{ "cells": [ { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 4, "hidden": false, "row": 0, "width": 4 }, "report_default": { "hidden": false } } } } }, "source": [ "# Project: Wrangling and Analysis of 'WeRateDogs' tweet archive data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Introduction\n", "\n", "This notebook shows the data wrangling and analysis of tweet archive data of a popular dog rating page on Twitter called 'WeRateDogs'. It demonstrates gathering, assessment and cleaning of the data following different methods all as part of the data wrangling process. It also demonstrates the analysis of this data and documents the various insights and visualizations.\n", "\n", "### Data description\n", "The data to be used in this project includes:\n", "\n", "#### 1. A file on hand containing tweet archive data including:\n", "\n", "##### tweet_id: \n", "unique identifier of a particular Tweet.\n", "\n", "##### in_reply_to_status_id: \n", "if the represented Tweet is a reply, this field will contain the integer representation of the original Tweet's ID.\n", "\n", "##### in_reply_to_user_id:\n", "if the represented Tweet is a reply, this field will contain the string representation of the original Tweet's author ID.\n", "\n", "##### timestamp: \n", "the date and time at which the tweet was posted\n", "\n", "##### source:\n", "utility used to post the tweet as a HTML-formatted string\n", "\n", "##### text: \n", "the actual UTF-8 text of the tweet.\n", "\n", "##### retweeted_status_id:\n", "if the represented Tweet is a retweet, this field will contain the integer representation of the original Tweet's ID\n", "\n", "##### retweeted_status_user_id:\n", "if the represented Tweet is a retweet, this field will contain the integer representation of the original Tweet's author ID\n", "\n", "##### retweeted_timestamp:\n", "the date and time at which the retweet was posted\n", "\n", "##### expanded_urls: \n", "the full url link of the tweet\n", "\n", "##### rating_numerator: \n", "the integer representation of the dog rating.\n", "\n", "##### rating_denominator:\n", "the integer representation of the overall value of the rating\n", "\n", "##### name: \n", "the name of the dog\n", "\n", "##### doggo: \n", "a big pupper usually older\n", "\n", "##### floofer:\n", "label given to a dog that is excessively fury\n", "\n", "##### pupper:\n", "a small doggo, usually younger.\n", "\n", "##### puppo:\n", "a transitional phase between pupper and doggo\n", "\n", "#### 2. A tweet image file containing tweet image prediction data including:\n", "\n", "##### tweet_id:\n", "unique identifier of a particular Tweet.\n", "\n", "##### jpg_url:\n", "url link to the image associated with the given Tweet.\n", "##### img_num:\n", "since a tweet can have multiple images, this indicates the number of the image corresponding to the most confident prediction.\n", "##### p1:\n", "p1 is the algorithm's #1 prediction for the image in the tweet\n", "##### p1_conf:\n", "p1_conf is how confident the algorithm is in its #1 prediction \n", "##### p1_dog:\n", "p1_dog is whether or not the #1 prediction is a breed of dog \n", "##### p2:\n", "p2 is the algorithm's second most likely prediction\n", "##### p2_conf:\n", "p2_conf is how confident the algorithm is in its #2 prediction\n", "##### p2_dog:\n", "p2_dog is whether or not the #2 prediction is a breed of dog\n", "##### p3:\n", "p3 is the algorithm's third most likely prediction\n", "##### p3_conf:\n", "p3_conf is how confident the algorithm is in its #3 prediction\n", "##### p3_dog:\n", "p3_dog is whether or not the #3 prediction is a breed of dog\n", "\n", "#### 3. Tweet retweet count and favorite count data including:\n", "\n", "##### tweet_id:\n", "unique identifier of a particular Tweet.\n", "##### retweet_count:\n", "the number of times a Tweet has been retweeted.\n", "##### favorite_count:\n", "the number of times a Tweet has been favorited.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tweepy in /opt/conda/lib/python3.6/site-packages (3.5.0)\n", "Requirement already satisfied: requests>=2.4.3 in /opt/conda/lib/python3.6/site-packages (from tweepy) (2.18.4)\n", "Requirement already satisfied: requests_oauthlib>=0.4.1 in /opt/conda/lib/python3.6/site-packages (from tweepy) (0.8.0)\n", "Requirement already satisfied: six>=1.7.3 in /opt/conda/lib/python3.6/site-packages (from tweepy) (1.11.0)\n", "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests>=2.4.3->tweepy) (3.0.4)\n", "Requirement already satisfied: idna<2.7,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests>=2.4.3->tweepy) (2.6)\n", "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests>=2.4.3->tweepy) (1.22)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests>=2.4.3->tweepy) (2019.11.28)\n", "Requirement already satisfied: oauthlib>=0.6.2 in /opt/conda/lib/python3.6/site-packages (from requests_oauthlib>=0.4.1->tweepy) (2.0.6)\n" ] } ], "source": [ "#installing tweepy into the environment\n", "!pip install tweepy" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# importing all the packages that will be required.\n", "import pandas as pd\n", "import requests\n", "import tweepy\n", "import json\n", "import numpy as np\n", "import re\n", "import functools\n", "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "import seaborn as sns\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Data Gathering\n", "\n", "1. Directly downloading the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": { "hidden": true } } } } }, "outputs": [ { "data": { "text/html": [ "
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tweet_idin_reply_to_status_idin_reply_to_user_idtimestampsourcetextretweeted_status_idretweeted_status_user_idretweeted_status_timestampexpanded_urlsrating_numeratorrating_denominatornamedoggoflooferpupperpuppo
0892420643555336193NaNNaN2017-08-01 16:23:56 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Phineas. He's a mystical boy. Only eve...NaNNaNNaNhttps://twitter.com/dog_rates/status/892420643...1310PhineasNoneNoneNoneNone
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2891815181378084864NaNNaN2017-07-31 00:18:03 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Archie. He is a rare Norwegian Pouncin...NaNNaNNaNhttps://twitter.com/dog_rates/status/891815181...1210ArchieNoneNoneNoneNone
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" ], "text/plain": [ " tweet_id in_reply_to_status_id in_reply_to_user_id \\\n", "0 892420643555336193 NaN NaN \n", "1 892177421306343426 NaN NaN \n", "2 891815181378084864 NaN NaN \n", "\n", " timestamp \\\n", "0 2017-08-01 16:23:56 +0000 \n", "1 2017-08-01 00:17:27 +0000 \n", "2 2017-07-31 00:18:03 +0000 \n", "\n", " source \\\n", "0 \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
tweet_idjpg_urlimg_nump1p1_confp1_dogp2p2_confp2_dogp3p3_confp3_dog
0666020888022790149https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg1Welsh_springer_spaniel0.465074Truecollie0.156665TrueShetland_sheepdog0.061428True
1666029285002620928https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg1redbone0.506826Trueminiature_pinscher0.074192TrueRhodesian_ridgeback0.072010True
2666033412701032449https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg1German_shepherd0.596461Truemalinois0.138584Truebloodhound0.116197True
\n", "" ], "text/plain": [ " tweet_id jpg_url \\\n", "0 666020888022790149 https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg \n", "1 666029285002620928 https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg \n", "2 666033412701032449 https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg \n", "\n", " img_num p1 p1_conf p1_dog p2 \\\n", "0 1 Welsh_springer_spaniel 0.465074 True collie \n", "1 1 redbone 0.506826 True miniature_pinscher \n", "2 1 German_shepherd 0.596461 True malinois \n", "\n", " p2_conf p2_dog p3 p3_conf p3_dog \n", "0 0.156665 True Shetland_sheepdog 0.061428 True \n", "1 0.074192 True Rhodesian_ridgeback 0.072010 True \n", "2 0.138584 True bloodhound 0.116197 True " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_df = pd.read_csv('image_predictions.tsv', sep = '\\t')\n", "image_df.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Using the Tweepy library to query additional data via the Twitter API (tweet_json.txt)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#api\n", "api_key = \"YOU API KEY HERE\"\n", "api_secrets = \"YOUR API SECRET KEY HERE\"\n", "access_token = \"YOUR ACCESS TOKEN KEY HERE\"\n", "access_secret = \"YOUR ACCESS TOKEN SECRET HERE\"\n", " \n", "# Authenticate to Twitter\n", "auth = tweepy.OAuthHandler(api_key,api_secrets)\n", "auth.set_access_token(access_token,access_secret)\n", " \n", "api = tweepy.API(auth,wait_on_rate_limit=True,wait_on_rate_limit_notify=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#create list of tweet ids from archive_df tweet_id column\n", "tweet_ids = []\n", "for id in tweet_archive.tweet_id:\n", " tweet_ids.append(str(id))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2356\n" ] } ], "source": [ "print(len(tweet_ids))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rate limit reached. Sleeping for: 743\n", "Rate limit reached. Sleeping for: 743\n" ] } ], "source": [ "#create empty list for available tweets\n", "tweets = []\n", "#create empty list for unavailable tweets\n", "unavailable_tweets = []\n", "#gather each tweet's json data by id\n", "for id in tweet_ids:\n", " try:\n", " tweet = (api.get_status(id))._json\n", " tweets.append({'tweet_id':tweet['id'],'retweet_count':tweet['retweet_count'],'favorite_count':tweet['favorite_count']})\n", " except:\n", " unavailable_tweets.append(id)\n", " \n", "indices = list(range(len(tweets)))\n", "with open('tweet_json.txt', mode = 'w') as file:\n", " for i in indices\n", " file.write(json.dumps(tweets[i]['tweet_id']))\n", " file.write('\\t') \n", " file.write(json.dumps(tweets[i]['retweet_count']))\n", " file.write('\\t')\n", " file.write(json.dumps(tweets[i]['favorite_count']))\n", " file.write('\\n')\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['created_at',\n", " 'id',\n", " 'id_str',\n", " 'text',\n", " 'truncated',\n", " 'entities',\n", " 'extended_entities',\n", " 'source',\n", " 'in_reply_to_status_id',\n", " 'in_reply_to_status_id_str',\n", " 'in_reply_to_user_id',\n", " 'in_reply_to_user_id_str',\n", " 'in_reply_to_screen_name',\n", " 'user',\n", " 'geo',\n", " 'coordinates',\n", " 'place',\n", " 'contributors',\n", " 'is_quote_status',\n", " 'retweet_count',\n", " 'favorite_count',\n", " 'favorited',\n", " 'retweeted',\n", " 'possibly_sensitive',\n", " 'possibly_sensitive_appealable',\n", " 'lang']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check for available attributes of the json data retrieved from the api\n", "list(tweet.keys())" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
tweet_idretweet_countfavorite_count
0892420643555336193697933728
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2891815181378084864346621987
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" ], "text/plain": [ " tweet_id retweet_count favorite_count\n", "0 892420643555336193 6979 33728\n", "1 892177421306343426 5280 29255\n", "2 891815181378084864 3466 21987\n", "3 891689557279858688 7198 36823\n", "4 891327558926688256 7723 35207" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#reading the 'tweet_json.txt' file into a dataframe\n", "tweet_counts = pd.read_csv('tweet_json.txt', sep ='\\t', header = None, names = ['tweet_id', 'retweet_count','favorite_count'])\n", "tweet_counts.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2326, 3)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_counts.shape" ] }, { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 4, "height": 4, "hidden": false, "row": 28, "width": 4 }, "report_default": { "hidden": false } } } } }, "source": [ "\n", "## Assessing Data\n", "#### 1. tweet_archive dataframe" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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tweet_idin_reply_to_status_idin_reply_to_user_idtimestampsourcetextretweeted_status_idretweeted_status_user_idretweeted_status_timestampexpanded_urlsrating_numeratorrating_denominatornamedoggoflooferpupperpuppo
0892420643555336193NaNNaN2017-08-01 16:23:56 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Phineas. He's a mystical boy. Only eve...NaNNaNNaNhttps://twitter.com/dog_rates/status/892420643...1310PhineasNoneNoneNoneNone
1892177421306343426NaNNaN2017-08-01 00:17:27 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Tilly. She's just checking pup on you....NaNNaNNaNhttps://twitter.com/dog_rates/status/892177421...1310TillyNoneNoneNoneNone
2891815181378084864NaNNaN2017-07-31 00:18:03 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Archie. He is a rare Norwegian Pouncin...NaNNaNNaNhttps://twitter.com/dog_rates/status/891815181...1210ArchieNoneNoneNoneNone
3891689557279858688NaNNaN2017-07-30 15:58:51 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Darla. She commenced a snooze mid meal...NaNNaNNaNhttps://twitter.com/dog_rates/status/891689557...1310DarlaNoneNoneNoneNone
4891327558926688256NaNNaN2017-07-29 16:00:24 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Franklin. He would like you to stop ca...NaNNaNNaNhttps://twitter.com/dog_rates/status/891327558...1210FranklinNoneNoneNoneNone
5891087950875897856NaNNaN2017-07-29 00:08:17 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a majestic great white breaching ...NaNNaNNaNhttps://twitter.com/dog_rates/status/891087950...1310NoneNoneNoneNoneNone
6890971913173991426NaNNaN2017-07-28 16:27:12 +0000<a href=\"http://twitter.com/download/iphone\" r...Meet Jax. He enjoys ice cream so much he gets ...NaNNaNNaNhttps://gofundme.com/ydvmve-surgery-for-jax,ht...1310JaxNoneNoneNoneNone
7890729181411237888NaNNaN2017-07-28 00:22:40 +0000<a href=\"http://twitter.com/download/iphone\" r...When you watch your owner call another dog a g...NaNNaNNaNhttps://twitter.com/dog_rates/status/890729181...1310NoneNoneNoneNoneNone
8890609185150312448NaNNaN2017-07-27 16:25:51 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Zoey. She doesn't want to be one of th...NaNNaNNaNhttps://twitter.com/dog_rates/status/890609185...1310ZoeyNoneNoneNoneNone
9890240255349198849NaNNaN2017-07-26 15:59:51 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Cassie. She is a college pup. Studying...NaNNaNNaNhttps://twitter.com/dog_rates/status/890240255...1410CassiedoggoNoneNoneNone
10890006608113172480NaNNaN2017-07-26 00:31:25 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Koda. He is a South Australian decksha...NaNNaNNaNhttps://twitter.com/dog_rates/status/890006608...1310KodaNoneNoneNoneNone
11889880896479866881NaNNaN2017-07-25 16:11:53 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Bruno. He is a service shark. Only get...NaNNaNNaNhttps://twitter.com/dog_rates/status/889880896...1310BrunoNoneNoneNoneNone
12889665388333682689NaNNaN2017-07-25 01:55:32 +0000<a href=\"http://twitter.com/download/iphone\" r...Here's a puppo that seems to be on the fence a...NaNNaNNaNhttps://twitter.com/dog_rates/status/889665388...1310NoneNoneNoneNonepuppo
13889638837579907072NaNNaN2017-07-25 00:10:02 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Ted. He does his best. Sometimes that'...NaNNaNNaNhttps://twitter.com/dog_rates/status/889638837...1210TedNoneNoneNoneNone
14889531135344209921NaNNaN2017-07-24 17:02:04 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Stuart. He's sporting his favorite fan...NaNNaNNaNhttps://twitter.com/dog_rates/status/889531135...1310StuartNoneNoneNonepuppo
15889278841981685760NaNNaN2017-07-24 00:19:32 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Oliver. You're witnessing one of his m...NaNNaNNaNhttps://twitter.com/dog_rates/status/889278841...1310OliverNoneNoneNoneNone
16888917238123831296NaNNaN2017-07-23 00:22:39 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Jim. He found a fren. Taught him how t...NaNNaNNaNhttps://twitter.com/dog_rates/status/888917238...1210JimNoneNoneNoneNone
17888804989199671297NaNNaN2017-07-22 16:56:37 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Zeke. He has a new stick. Very proud o...NaNNaNNaNhttps://twitter.com/dog_rates/status/888804989...1310ZekeNoneNoneNoneNone
18888554962724278272NaNNaN2017-07-22 00:23:06 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Ralphus. He's powering up. Attempting ...NaNNaNNaNhttps://twitter.com/dog_rates/status/888554962...1310RalphusNoneNoneNoneNone
19888202515573088257NaNNaN2017-07-21 01:02:36 +0000<a href=\"http://twitter.com/download/iphone\" r...RT @dog_rates: This is Canela. She attempted s...8.874740e+174.196984e+092017-07-19 00:47:34 +0000https://twitter.com/dog_rates/status/887473957...1310CanelaNoneNoneNoneNone
20888078434458587136NaNNaN2017-07-20 16:49:33 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Gerald. He was just told he didn't get...NaNNaNNaNhttps://twitter.com/dog_rates/status/888078434...1210GeraldNoneNoneNoneNone
21887705289381826560NaNNaN2017-07-19 16:06:48 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Jeffrey. He has a monopoly on the pool...NaNNaNNaNhttps://twitter.com/dog_rates/status/887705289...1310JeffreyNoneNoneNoneNone
22887517139158093824NaNNaN2017-07-19 03:39:09 +0000<a href=\"http://twitter.com/download/iphone\" r...I've yet to rate a Venezuelan Hover Wiener. Th...NaNNaNNaNhttps://twitter.com/dog_rates/status/887517139...1410suchNoneNoneNoneNone
23887473957103951883NaNNaN2017-07-19 00:47:34 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Canela. She attempted some fancy porch...NaNNaNNaNhttps://twitter.com/dog_rates/status/887473957...1310CanelaNoneNoneNoneNone
24887343217045368832NaNNaN2017-07-18 16:08:03 +0000<a href=\"http://twitter.com/download/iphone\" r...You may not have known you needed to see this ...NaNNaNNaNhttps://twitter.com/dog_rates/status/887343217...1310NoneNoneNoneNoneNone
25887101392804085760NaNNaN2017-07-18 00:07:08 +0000<a href=\"http://twitter.com/download/iphone\" r...This... is a Jubilant Antarctic House Bear. We...NaNNaNNaNhttps://twitter.com/dog_rates/status/887101392...1210NoneNoneNoneNoneNone
26886983233522544640NaNNaN2017-07-17 16:17:36 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Maya. She's very shy. Rarely leaves he...NaNNaNNaNhttps://twitter.com/dog_rates/status/886983233...1310MayaNoneNoneNoneNone
27886736880519319552NaNNaN2017-07-16 23:58:41 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Mingus. He's a wonderful father to his...NaNNaNNaNhttps://www.gofundme.com/mingusneedsus,https:/...1310MingusNoneNoneNoneNone
28886680336477933568NaNNaN2017-07-16 20:14:00 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Derek. He's late for a dog meeting. 13...NaNNaNNaNhttps://twitter.com/dog_rates/status/886680336...1310DerekNoneNoneNoneNone
29886366144734445568NaNNaN2017-07-15 23:25:31 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Roscoe. Another pupper fallen victim t...NaNNaNNaNhttps://twitter.com/dog_rates/status/886366144...1210RoscoeNoneNonepupperNone
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2326666411507551481857NaNNaN2015-11-17 00:24:19 +0000<a href=\"http://twitter.com/download/iphone\" r...This is quite the dog. Gets really excited whe...NaNNaNNaNhttps://twitter.com/dog_rates/status/666411507...210quiteNoneNoneNoneNone
2327666407126856765440NaNNaN2015-11-17 00:06:54 +0000<a href=\"http://twitter.com/download/iphone\" r...This is a southern Vesuvius bumblegruff. Can d...NaNNaNNaNhttps://twitter.com/dog_rates/status/666407126...710aNoneNoneNoneNone
2328666396247373291520NaNNaN2015-11-16 23:23:41 +0000<a href=\"http://twitter.com/download/iphone\" r...Oh goodness. A super rare northeast Qdoba kang...NaNNaNNaNhttps://twitter.com/dog_rates/status/666396247...910NoneNoneNoneNoneNone
2329666373753744588802NaNNaN2015-11-16 21:54:18 +0000<a href=\"http://twitter.com/download/iphone\" r...Those are sunglasses and a jean jacket. 11/10 ...NaNNaNNaNhttps://twitter.com/dog_rates/status/666373753...1110NoneNoneNoneNoneNone
2330666362758909284353NaNNaN2015-11-16 21:10:36 +0000<a href=\"http://twitter.com/download/iphone\" r...Unique dog here. Very small. Lives in containe...NaNNaNNaNhttps://twitter.com/dog_rates/status/666362758...610NoneNoneNoneNoneNone
2331666353288456101888NaNNaN2015-11-16 20:32:58 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a mixed Asiago from the Galápagos...NaNNaNNaNhttps://twitter.com/dog_rates/status/666353288...810NoneNoneNoneNoneNone
2332666345417576210432NaNNaN2015-11-16 20:01:42 +0000<a href=\"http://twitter.com/download/iphone\" r...Look at this jokester thinking seat belt laws ...NaNNaNNaNhttps://twitter.com/dog_rates/status/666345417...1010NoneNoneNoneNoneNone
2333666337882303524864NaNNaN2015-11-16 19:31:45 +0000<a href=\"http://twitter.com/download/iphone\" r...This is an extremely rare horned Parthenon. No...NaNNaNNaNhttps://twitter.com/dog_rates/status/666337882...910anNoneNoneNoneNone
2334666293911632134144NaNNaN2015-11-16 16:37:02 +0000<a href=\"http://twitter.com/download/iphone\" r...This is a funny dog. Weird toes. Won't come do...NaNNaNNaNhttps://twitter.com/dog_rates/status/666293911...310aNoneNoneNoneNone
2335666287406224695296NaNNaN2015-11-16 16:11:11 +0000<a href=\"http://twitter.com/download/iphone\" r...This is an Albanian 3 1/2 legged Episcopalian...NaNNaNNaNhttps://twitter.com/dog_rates/status/666287406...12anNoneNoneNoneNone
2336666273097616637952NaNNaN2015-11-16 15:14:19 +0000<a href=\"http://twitter.com/download/iphone\" r...Can take selfies 11/10 https://t.co/ws2AMaNwPWNaNNaNNaNhttps://twitter.com/dog_rates/status/666273097...1110NoneNoneNoneNoneNone
2337666268910803644416NaNNaN2015-11-16 14:57:41 +0000<a href=\"http://twitter.com/download/iphone\" r...Very concerned about fellow dog trapped in com...NaNNaNNaNhttps://twitter.com/dog_rates/status/666268910...1010NoneNoneNoneNoneNone
2338666104133288665088NaNNaN2015-11-16 04:02:55 +0000<a href=\"http://twitter.com/download/iphone\" r...Not familiar with this breed. No tail (weird)....NaNNaNNaNhttps://twitter.com/dog_rates/status/666104133...110NoneNoneNoneNoneNone
2339666102155909144576NaNNaN2015-11-16 03:55:04 +0000<a href=\"http://twitter.com/download/iphone\" r...Oh my. Here you are seeing an Adobe Setter giv...NaNNaNNaNhttps://twitter.com/dog_rates/status/666102155...1110NoneNoneNoneNoneNone
2340666099513787052032NaNNaN2015-11-16 03:44:34 +0000<a href=\"http://twitter.com/download/iphone\" r...Can stand on stump for what seems like a while...NaNNaNNaNhttps://twitter.com/dog_rates/status/666099513...810NoneNoneNoneNoneNone
2341666094000022159362NaNNaN2015-11-16 03:22:39 +0000<a href=\"http://twitter.com/download/iphone\" r...This appears to be a Mongolian Presbyterian mi...NaNNaNNaNhttps://twitter.com/dog_rates/status/666094000...910NoneNoneNoneNoneNone
2342666082916733198337NaNNaN2015-11-16 02:38:37 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a well-established sunblockerspan...NaNNaNNaNhttps://twitter.com/dog_rates/status/666082916...610NoneNoneNoneNoneNone
2343666073100786774016NaNNaN2015-11-16 01:59:36 +0000<a href=\"http://twitter.com/download/iphone\" r...Let's hope this flight isn't Malaysian (lol). ...NaNNaNNaNhttps://twitter.com/dog_rates/status/666073100...1010NoneNoneNoneNoneNone
2344666071193221509120NaNNaN2015-11-16 01:52:02 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a northern speckled Rhododendron....NaNNaNNaNhttps://twitter.com/dog_rates/status/666071193...910NoneNoneNoneNoneNone
2345666063827256086533NaNNaN2015-11-16 01:22:45 +0000<a href=\"http://twitter.com/download/iphone\" r...This is the happiest dog you will ever see. Ve...NaNNaNNaNhttps://twitter.com/dog_rates/status/666063827...1010theNoneNoneNoneNone
2346666058600524156928NaNNaN2015-11-16 01:01:59 +0000<a href=\"http://twitter.com/download/iphone\" r...Here is the Rand Paul of retrievers folks! He'...NaNNaNNaNhttps://twitter.com/dog_rates/status/666058600...810theNoneNoneNoneNone
2347666057090499244032NaNNaN2015-11-16 00:55:59 +0000<a href=\"http://twitter.com/download/iphone\" r...My oh my. This is a rare blond Canadian terrie...NaNNaNNaNhttps://twitter.com/dog_rates/status/666057090...910aNoneNoneNoneNone
2348666055525042405380NaNNaN2015-11-16 00:49:46 +0000<a href=\"http://twitter.com/download/iphone\" r...Here is a Siberian heavily armored polar bear ...NaNNaNNaNhttps://twitter.com/dog_rates/status/666055525...1010aNoneNoneNoneNone
2349666051853826850816NaNNaN2015-11-16 00:35:11 +0000<a href=\"http://twitter.com/download/iphone\" r...This is an odd dog. Hard on the outside but lo...NaNNaNNaNhttps://twitter.com/dog_rates/status/666051853...210anNoneNoneNoneNone
2350666050758794694657NaNNaN2015-11-16 00:30:50 +0000<a href=\"http://twitter.com/download/iphone\" r...This is a truly beautiful English Wilson Staff...NaNNaNNaNhttps://twitter.com/dog_rates/status/666050758...1010aNoneNoneNoneNone
2351666049248165822465NaNNaN2015-11-16 00:24:50 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a 1949 1st generation vulpix. Enj...NaNNaNNaNhttps://twitter.com/dog_rates/status/666049248...510NoneNoneNoneNoneNone
2352666044226329800704NaNNaN2015-11-16 00:04:52 +0000<a href=\"http://twitter.com/download/iphone\" r...This is a purebred Piers Morgan. Loves to Netf...NaNNaNNaNhttps://twitter.com/dog_rates/status/666044226...610aNoneNoneNoneNone
2353666033412701032449NaNNaN2015-11-15 23:21:54 +0000<a href=\"http://twitter.com/download/iphone\" r...Here is a very happy pup. Big fan of well-main...NaNNaNNaNhttps://twitter.com/dog_rates/status/666033412...910aNoneNoneNoneNone
2354666029285002620928NaNNaN2015-11-15 23:05:30 +0000<a href=\"http://twitter.com/download/iphone\" r...This is a western brown Mitsubishi terrier. Up...NaNNaNNaNhttps://twitter.com/dog_rates/status/666029285...710aNoneNoneNoneNone
2355666020888022790149NaNNaN2015-11-15 22:32:08 +0000<a href=\"http://twitter.com/download/iphone\" r...Here we have a Japanese Irish Setter. Lost eye...NaNNaNNaNhttps://twitter.com/dog_rates/status/666020888...810NoneNoneNoneNoneNone
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2356 rows × 17 columns

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1062741099773336379392NaNNaN2016-06-10 02:48:49 +0000<a href=\"http://vine.co\" rel=\"nofollow\">Vine -...This is Ted. He's given up. 11/10 relatable af...NaNNaNNaNhttps://vine.co/v/ixHYvdxUx1L1110TedNoneNoneNoneNone
2347666057090499244032NaNNaN2015-11-16 00:55:59 +0000<a href=\"http://twitter.com/download/iphone\" r...My oh my. This is a rare blond Canadian terrie...NaNNaNNaNhttps://twitter.com/dog_rates/status/666057090...910aNoneNoneNoneNone
555803692223237865472NaNNaN2016-11-29 20:08:52 +0000<a href=\"http://twitter.com/download/iphone\" r...RT @dog_rates: I present to you... Dog Jesus. ...6.914169e+174.196984e+092016-01-25 00:26:41 +0000https://twitter.com/dog_rates/status/691416866...1310NoneNoneNoneNoneNone
1305707387676719185920NaNNaN2016-03-09 02:08:59 +0000<a href=\"http://twitter.com/download/iphone\" r...Meet Clarkus. He's a Skinny Eastern Worcesters...NaNNaNNaNhttps://twitter.com/dog_rates/status/707387676...1010ClarkusNoneNoneNoneNone
1060741438259667034112NaNNaN2016-06-11 01:13:51 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Tucker. He's still figuring out couche...NaNNaNNaNhttps://twitter.com/dog_rates/status/741438259...910TuckerNoneNoneNoneNone
1498630795471887851546.671522e+174.196984e+092017-05-12 17:12:53 +0000<a href=\"http://twitter.com/download/iphone\" r...Ladies and gentlemen... I found Pipsy. He may ...NaNNaNNaNhttps://twitter.com/dog_rates/status/863079547...1410NoneNoneNoneNoneNone
1073739932936087216128NaNNaN2016-06-06 21:32:13 +0000<a href=\"http://twitter.com/download/iphone\" r...Say hello to Rorie. She's zen af. Just enjoyin...NaNNaNNaNhttps://twitter.com/dog_rates/status/739932936...1010RorieNoneNoneNoneNone
1828676263575653122048NaNNaN2015-12-14 04:52:55 +0000<a href=\"http://twitter.com/download/iphone\" r...All this pupper wanted to do was go skiing. No...NaNNaNNaNhttps://twitter.com/dog_rates/status/676263575...1010NoneNoneNonepupperNone
1938673906403526995968NaNNaN2015-12-07 16:46:21 +0000<a href=\"http://twitter.com/download/iphone\" r...Guys I'm getting real tired of this. We only r...NaNNaNNaNhttps://twitter.com/dog_rates/status/673906403...310NoneNoneNoneNoneNone
1054742423170473463808NaNNaN2016-06-13 18:27:32 +0000<a href=\"http://twitter.com/download/iphone\" r...This is Bell. She likes holding hands. 12/10 w...NaNNaNNaNhttps://twitter.com/dog_rates/status/742423170...1210BellNoneNoneNoneNone
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Koda\n", "11 Bruno\n", "12 None\n", "13 Ted\n", "14 Stuart\n", "15 Oliver\n", "16 Jim\n", "17 Zeke\n", "18 Ralphus\n", "19 Canela\n", "20 Gerald\n", "21 Jeffrey\n", "22 such\n", "23 Canela\n", "24 None\n", "25 None\n", "26 Maya\n", "27 Mingus\n", "28 Derek\n", "29 Roscoe\n", " ... \n", "2326 quite\n", "2327 a\n", "2328 None\n", "2329 None\n", "2330 None\n", "2331 None\n", "2332 None\n", "2333 an\n", "2334 a\n", "2335 an\n", "2336 None\n", "2337 None\n", "2338 None\n", "2339 None\n", "2340 None\n", "2341 None\n", "2342 None\n", "2343 None\n", "2344 None\n", "2345 the\n", "2346 the\n", "2347 a\n", "2348 a\n", "2349 an\n", "2350 a\n", "2351 None\n", "2352 a\n", "2353 a\n", "2354 a\n", "2355 None\n", "Name: name, Length: 2356, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_archive.name" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "None 745\n", "a 55\n", "Charlie 12\n", "Lucy 11\n", "Oliver 11\n", "Cooper 11\n", "Penny 10\n", "Tucker 10\n", "Lola 10\n", "Winston 9\n", "Bo 9\n", "the 8\n", "Sadie 8\n", "an 7\n", "Bailey 7\n", "Buddy 7\n", "Daisy 7\n", "Toby 7\n", "Scout 6\n", "Dave 6\n", "Jack 6\n", "Oscar 6\n", "Bella 6\n", "Koda 6\n", "Jax 6\n", "Rusty 6\n", "Milo 6\n", "Leo 6\n", "Stanley 6\n", "Chester 5\n", " ... \n", "Thor 1\n", "Rueben 1\n", "by 1\n", "Jeremy 1\n", "Bobble 1\n", "Liam 1\n", "Stella 1\n", "General 1\n", "Cheryl 1\n", "Lilli 1\n", "Travis 1\n", "Berkeley 1\n", "Banditt 1\n", "Ralphie 1\n", "Dixie 1\n", "Grizzwald 1\n", "Strudel 1\n", "Orion 1\n", "Kona 1\n", "Lupe 1\n", "Ace 1\n", "Zuzu 1\n", "Chloe 1\n", "Remy 1\n", "Rufio 1\n", "Vinscent 1\n", "Sprinkles 1\n", "Eevee 1\n", "Pancake 1\n", "Mosby 1\n", "Name: name, Length: 957, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_archive.name.value_counts()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['a', 'actually', 'all', 'an', 'by', 'getting', 'his', 'incredibly', 'infuriating', 'just', 'life', 'light', 'mad', 'my', 'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the', 'this', 'unacceptable', 'very']\n" ] } ], "source": [ "words = []\n", "for n in tweet_archive.name:\n", " if n[0].islower():\n", " words.append(n)\n", " \n", "other_words = list(np.unique(words))\n", "print(other_words)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "None 2099\n", "pupper 257\n", "Name: pupper, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_archive.pupper.value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "None 2259\n", "doggo 97\n", "Name: doggo, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_archive.doggo.value_counts()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(tweet_archive.duplicated())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. image_df dataframe" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2666033412701032449https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg1German_shepherd0.596461Truemalinois0.138584Truebloodhound0.116197True
3666044226329800704https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg1Rhodesian_ridgeback0.408143Trueredbone0.360687Trueminiature_pinscher0.222752True
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9666058600524156928https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg1miniature_poodle0.201493Truekomondor0.192305Truesoft-coated_wheaten_terrier0.082086True
10666063827256086533https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg1golden_retriever0.775930TrueTibetan_mastiff0.093718TrueLabrador_retriever0.072427True
11666071193221509120https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg1Gordon_setter0.503672TrueYorkshire_terrier0.174201TruePekinese0.109454True
12666073100786774016https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg1Walker_hound0.260857TrueEnglish_foxhound0.175382TrueIbizan_hound0.097471True
13666082916733198337https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg1pug0.489814Truebull_mastiff0.404722TrueFrench_bulldog0.048960True
14666094000022159362https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg1bloodhound0.195217TrueGerman_shepherd0.078260Truemalinois0.075628True
15666099513787052032https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg1Lhasa0.582330TrueShih-Tzu0.166192TrueDandie_Dinmont0.089688True
16666102155909144576https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg1English_setter0.298617TrueNewfoundland0.149842Trueborzoi0.133649True
17666104133288665088https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg1hen0.965932Falsecock0.033919Falsepartridge0.000052False
18666268910803644416https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg1desktop_computer0.086502Falsedesk0.085547Falsebookcase0.079480False
19666273097616637952https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg1Italian_greyhound0.176053Truetoy_terrier0.111884Truebasenji0.111152True
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21666293911632134144https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg1three-toed_sloth0.914671Falseotter0.015250Falsegreat_grey_owl0.013207False
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23666345417576210432https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg1golden_retriever0.858744TrueChesapeake_Bay_retriever0.054787TrueLabrador_retriever0.014241True
24666353288456101888https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg1malamute0.336874TrueSiberian_husky0.147655TrueEskimo_dog0.093412True
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26666373753744588802https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg1soft-coated_wheaten_terrier0.326467TrueAfghan_hound0.259551Truebriard0.206803True
27666396247373291520https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg1Chihuahua0.978108Truetoy_terrier0.009397Truepapillon0.004577True
28666407126856765440https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg1black-and-tan_coonhound0.529139Truebloodhound0.244220Trueflat-coated_retriever0.173810True
29666411507551481857https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg1coho0.404640Falsebarracouta0.271485Falsegar0.189945False
.......................................
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2058888917238123831296https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg1golden_retriever0.714719TrueTibetan_mastiff0.120184TrueLabrador_retriever0.105506True
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French_bulldog 0.999201 True \n", "2046 1 convertible 0.738995 False \n", "2047 1 kuvasz 0.309706 True \n", "2048 2 Chihuahua 0.793469 True \n", "2049 1 Samoyed 0.733942 True \n", "2050 1 Mexican_hairless 0.330741 True \n", "2051 2 Pembroke 0.809197 True \n", "2052 1 limousine 0.130432 False \n", "2053 1 basset 0.821664 True \n", "2054 1 French_bulldog 0.995026 True \n", "2055 2 Pembroke 0.809197 True \n", "2056 3 Siberian_husky 0.700377 True \n", "2057 1 golden_retriever 0.469760 True \n", "2058 1 golden_retriever 0.714719 True \n", "2059 1 whippet 0.626152 True \n", "2060 1 golden_retriever 0.953442 True \n", "2061 1 French_bulldog 0.991650 True \n", "2062 1 Pembroke 0.966327 True \n", "2063 1 French_bulldog 0.377417 True \n", "2064 1 Samoyed 0.957979 True \n", "2065 1 Pembroke 0.511319 True \n", "2066 1 Irish_terrier 0.487574 True \n", "2067 2 Pomeranian 0.566142 True \n", "2068 1 Appenzeller 0.341703 True \n", "2069 1 Chesapeake_Bay_retriever 0.425595 True \n", "2070 2 basset 0.555712 True \n", "2071 1 paper_towel 0.170278 False \n", "2072 1 Chihuahua 0.716012 True \n", "2073 1 Chihuahua 0.323581 True \n", "2074 1 orange 0.097049 False \n", "\n", " p2 p2_conf p2_dog p3 \\\n", "0 collie 0.156665 True Shetland_sheepdog \n", "1 miniature_pinscher 0.074192 True Rhodesian_ridgeback \n", "2 malinois 0.138584 True bloodhound \n", "3 redbone 0.360687 True miniature_pinscher \n", "4 Rottweiler 0.243682 True Doberman \n", "5 English_springer 0.263788 True Greater_Swiss_Mountain_dog \n", "6 mud_turtle 0.045885 False terrapin \n", "7 Tibetan_mastiff 0.058279 True fur_coat \n", "8 shopping_basket 0.014594 False golden_retriever \n", "9 komondor 0.192305 True soft-coated_wheaten_terrier \n", "10 Tibetan_mastiff 0.093718 True Labrador_retriever \n", "11 Yorkshire_terrier 0.174201 True Pekinese \n", "12 English_foxhound 0.175382 True Ibizan_hound \n", "13 bull_mastiff 0.404722 True French_bulldog \n", "14 German_shepherd 0.078260 True malinois \n", "15 Shih-Tzu 0.166192 True Dandie_Dinmont \n", "16 Newfoundland 0.149842 True borzoi \n", "17 cock 0.033919 False partridge \n", "18 desk 0.085547 False bookcase \n", "19 toy_terrier 0.111884 True basenji \n", "20 toy_poodle 0.063064 True miniature_poodle \n", "21 otter 0.015250 False great_grey_owl \n", "22 Newfoundland 0.278407 True groenendael \n", "23 Chesapeake_Bay_retriever 0.054787 True Labrador_retriever \n", "24 Siberian_husky 0.147655 True Eskimo_dog \n", "25 skunk 0.002402 False hamster \n", "26 Afghan_hound 0.259551 True briard \n", "27 toy_terrier 0.009397 True papillon \n", "28 bloodhound 0.244220 True flat-coated_retriever \n", "29 barracouta 0.271485 False gar \n", "... ... ... ... ... \n", "2045 Chihuahua 0.000361 True Boston_bull \n", "2046 sports_car 0.139952 False car_wheel \n", "2047 Great_Pyrenees 0.186136 True Dandie_Dinmont \n", "2048 toy_terrier 0.143528 True can_opener \n", "2049 Eskimo_dog 0.035029 True Staffordshire_bullterrier \n", "2050 sea_lion 0.275645 False Weimaraner \n", "2051 Rhodesian_ridgeback 0.054950 True beagle \n", "2052 tow_truck 0.029175 False shopping_cart \n", "2053 redbone 0.087582 True Weimaraner \n", "2054 pug 0.000932 True bull_mastiff \n", "2055 Rhodesian_ridgeback 0.054950 True beagle \n", "2056 Eskimo_dog 0.166511 True malamute \n", "2057 Labrador_retriever 0.184172 True English_setter \n", "2058 Tibetan_mastiff 0.120184 True Labrador_retriever \n", "2059 borzoi 0.194742 True Saluki \n", "2060 Labrador_retriever 0.013834 True redbone \n", "2061 boxer 0.002129 True Staffordshire_bullterrier \n", "2062 Cardigan 0.027356 True basenji \n", "2063 Labrador_retriever 0.151317 True muzzle \n", "2064 Pomeranian 0.013884 True chow \n", "2065 Cardigan 0.451038 True Chihuahua \n", "2066 Irish_setter 0.193054 True Chesapeake_Bay_retriever \n", "2067 Eskimo_dog 0.178406 True Pembroke \n", "2068 Border_collie 0.199287 True ice_lolly \n", "2069 Irish_terrier 0.116317 True Indian_elephant \n", "2070 English_springer 0.225770 True German_short-haired_pointer \n", "2071 Labrador_retriever 0.168086 True spatula \n", "2072 malamute 0.078253 True kelpie \n", "2073 Pekinese 0.090647 True papillon \n", "2074 bagel 0.085851 False banana \n", "\n", " p3_conf p3_dog \n", "0 0.061428 True \n", "1 0.072010 True \n", "2 0.116197 True \n", "3 0.222752 True \n", "4 0.154629 True \n", "5 0.016199 True \n", "6 0.017885 False \n", "7 0.054449 False \n", "8 0.007959 True \n", "9 0.082086 True \n", "10 0.072427 True \n", "11 0.109454 True \n", "12 0.097471 True \n", "13 0.048960 True \n", "14 0.075628 True \n", "15 0.089688 True \n", "16 0.133649 True \n", "17 0.000052 False \n", "18 0.079480 False \n", "19 0.111152 True \n", "20 0.025581 True \n", "21 0.013207 False \n", "22 0.102643 True \n", "23 0.014241 True \n", "24 0.093412 True \n", "25 0.000461 False \n", "26 0.206803 True \n", "27 0.004577 True \n", "28 0.173810 True \n", "29 0.189945 False \n", "... ... ... \n", "2045 0.000076 True \n", "2046 0.044173 False \n", "2047 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" ], "text/plain": [ " tweet_id jpg_url \\\n", "1881 847116187444137987 https://pbs.twimg.com/media/C8GPrNDW4AAkLde.jpg \n", "1837 837366284874571778 https://pbs.twimg.com/media/C57sMJwXMAASBSx.jpg \n", "995 708149363256774660 https://pbs.twimg.com/media/CdPaEkHW8AA-Wom.jpg \n", "481 675362609739206656 https://pbs.twimg.com/media/CV9etctWUAAl5Hp.jpg \n", "1937 860276583193509888 https://pbs.twimg.com/media/C_BQ_NlVwAAgYGD.jpg \n", "\n", " img_num p1 p1_conf p1_dog \\\n", "1881 1 white_wolf 0.128935 False \n", "1837 1 American_Staffordshire_terrier 0.660085 True \n", "995 1 Cardigan 0.350993 True \n", "481 1 Labrador_retriever 0.479008 True \n", "1937 1 lakeside 0.312299 False \n", "\n", " p2 p2_conf p2_dog p3 p3_conf \\\n", "1881 American_Staffordshire_terrier 0.113434 True dingo 0.081231 \n", "1837 Staffordshire_bullterrier 0.334947 True dalmatian 0.002697 \n", "995 basset 0.164555 True toy_terrier 0.080484 \n", "481 ice_bear 0.218289 False kuvasz 0.139911 \n", "1937 dock 0.159842 False canoe 0.070795 \n", "\n", " p3_dog \n", "1881 False \n", "1837 True \n", "995 True \n", "481 True \n", "1937 False " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_df.sample(5)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(image_df.duplicated())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2075 entries, 0 to 2074\n", "Data columns (total 12 columns):\n", "tweet_id 2075 non-null int64\n", "jpg_url 2075 non-null object\n", "img_num 2075 non-null int64\n", "p1 2075 non-null object\n", "p1_conf 2075 non-null float64\n", "p1_dog 2075 non-null bool\n", "p2 2075 non-null object\n", "p2_conf 2075 non-null float64\n", "p2_dog 2075 non-null bool\n", "p3 2075 non-null object\n", "p3_conf 2075 non-null float64\n", "p3_dog 2075 non-null bool\n", "dtypes: bool(3), float64(3), int64(2), object(4)\n", "memory usage: 152.1+ KB\n" ] } ], "source": [ "image_df.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#checking for non-original tweets' tweet ids in the image_df dataframe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "img_list = (list(image_df.tweet_id))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rate limit reached. Sleeping for: 741\n", "Rate limit reached. Sleeping for: 742\n" ] } ], "source": [ "#takes approximately 30 mins\n", "#create empty list for unoriginal tweets\n", "unoriginal_tweets = []\n", "#create empty list for unavailable tweets\n", "unavailable_tweets = []\n", "#create empty list for original tweets\n", "original_tweets = []\n", "#gather each tweet's json data by id\n", "for id in img_list:\n", " try:\n", " img_tweet = (api.get_status(id))._json\n", " if pd.isna(img_tweet['in_reply_to_status_id']) is False or 'retweeted_status' in img_tweet.keys():\n", " unoriginal_tweets.append(id)\n", " elif pd.isna(img_tweet['in_reply_to_status_id']) is True and 'retweeted_status' not in img_tweet.keys():\n", " original_tweets.append(id)\n", " except:\n", " unavailable_tweets.append(id)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[667550882905632768, 667550904950915073, 669353438988365824, 671729906628341761, 674754018082705410, 674793399141146624, 674999807681908736, 675349384339542016, 675707330206547968, 675870721063669760, 684225744407494656, 684538444857667585, 692142790915014657, 694356675654983680, 695767669421768709, 703425003149250560, 704871453724954624, 705786532653883392, 711998809858043904, 729838605770891264, 746818907684614144, 746906459439529985, 752309394570878976, 754874841593970688, 757597904299253760, 757729163776290825, 759159934323924993, 761371037149827077, 761750502866649088, 766078092750233600, 770093767776997377, 771171053431250945, 772615324260794368, 775898661951791106, 776819012571455488, 777641927919427584, 778396591732486144, 780476555013349377, 780496263422808064, 782021823840026624, 783347506784731136, 786036967502913536, 788070120937619456, 790723298204217344, 791026214425268224, 793614319594401792, 794355576146903043, 794983741416415232, 796177847564038144, 798340744599797760, 798628517273620480, 798644042770751489, 798665375516884993, 798673117451325440, 798694562394996736, 798697898615730177, 799774291445383169, 800443802682937345, 802265048156610565, 802624713319034886, 803692223237865472, 804413760345620481, 805958939288408065, 806242860592926720, 807059379405148160, 808134635716833280, 809808892968534016, 813944609378369540, 816014286006976512, 816829038950027264, 817181837579653120, 818588835076603904, 819015331746349057, 819015337530290176, 820446719150292993, 821813639212650496, 822647212903690241, 823269594223824897, 824796380199809024, 829878982036299777, 832040443403784192, 832215726631055365, 832769181346996225, 838916489579200512, 839290600511926273, 841833993020538882, 844979544864018432, 847971574464610304, 856526610513747968, 860924035999428608, 863079547188785154, 867072653475098625, 877611172832227328, 885311592912609280]\n" ] } ], "source": [ "print(unoriginal_tweets)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[680055455951884288, 754011816964026368, 759566828574212096, 759923798737051648, 771004394259247104, 779123168116150273, 802247111496568832, 829374341691346946, 837012587749474308, 837366284874571778, 842892208864923648, 844704788403113984, 851861385021730816, 851953902622658560, 861769973181624320, 872261713294495745, 873697596434513921, 888202515573088257]\n" ] } ], "source": [ "print(unavailable_tweets)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. tweet_counts dataframe" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " tweet_id retweet_count favorite_count\n", "0 892420643555336193 6979 33728\n", "1 892177421306343426 5280 29255\n", "2 891815181378084864 3466 21987\n", "3 891689557279858688 7198 36823\n", "4 891327558926688256 7723 35207" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_counts.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2326 entries, 0 to 2325\n", "Data columns (total 3 columns):\n", "tweet_id 2326 non-null int64\n", "retweet_count 2326 non-null int64\n", "favorite_count 2326 non-null int64\n", "dtypes: int64(3)\n", "memory usage: 54.6 KB\n" ] } ], "source": [ "tweet_counts.info()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " tweet_id retweet_count favorite_count\n", "count 2.326000e+03 2326.000000 2326.000000\n", "mean 7.417480e+17 2457.867154 7018.926913\n", "std 6.818802e+16 4166.197661 10908.789726\n", "min 6.660209e+17 1.000000 0.000000\n", "25% 6.780814e+17 492.250000 1219.500000\n", "50% 7.178159e+17 1145.500000 3040.500000\n", "75% 7.986402e+17 2843.500000 8562.750000\n", "max 8.924206e+17 70429.000000 144312.000000" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweet_counts.describe()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(tweet_counts.duplicated())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17 tweet_id\n", "20 tweet_id\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_columns = pd.Series(list(tweet_archive) + list(tweet_counts) + list(image_df))\n", "all_columns[all_columns.duplicated()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quality issues\n", "#### `tweet_archive table`\n", "\n", "1. 'name', 'doggo', 'floofer', 'pupper' and 'puppo' columns have missing values misrepresented as strings('None')\n", "\n", "2. 'name' column has non-valid words (adjectives,articles,adverbs) as values in some entries\n", "\n", "3. 'name', 'doggo', 'floofer', 'pupper' and 'puppo' columns have missing values.\n", "\n", "4. presence of some records with non null values in the 'retweeted_status_id' column,these are not original tweets\n", "\n", "5. presence of some records with non null values in 'reply_to_status_id' column, these are not original tweets\n", "\n", "6. records dating beyond 2017-08-01\n", "\n", "7. 'time_stamp' column is an object datatype\n", "\n", "#### `image_df table`\n", "\n", "8. records with 'false' values in all 3 of the dog prediction columns (p1_dog, p2_dog, p3_dog),these are not dogs\n", "9. non-original tweet records\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 0, "height": 7, "hidden": false, "row": 40, "width": 12 }, "report_default": { "hidden": false } } } } }, "source": [ "### Tidiness issues\n", "1. doggo,pupper,puppo,floofer are represented as different column headers instead of individual values of one column(tweet_archive table)\n", "\n", "2. dog breeds are spread out in separate columns(image_df table)\n", "\n", "3. data is in separate tables." ] }, { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "col": 4, "height": 4, "hidden": false, "row": 32, "width": 4 }, "report_default": { "hidden": false } } } } }, "source": [ "\n", "## Cleaning Data" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "# Make copies of original pieces of data\n", "tweetarch_clean = tweet_archive.copy()\n", "img_clean = image_df.copy()\n", "tweetcounts_clean = tweet_counts.copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quality issue #1:\n", "#### tweet_archive table:\n", "#### 'name', 'doggo', 'floofer', 'pupper' and 'puppo' columns have missing values misrepresented as strings('None')\n", "\n", "### Quality issue #2:\n", "#### tweet_archive table:\n", "#### 'name' column has non-valid words (adjectives,articles,adverbs) as values in some entries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define: \n", "Use the .replace() function to replace the specified values from the assessment with null values.This fixes both issue 1 and 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [], "source": [ "other_words.append('None')\n", "for word in other_words:\n", " tweetarch_clean.replace(to_replace = word , value = np.nan, inplace = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tweet_idin_reply_to_status_idin_reply_to_user_idtimestampsourcetextretweeted_status_idretweeted_status_user_idretweeted_status_timestampexpanded_urlsrating_numeratorrating_denominatornamedoggoflooferpupperpuppo
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [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]\n", "Index: []" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweetarch_clean.query(f'name == {other_words}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quality issue #3:\n", "#### tweet_archive table:\n", "#### 'name', 'doggo', 'floofer', 'pupper' and 'puppo' columns have missing values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define: \n", "Use the values of the text column and regex methods to extract the required values for these columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "#create a function to find and replace null values in the dog stages\n", "\n", "def fill_dog_stage(stage):\n", " \n", " '''loops through all the values of the specified column,searches for null values, which if True,\n", " loops through the corresponding values of the text column, searches for the appropriate value and replaces this value\n", " into the specified position'''\n", " \n", " indices = list(range(len(tweetarch_clean[stage])))\n", " for i in indices:\n", " if pd.isna(tweetarch_clean[stage][i]):\n", " try:\n", " tweetarch_clean.loc[i,stage] = re.findall(stage,tweetarch_clean.text[i])[0]\n", " except:\n", " tweetarch_clean.loc[i,stage] = np.nan" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "#create a list of the dog stages loop through each one, passing to the function\n", "\n", "stages = ['pupper','puppo','doggo','floofer']\n", "\n", "for stage in stages:\n", " fill_dog_stage(stage)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2356 entries, 0 to 2355\n", "Data columns (total 17 columns):\n", "tweet_id 2356 non-null int64\n", "in_reply_to_status_id 78 non-null float64\n", "in_reply_to_user_id 78 non-null float64\n", "timestamp 2356 non-null object\n", "source 2356 non-null object\n", "text 2356 non-null object\n", "retweeted_status_id 181 non-null float64\n", "retweeted_status_user_id 181 non-null float64\n", "retweeted_status_timestamp 181 non-null object\n", "expanded_urls 2297 non-null object\n", "rating_numerator 2356 non-null int64\n", "rating_denominator 2356 non-null int64\n", "name 1502 non-null object\n", "doggo 107 non-null object\n", "floofer 10 non-null object\n", "pupper 281 non-null object\n", "puppo 38 non-null object\n", "dtypes: float64(4), int64(3), object(10)\n", "memory usage: 313.0+ KB\n" ] } ], "source": [ "tweetarch_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tidiness issue #1: \n", "#### tweet_archive table:\n", "#### doggo,pupper,puppo,floofer are represented as different column headers instead of individual values of one column" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "extensions": { "jupyter_dashboards": { "version": 1, "views": { "grid_default": { "hidden": true }, "report_default": { "hidden": true } } } } }, "source": [ "#### Define:\n", "Create a new column for dog stages and loop through each of these columns for appropriate values for the dog stage column to unpivot the three columns. Drop the three columns when done." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "dog_stages = [] #doggo,floofer,pupper,puppo\n", "indices = list(range(len(tweetarch_clean)))\n", "for i in indices:\n", " if not pd.isna(tweetarch_clean.floofer[i]):\n", " dog_stages.append('floofer')\n", " elif not pd.isna(tweetarch_clean.puppo[i]):\n", " dog_stages.append('puppo')\n", " elif not pd.isna(tweetarch_clean.pupper[i]):\n", " dog_stages.append('pupper')\n", " elif not pd.isna(tweetarch_clean.doggo[i]):\n", " dog_stages.append('doggo')\n", " else:\n", " dog_stages.append(np.nan)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "tweetarch_clean['dog_stage'] = dog_stages" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "tweetarch_clean = tweetarch_clean.drop(['pupper','doggo','puppo','floofer'], axis = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pupper 281\n", "doggo 92\n", "puppo 38\n", "floofer 10\n", "Name: dog_stage, dtype: int64" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweetarch_clean.dog_stage.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that some records had values for either both doggo & pupper, doggo & puppo and doggo & floofer.For streamlining purposes, one had to be chosen over the other, hence the slight changes in some of the dog stage value counts. I chose the other stages over 'doggo', placing it last in the loop statement because this term is generally used more loosely relative to the rest, as per my personal judgement." ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n", " 'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',\n", " 'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',\n", " 'rating_denominator', 'name', 'dog_stage'],\n", " dtype='object')" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweetarch_clean.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quality issue #9:\n", "#### image_df table:\n", "#### non-original tweet records" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define:\n", "Delete all the non-original tweets using the original_tweets ids list and vectorization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [], "source": [ "for id in img_clean.tweet_id:\n", " if id not in original_tweets:\n", " img_clean.drop(img_clean[img_clean['tweet_id'] == id].index, inplace = True)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "#reset the index\n", "img_clean.reset_index(inplace = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#should evaluate to True\n", "list(img_clean.tweet_id) == original_tweets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tidiness issue #2:\n", "#### image_df table:\n", "#### dog breeds are spread out in separate columns\n", "\n", "### Quality issue#8:\n", "#### image_df table:\n", "#### records with 'false' values in all 3 of the dog prediction columns (p1_dog, p2_dog, p3_dog),these are not dogs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define:\n", "Create a new column for dog breeds and use the p and p_dog columns to populate the column, then drop all rows with null values in the dog breeds column. This also takes care of issue `#8`\n", "Then drop the columns that will no longer be needed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "breeds = []\n", "indices = list(range(len(img_clean.tweet_id)))\n", "for i in indices:\n", " if img_clean.p1_dog[i]:\n", " breeds.append(img_clean.p1[i])\n", " elif img_clean.p2_dog[i]:\n", " breeds.append(img_clean.p2[i])\n", " elif img_clean.p3_dog[i]:\n", " breeds.append(img_clean.p3[i])\n", " else:\n", " breeds.append(np.nan)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [], "source": [ "img_clean['dog_breed'] = breeds" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "img_clean.dropna(inplace = True)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "img_clean = img_clean.drop([\"img_num\",'p1','p1_conf','p1_dog','p2','p2_conf','p2_dog','p3','p3_conf','p3_dog'],axis = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'tweet_id', 'jpg_url', 'dog_breed'], dtype='object')" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img_clean.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quality issue #4:\n", "#### tweet_archive table:\n", "#### presence of some records with non null values in the 'retweeted_status_id' column,these are not original tweets\n", "\n", "\n", "### Quality issue #5:\n", "#### tweet_archive table:\n", "#### presence of some records with non null values in 'reply_to_status_id' column, these are not original tweets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define:\n", "Filter out these records using boolean indexing. Afterwards drop associated columns and any other columns that will not be further used." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [], "source": [ "tweetarch_clean = tweetarch_clean.loc[pd.isna(tweetarch_clean['in_reply_to_status_id'])]" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "tweetarch_clean = tweetarch_clean.loc[pd.isna(tweetarch_clean['retweeted_status_id'])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 2097 entries, 0 to 2355\n", "Data columns (total 14 columns):\n", "tweet_id 2097 non-null int64\n", "in_reply_to_status_id 0 non-null float64\n", "in_reply_to_user_id 0 non-null float64\n", "timestamp 2097 non-null object\n", "source 2097 non-null object\n", "text 2097 non-null object\n", "retweeted_status_id 0 non-null float64\n", "retweeted_status_user_id 0 non-null float64\n", "retweeted_status_timestamp 0 non-null object\n", "expanded_urls 2094 non-null object\n", "rating_numerator 2097 non-null int64\n", "rating_denominator 2097 non-null int64\n", "name 1390 non-null object\n", "dog_stage 372 non-null object\n", "dtypes: float64(4), int64(3), object(7)\n", "memory usage: 245.7+ KB\n" ] } ], "source": [ "tweetarch_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "tweetarch_clean = tweetarch_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id','retweeted_status_id', 'retweeted_status_user_id','retweeted_status_timestamp','source','expanded_urls',], axis = 1 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 2097 entries, 0 to 2355\n", "Data columns (total 7 columns):\n", "tweet_id 2097 non-null int64\n", "timestamp 2097 non-null object\n", "text 2097 non-null object\n", "rating_numerator 2097 non-null int64\n", "rating_denominator 2097 non-null int64\n", "name 1390 non-null object\n", "dog_stage 372 non-null object\n", "dtypes: int64(3), object(4)\n", "memory usage: 131.1+ KB\n" ] } ], "source": [ "tweetarch_clean.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tidiness issue #3: \n", "#### Data is in separate tables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define:\n", "Merge the three dataframes on the tweet_id column to make one master dataframe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [], "source": [ "data_frames = [img_clean,tweetarch_clean,tweetcounts_clean]\n", "master_df = functools.reduce(lambda left,right: pd.merge(left,right,on=['tweet_id'],\n", " how='left'), data_frames)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tweet_idjpg_urldog_breedtimestamptextrating_numeratorrating_denominatornamedog_stageretweet_countfavorite_count
0666020888022790149https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpgWelsh_springer_spaniel2015-11-15 22:32:08 +0000Here we have a Japanese Irish Setter. Lost eye...8.010.0NaNNaN421.02285.0
1666029285002620928https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpgredbone2015-11-15 23:05:30 +0000This is a western brown Mitsubishi terrier. Up...7.010.0NaNNaN39.0112.0
2666033412701032449https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpgGerman_shepherd2015-11-15 23:21:54 +0000Here is a very happy pup. Big fan of well-main...9.010.0NaNNaN36.0100.0
3666044226329800704https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpgRhodesian_ridgeback2015-11-16 00:04:52 +0000This is a purebred Piers Morgan. Loves to Netf...6.010.0NaNNaN115.0245.0
4666049248165822465https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpgminiature_pinscher2015-11-16 00:24:50 +0000Here we have a 1949 1st generation vulpix. Enj...5.010.0NaNNaN36.088.0
5666050758794694657https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpgBernese_mountain_dog2015-11-16 00:30:50 +0000This is a truly beautiful English Wilson Staff...10.010.0NaNNaN50.0115.0
6666055525042405380https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpgchow2015-11-16 00:49:46 +0000Here is a Siberian heavily armored polar bear ...10.010.0NaNNaN196.0367.0
7666057090499244032https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpggolden_retriever2015-11-16 00:55:59 +0000My oh my. This is a rare blond Canadian terrie...9.010.0NaNNaN112.0247.0
8666058600524156928https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpgminiature_poodle2015-11-16 01:01:59 +0000Here is the Rand Paul of retrievers folks! He'...8.010.0NaNNaN47.099.0
9666063827256086533https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpggolden_retriever2015-11-16 01:22:45 +0000This is the happiest dog you will ever see. Ve...10.010.0NaNNaN180.0395.0
10666071193221509120https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpgGordon_setter2015-11-16 01:52:02 +0000Here we have a northern speckled Rhododendron....9.010.0NaNNaN51.0127.0
11666073100786774016https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpgWalker_hound2015-11-16 01:59:36 +0000Let's hope this flight isn't Malaysian (lol). ...10.010.0NaNNaN130.0274.0
12666082916733198337https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpgpug2015-11-16 02:38:37 +0000Here we have a well-established sunblockerspan...6.010.0NaNNaN37.092.0
13666094000022159362https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpgbloodhound2015-11-16 03:22:39 +0000This appears to be a Mongolian Presbyterian mi...9.010.0NaNNaN63.0142.0
14666099513787052032https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpgLhasa2015-11-16 03:44:34 +0000Can stand on stump for what seems like a while...8.010.0NaNNaN53.0133.0
15666102155909144576https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpgEnglish_setter2015-11-16 03:55:04 +0000Oh my. Here you are seeing an Adobe Setter giv...11.010.0NaNNaN11.066.0
16666273097616637952https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpgItalian_greyhound2015-11-16 15:14:19 +0000Can take selfies 11/10 https://t.co/ws2AMaNwPW11.010.0NaNNaN66.0151.0
17666287406224695296https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpgMaltese_dog2015-11-16 16:11:11 +0000This is an Albanian 3 1/2 legged Episcopalian...1.02.0NaNNaN56.0123.0
18666337882303524864https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpgNewfoundland2015-11-16 19:31:45 +0000This is an extremely rare horned Parthenon. No...9.010.0NaNNaN79.0168.0
19666345417576210432https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpggolden_retriever2015-11-16 20:01:42 +0000Look at this jokester thinking seat belt laws ...10.010.0NaNNaN122.0241.0
20666353288456101888https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpgmalamute2015-11-16 20:32:58 +0000Here we have a mixed Asiago from the Galápagos...8.010.0NaNNaN56.0179.0
21666373753744588802https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpgsoft-coated_wheaten_terrier2015-11-16 21:54:18 +0000Those are sunglasses and a jean jacket. 11/10 ...11.010.0NaNNaN73.0162.0
22666396247373291520https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpgChihuahua2015-11-16 23:23:41 +0000Oh goodness. A super rare northeast Qdoba kang...9.010.0NaNNaN68.0147.0
23666407126856765440https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpgblack-and-tan_coonhound2015-11-17 00:06:54 +0000This is a southern Vesuvius bumblegruff. Can d...7.010.0NaNNaN30.093.0
24666418789513326592https://pbs.twimg.com/media/CT-YWb7U8AA7QnN.jpgtoy_terrier2015-11-17 00:53:15 +0000This is Walter. He is an Alaskan Terrapin. Lov...10.010.0WalterNaN39.0107.0
25666421158376562688https://pbs.twimg.com/media/CT-aggCXAAIMfT3.jpgBlenheim_spaniel2015-11-17 01:02:40 +0000*internally screaming* 12/10 https://t.co/YMcr...12.010.0NaNNaN91.0272.0
26666428276349472768https://pbs.twimg.com/media/CT-g-0DUwAEQdSn.jpgPembroke2015-11-17 01:30:57 +0000Here we have an Austrian Pulitzer. Collectors ...7.010.0NaNNaN67.0139.0
27666430724426358785https://pbs.twimg.com/media/CT-jNYqW4AAPi2M.jpgIrish_terrier2015-11-17 01:40:41 +0000Oh boy what a pup! Sunglasses take this one to...6.010.0NaNNaN160.0276.0
28666435652385423360https://pbs.twimg.com/media/CT-nsTQWEAEkyDn.jpgChesapeake_Bay_retriever2015-11-17 02:00:15 +0000\"Can you behave? You're ruining my wedding day...10.010.0NaNNaN42.0137.0
29666437273139982337https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpgChihuahua2015-11-17 02:06:42 +0000Here we see a lone northeastern Cumberbatch. H...7.010.0NaNNaN40.0106.0
....................................
1721885528943205470208https://pbs.twimg.com/media/DEoH3yvXgAAzQtS.jpgpug2017-07-13 15:58:47 +0000This is Maisey. She fell asleep mid-excavation...13.010.0MaiseyNaN5311.031522.0
1722885984800019947520https://pbs.twimg.com/media/DEumeWWV0AA-Z61.jpgBlenheim_spaniel2017-07-14 22:10:11 +0000Viewer discretion advised. This is Jimbo. He w...12.010.0JimboNaN5592.028526.0
1723886258384151887873https://pbs.twimg.com/media/DEyfTG4UMAE4aE9.jpgpug2017-07-15 16:17:19 +0000This is Waffles. His doggles are pupside down....13.010.0WafflesNaN5262.024459.0
1724886366144734445568https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpgFrench_bulldog2017-07-15 23:25:31 +0000This is Roscoe. Another pupper fallen victim t...12.010.0Roscoepupper2614.018501.0
1725886736880519319552https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpgkuvasz2017-07-16 23:58:41 +0000This is Mingus. He's a wonderful father to his...13.010.0MingusNaN2619.010466.0
1726886983233522544640https://pbs.twimg.com/media/DE8yicJW0AAAvBJ.jpgChihuahua2017-07-17 16:17:36 +0000This is Maya. She's very shy. Rarely leaves he...13.010.0MayaNaN6294.030275.0
1727887101392804085760https://pbs.twimg.com/media/DE-eAq6UwAA-jaE.jpgSamoyed2017-07-18 00:07:08 +0000This... is a Jubilant Antarctic House Bear. We...12.010.0NaNNaN4975.026916.0
1728887343217045368832https://pbs.twimg.com/ext_tw_video_thumb/88734...Mexican_hairless2017-07-18 16:08:03 +0000You may not have known you needed to see this ...13.010.0NaNNaN8788.029515.0
1729887473957103951883https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpgPembroke2017-07-19 00:47:34 +0000This is Canela. She attempted some fancy porch...13.010.0CanelaNaN14973.060028.0
1730887705289381826560https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpgbasset2017-07-19 16:06:48 +0000This is Jeffrey. He has a monopoly on the pool...13.010.0JeffreyNaN4522.026553.0
1731888078434458587136https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpgFrench_bulldog2017-07-20 16:49:33 +0000This is Gerald. He was just told he didn't get...12.010.0GeraldNaN2886.019115.0
1732888202515573088257https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpgPembrokeNaNNaNNaNNaNNaNNaNNaNNaN
1733888554962724278272https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpgSiberian_husky2017-07-22 00:23:06 +0000This is Ralphus. He's powering up. Attempting ...13.010.0RalphusNaN2868.017266.0
1734888804989199671297https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpggolden_retriever2017-07-22 16:56:37 +0000This is Zeke. He has a new stick. Very proud o...13.010.0ZekeNaN3518.022409.0
1735888917238123831296https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpggolden_retriever2017-07-23 00:22:39 +0000This is Jim. He found a fren. Taught him how t...12.010.0JimNaN3747.025551.0
1736889278841981685760https://pbs.twimg.com/ext_tw_video_thumb/88927...whippet2017-07-24 00:19:32 +0000This is Oliver. You're witnessing one of his m...13.010.0OliverNaN4426.022052.0
1737889531135344209921https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpggolden_retriever2017-07-24 17:02:04 +0000This is Stuart. He's sporting his favorite fan...13.010.0Stuartpuppo1874.013321.0
1738889638837579907072https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpgFrench_bulldog2017-07-25 00:10:02 +0000This is Ted. He does his best. Sometimes that'...12.010.0TedNaN3702.023605.0
1739889665388333682689https://pbs.twimg.com/media/DFi579UWsAAatzw.jpgPembroke2017-07-25 01:55:32 +0000Here's a puppo that seems to be on the fence a...13.010.0NaNpuppo8312.041910.0
1740889880896479866881https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpgFrench_bulldog2017-07-25 16:11:53 +0000This is Bruno. He is a service shark. Only get...13.010.0BrunoNaN4145.024506.0
1741890006608113172480https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpgSamoyed2017-07-26 00:31:25 +0000This is Koda. He is a South Australian decksha...13.010.0KodaNaN6120.026973.0
1742890240255349198849https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpgPembroke2017-07-26 15:59:51 +0000This is Cassie. She is a college pup. Studying...14.010.0Cassiedoggo6081.027878.0
1743890609185150312448https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpgIrish_terrier2017-07-27 16:25:51 +0000This is Zoey. She doesn't want to be one of th...13.010.0ZoeyNaN3605.024455.0
1744890729181411237888https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpgPomeranian2017-07-28 00:22:40 +0000When you watch your owner call another dog a g...13.010.0NaNNaN15695.056701.0
1745890971913173991426https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpgAppenzeller2017-07-28 16:27:12 +0000Meet Jax. He enjoys ice cream so much he gets ...13.010.0JaxNaN1649.010340.0
1746891087950875897856https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpgChesapeake_Bay_retriever2017-07-29 00:08:17 +0000Here we have a majestic great white breaching ...13.010.0NaNNaN2590.017757.0
1747891327558926688256https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpgbasset2017-07-29 16:00:24 +0000This is Franklin. He would like you to stop ca...12.010.0FranklinNaN7723.035207.0
1748891689557279858688https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpgLabrador_retriever2017-07-30 15:58:51 +0000This is Darla. She commenced a snooze mid meal...13.010.0DarlaNaN7198.036823.0
1749891815181378084864https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpgChihuahua2017-07-31 00:18:03 +0000This is Archie. He is a rare Norwegian Pouncin...12.010.0ArchieNaN3466.021987.0
1750892177421306343426https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpgChihuahua2017-08-01 00:17:27 +0000This is Tilly. She's just checking pup on you....13.010.0TillyNaN5280.029255.0
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1751 rows × 11 columns

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miniature_pinscher 2015-11-16 00:24:50 +0000 \n", "5 Bernese_mountain_dog 2015-11-16 00:30:50 +0000 \n", "6 chow 2015-11-16 00:49:46 +0000 \n", "7 golden_retriever 2015-11-16 00:55:59 +0000 \n", "8 miniature_poodle 2015-11-16 01:01:59 +0000 \n", "9 golden_retriever 2015-11-16 01:22:45 +0000 \n", "10 Gordon_setter 2015-11-16 01:52:02 +0000 \n", "11 Walker_hound 2015-11-16 01:59:36 +0000 \n", "12 pug 2015-11-16 02:38:37 +0000 \n", "13 bloodhound 2015-11-16 03:22:39 +0000 \n", "14 Lhasa 2015-11-16 03:44:34 +0000 \n", "15 English_setter 2015-11-16 03:55:04 +0000 \n", "16 Italian_greyhound 2015-11-16 15:14:19 +0000 \n", "17 Maltese_dog 2015-11-16 16:11:11 +0000 \n", "18 Newfoundland 2015-11-16 19:31:45 +0000 \n", "19 golden_retriever 2015-11-16 20:01:42 +0000 \n", "20 malamute 2015-11-16 20:32:58 +0000 \n", "21 soft-coated_wheaten_terrier 2015-11-16 21:54:18 +0000 \n", "22 Chihuahua 2015-11-16 23:23:41 +0000 \n", "23 black-and-tan_coonhound 2015-11-17 00:06:54 +0000 \n", "24 toy_terrier 2015-11-17 00:53:15 +0000 \n", "25 Blenheim_spaniel 2015-11-17 01:02:40 +0000 \n", "26 Pembroke 2015-11-17 01:30:57 +0000 \n", "27 Irish_terrier 2015-11-17 01:40:41 +0000 \n", "28 Chesapeake_Bay_retriever 2015-11-17 02:00:15 +0000 \n", "29 Chihuahua 2015-11-17 02:06:42 +0000 \n", "... ... ... \n", "1721 pug 2017-07-13 15:58:47 +0000 \n", "1722 Blenheim_spaniel 2017-07-14 22:10:11 +0000 \n", "1723 pug 2017-07-15 16:17:19 +0000 \n", "1724 French_bulldog 2017-07-15 23:25:31 +0000 \n", "1725 kuvasz 2017-07-16 23:58:41 +0000 \n", "1726 Chihuahua 2017-07-17 16:17:36 +0000 \n", "1727 Samoyed 2017-07-18 00:07:08 +0000 \n", "1728 Mexican_hairless 2017-07-18 16:08:03 +0000 \n", "1729 Pembroke 2017-07-19 00:47:34 +0000 \n", "1730 basset 2017-07-19 16:06:48 +0000 \n", "1731 French_bulldog 2017-07-20 16:49:33 +0000 \n", "1732 Pembroke NaN \n", "1733 Siberian_husky 2017-07-22 00:23:06 +0000 \n", "1734 golden_retriever 2017-07-22 16:56:37 +0000 \n", "1735 golden_retriever 2017-07-23 00:22:39 +0000 \n", "1736 whippet 2017-07-24 00:19:32 +0000 \n", "1737 golden_retriever 2017-07-24 17:02:04 +0000 \n", "1738 French_bulldog 2017-07-25 00:10:02 +0000 \n", "1739 Pembroke 2017-07-25 01:55:32 +0000 \n", "1740 French_bulldog 2017-07-25 16:11:53 +0000 \n", "1741 Samoyed 2017-07-26 00:31:25 +0000 \n", "1742 Pembroke 2017-07-26 15:59:51 +0000 \n", "1743 Irish_terrier 2017-07-27 16:25:51 +0000 \n", "1744 Pomeranian 2017-07-28 00:22:40 +0000 \n", "1745 Appenzeller 2017-07-28 16:27:12 +0000 \n", "1746 Chesapeake_Bay_retriever 2017-07-29 00:08:17 +0000 \n", "1747 basset 2017-07-29 16:00:24 +0000 \n", "1748 Labrador_retriever 2017-07-30 15:58:51 +0000 \n", "1749 Chihuahua 2017-07-31 00:18:03 +0000 \n", "1750 Chihuahua 2017-08-01 00:17:27 +0000 \n", "\n", " text rating_numerator \\\n", "0 Here we have a Japanese Irish Setter. Lost eye... 8.0 \n", "1 This is a western brown Mitsubishi terrier. Up... 7.0 \n", "2 Here is a very happy pup. Big fan of well-main... 9.0 \n", "3 This is a purebred Piers Morgan. Loves to Netf... 6.0 \n", "4 Here we have a 1949 1st generation vulpix. Enj... 5.0 \n", "5 This is a truly beautiful English Wilson Staff... 10.0 \n", "6 Here is a Siberian heavily armored polar bear ... 10.0 \n", "7 My oh my. This is a rare blond Canadian terrie... 9.0 \n", "8 Here is the Rand Paul of retrievers folks! He'... 8.0 \n", "9 This is the happiest dog you will ever see. Ve... 10.0 \n", "10 Here we have a northern speckled Rhododendron.... 9.0 \n", "11 Let's hope this flight isn't Malaysian (lol). ... 10.0 \n", "12 Here we have a well-established sunblockerspan... 6.0 \n", "13 This appears to be a Mongolian Presbyterian mi... 9.0 \n", "14 Can stand on stump for what seems like a while... 8.0 \n", "15 Oh my. Here you are seeing an Adobe Setter giv... 11.0 \n", "16 Can take selfies 11/10 https://t.co/ws2AMaNwPW 11.0 \n", "17 This is an Albanian 3 1/2 legged Episcopalian... 1.0 \n", "18 This is an extremely rare horned Parthenon. No... 9.0 \n", "19 Look at this jokester thinking seat belt laws ... 10.0 \n", "20 Here we have a mixed Asiago from the Galápagos... 8.0 \n", "21 Those are sunglasses and a jean jacket. 11/10 ... 11.0 \n", "22 Oh goodness. A super rare northeast Qdoba kang... 9.0 \n", "23 This is a southern Vesuvius bumblegruff. Can d... 7.0 \n", "24 This is Walter. He is an Alaskan Terrapin. Lov... 10.0 \n", "25 *internally screaming* 12/10 https://t.co/YMcr... 12.0 \n", "26 Here we have an Austrian Pulitzer. Collectors ... 7.0 \n", "27 Oh boy what a pup! Sunglasses take this one to... 6.0 \n", "28 \"Can you behave? You're ruining my wedding day... 10.0 \n", "29 Here we see a lone northeastern Cumberbatch. H... 7.0 \n", "... ... ... \n", "1721 This is Maisey. She fell asleep mid-excavation... 13.0 \n", "1722 Viewer discretion advised. This is Jimbo. He w... 12.0 \n", "1723 This is Waffles. His doggles are pupside down.... 13.0 \n", "1724 This is Roscoe. Another pupper fallen victim t... 12.0 \n", "1725 This is Mingus. He's a wonderful father to his... 13.0 \n", "1726 This is Maya. She's very shy. Rarely leaves he... 13.0 \n", "1727 This... is a Jubilant Antarctic House Bear. We... 12.0 \n", "1728 You may not have known you needed to see this ... 13.0 \n", "1729 This is Canela. She attempted some fancy porch... 13.0 \n", "1730 This is Jeffrey. He has a monopoly on the pool... 13.0 \n", "1731 This is Gerald. He was just told he didn't get... 12.0 \n", "1732 NaN NaN \n", "1733 This is Ralphus. He's powering up. Attempting ... 13.0 \n", "1734 This is Zeke. He has a new stick. Very proud o... 13.0 \n", "1735 This is Jim. He found a fren. Taught him how t... 12.0 \n", "1736 This is Oliver. You're witnessing one of his m... 13.0 \n", "1737 This is Stuart. He's sporting his favorite fan... 13.0 \n", "1738 This is Ted. He does his best. Sometimes that'... 12.0 \n", "1739 Here's a puppo that seems to be on the fence a... 13.0 \n", "1740 This is Bruno. He is a service shark. Only get... 13.0 \n", "1741 This is Koda. He is a South Australian decksha... 13.0 \n", "1742 This is Cassie. She is a college pup. Studying... 14.0 \n", "1743 This is Zoey. She doesn't want to be one of th... 13.0 \n", "1744 When you watch your owner call another dog a g... 13.0 \n", "1745 Meet Jax. He enjoys ice cream so much he gets ... 13.0 \n", "1746 Here we have a majestic great white breaching ... 13.0 \n", "1747 This is Franklin. He would like you to stop ca... 12.0 \n", "1748 This is Darla. She commenced a snooze mid meal... 13.0 \n", "1749 This is Archie. He is a rare Norwegian Pouncin... 12.0 \n", "1750 This is Tilly. She's just checking pup on you.... 13.0 \n", "\n", " rating_denominator name dog_stage retweet_count favorite_count \n", "0 10.0 NaN NaN 421.0 2285.0 \n", "1 10.0 NaN NaN 39.0 112.0 \n", "2 10.0 NaN NaN 36.0 100.0 \n", "3 10.0 NaN NaN 115.0 245.0 \n", "4 10.0 NaN NaN 36.0 88.0 \n", "5 10.0 NaN NaN 50.0 115.0 \n", "6 10.0 NaN NaN 196.0 367.0 \n", "7 10.0 NaN NaN 112.0 247.0 \n", "8 10.0 NaN NaN 47.0 99.0 \n", "9 10.0 NaN NaN 180.0 395.0 \n", "10 10.0 NaN NaN 51.0 127.0 \n", "11 10.0 NaN NaN 130.0 274.0 \n", "12 10.0 NaN NaN 37.0 92.0 \n", "13 10.0 NaN NaN 63.0 142.0 \n", "14 10.0 NaN NaN 53.0 133.0 \n", "15 10.0 NaN NaN 11.0 66.0 \n", "16 10.0 NaN NaN 66.0 151.0 \n", "17 2.0 NaN NaN 56.0 123.0 \n", "18 10.0 NaN NaN 79.0 168.0 \n", "19 10.0 NaN NaN 122.0 241.0 \n", "20 10.0 NaN NaN 56.0 179.0 \n", "21 10.0 NaN NaN 73.0 162.0 \n", "22 10.0 NaN NaN 68.0 147.0 \n", "23 10.0 NaN NaN 30.0 93.0 \n", "24 10.0 Walter NaN 39.0 107.0 \n", "25 10.0 NaN NaN 91.0 272.0 \n", "26 10.0 NaN NaN 67.0 139.0 \n", "27 10.0 NaN NaN 160.0 276.0 \n", "28 10.0 NaN NaN 42.0 137.0 \n", "29 10.0 NaN NaN 40.0 106.0 \n", "... ... ... ... ... ... \n", "1721 10.0 Maisey NaN 5311.0 31522.0 \n", "1722 10.0 Jimbo NaN 5592.0 28526.0 \n", "1723 10.0 Waffles NaN 5262.0 24459.0 \n", "1724 10.0 Roscoe pupper 2614.0 18501.0 \n", "1725 10.0 Mingus NaN 2619.0 10466.0 \n", "1726 10.0 Maya NaN 6294.0 30275.0 \n", "1727 10.0 NaN NaN 4975.0 26916.0 \n", "1728 10.0 NaN NaN 8788.0 29515.0 \n", "1729 10.0 Canela NaN 14973.0 60028.0 \n", "1730 10.0 Jeffrey NaN 4522.0 26553.0 \n", "1731 10.0 Gerald NaN 2886.0 19115.0 \n", "1732 NaN NaN NaN NaN NaN \n", "1733 10.0 Ralphus NaN 2868.0 17266.0 \n", "1734 10.0 Zeke NaN 3518.0 22409.0 \n", "1735 10.0 Jim NaN 3747.0 25551.0 \n", "1736 10.0 Oliver NaN 4426.0 22052.0 \n", "1737 10.0 Stuart puppo 1874.0 13321.0 \n", "1738 10.0 Ted NaN 3702.0 23605.0 \n", "1739 10.0 NaN puppo 8312.0 41910.0 \n", "1740 10.0 Bruno NaN 4145.0 24506.0 \n", "1741 10.0 Koda NaN 6120.0 26973.0 \n", "1742 10.0 Cassie doggo 6081.0 27878.0 \n", "1743 10.0 Zoey NaN 3605.0 24455.0 \n", "1744 10.0 NaN NaN 15695.0 56701.0 \n", "1745 10.0 Jax NaN 1649.0 10340.0 \n", "1746 10.0 NaN NaN 2590.0 17757.0 \n", "1747 10.0 Franklin NaN 7723.0 35207.0 \n", "1748 10.0 Darla NaN 7198.0 36823.0 \n", "1749 10.0 Archie NaN 3466.0 21987.0 \n", "1750 10.0 Tilly NaN 5280.0 29255.0 \n", "\n", "[1751 rows x 11 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Issue #6:\n", "#### records dating beyond 2017-08-01\n", "\n", "### Issue #7:\n", "#### 'timestamp' column is an object datatype\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define:\n", "Merging the data sets filters out all records dating beyond 2017-08-01, fixing issue`#6`.This also means that issue`#7` is now a non-issue because the column will be dropped, as the main reason for converting it's data type would have been to filter out the records that dated beyond the specified date." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code:" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "master_df = master_df.drop(['timestamp'], axis = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Test:" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'tweet_id', 'jpg_url', 'dog_breed', 'text', 'rating_numerator',\n", " 'rating_denominator', 'name', 'dog_stage', 'retweet_count',\n", " 'favorite_count'],\n", " dtype='object')" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "master_df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Storing Data" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "# storing the cleaned master dataframe in a csv file\n", "master_df.to_csv('twitter_archive_master.csv',index=False) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Analyzing and Visualizing Data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Questions for analysis\n", "\n", "1. What is the lowest dog rating on WeRateDogs?\n", "2. Which tweet has the lowest dog rating on WeRateDogs?\n", "3. Which tweet has the highest retweet count?\n", "4. What are some of the most common dog names?\n", "5. What are the most popular dog breeds by favorite count?\n", "6. Are WeRateDogs tweets more likely to be favorited or retweeted?\n", "7. Describe the correlation between retweet count and favorite count.\n", "8. Describe the correlation between dog rating and retweet count.\n", "9. Describe the correlation between dog rating and favorite count.\n" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [], "source": [ "# importing data into a dataframe\n", "tweets = pd.read_csv('twitter_archive_master.csv')\n" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indextweet_idrating_numeratorrating_denominatorretweet_countfavorite_count
count1658.0000001.658000e+031658.0000001658.0000001657.0000001657.000000
mean1048.1628477.392385e+1711.38540410.4710492283.7465308004.847314
std594.0204136.794971e+167.5065346.3591524158.93629311787.806358
min0.0000006.660209e+170.0000002.00000011.00000066.000000
25%548.2500006.773835e+1710.00000010.000000514.0000001806.000000
50%1049.5000007.138309e+1711.00000010.0000001131.0000003723.000000
75%1552.7500007.931619e+1712.00000010.0000002587.0000009904.000000
max2073.0000008.921774e+17165.000000150.00000070429.000000144312.000000
\n", "
" ], "text/plain": [ " index tweet_id rating_numerator rating_denominator \\\n", "count 1658.000000 1.658000e+03 1658.000000 1658.000000 \n", "mean 1048.162847 7.392385e+17 11.385404 10.471049 \n", "std 594.020413 6.794971e+16 7.506534 6.359152 \n", "min 0.000000 6.660209e+17 0.000000 2.000000 \n", "25% 548.250000 6.773835e+17 10.000000 10.000000 \n", "50% 1049.500000 7.138309e+17 11.000000 10.000000 \n", "75% 1552.750000 7.931619e+17 12.000000 10.000000 \n", "max 2073.000000 8.921774e+17 165.000000 150.000000 \n", "\n", " retweet_count favorite_count \n", "count 1657.000000 1657.000000 \n", "mean 2283.746530 8004.847314 \n", "std 4158.936293 11787.806358 \n", "min 11.000000 66.000000 \n", "25% 514.000000 1806.000000 \n", "50% 1131.000000 3723.000000 \n", "75% 2587.000000 9904.000000 \n", "max 70429.000000 144312.000000 " ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cheching summary statistics for the data\n", "tweets.describe()" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10 1642\n", "50 3\n", "80 2\n", "11 2\n", "150 1\n", "120 1\n", "110 1\n", "90 1\n", "70 1\n", "40 1\n", "20 1\n", "7 1\n", "2 1\n", "Name: rating_denominator, dtype: int64" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#checking the denominator values\n", "tweets.rating_denominator.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the values of the denominator vary, to standardize the ratings for analysis, create an additional percentage column" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "#creating a percentage rating column\n", "tweets['rating'] = (tweets.rating_numerator / tweets.rating_denominator)* 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Insights:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 1. What is the lowest dog rating on WeRateDogs? " ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweets.rating.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The lowest dog rating given on WeRateDogs is 0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 2. Which tweet has the lowest dog rating on WeRateDogs?" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indextweet_idjpg_urldog_breedtextrating_numeratorrating_denominatornamedog_stageretweet_countfavorite_countrating
14551824835152434251116546https://pbs.twimg.com/media/C5cOtWVWMAEjO5p.jpgAmerican_Staffordshire_terrierWhen you're so blinded by your systematic plag...010NaNNaN2755.020923.00.0
\n", "
" ], "text/plain": [ " index tweet_id \\\n", "1455 1824 835152434251116546 \n", "\n", " jpg_url \\\n", "1455 https://pbs.twimg.com/media/C5cOtWVWMAEjO5p.jpg \n", "\n", " dog_breed \\\n", "1455 American_Staffordshire_terrier \n", "\n", " text rating_numerator \\\n", "1455 When you're so blinded by your systematic plag... 0 \n", "\n", " rating_denominator name dog_stage retweet_count favorite_count rating \n", "1455 10 NaN NaN 2755.0 20923.0 0.0 " ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#tweet with the lowest dog rating\n", "tweets.loc[tweets.rating == tweets.rating.min()]" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag\"" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# getting the tweet text\n", "tweets.loc[tweets.rating == tweets.rating.min()].text[1455]" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://pbs.twimg.com/media/C5cOtWVWMAEjO5p.jpg'" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#getting the tweet image url for download\n", "tweets.loc[tweets.rating == tweets.rating.min()].jpg_url[1455]" ] }, { "attachments": { "Terrier.jpg": { "image/jpeg": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "The tweet with the lowest rating on WeRateDogs, at 0/10 is of an American Staffordshire terrier stating:\n", "\n", "\"When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10\"\n", "![Terrier.jpg](attachment:Terrier.jpg)\n", "\n", "This tweet however still has a favorite count of 20,923 which is more than double the average favorite count, and a retweet count of 2,755 which is still slightly higher than the average retweet count." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 3. Which tweet has the highest retweet count?" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indextweet_idjpg_urldog_breedtextrating_numeratorrating_denominatornamedog_stageretweet_countfavorite_countrating
9781221744234799360020481https://pbs.twimg.com/ext_tw_video_thumb/74423...Labrador_retrieverHere's a doggo realizing you can stand in a po...1310NaNdoggo70429.0144312.0130.0
\n", "
" ], "text/plain": [ " index tweet_id \\\n", "978 1221 744234799360020481 \n", "\n", " jpg_url dog_breed \\\n", "978 https://pbs.twimg.com/ext_tw_video_thumb/74423... Labrador_retriever \n", "\n", " text rating_numerator \\\n", "978 Here's a doggo realizing you can stand in a po... 13 \n", "\n", " rating_denominator name dog_stage retweet_count favorite_count rating \n", "978 10 NaN doggo 70429.0 144312.0 130.0 " ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#tweet with the highest retweet count\n", "tweets.loc[tweets.retweet_count == tweets.retweet_count.max()]" ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Here's a doggo realizing you can stand in a pool. 13/10 enlightened af (vid by Tina Conrad) https://t.co/7wE9LTEXC4\"" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#getting the tweet text\n", "tweets.loc[tweets.retweet_count == tweets.retweet_count.max()].text[978]" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://pbs.twimg.com/ext_tw_video_thumb/744234667679821824/pu/img/1GaWmtJtdqzZV7jy.jpg'" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#getting the tweet image for downaload\n", "tweets.loc[tweets.retweet_count == tweets.retweet_count.max()].jpg_url[978]" ] }, { "attachments": { "Labrador_retriever.jpg": { "image/jpeg": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "The tweet with the highest retweet count at 70,429 retweets, also happening to have the highest favorite count at 144,312 likes, is of a Labrador retriever stating:\n", "\n", "\"Here's a doggo realizing you can stand in a pool. 13/10 enlightened af\"\n", "![Labrador_retriever.jpg](attachment:Labrador_retriever.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 4. What are some of the most common dog names?" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Cooper 10\n", "Charlie 9\n", "Lucy 9\n", "Tucker 9\n", "Oliver 9\n", "Penny 8\n", "Sadie 7\n", "Daisy 7\n", "Winston 7\n", "Toby 6\n", "Jax 6\n", "Lola 6\n", "Koda 6\n", "Stanley 5\n", "Bella 5\n", "Oscar 5\n", "Leo 5\n", "Bo 5\n", "Rusty 5\n", "Bear 4\n", "Gus 4\n", "Larry 4\n", "Louis 4\n", "Winnie 4\n", "Alfie 4\n", "Duke 4\n", "Brody 4\n", "Maggie 4\n", "Bentley 4\n", "Cassie 4\n", " ..\n", "Tayzie 1\n", "Lipton 1\n", "Aqua 1\n", "Rocco 1\n", "Clybe 1\n", "Carll 1\n", "Humphrey 1\n", "Brownie 1\n", "Jay 1\n", "Asher 1\n", "Brat 1\n", "Lili 1\n", "Eve 1\n", "Ed 1\n", "Grizz 1\n", "Travis 1\n", "Cheesy 1\n", "Sage 1\n", "Jockson 1\n", "Hero 1\n", "Antony 1\n", "Buddah 1\n", "Jarvis 1\n", "Snickers 1\n", "Bonaparte 1\n", "Klevin 1\n", "Betty 1\n", "Cora 1\n", "Bruno 1\n", "Dug 1\n", "Name: name, Length: 830, dtype: int64" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweets.name.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the most common dog names on WeRateDogs include Cooper at the very top, with 10 dogs, Charlie, Lucy, Tucker, Oliver. Each having 9 dogs with the stated names and Penny having 8 dogs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 5. What are the most popular dog breeds by favorite count?" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dog_breed\n", "golden_retriever 1639666.0\n", "Labrador_retriever 1028020.0\n", "Pembroke 902671.0\n", "Chihuahua 664894.0\n", "French_bulldog 524718.0\n", "Samoyed 480684.0\n", "chow 388436.0\n", "cocker_spaniel 351165.0\n", "pug 324429.0\n", "malamute 303845.0\n", "toy_poodle 274019.0\n", "Pomeranian 273973.0\n", "Chesapeake_Bay_retriever 265152.0\n", "Eskimo_dog 242658.0\n", "Cardigan 229040.0\n", "German_shepherd 184672.0\n", "Lakeland_terrier 182833.0\n", "basset 170964.0\n", "miniature_pinscher 168423.0\n", "Great_Pyrenees 157068.0\n", "whippet 139361.0\n", "standard_poodle 131277.0\n", "Shetland_sheepdog 130964.0\n", "Bedlington_terrier 128905.0\n", "Staffordshire_bullterrier 128875.0\n", "English_springer 121099.0\n", "Italian_greyhound 120516.0\n", "Siberian_husky 119303.0\n", "Rottweiler 118892.0\n", "flat-coated_retriever 115892.0\n", " ... \n", "Dandie_Dinmont 20572.0\n", "Australian_terrier 19072.0\n", "basenji 18967.0\n", "Gordon_setter 18523.0\n", "Welsh_springer_spaniel 17243.0\n", "bluetick 17055.0\n", "keeshond 16513.0\n", "redbone 16486.0\n", "Bouvier_des_Flandres 15318.0\n", "cairn 15302.0\n", "miniature_schnauzer 14438.0\n", "wire-haired_fox_terrier 14355.0\n", "Rhodesian_ridgeback 13811.0\n", "Appenzeller 12507.0\n", "curly-coated_retriever 11830.0\n", "Lhasa 11146.0\n", "Ibizan_hound 10865.0\n", "toy_terrier 8100.0\n", "Scottish_deerhound 7650.0\n", "Sussex_spaniel 6853.0\n", "silky_terrier 6222.0\n", "Tibetan_terrier 6218.0\n", "clumber 6184.0\n", "Scotch_terrier 3018.0\n", "EntleBucher 2246.0\n", "Brabancon_griffon 2229.0\n", "groenendael 1949.0\n", "standard_schnauzer 1686.0\n", "Irish_wolfhound 1285.0\n", "Japanese_spaniel 1111.0\n", "Name: favorite_count, Length: 113, dtype: float64" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tweets.groupby('dog_breed').favorite_count.sum().sort_values(ascending = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top 5 most popular dog breeds on WeRateDogs in descending order are:\n", "\n", "1.Golden retrievers having 1,639,666 total favorite counts\n", "\n", "2.Labrador retrievers at 1,028,020 favorite counts\n", "\n", "3.Pembrokes at 902,671 favorite counts\n", "\n", "4.Chihuahuas at 664,894 favorite counts\n", "\n", "5.French Bulldogs at 524,718 favorite counts\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 6. Are WeRateDogs tweets more likely to be retweeted or favorited?" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of retweets: 3784168.0\n", "Total number of likes: 13264032.0\n", "Difference: 9479864.0\n" ] } ], "source": [ "print(f'Total number of retweets: {tweets.retweet_count.sum()}') \n", "print(f'Total number of likes: {tweets.favorite_count.sum()}')\n", "print(f'Difference: {tweets.favorite_count.sum()-tweets.retweet_count.sum()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "WeRateDogs tweets are more likely to be favorited, having 9.4 million more favorite counts than retweet counts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 1. Describe the correlation between retweet count and favorite count." ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "image/png": 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2PL42M7vEzOrMbJGZ7ZqYd7qZvRgf0xPlu5nZU3GZS8zMYvnmZjY7zj/bzIYW8n2KiPQ2wwbWcOERO9G/uoJBNVX0r67gwiN2YtjAmmKHlneFboxyLXApcH2y0MzGAJ8GXksUHwRMiI/JwOXAZDPbHDgbqAUcmG9ms9x9RZznRGAOcBcwFbgbOAO4193PN7Mz4usZBXqPIiK90rRdRjFl/HAaVqxh9NABZZnkoMBndO7+ELA8w6SLgdMJiSvlMOB6D+YAQ8xsa+BAYLa7L4/JbTYwNU7bzN0fdXcnJNPDE+u6Lj6/LlEuIiIJwwbWsPOYIWWb5KAI1+jMbBqwxN0Xpk0aBdQnXjfEsvbKGzKUA2zp7m8AxL9b5O0NiIhIr9Kj99GZ2SbAD4ADMk3OUOZdKO9sTCcSqj8ZO3ZsZxcXEZES19NndB8CtgUWmtkrwGjgCTPbinBGNiYx72jg9Q7KR2coB3grVm0S/76dLSB3n+nute5eO2JEyY4bKCIiXdSjic7dn3L3Ldx9nLuPIySrXd39TWAWcFxsfbkHsCpWO94DHGBmQ2PryQOAe+K01Wa2R2xteRxwR9zULCDVOnN6olxERPqYQt9ecCPwKLC9mTWY2QntzH4X8BJQB/wf8HUAd18OnAc8Hh8/jmUAJwNXxmX+Q2hxCXA+8Gkze5HQuvP8fL4vERHpPSw0WBSA2tpanzdvXrHDEBHpVcxsvrvXFjuObNQzioiIlDUlOhERKWtKdCIiUtaU6EREpKwp0YmISFlTohMRkbKmRCciImVNiU5ERMqaEp2IiJQ1JToRESlrSnQiIlLWlOhERKSsKdGJiEhZU6ITEZGypkQn0scsa2xiYf1KljU2FTsUkR5RVewARKTn3LFgCTNuX0R1RQXrWlq48IidmLbLqGKHJVJQOqMT6SOWNTYx4/ZFfLCuhdVN6/lgXQun375IZ3ZS9pToRPqIhhVrqK5o/ZOvrqigYcWaIkUk0jMKmujM7Goze9vMnk6U/cLMnjOzRWb2ZzMbkph2ppnVmdnzZnZgonxqLKszszMS5dua2Vwze9HMbjazfrG8Jr6ui9PHFfJ9ivQGo4cOYF1LS6uydS0tjB46oEgRifSMQp/RXQtMTSubDezo7jsBLwBnApjZROAoYFJc5rdmVmlmlcBlwEHARODoOC/ABcDF7j4BWAGcEMtPAFa4+3jg4jifSJ82bGANFx6xE/2rKxhUU0X/6gouPGInhg2sKXZoIgVV0MYo7v5Q+tmUu/8j8XIOcGR8fhhwk7s3AS+bWR2we5xW5+4vAZjZTcBhZvYssB9wTJznOuAc4PK4rnNi+W3ApWZm7u55e3MivdC0XUYxZfxwGlasYfTQAUpy0icUu9XlV4Cb4/NRhMSX0hDLAOrTyicDw4CV7r4+w/yjUsu4+3ozWxXnfyffb0Cktxk2sEYJTvqUojVGMbMfAOuBG1JFGWbzLpS3t65McZxoZvPMbN7SpUvbD1pERHqdoiQ6M5sOHAp8MVGd2ACMScw2Gni9nfJ3gCFmVpVW3mpdcfpgYHmmWNx9prvXunvtiBEjuvvWRESkxPR4ojOzqcAMYJq7v5+YNAs4KraY3BaYADwGPA5MiC0s+xEarMyKCfJ+Nl7jmw7ckVjX9Pj8SOA+XZ8TEembCnqNzsxuBPYBhptZA3A2oZVlDTDbzADmuPtJ7r7YzG4BniFUaX7D3Zvjek4B7gEqgavdfXHcxAzgJjP7CfAkcFUsvwr4fWzQspyQHEVEpA8ynehsVFtb6/PmzSt2GCIivYqZzXf32mLHkY16RhERkbKmRCciImVNiU5ERMqaEp2IiJQ1JToR6bU0iKzkothdgImIdIkGkZVc6YxORHodDSIrnaFEJyK9jgaRlc5QohPJE10v6jkaRFY6Q9foRPJA14t6VmoQ2dPTPnMNPySZKNGJdFPyetEHhLOM029fxJTxw3XgLaBsg8gua2zSwLLSihKdSDelrhelkhxsvF6kA21hpQ8iqzNryUTX6ES6SdeLSoNaYko2SnQi3ZS6XtS/uoJBNVX0r67Q9aIiUEtMyUZVlyJ5kO16kfQcnVlLNjqjE8mTYQNr2HnMECW5ItGZtWSjMzoRKRs6s5ZMlOhEpKykt8QUUdWliIiUtYImOjO72szeNrOnE2Wbm9lsM3sx/h0ay83MLjGzOjNbZGa7JpaZHud/0cymJ8p3M7On4jKXmJm1tw0REel7Cn1Gdy0wNa3sDOBed58A3BtfAxwETIiPE4HLISQt4GxgMrA7cHYicV0e500tN7WDbYiISB9T0ETn7g8By9OKDwOui8+vAw5PlF/vwRxgiJltDRwIzHb35e6+ApgNTI3TNnP3R93dgevT1pVpGyIi0scU4xrdlu7+BkD8u0UsHwXUJ+ZriGXtlTdkKG9vGyIi0seUUmMUy1DmXSjv3EbNTjSzeWY2b+nSpZ1dXERESlwxEt1bsdqR+PftWN4AjEnMNxp4vYPy0RnK29tGG+4+091r3b12xIgRXX5TIiJSmoqR6GYBqZaT04E7EuXHxdaXewCrYrXjPcABZjY0NkI5ALgnTlttZnvE1pbHpa0r0zZERKSPKegN42Z2I7APMNzMGgitJ88HbjGzE4DXgM/F2e8CDgbqgPeB4wHcfbmZnQc8Huf7sbunGricTGjZOQC4Oz5oZxsiItLHWGiw2MFMZt9y9193VNbb1dbW+rx584odhohIr2Jm8929tthxZJNr1eX0DGVfzmMcIiIiBdFu1aWZHQ0cA2xrZrMSkwYBywoZmIiISD50dI3uEeANYDjwv4ny1cCiQgUlIp2zrLFJPfaLZNFuonP3V4FXgT17JhwR6aw7Fixhxu2LqK6oYF1LCxcesRPTdhnV8YIifURO1+jM7L9iB8mrzOxdM1ttZu8WOjgRad+yxiZm3L6ID9a1sLppPR+sa+H02xexrLGp2KGJlIxcG6NcCExz98Huvpm7D3L3zQoZmIh0rGHFGqorWv+MqysqaFixpkgRiZSeXBPdW+7+bEEjEZFOGz10AOtaWlqVrWtpYfTQAUWKSKT05Jro5pnZzWZ2dKzG/C8z+6+CRiYiHRo2sIYLj9iJ/tUVDKqpon91BRcesZMapIgk5NozymaE3koOSJQ58Ke8RyQinTJtl1FMGT9crS5Fssgp0bn78YUORES6btjAGiU4kSxySnRmdg0ZhsBx96/kPSIREZE8yrXq8s7E8/7AZ9k4JI6IiEjJyrXq8vbk6zgqwT8LEpGIiEgedXU8ugnA2HwGIiIiUgi5XqNbTbhGZ/Hvm8CMAsYlIiKSF7lWXQ4qdCAiIiKFkPMI42Y2Ddg7vnzA3e9sb34R6TqNRiCSP7lWXZ4PfAy4IRZ9y8ymuPuZBYtMpI/SaAQi+ZVrY5SDgU+7+9XufjUwFTikOxs2s++Y2WIze9rMbjSz/ma2rZnNjSMl3Gxm/eK8NfF1XZw+LrGeM2P582Z2YKJ8aiyrM7MzuhOrlL9ljU0srF9Z9F7/NRqBSP51ptXlkMTzwd3ZqJmNAk4Fat19R6ASOAq4ALjY3ScAK4AT4iInACvcfTxwcZwPM5sYl5tESL6/NbNKM6sELgMOAiYCR8d5Rdq4Y8ESplxwH8deOZcpF9zHrAVLihaLRiMQyb9cE93PgSfN7Fozuw6YD/ysm9uuAgaYWRWwCWEk8/2A2+L064DD4/PD4mvi9P3NzGL5Te7e5O4vA3XA7vFR5+4vufta4KY4r0grpXYGpdEIRPIvp0Tn7jcCexA6cf4TsKe739TVjbr7EuCXwGuEBLeKkDxXuvv6OFsDkLowMQqoj8uuj/MPS5anLZOtXKSVUjuD0mgEIvmXa2OUzwL3ufus+HqImR3u7n/pykbNbCjhDGtbYCVwK6GaMV2qf03LMi1beaYE3qavzhjLicCJAGPH6h74vqYUz6A0GoFIfuVadXm2u69KvXD3lcDZ3djup4CX3X2pu68jnCV+HBgSqzIBRrOxP80GYAxAnD4YWJ4sT1smW3kb7j7T3WvdvXbEiBHdeEvSG5XqGdSwgTXsPGZI0eMQKQe53keXKSHmfA9eBq8Be5jZJsAaYH9gHnA/cCThmtp04I44/6z4+tE4/T53dzObBfzRzC4CRhK6JnuMcKY3wcy2BZYQGqwc0414pYzpDEqkvOWarObFZHIZoQrwm4Rral3i7nPN7DbgCWA98CQwE/gbcJOZ/SSWXRUXuQr4vZnVEc7kjorrWWxmtwDPxPV8w92bAczsFOAeQovOq919cVfjlfKn8dxEype5Z7x01Xoms02BswhVjgD/AH7q7u8VMLYeV1tb6/PmzSt2GCIivYqZzXf32mLHkU2ufV2+B2S96drMfuPu38xbVCJdoG6zRCST7lxnS5qSp/WIdElPdZulZCrS++Qr0YkUTfKm7w8ItwqcfvsipowfntdkpD4oRXqnrg68KlIyeuKm71LrQUVEcpevRJfpxm2RHtETN32XWg8qIpK7TiW62Poyk1/nIRaRLin0Td/LGptYtWYda5u7n0xLZZQEkb4k1y7APg5cCQwExprZzsDX3P3rAO5+bcEiFMlBoW76Tl6Xa25pobrS6F9VueEaXWe2o2t8IsWRa2OUi4EDCT2U4O4LzWzv9heRvqpYLRPzfdN3pkYuNVVw2Rc/yqSRgzu1rZ5qMCMibeXc6tLd68PIOBs05z8c6e3K6awldV0ulZgA+lVWMnhAv04np0zrSl3jU6ITKaxcr9HVx+pLN7N+ZvY94NkCxiW9ULm1TMxnI5dSHCVBpK/INdGdBHyDMKZbA7AL8PVCBSW9z7LGJu5/7m0qW5/19+qWifls5FKqoySI9AW5Vl1u7+5fTBaY2RTg4fyHJL1NqrqyqsJ4b23rGu3unLWUQi8k+WzkolESRIoj10T3G2DXHMqkj0lWVyZt2q+SZvcun7WU0rW+fDZy0SgJIj2v3URnZnsSBkQdYWanJSZtRhj+Rvq4TI0sNq2p5NzPTGLfHbbo0kFdLRRFJJ86ukbXj3DvXBUwKPF4lzAAqvRxmRpZNLd4l5McqBcSEcmvds/o3P1B4EEzu9bdX+2hmKQXSTWyOD2tmrE7Z15qoSgi+dRR1eWv3P3bwKVm1maEVnefVrDIpNdINrLYtF8l761tZlljU5eTXSGSp4j0XR01Rvl9/PvLQgcivduwgTX8u+6dvDUgUQtFEcmXjqou55tZJfDf7n5sD8UkvVAhGpCohaKI5EOHN4y7ezOh1WW/fG7YzIaY2W1m9pyZPWtme5rZ5mY228xejH+HxnnNzC4xszozW2RmuybWMz3O/6KZTU+U72ZmT8VlLrG0/sskvxa//i4VlM/N4iJSPnLtGeUV4GEzO8vMTks9urntXwN/d/cdgJ0JXYqdAdzr7hOAe+NrgIOACfFxInA5gJltDpwNTAZ2B85OJcc4z4mJ5aZ2M17J4o4FS/jv6+fx/rr83SwuIpIvuSa614E74/zJ2wy6xMw2A/YGrgJw97XuvhI4DLguznYdcHh8fhhwvQdzgCFmtjVhRIXZ7r7c3VcAs4Gpcdpm7v6ouztwfWJdkkepKsum9a1bSdZUmRqQiEhJyKlnFHc/F8DMBoWX3tjN7W4HLAWuiWPbzQe+BWzp7m/Ebb5hZlvE+UcB9YnlG2JZe+UNGcrbMLMTCWd+jB07tnvvqozk2v1WphvGN+lXyRXH7sreH94i63IiIj0lpzM6M9vRzJ4EngYWm9l8M5vUje1WEboPu9zdPwq8x8ZqyowhZCjzLpS3LXSf6e617l47YsSI9qPuI+5YsIQpF9zHsVfOZcoF9zFrwZKs82a6563FnUkjBxc6TBGRnORadTkTOM3dt3H3bYDvAv/Xje02AA3uPje+vo2Q+N6K1Y7Ev28n5h+TWH40oTq1vfLRGcqlA50daidTr/xnHTKRhhVreu3wPCJSXnLt1HlTd78/9cLdHzCzTbu6UXd/08zqzWx7d38e2B94Jj6mA+fHv3fERWYBp5jZTYSGJ6ti1eY9wM8SDVAOAM509+VmttrM9gDmAscROqGWDnRlgNDkPW9PL1nFeX97piQ6YxYRgdwT3UtmdhYbbyA/Fni5m9v+JnBDvG3hJeB4whnmLWZ2AvAa8LlpEDq4AAAaRElEQVQ4713AwUAd8H6cl5jQzgMej/P92N2Xx+cnA9cCA4C740M60NXut1JJ8AszH1VnzCJSUnJNdF8BzgX+RLj+9SAx2XSVuy8AajNM2j/DvE4Y+DXTeq4Grs5QPg/YsTsx9kXd6X6rK2eDIiKFlmuiG+fupxY0EikZXe1+K9PZ4NrmFlatWdetvi9FRLoj18YoF8UeTM7rZmtL6SWGDaxh5zFDOpWc0humVFcazS0tfOOGJzpsvSkiUig5JTp33xfYh3Dv28zYtdYPCxmY9E7TdhnFwzP247IvfpQKg/Ut5NR6U0SkUHI9o8Pd33T3S4CTgAXAjwoWlfRqwwbWMHhAP/pVth6EXn1fikgx5HrD+EfM7Bwzexq4FHiE1vepibSiwVNFpFTkekZ3DbACOMDdP+nul7v72x0tJH3HssYmFtav3FA1melGcvV9KSLFkGtfl3sUOhDpve5YsGTDgKtrm1s4Zd/xHDN5rAZPFZGSYOEWtQ5mMpsA/ByYCPRPlbv7doULrefV1tb6vHnzih1Gr7KssYkpF9zHB+vajl7wiyN3LsleUXLtsFpEcmNm8909033RJSHX++iuIYz7djGwL+FmcQ1kKhlvEgdoWu8l2StK8uxTXZSJ9A25XqMb4O73Es4AX3X3c4D9CheW9BaZGp2klFory852WC0i5SHXRPeBmVUAL5rZKWb2WUCDjZWx9MYl2aQandRUtd2VeqKVZa5xwsazz6RSS8Yikn/tVl2a2e/d/UuEUQQ2AU4FziOczU0vfHhSDJ2t3ks1Ovnj3Ne49P4X6VdZ2ak+MnsqTt3yINI3tdsYxcyeAQ4iDJOzD2nX5RIjBZQFNUbJ3Likf3UFD8/YL6ek1VMNPboa56wFS9p0WK1rdCLd09sbo1wB/B3YDphPSHSe+FtWrS7LUWcTT3dHIBg2sKZHGp90NU7d8iDS97Sb6GKXX5eY2eXufnIPxSR50pUWhu1V75VSs/zuVEP2VDIWkdKQa6fOSnK9TFdbGGbr0eTfde8w5YL7OPbKuSUxEoF6XhGRXOV6H530Issam7j/ubeptNa3OuZaBZlevQdsuB6WPnI4kPUsr9BngKqGFJFcKNGVmVR1ZVWF8d7a5lbTOtPCMFm9t7B+ZcbrYTfMfY3fPlCXsWr0jgVLOP22RVRWGM0tzi+OLEyjD1VDikhHch6mpxDMrNLMnjSzO+Prbc1srpm9aGY3m1m/WF4TX9fF6eMS6zgzlj9vZgcmyqfGsjozO6On31sxJKsrG5s2JrlN+1V2q2ov28jhl93/Ysaq0WWNTXzv1oU0rW/h/bXNNK1v4bu3LtSN2SJSFEVNdMC3gGcTry8ALnb3CYTREk6I5ScAK9x9PKEbsgsAzGwicBQwCZgK/DYmz0rgMsKtEROBo+O8ZS3TDdGb1lRy7rRJPDxjvy6fUWW6HnbKvuOzjje3+PVVrGtufdvKumZn8eururR9EZHuKFrVpZmNBg4BfgqcZmZGuBH9mDjLdcA5wOXAYfE5wG3ApXH+w4Cb3L0JeNnM6oDd43x17v5S3NZNcd5nCvy2imr00AF8sL51deXa9S3su0PoxGZh/couX8vKdN3usgfqWs2TqhpdtWZdlrWoe1QR6XnFvEb3K+B0YFB8PQxY6e7r4+sGIHUKMgqoB3D39Wa2Ks4/CpiTWGdymfq08sn5fgOlKL0DAHfn70+/yXl/e6bbN0mnXw+78Iid2tx8PWxgDZNGbkZVBaxP1HZWVcCkkZt1+X2JiHRVURKdmR0KvO3u881sn1Rxhlm9g2nZyjNVyWbsAsbMTgROBBg7dmw7UZe+hhVrGFBdxeqm9RvK+lVVcu5fF7O22du0mOxuI45srR6HDazhos/vwvdvW0ilVdDsLfziyJ3VaEREiqJYZ3RTgGlmdjBhfLvNCGd4Q8ysKp7VjQZej/M3AGOABjOrAgYDyxPlKcllspW34u4zgZkQugDr/lsrjGxN9ZPlmRqNvL+2mZqqrt1mkItsrR7V9F9ESkVREp27nwmcCRDP6L7n7l80s1uBI4GbCJ1G3xEXmRVfPxqn3+fubmazgD+a2UXASGAC8BjhTG+CmW0LLCE0WEld++t1svVwkmlk79M+9WF+dvdzrZZvWp/WMKSHOjLuStP/Uup9RUTKQ6ndRzcDuMnMfgI8CVwVy68Cfh8bmywnJC7cfbGZ3UJoZLIe+Ia7NwOY2SnAPUAlcLW7L+7Rd5InyVsGklWPE7ferE35/85+gaoK6FcBaxMndjWVhptRU1nRI6MKdJUGRRWRQih6onP3B4AH4vOX2NhqMjnPB8Dnsiz/U0LLzfTyu4C78hhqUWTrvHhBhpu4oXUDkBSrMP52yid4b21zl86UeuIsK1tCL7URykWk9yl6opP2Zeu8eJcxQ7KO7A1QXQH9q6tY19LCWYdObJPkck1euZxl5SMRdnfUBBGRbJToSlzqZu30ZvzjtxzEhUfsxPduXcja5rZtaC7+wkcZs/kmPL1kFefOWoyZ4e788nM745BT91x1b63m+7ctYu367GdZ+apu1KCoIlIo7Q682teU6sCryxqbYq8ixqSRm7U6w6l7azUH/vohmhM5orrSmHPm/gB87Kf/pCXxFRtQVWmtei5JzZ8827th7mv85r4XWNf6/nMG1VTxh69OZucxQ7o9SGs6DYoq0jv19oFXpchumPMq5/51MdWVFTS7tzn4D920H9/e/8Ncen9dqzO0YQNruHHuq62SHISbCbN1z7X3h7fY0BlzU6aLfcDa5mZWrVm7oboyn9WNuiVBRApBia6E3TDnVX7wl6eBkGCgddVhstoQnJM/OZ5jJo/dMO2Hcdnc2IYGIdmSXKVBi8M3bnhyw7W/fFc3ajQCEcm3YnfqLFksa2zi3Dvbds1pONc98gr3PvMm37914YbRA5rW+4a+J5c1NnH6bQvJcOkurqO1VPdcmTqFTqmugMqKCtY1+4bRCs678xnOOmSiBj8VkZKmM7oS1bBiDf0qjbXrW5evWedccl9dxmVS1YYPvrC0zU3iSafuN54rHvpPxu65MrXkrKky9t9hC+56+q0229tx1GDuPOUTLKhfyS5jhjB+y0FtlgfdCC4ixaNEV6JGDx3A+vQLbB1Y19LCuvXNXJolEaZc/uB/OHvaJHYcObhNH5XJFp6p3lYO2nErDvnNv9qsZ21zC08vWdVhh9G6EVxEikmtLhNKrdXlj/7yFNfPeS2neftVGkd9bAw3Pv5am5aSmbTXOjL97Gth/UqOvXJuq86iAU7+5HZc/fDLrc4e09eb75aZIlJ6Sr3Vpa7RlahljU3cMr8h5/kv/+Ku/PGx3JIcbKzmXNbYxML6la1G/x42sIadxwzZkIgy3eNWU2U4bfvRTK03JdN1v/R5REQKSYmuBC1rbOL+596mohMDld799BsZu//KZl1LqHaccsF9HHvlXKZccB+zFizJOG+mEcZ/9JlJXPPwK23mXdvc3KrVpW4EF5Fi0zW6EpK6Ufuy++sAb7dBSbrbnsg4ClEb/SorMIOzDp3IeXc+06pvye/ftpAhm1QzaeTgNtWKU8YPZ+aXdiN103poLFPR5laEU/ad0GrZbD27qNpSRHqKEl2RJK+DAdww9zUuve8F1uZY9ZirmkrDcT71kS3557NvUVVhNHsLyxvXUlXR+oyxab1z0h+eoCXtxvRMjUmmjB+eoTqzgmMmtx28VjeCi0gxqTFKQk81RkkmjjXr1uNO1nveuqPC4Nrjd2fk4P4ceum/WzUIqU7rBixdqsEIkLUxycN176jLLhEp+cYoOqPrYZmGoymU8w7bkb0/PIKFGYb0aS/JQesGI9m6+dKZmoj0Bkp0PSxT/5D5VmFwxtQd2HHUYJY1NmVsENKRZIOR9hqTqMsuESl1anXZw7qSdDqjMia5i/75wobWlA/XvdOq1WRNVQVVad98VUW4xpbelVemFpdqTCIivYmu0SX01DW6G+a8yo9mPd1qaJ18OfmT23HNI69kvKYGbKhmzHR9rb1qSHXhJSLZ6BpdBmY2Brge2ApoAWa6+6/NbHPgZmAc8ArweXdfYWYG/Bo4GHgf+LK7PxHXNR34YVz1T9z9uli+G3AtMAC4C/iWl0BWTw27U4iTuupKY8vN+lOZdvtd6ppa8ibwbNfXsiWx1Nld6gZzJTwR6S2KdY1uPfBdd3/CzAYB881sNvBl4F53P9/MzgDOAGYABwET4mMycDkwOSbGs4FawlBr881slruviPOcCMwhJLqpwN09+B7bSA67UwgtLc7P736uzb1t2W7Q7uz1NfVZKSK9UVGu0bn7G6kzMndfDTwLjAIOA66Ls10HHB6fHwZc78EcYIiZbQ0cCMx29+Uxuc0GpsZpm7n7o/Es7vrEuopiWWMT5/x1cUG30exkHEvurEMntkpombr96kiytWhqmJ7Tb1/UqXWIiBRD0Vtdmtk44KPAXGBLd38DQjI0sy3ibKOA+sRiDbGsvfKGDOVF07BiDZVmrKNna0837VfJjiMHb3jd1bOyfI8mLiLSU4ra6tLMBgK3A99293fbmzVDmXehPFMMJ5rZPDObt3Tp0o5C7rLRQwewtjOdUebJ2uaN1ZbdOStTn5Ui0lsVLdGZWTUhyd3g7n+KxW/Fakfi37djeQMwJrH4aOD1DspHZyhvw91nunutu9eOGDGie2+qAz2f5iDZ/qY7IwnoNgMR6a2K1erSgKuAZ939osSkWcB04Pz4945E+SlmdhOhMcqqWLV5D/AzMxsa5zsAONPdl5vZajPbg1Alehzwm4K/sTTJJvn/WPxmT28egAHVVRuqF7t7VqaeUESkNyrWNbopwJeAp8xsQSz7H0KCu8XMTgBeAz4Xp91FuLWgjnB7wfEAMaGdBzwe5/uxuy+Pz09m4+0Fd9PDLS7T+7MsQq0l0LYXk+6OJKCeUESkt9EN4wn5umE806jaPaVfZQVrm1uoqTSswjI2NtHN3yKST7phvA/qif4ss7no8zuzw1aDeG9tc9ZEprMyEelLlOgKoND9WWZTU2mM2XwTxm85qMe3LSJSqtSpcwGkroXVpPecXGBWYWruLyKSRomuQKbtMopzPjOx4NuprIBNayrV3F9EJAslugJqWt9c8G3UVFawbn0LZx0yMaceTrrS/ZeISG+ma3QFlOx6q1Dejy07z/vbM0zdcat2z+jUKbOI9EU6oyuQG+a8yudnzumx7XXUw4k6ZRaRvkqJrgBSw/G09OAtih31cNKd7r9ERHozVV3m2bLGJn40q3BjzqXbtKaS5hbvsCGKOmUWkb5KiS7PFr++iuYeuIWupsr40aGT2HHU4Jx6OMlH918iIr2REl3eZRohKH+qKoxv7T+BYyaP7XSSUqfMItIXKdHl2Z0Ll+RtXdWVxqn7TeCgHbfi9VUfAM6kkYO7laDU/ZeI9DVKdHlU99Zqbpmfn0RXaXD3qXtt6M5L3XqJiHSNWl3m0YL6lXlZT3UFXPyFXZTcRETyQGd0eTR0k+pur+OQHbfix4fvqOpFEZE8UaLLo3P/+ky3lr/quN3Yf+JWeYpGRERAiS5v9jr/n9Sv7HovI5+vHa0kJyJSAEp0efDTO5/pcpIz4MyDduDET34ov0GJiAigRNdtyxqb+L9/v9zp5b685zZ8dtfRup9NRKTAyrrVpZlNNbPnzazOzM4oxDYumf1Cp5epqTK+uf8Edh4zRElORKTAyjbRmVklcBlwEDARONrM8j4S6nVzX+vU/P2rK/jFkTsrwYmI9JByrrrcHahz95cAzOwm4DCge00jE8ad8bec5x2+SSVXHb+HqipFRHpYOSe6UUB94nUDMLkYgUweN4SbT5pSjE2LiPR5ZVt1SebelduMEGdmJ5rZPDObt3Tp0rwHMW3nrZTkRESKqJwTXQMwJvF6NPB6+kzuPtPda929dsSIEZ3awPwffirrtH7AP7+zN5ccvVun1ikiIvlVzonucWCCmW1rZv2Ao4BZ+dzAsIE1XHLULm3KrzpuN144/xD1VSkiUgLK9hqdu683s1OAe4BK4Gp3X5zv7WiMNxGR0la2iQ7A3e8C7ir0djTGm4hI6SrnqksRERElOhERKW9KdCIiUtaU6EREpKyZe5t7qPssM1sKvNrFxYcD7+QxnEJSrIXTm+JVrIXRm2KF/MS7jbt37kbkHqRElydmNs/da4sdRy4Ua+H0pngVa2H0plih98XbFaq6FBGRsqZEJyIiZU2JLn9mFjuATlCshdOb4lWshdGbYoXeF2+n6RqdiIiUNZ3RiYhIWVOiExGRsqZElwdmNtXMnjezOjM7owe3e7WZvW1mTyfKNjez2Wb2Yvw7NJabmV0SY1xkZrsmlpke53/RzKYnynczs6fiMpeYWabBbHONdYyZ3W9mz5rZYjP7VqnGa2b9zewxM1sYYz03lm9rZnPjdm+Owz9hZjXxdV2cPi6xrjNj+fNmdmCiPK/7jJlVmtmTZnZnKcdqZq/E72iBmc2LZSW3D8R1DTGz28zsubjf7lnCsW4fP9PU410z+3apxtvj3F2PbjwIQwD9B9iOMN7qQmBiD217b2BX4OlE2YXAGfH5GcAF8fnBwN2Ekdf3AObG8s2Bl+LfofH50DjtMWDPuMzdwEHdiHVrYNf4fBDwAjCxFOONyw+Mz6uBuTGGW4CjYvkVwMnx+deBK+Lzo4Cb4/OJcX+oAbaN+0llIfYZ4DTgj8Cd8XVJxgq8AgxPKyu5fSCu6zrgq/F5P2BIqcaaFncl8CawTW+ItyceRQ+gtz/iF39P4vWZwJk9uP1xtE50zwNbx+dbA8/H578Djk6fDzga+F2i/HexbGvguUR5q/nyEPcdwKdLPV5gE+AJYDKh94iq9O+dMObhnvF5VZzP0veF1Hz53meA0cC9wH7AnXHbpRrrK7RNdCW3DwCbAS8TG+yVcqwZYj8AeLi3xNsTD1Vddt8ooD7xuiGWFcuW7v4GQPy7RSzPFmd75Q0ZyrstVpd9lHCmVJLxxqrABcDbwGzCWc1Kd1+fYf0bYorTVwHDuvAeuupXwOlAS3w9rIRjdeAfZjbfzE6MZaW4D2wHLAWuiVXCV5rZpiUaa7qjgBvj894Qb8Ep0XVfpnrqUrxnI1ucnS3vXhBmA4HbgW+7+7vtzdrJuPIar7s3u/suhLOl3YGPtLP+osVqZocCb7v7/GRxO+sv9n4wxd13BQ4CvmFme7czbzFjrSJcFrjc3T8KvEeo+sum2J9rCCJci50G3NrRrJ2Mq7cc5zJSouu+BmBM4vVo4PUixQLwlpltDRD/vh3Ls8XZXvnoDOVdZmbVhCR3g7v/qdTjBXD3lcADhOsYQ8ysKsP6N8QUpw8GlnfhPXTFFGCamb0C3ESovvxVicaKu78e/74N/JnwT0Qp7gMNQIO7z42vbyMkvlKMNekg4Al3fyu+LvV4e0ax6057+4Pwn99LhAv4qYv1k3pw++NofY3uF7S++HxhfH4IrS8+PxbLNydcixgaHy8Dm8dpj8d5UxefD+5GnAZcD/wqrbzk4gVGAEPi8wHAv4BDCf8lJxt4fD0+/watG3jcEp9PonUDj5cIDQUKss8A+7CxMUrJxQpsCgxKPH8EmFqK+0Bc17+A7ePzc2KcJRlrIuabgONL+fdVjEfRAyiHB6EF0wuE6zg/6MHt3gi8Aawj/Md1AuF6y73Ai/Fvaic14LIY41NAbWI9XwHq4iP5I6kFno7LXErahflOxvoJQlXHImBBfBxcivECOwFPxlifBn4Uy7cjtDyrIySSmljeP76ui9O3S6zrBzGe50m0UivEPkPrRFdyscaYFsbH4tS6SnEfiOvaBZgX94O/EA78JRlrXN8mwDJgcKKsZOPtyYe6ABMRkbKma3QiIlLWlOhERKSsKdGJiEhZU6ITEZGypkQn0k2x89xNCrj+w81sYqHWn2MM+5jZx4sZg0hXKdGJ5CD29p7t9/JtQtPuQjmc0OlyMe0DKNFJr6REJ5KFmY2Lw7P8ltCx85fM7FEze8LMbjWzgWZ2KjASuN/CMESfN7OL4vLfMrOX4vMPmdm/4/PdzOzB2N/jPYmeKz5kZn+P5f8ysx3iWdQ04Bdx+JUPZYl1vJn908LQQk/EdZmZ/cLMno7Dq3whzruPxeF84utLzezL8fkrZnZuXMdTMYZxwEnAd2IMexXg4xYpmKqOZxHp07YHjgd+BPwJ+JS7v2dmM4DT3P3HZnYasK+7v2NmWwHfj8vuBSwzs1GEG+b/FbtB+w1wmLsvjcnnp4SbdGcCJ7n7i2Y2Gfitu+9nZrMIN4Lf1k6cNwDnu/ufzaw/4Z/Y/yLc9LwzMBx43MweyuE9v+Puu5rZ14HvuftXzewKoNHdf5nrBydSKpToRNr3qrvPiZ0nTwQejuNN9gMeTZ/Z3d+MZ3qDCH0G/pEwbuBehES5PbAjMDuupxJ4I3Z2/XHg1sR4ljW5BBi3Ncrd/xxj+CCWfwK40d2bCX0ePgh8DGivM21inADzCclSpFdTohNp33vxrwGz3f3oHJZ5lHAW+Dyhv8SvEMZ1+y4wFljs7nsmFzCzzQhD6+zShRizjfScrXw9rS9b9E+b3hT/NqNjhJQBXaMTyc0cYIqZjQcws03M7MNx2mrCqOkpDwHfi3+fBPYFmtx9FSH5jTCzPeN6qs1skochi142s8/FcjOznbOsv5W4bIOZHR6XrYmtQB8CvmBhbL0RhDPLx4BXgYlxvsHA/jm8/3ZjECllSnQiOXD3pcCXgRvNbBEh8e0QJ88E7jaz++PrfxGqLR+K1Yb1wL/jetYCRwIXmNlCQufWqdaMXwROiOWLgcNi+U3A9y0MAJqxMQrwJeDUGNsjwFaEYXAWETpRvg843d3fdPd64JY47QZCMu7IX4HPqjGK9Ebq1FlERMqazuhERKSs6UKzSC9iZpcRRhVP+rW7X1OMeER6A1VdiohIWVPVpYiIlDUlOhERKWtKdCIiUtaU6EREpKwp0YmISFlTohMRkbL2/wG6+gpwqvXF4QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#plotting to show correlation\n", "tweets.plot(x = 'retweet_count',y = 'favorite_count', kind = 'scatter');\n", "plt.title('Correlation between tweet retweet count and tweet favorite count');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is generally a positive correlation between retweet count and favorite count. As a tweet's favorite count increases, the number of retweets is also highly likely to increase." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 2. Describe the correlation between dog rating and retweet count" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tweets.plot(x = 'retweet_count',y = 'rating', kind = 'scatter');\n", "plt.title('Correlation between tweet retweet count and dog rating');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The scatter plot reveals a horizontal line of best fit, indicating there is no correlation between dog rating and retweet count. The rating given to a dog therefore does not affect the number of times a particular dog's tweet will be retweeted." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### 3. Describe the correlation between dog rating and favorite count." ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tweets.plot(x = 'favorite_count',y = 'rating', kind = 'scatter');\n", "plt.title('Correlation between tweet favorite count and dog rating');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much like with retweet count, this scatter plot also reveals a horizontal line of best fit, indicating, there is no correlation between dog rating and favorite count. The rating given to a dog therefore does not affect the number of times that the particular dog's tweet will be favorited." ] } ], "metadata": { "extensions": { "jupyter_dashboards": { "activeView": "report_default", "version": 1, "views": { "grid_default": { "cellMargin": 10, "defaultCellHeight": 20, "maxColumns": 12, "name": "grid", "type": "grid" }, "report_default": { "name": "report", "type": "report" } } } }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 2 }