cabinet',
  'Chain_mail',
  'Breakwater',
  'Computer_keyboard',
  'Goose',
  'Knee_pad',
  'Solar_dish',
  'Table_lamp',
  'Windsor_tie',
  'Comic_book',
  'Dam',
  'Armadillo',
  'Timber_wolf',
  'Confectionery',
  'Cliff',
  'Killer_whale',
  'Canoe',
  'Sweatshirt',
  'White_wolf',
  'Sliding_door',
  'Academic_gown',
  'Cowboy_hat',
  'Can_opener',
  'Drake',
  'Rule',
  'Soccer_ball',
  'Bucket',
  'Racket',
  'Menu',
  'Purse',
  'Bernese_mountain_dog',
  'Dumbbell',
  'Projectile',
  'Dock',
  'Oxygen_mask',
  'Sandbar',
  'Umbrella',
  'Shower_cap',
  'Jigsaw_puzzle',
  'Snorkel',
  'Cab',
  'Cradle',
  'Madagascar_cat',
  'Wallaby',
  'Boathouse',
  'Coral_reef',
  'Maillot',
  'Ipod',
  'Hand_held_computer',
  'Necklace',
  'Dining_table',
  'Sea_urchin',
  'Shower_curtain',
  'Cougar',
  'Entlebucher',
  'Basketball',
  'Lighter',
  'Saltshaker',
  'Hummingbird',
  'Television',
  'Web_site',
  'Spotlight',
  'Standard_schnauzer',
  'Turnstile',
  'Coffee_mug',
  'Desk',
  'Tiger',
  'Rain_barrel',
  'Sandal',
  'Leafhopper',
  'Water_buffalo',
  'Polecat',
  'Wood_rabbit',
  'Folding_chair',
  'Trench_coat',
  'Junco',
  'Snail',
  'Crib',
  'Accordion',
  'Coral_fungus',
  'Streetcar',
  'Tarantula',
  'Hair_slide',
  'Cannon',
  'Sulphur_butterfly',
  'Otter',
  'Mailbox',
  'Space_heater',
  'Sombrero',
  'Komondor',
  'Bobsled',
  'Lifeboat',
  'Cockroach',
  'Brown_bear',
  'Grey_whale',
  'Crate',
  'Shopping_cart',
  'Toucan',
  'Ashcan',
  'Bearskin',
  'Tick',
  'Kerry_blue_terrier',
  'Ice_lolly',
  'Platypus',
  'Mosquito_net',
  'Mud_turtle',
  'Nail',
  'Water_bottle',
  'Promontory',
  'Sarong',
  'Hay',
  'Apron',
  'Home_theater',
  'Shoji',
  'Barracouta',
  'Tray',
  'Hair_spray',
  'Neck_brace',
  'Grey_fox',
  'Lampshade',
  'Pickup',
  'Spatula',
  'Paper_towel',
  'Breastplate',
  'Siamang',
  'Banded_gecko',
  'Peacock',
  'Tree_frog',
  'Wombat',
  'Police_van',
  'Bib',
  'Gila_monster',
  'Waffle_iron',
  'Handkerchief',
  'Birdhouse',
  'Seashore',
  'Hyena',
  'Moped',
  'Rifle',
  'Chimpanzee',
  'Indri',
  'Torch',
  'European_gallinule',
  'Spindle',
  'Triceratops',
  'Stove',
  'Porcupine',
  'Spotted_salamander',
  'Common_newt',
  'Medicine_chest',
  'Quail',
  'Horse_cart',
  'Pier',
  'Red_fox',
  'Affenpinscher',
  'Assault_rifle',
  'Mashed_potato',
  'Bagel'],
 ['Labrador_retriever',
  'Chihuahua',
  'Golden_retriever',
  'Eskimo_dog',
  'Kuvasz',
  'Staffordshire_bullterrier',
  'Chow',
  'Beagle',
  'Cocker_spaniel',
  'Toy_poodle',
  'Pomeranian',
  'Pembroke',
  'Pekinese',
  'Great_pyrenees',
  'Chesapeake_bay_retriever',
  'Malamute',
  'French_bulldog',
  'American_staffordshire_terrier',
  'Pug',
  'Cardigan',
  'Siberian_husky',
  'Basenji',
  'Toy_terrier',
  'Bull_mastiff',
  'Boston_bull',
  'Lakeland_terrier',
  'Shetland_sheepdog',
  'Seat_belt',
  'Boxer',
  'Miniature_pinscher',
  'Dingo',
  'Doormat',
  'German_shepherd',
  'Maltese_dog',
  'Standard_poodle',
  'Collie',
  'Miniature_poodle',
  'Tibetan_mastiff',
  'Saluki',
  'West_highland_white_terrier',
  'Keeshond',
  'Siamese_cat',
  'Weimaraner',
  'Tibetan_terrier',
  'Soft_coated_wheaten_terrier',
  'Bloodhound',
  'Teddy',
  'Norfolk_terrier',
  'English_setter',
  'Irish_terrier',
  'Border_collie',
  'Appenzeller',
  'Brittany_spaniel',
  'Airedale',
  'Norwegian_elkhound',
  'Papillon',
  'Italian_greyhound',
  'Welsh_springer_spaniel',
  'Vizsla',
  'White_wolf',
  'Rottweiler',
  'Rhodesian_ridgeback',
  'Curly_coated_retriever',
  'Great_dane',
  'Newfoundland',
  'Shih_tzu',
  'Whippet',
  'Saint_bernard',
  'German_short_haired_pointer',
  'Irish_setter',
  'Borzoi',
  'Australian_terrier',
  'Basset',
  'Samoyed',
  'Bathtub',
  'Clumber',
  'English_springer',
  'Otterhound',
  'Redbone',
  'Old_english_sheepdog',
  'Doberman',
  'Yorkshire_terrier',
  'Toilet_tissue',
  'Ibizan_hound',
  'Brabancon_griffon',
  'Giant_schnauzer',
  'Bluetick',
  'Cairn',
  'English_foxhound',
  'Lhasa',
  'Norwich_terrier',
  'Ice_bear',
  'Muzzle',
  'Entlebucher',
  'Schipperke',
  'Border_terrier',
  'Hog',
  'Bath_towel',
  'Black_footed_ferret',
  'Leonberg',
  'Persian_cat',
  'Three_toed_sloth',
  'Arctic_fox',
  'Dalmatian',
  'Flat_coated_retriever',
  'Groenendael',
  'Water_buffalo',
  'Tennis_ball',
  'Sandbar',
  'Weasel',
  'Silky_terrier',
  'Greater_swiss_mountain_dog',
  'Miniature_schnauzer',
  'Malinois',
  'Ice_lolly',
  'Fur_coat',
  'Afghan_hound',
  'Wallaby',
  'Printer',
  'Sea_lion',
  'Sombrero',
  'Gordon_setter',
  'Arabian_camel',
  'Swing',
  'Fountain',
  'Irish_water_spaniel',
  'Television',
  'Paper_towel',
  'Dandie_dinmont',
  'Japanese_spaniel',
  'Mexican_hairless',
  'Quilt',
  'Black_and_tan_coonhound',
  'Pillow',
  'Wire_haired_fox_terrier',
  'Scottish_deerhound',
  'Shower_curtain',
  'Limousine',
  'Sunglasses',
  'Tub',
  'Umbrella',
  'Gibbon',
  'Koala',
  'Microwave',
  'Wool',
  'Carton',
  'Convertible',
  'Sundial',
  'Badger',
  'Wood_rabbit',
  'Hamster',
  'Purse',
  'Bubble',
  'Toilet_seat',
  'Polecat',
  'Guinea_pig',
  'Box_turtle',
  'Echidna',
  'Ski_mask',
  'Bagel',
  'Bookcase',
  'Racket',
  'Crane',
  'Macaque',
  'Scuba_diver',
  'Menu',
  'Bernese_mountain_dog',
  'Irish_wolfhound',
  'Cougar',
  'Komondor',
  'Briard',
  'Terrapin',
  'Beaver',
  'Refrigerator',
  'Walker_hound',
  'Swab',
  'Paddlewheel',
  'French_loaf',
  'Lakeside',
  'Wreck',
  'Squirrel_monkey',
  'Shopping_basket',
  'Cheetah',
  'Mousetrap',
  'Sealyham_terrier',
  'Bucket',
  'Snow_leopard',
  'Warthog',
  'Bouvier_des_flandres',
  'Washbasin',
  'Jigsaw_puzzle',
  'Chime',
  'Bison',
  'Ashcan',
  'Bolete',
  'Prison',
  'Lumbermill',
  'Minibus',
  'Sliding_door',
  'Llama',
  'Christmas_stocking',
  'Blenheim_spaniel',
  'Angora',
  'Titi',
  'Mouse',
  'Book_jacket',
  'Feather_boa',
  'Ram',
  'Bassinet',
  'Brass',
  'Giant_panda',
  'Spatula',
  'Space_heater',
  'Coyote',
  'Agama',
  'Envelope',
  'Abaya',
  'Dhole',
  'Poncho',
  'Beacon',
  'File',
  'Hippopotamus',
  'King_penguin',
  'Mask',
  'Pizza',
  'Bathing_cap',
  'Shopping_cart',
  'Common_iguana',
  'Leafhopper',
  'Oxcart',
  'Wig',
  'Egyptian_cat',
  'Car_wheel',
  'Space_shuttle',
  'Affenpinscher',
  'Wok',
  'Snorkel',
  'Rotisserie',
  'Desktop_computer',
  'Hand_blower',
  'Neck_brace',
  'Cup',
  'Padlock',
  'Cab',
  'Viaduct',
  'American_black_bear',
  'Passenger_car',
  'Parachute',
  'Mongoose',
  'Consomme',
  'Valley',
  'Park_bench',
  'Mushroom',
  'Barrow',
  'Rain_barrel',
  'Triceratops',
  'Ballplayer',
  'Restaurant',
  'Bow',
  'Grocery_store',
  'Boathouse',
  'Conch',
  'Grey_fox',
  'Meerkat',
  'Grand_piano',
  'Screen',
  'Coffeepot',
  'Otter',
  'Bonnet',
  'Mountain_tent',
  'Crossword_puzzle',
  'Go_kart',
  'Sussex_spaniel',
  'Orangutan',
  'Canoe',
  'Barber_chair',
  'Traffic_light',
  'Ibex',
  'Can_opener',
  'Indian_elephant',
  'Standard_schnauzer',
  'Nail',
  'Guillotine',
  'Entertainment_center',
  'Buckeye',
  'Sunglass',
  'Croquet_ball',
  'Tripod',
  'Rapeseed',
  'Tiger_cat',
  'Notebook',
  'Maraca',
  'Pool_table',
  'Theater_curtain',
  'Pier',
  'Pop_bottle',
  'Soccer_ball',
  'Wombat',
  'Rhinoceros_beetle',
  'Paintbrush',
  'Maze',
  'Chain',
  'Switch',
  'Kerry_blue_terrier',
  'Barbell',
  'Hatchet',
  'Parallel_bars',
  'Golfcart',
  'Acorn',
  'Swimming_trunks',
  'Prairie_chicken',
  'Red_wolf',
  'Power_drill',
  'Snail',
  'Black_swan',
  'Cowboy_boot',
  'Gar',
  'Bib',
  'Steam_locomotive',
  'Chickadee',
  'Chimpanzee',
  'Hammerhead',
  'Sea_cucumber',
  'Seashore',
  'Nipple',
  'Moped',
  'Crayfish',
  'Great_grey_owl',
  'Pajama',
  'Shovel',
  'Plastic_bag',
  'Drumstick',
  'Mitten',
  'Stinkhorn',
  'Band_aid',
  'African_chameleon',
  'French_horn',
  'African_grey',
  'Jeep',
  'Oscilloscope',
  'Lampshade',
  'Panpipe',
  'Balance_beam',
  'Goldfish',
  'Jersey',
  'Bulletproof_vest',
  'Beach_wagon',
  'Plunger',
  'Wing',
  'Cardoon',
  'Window_screen',
  'Common_newt',
  'Gorilla',
  'Brown_bear',
  'Wolf_spider',
  'Windsor_tie',
  'Quill',
  'European_fire_salamander',
  'Coral_reef',
  'Jaguar',
  'Lion',
  'Hand_held_computer',
  'Whiptail',
  'Rock_crab',
  'Hare',
  'Shoji',
  'Bell_cote',
  'Rifle',
  'Goose',
  'Pickup',
  'Bow_tie',
  'Pretzel',
  'Marmot',
  'Vacuum',
  'Shower_cap',
  'Dugong',
  'Eel',
  'Binder',
  'Bullfrog',
  'Soap_dispenser',
  'Wild_boar',
  'Loupe',
  'Cliff',
  'Cloak',
  'Partridge',
  'Mink',
  'Screw',
  'Kimono',
  'Joystick',
  'Loggerhead',
  'Greenhouse',
  'Broccoli',
  'Green_lizard',
  'Chest',
  'Toyshop',
  'Mosquito_net',
  'Cuirass',
  'Zebra',
  'Wallet',
  'Electric_fan',
  'Ox',
  'Assault_rifle',
  'Axolotl',
  'Pot',
  'Banana'])

Issue #3: Rd_df numerator and denominators should be a float.¶

Define: These values will be changed to a float type¶

Code¶

In [22]:

#changing type
rd_df.rating_numerator=rd_df.rating_numerator.astype(float)
rd_df.rating_denominator=rd_df.rating_denominator.astype(float)

Test¶

In [23]:

rd_df.rating_numerator.dtypes, rd_df.rating_denominator.dtypes

Out[23]:

(dtype('float64'), dtype('float64'))

Issue #4: Some of the jpg_urls in image_predict are incomplete¶

Define: Because this code is corrupted and I don't have access to a verision that isn't (also there are only a few variables) I will be removing these rows.¶

Code¶

In [24]:

#dropping the unfinished url
ip_df.jpg_url[ip_df.jpg_url.str.endswith('.jpg')== False]

Out[24]:

320    https://pbs.twimg.com/tweet_video_thumb/CVKtH-...
815    https://pbs.twimg.com/tweet_video_thumb/CZ0mhd...
Name: jpg_url, dtype: object

In [25]:

ip_df.drop([320, 815], inplace=True)

Test¶

In [26]:

ip_df.jpg_url[ip_df.jpg_url.str.endswith('.jpg')== False]

Out[26]:

Series([], Name: jpg_url, dtype: object)

Issue #5: Timestamp dtypes are objects in ratingDogsDF¶

Define: They should be timestamps¶

Code¶

In [27]:

#changing the dtype from an object to a tiemstamp
rd_df.timestamp=pd.to_datetime(rd_df.timestamp, infer_datetime_format=True)
rd_df.retweeted_status_timestamp=pd.to_datetime(rd_df.retweeted_status_timestamp, infer_datetime_format=True)

Test¶

In [28]:

rd_df.timestamp.dtypes, rd_df.retweeted_status_timestamp.dtypes

Out[28]:

(dtype('<M8[ns]'), dtype('<M8[ns]'))

Issue #6: Retweets and other rows in rd_df are not necessary for examination.¶

Define: Create a list of indexes with the retweets in the rows and use that to drop those rows. Then drop unneeded columns.¶

Code¶

In [29]:

#making a more usuable dataframe by getting rid of data I won't be using
index_retweets=rd_df.retweeted_status_id[rd_df.retweeted_status_id.notnull()==True].index
rd_df.drop(index_retweets, inplace=True)

In [30]:

dropping_rows=["in_reply_to_status_id", "in_reply_to_user_id", "retweeted_status_id", "retweeted_status_user_id", "retweeted_status_timestamp", "expanded_urls"]
rd_df.drop(dropping_rows, axis=1, inplace=True)

Test¶

In [31]:

rd_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2175 entries, 0 to 2355
Data columns (total 11 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   tweet_id            2175 non-null   int64         
 1   timestamp           2175 non-null   datetime64[ns]
 2   source              2175 non-null   object        
 3   text                2175 non-null   object        
 4   rating_numerator    2175 non-null   float64       
 5   rating_denominator  2175 non-null   float64       
 6   name                2175 non-null   object        
 7   doggo               2175 non-null   object        
 8   floofer             2175 non-null   object        
 9   pupper              2175 non-null   object        
 10  puppo               2175 non-null   object        
dtypes: datetime64[ns](1), float64(2), int64(1), object(7)
memory usage: 203.9+ KB

Issue #7: The remaining IDs should all be qualitative¶

Define: Change the dtype to str¶

Code¶

In [32]:

#changing types to a str, tweet_id was already an object, but there are different kinds of objects in pandas so I included it
rd_df.tweet_id=rd_df.tweet_id.astype(str)
ip_df.tweet_id=ip_df.tweet_id.astype(str)

Test¶

In [33]:

rd_df.tweet_id.dtypes, ip_df.tweet_id.dtypes

Out[33]:

(dtype('O'), dtype('O'))

Issue #8: For ratingDogsDF, we will get rid of "None" values in the doggo - puppo columns and replace an empty value.¶

Define: replace "None" with "".¶

Code¶

In [34]:

#checking count for Issue 9
rd_df.doggo.value_counts()[1], rd_df.floofer.value_counts()[1], rd_df.pupper.value_counts()[1], rd_df.puppo.value_counts()[1]

Out[34]:

(87, 10, 234, 25)

In [35]:

#making sure all "None" values are empy values
rd_df.replace("None", "", inplace=True)

Test

In [36]:

rd_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2175 entries, 0 to 2355
Data columns (total 11 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   tweet_id            2175 non-null   object        
 1   timestamp           2175 non-null   datetime64[ns]
 2   source              2175 non-null   object        
 3   text                2175 non-null   object        
 4   rating_numerator    2175 non-null   float64       
 5   rating_denominator  2175 non-null   float64       
 6   name                2175 non-null   object        
 7   doggo               2175 non-null   object        
 8   floofer             2175 non-null   object        
 9   pupper              2175 non-null   object        
 10  puppo               2175 non-null   object        
dtypes: datetime64[ns](1), float64(2), object(8)
memory usage: 203.9+ KB

Issue #9: There are a lot of null values in rd_df columns doggo, floofer, puppo, and pupper.¶

Define: The columns need to be concatenated and make sure the multiple stages are addressed using LOC then dropping the remaining doggo, floofer, puppo and pupper columns leaving dog_stages¶

Code¶

In [37]:

#creating a new column
rd_df['dog_stage'] = rd_df.doggo + rd_df.floofer + rd_df.pupper + rd_df.puppo

In [38]:

# Then format entries with multiple dog stages which appeared like doggopupper.
rd_df.loc[rd_df.dog_stage == 'doggopupper', 'dog_stage'] = 'doggo, pupper'
rd_df.loc[rd_df.dog_stage == 'doggopuppo', 'dog_stage'] = 'doggo, puppo'
rd_df.loc[rd_df.dog_stage == 'doggofloofer', 'dog_stage'] = 'doggo, floofer'

In [39]:

rd_df.drop(["doggo", "floofer", "pupper", "puppo"], axis=1, inplace=True)

Test¶

In [40]:

#confirmed with #8 value counts 234 pupper (add the multistage), 86 doggo, 25 puppo and 10 floofer
rd_df.dog_stage.value_counts()

Out[40]:

                  1831
pupper             224
doggo               75
puppo               24
doggo, pupper       10
floofer              9
doggo, puppo         1
doggo, floofer       1
Name: dog_stage, dtype: int64

Issue #10: Merge data frames on shared tweet_ids.¶

Define: Merging on tweet_ids¶

Code¶

In [41]:

#merge 1
df12=pd.merge(tt_df, rd_df, on='tweet_id')

In [42]:

#merge2
df4=pd.merge(df12, ip_df, on='tweet_id')

Test¶

In [43]:

df4.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1984 entries, 0 to 1983
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   tweet_id            1984 non-null   object        
 1   retweet_count       1984 non-null   object        
 2   favorite_count      1984 non-null   object        
 3   timestamp           1984 non-null   datetime64[ns]
 4   source              1984 non-null   object        
 5   text                1984 non-null   object        
 6   rating_numerator    1984 non-null   float64       
 7   rating_denominator  1984 non-null   float64       
 8   name                1984 non-null   object        
 9   dog_stage           1984 non-null   object        
 10  jpg_url             1984 non-null   object        
 11  img_num             1984 non-null   int64         
 12  p1                  1984 non-null   object        
 13  p1_conf             1984 non-null   float64       
 14  p1_dog              1984 non-null   bool          
 15  p2                  1984 non-null   object        
 16  p2_conf             1984 non-null   float64       
 17  p2_dog              1984 non-null   bool          
 18  p3                  1984 non-null   object        
 19  p3_conf             1984 non-null   float64       
 20  p3_dog              1984 non-null   bool          
dtypes: bool(3), datetime64[ns](1), float64(5), int64(1), object(11)
memory usage: 300.3+ KB

Storing Data¶

Save gathered, assessed, and cleaned master dataset to a CSV file named "twitter_archive_master.csv".

In [44]:

df4.to_csv('twitter_archive_master.csv', index=False)

Analyzing and Visualizing Data¶

In this section, analyze and visualize the wrangled data, at least three (3) insights and one (1) visualization.

In [45]:

df_all=pd.read_csv("twitter_archive_master.csv")

In [46]:

df_all.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1984 entries, 0 to 1983
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   tweet_id            1984 non-null   int64  
 1   retweet_count       1984 non-null   int64  
 2   favorite_count      1984 non-null   int64  
 3   timestamp           1984 non-null   object 
 4   source              1984 non-null   object 
 5   text                1984 non-null   object 
 6   rating_numerator    1984 non-null   float64
 7   rating_denominator  1984 non-null   float64
 8   name                1401 non-null   object 
 9   dog_stage           304 non-null    object 
 10  jpg_url             1984 non-null   object 
 11  img_num             1984 non-null   int64  
 12  p1                  1984 non-null   object 
 13  p1_conf             1984 non-null   float64
 14  p1_dog              1984 non-null   bool   
 15  p2                  1984 non-null   object 
 16  p2_conf             1984 non-null   float64
 17  p2_dog              1984 non-null   bool   
 18  p3                  1984 non-null   object 
 19  p3_conf             1984 non-null   float64
 20  p3_dog              1984 non-null   bool   
dtypes: bool(3), float64(5), int64(4), object(9)
memory usage: 284.9+ KB

In [47]:

#seeing if there are any tweets that have not been retweeted
(df_all.retweet_count)

Out[47]:

0       7090
1       5345
2       3517
3       7294
4       7833
        ... 
1979      37
1980     117
1981      36
1982      39
1983     427
Name: retweet_count, Length: 1984, dtype: int64

In [48]:

#looking at how many times a tweet has 2x (or more) as many favorites as retweets compared to retweets.
((df_all.retweet_count/df_all.favorite_count)<.5).sum()/df_all.retweet_count.count()

Out[48]:

0.9506048387096774

In [49]:

#double checking my work
((df_all.retweet_count*2)<df_all.favorite_count).sum()/df_all.retweet_count.count()

Out[49]:

0.9506048387096774

In [50]:

#how many dogs do not have names in a tweet compared to number of tweets
df_all.name.count()
numberofNoNames=df_all.name.isnull().sum()/df_all.shape[0]
numberofNoNames

Out[50]:

0.2938508064516129

In [51]:

#how many retweets have dog names compared to retweet total and total of tweets with dog names
df_all.retweet_count[df_all.name.count()].sum()/(df_all.retweet_count.count())*(1-numberofNoNames)

Out[51]:

0.36909108553915193

In [52]:

#finding the overall rating per tweet
df_all['rating']= df_all.rating_numerator/df_all.rating_denominator

In [53]:

#finding the tweets that have a rating greater than or equal to 100% i.e. 10/10 or 13/13
df_all.retweet_count[df_all.rating>=1].count()/df_all.retweet_count.count()

Out[53]:

0.7908266129032258

Insights:¶

Relationship between retweets and favorites: 95% of all tweets have more than double the amount of favorites than retweets.
Relationship between retweets and dog names: 38% of all tweets that had a dog's name in it are retweeted.
Relationship between retweets and rating: 79% of all retweets have a rating equal to or higher than a 100% rating.

In [54]:

mean_count=df_all.retweet_count.mean()
mean_count

Out[54]:

2269.1491935483873

Visualization¶

In [55]:

#turns out csv doesn't keep datetime dtypes and will convert them back to objects
df_all.timestamp=pd.to_datetime(df_all.timestamp, infer_datetime_format=True)

In [56]:

#a visualization of retweet_count over time
fig, axs = plt.subplots(figsize=(12, 4))
df_all.groupby(df_all["timestamp"].dt.dayofweek)["retweet_count"].mean().plot(kind='bar', rot=90, ax=axs, color="purple")
plt.xlabel("Time")
plt.xticks([0,1,2,3,4,5,6],["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"])
plt.ylabel("Average Retweet Count per Day")
plt.title("Retweets Over Time");

In [57]:

#visualization of dog nickname count
values=df_all.dog_stage.value_counts().sort_values()
df_all.dog_stage.value_counts().plot(kind="bar", title="Dog Nicknames", color="orange")
plt.tight_layout()
plt.xticks(rotation=90)
plt.ylabel("Count");

In [ ]: