wrangle_act.ipynb

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   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import os\n",
    "import twitter \n",
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "from timeit import default_timer as timer\n",
    "import json\n",
    "import sys\n",
    "import copy\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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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",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    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",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# enhanced twitter archive data\n",
    "df=pd.read_csv('twitter-archive-enhanced.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.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",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \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  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "# import .tsv data\n",
    "df1 = pd.read_csv('image-predictions.tsv', sep = '\\t')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "(2075, 12)"
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   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# # These are hidden to comply with Twitter's API terms and conditions\n",
    "# consumer_key = 'HIDDEN'\n",
    "# consumer_secret = 'HIDDEN'\n",
    "# access_token = 'HIDDEN'\n",
    "# access_secret = 'HIDDEN'\n",
    "\n",
    "# auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "# auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "# api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "# # input: tweet ids from enhanced twitter archive\n",
    "# tweet_ids = df.tweet_id.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # dict-object containing failed tweet ids and error details\n",
    "# error_dict = {}\n",
    "\n",
    "# # track progress of iteration over all tweets in archive\n",
    "# count = 0\n",
    "# start = timer()\n",
    "\n",
    "# # query Twitter API for JSON data\n",
    "# with open('data/tweet-json.txt', 'w') as outfile:\n",
    "#     for tweet_id in tweet_ids:\n",
    "#         count += 1\n",
    "#         print('Tweet ID: {} (Count: {})'.format(tweet_id, count)) # tweet id being queried\n",
    "#         try: # query with tweet id and obtain status object\n",
    "#             api_data = api.get_status(tweet_id, tweet_mode = 'extended')\n",
    "#         except tweepy.errors.TweepyException as e: # failed query\n",
    "#             print(' - Fail\\n')\n",
    "#             error_dict[tweet_id] = e\n",
    "#         else: # write JSON data to .txt file\n",
    "#             print(' - Success\\n')\n",
    "#             json.dump(api_data._json, outfile)\n",
    "#             outfile.write('\\n') # change line for the next tweet id\n",
    "# end = timer()\n",
    "# print('Query took {} minutes.'.format(round((end-start)/60, 2))) # duration of iteration\n",
    "# print('Query failed for {} tweets.'.format(len(error_dict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  retweet_count  favorite_count\n",
       "0  892420643555336193           7009           33810\n",
       "1  892177421306343426           5301           29327\n",
       "2  891815181378084864           3481           22049"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_list = []\n",
    "json_attributes = ['id', 'retweet_count','favorite_count']\n",
    "\n",
    "# append each JSON data as dict-object to the list\n",
    "with open('data/tweet-json.txt') as file:\n",
    "    for line in file:\n",
    "        json_data = json.loads(line[:-1]) # exclude \\n and load JSON data only\n",
    "        json_list.append({json_attributes[0]: json_data[json_attributes[0]]\n",
    "                        , json_attributes[1]: json_data[json_attributes[1]]\n",
    "                        , json_attributes[2]: json_data[json_attributes[2]]})\n",
    "\n",
    "# create dataframe from the list of dict-objects containing JSON data\n",
    "df2 = pd.DataFrame(data = json_list, columns = json_attributes)\n",
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2327, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.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": [
    "## Assessing Data\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "* Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "* The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating system](http://knowyourmeme.com/memes/theyre-good-dogs-brent) is a big part of the popularity of WeRateDogs.\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## In this step of the data wrangling phase, a copy of each dataset was created and assessed. However, the actual datasets can still be reviewed for quality and tidiness issues, and creating copies of these datasets can be postponed to the next step of the data wrangling phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # copy of twitter_archive_enhanced.csv\n",
    "# df = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "# df_clean = df.copy()\n",
    "\n",
    "# # # copy of image-predictions.tsv \n",
    "# df1 = pd.read_csv('data/image-predictions.tsv', sep = '\\t')\n",
    "# df1_clean = df1.copy()\n",
    "\n",
    "# # # copy of JSON data from tweet_json.txt\n",
    "# df2_clean = df2.copy()\n",
    "# df1_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Enhanced Twitter Archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    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",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>\n",
      "2 <a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>\n",
      "3 <a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>\n",
      "4 <a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>\n"
     ]
    }
   ],
   "source": [
    "for i, source in enumerate(df.source.unique()):\n",
    "    print(i+1, source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of non-na's in retweeted_status_user_id column\n",
    "df.retweeted_status_id.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of non-na's in retweeted_status_timestamp column\n",
    "df.retweeted_status_timestamp.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of values in rating_denominator column besides 10\n",
    "(df.rating_denominator != 10).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>13/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>14/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>14/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>13/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "313                                                                          @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho   \n",
       "342                                                                                                                                    @docmisterio account started on 11/15/15   \n",
       "433                                                                         The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "784                                RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…   \n",
       "902                                                                                              Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "1068                               After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ   \n",
       "1120                                                  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "1165                                                                                                           Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1202                                                      This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1228                                                                              Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1254                                                               Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1274                         From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK   \n",
       "1351                                                                                         Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1433                                                                               Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1598                                   Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating   \n",
       "1634                                             Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3   \n",
       "1635                               Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55   \n",
       "1662                                This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5   \n",
       "1663                                I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible   \n",
       "1779                                                                                                     IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1843                                                                Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "2335                                   This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "\n",
       "      rating_numerator  rating_denominator rating  \n",
       "313                960                   0  13/10  \n",
       "342                 11                  15    NaN  \n",
       "433                 84                  70    NaN  \n",
       "516                 24                   7    NaN  \n",
       "784                  9                  11  14/10  \n",
       "902                165                 150    NaN  \n",
       "1068                 9                  11  14/10  \n",
       "1120               204                 170    NaN  \n",
       "1165                 4                  20  13/10  \n",
       "1202                50                  50  11/10  \n",
       "1228                99                  90    NaN  \n",
       "1254                80                  80    NaN  \n",
       "1274                45                  50    NaN  \n",
       "1351                60                  50    NaN  \n",
       "1433                44                  40    NaN  \n",
       "1598                 4                  20    NaN  \n",
       "1634               143                 130    NaN  \n",
       "1635               121                 110    NaN  \n",
       "1662                 7                  11  10/10  \n",
       "1663                20                  16    NaN  \n",
       "1779               144                 120    NaN  \n",
       "1843                88                  80    NaN  \n",
       "2335                 1                   2   9/10  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sub-dataframe for rows with rating_denominator values besides 10 \n",
    "df_ratings = df.query('rating_denominator != 10').loc[:,['text', 'rating_numerator', 'rating_denominator']]\n",
    "\n",
    "# extract rating, if any, of format digit(s)/10\n",
    "rating_format = '(\\d+/10)'\n",
    "df_ratings['rating'] = df_ratings.text.str.extract(pat = rating_format)\n",
    "\n",
    "# compare given ratings and ratings extracted from text\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## reset the option configured earlier\n",
    "pd.reset_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_clean.name.str.extract(pat = '(^[a-z])').dropna().shape[0]\n",
    "df.name.str.extract(pat = '(^[a-z])').dropna().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of values in name column that begins with a lower case alphabet\n",
    "error_name = [name for name in df.name.unique() if name.lower() == name]\n",
    "df.query('name in @error_name').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "such\n",
      "a\n",
      "quite\n",
      "not\n",
      "one\n",
      "incredibly\n",
      "mad\n",
      "an\n",
      "very\n",
      "just\n",
      "my\n",
      "his\n",
      "actually\n",
      "getting\n",
      "this\n",
      "unacceptable\n",
      "all\n",
      "old\n",
      "infuriating\n",
      "the\n",
      "by\n",
      "officially\n",
      "life\n",
      "light\n",
      "space\n"
     ]
    }
   ],
   "source": [
    "#values in name column that begins with a lower case alphabet\n",
    "for name in error_name:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>new_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba</td>\n",
       "      <td>such</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text  \\\n",
       "22                                  I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba   \n",
       "56           Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF   \n",
       "118                         RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…   \n",
       "169                 We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9   \n",
       "193   Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x   \n",
       "...                                                                                                                                                                  ...   \n",
       "2349                          This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc   \n",
       "2350                        This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe   \n",
       "2352                           This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx   \n",
       "2353                                  Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR   \n",
       "2354                         This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI   \n",
       "\n",
       "       name new_name  \n",
       "22     such      NaN  \n",
       "56        a      NaN  \n",
       "118   quite      NaN  \n",
       "169   quite      NaN  \n",
       "193   quite      NaN  \n",
       "...     ...      ...  \n",
       "2349     an      NaN  \n",
       "2350      a      NaN  \n",
       "2352      a      NaN  \n",
       "2353      a      NaN  \n",
       "2354      a      NaN  \n",
       "\n",
       "[109 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract name-like string from tweet text\n",
    "\n",
    "# sub-dataframe of rows with dog names which are oridinary vocabularies\n",
    "df_names = df.query('name in @error_name').loc[:, ['text','name']]\n",
    "\n",
    "# extract dog-name, if any, following 'named' or 'name is'\n",
    "name_format = '(?:named|name is)\\s([A-Z][a-z]+)'\n",
    "df_names['new_name'] = df_names.text.str.extract(pat = name_format)\n",
    "\n",
    "# compare given names and names extracted from text\n",
    "pd.set_option('display.max_colwidth',None)\n",
    "df_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 17)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reset the option configured earlier\n",
    "pd.reset_option('display.max_colwidth')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "1.Data type for the tweet_id column is integer, instead of string (object).\n",
    "\n",
    "2.It appears that the retweeted fields are not relevant to this project.\n",
    "\n",
    "3.Instead of datetime, the data type for the timestamp column is object.\n",
    "\n",
    "4.The four potential values for the source column include not only the tweet's source but also the HTML elements and attributes.\n",
    "\n",
    "5.By retweeting already-existing tweets, 181 new ones were generated.\n",
    "\n",
    "6.The expanded_urls column displays no significant information other than the tweet_id, which is also displayed in the tweet_id column.\n",
    "\n",
    "7.In the name column, 109 dog names are incorrect.\n",
    "\n",
    "8.23 dog ratings that were taken from the tweet are incorrect because they had values for the rating_denominator column that are different than 10."
   ]
  },
  {
   "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.The dog stage is one variable and hence should form single column. But this variable is spread across 4 columns - doggo, floofer, pupper, puppo\n",
    "\n",
    "2.Information about one type of observational unit (tweets) is spread across three different files/dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Image Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.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",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \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  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Tweet Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>7009</td>\n",
       "      <td>33810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>5301</td>\n",
       "      <td>29327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>3481</td>\n",
       "      <td>22049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>7225</td>\n",
       "      <td>36935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>7760</td>\n",
       "      <td>35308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  retweet_count  favorite_count\n",
       "0  892420643555336193           7009           33810\n",
       "1  892177421306343426           5301           29327\n",
       "2  891815181378084864           3481           22049\n",
       "3  891689557279858688           7225           36935\n",
       "4  891327558926688256           7760           35308"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2327 entries, 0 to 2326\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   id              2327 non-null   int64\n",
      " 1   retweet_count   2327 non-null   int64\n",
      " 2   favorite_count  2327 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.7 KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "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": [
    "## Cleaning Data\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy of twitter_archive_enhanced.csv\n",
    "df = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "df_clean = df.copy()\n",
    "\n",
    "# # copy of image-predictions.tsv \n",
    "df1 = pd.read_csv('data/image-predictions.tsv', sep = '\\t')\n",
    "df1_clean = df1.copy()\n",
    "\n",
    "# # copy of JSON data from tweet_json.txt\n",
    "df2_clean = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Enhanced Twitter Archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #1: \n",
    "Data type for the tweet_id column is integer, instead of string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Convert the data type of the tweet_id column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 17)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.tweet_id = df_clean.tweet_id.astype(dtype = 'str')\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2: \n",
    "The three columns with relation to retweeted appear to be outside the scope of interest of this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define \n",
    "Remove from the three columns retweeted_status_timestamp, in_reply_to_status_id and in_reply_to_user_id rows that have value from the dataframe object "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df_clean[df_clean.retweeted_status_id.isnull()]\n",
    "df_clean = df_clean[df_clean.retweeted_status_user_id.isnull()]\n",
    "df_clean = df_clean[df_clean.retweeted_status_timestamp.isnull()]\n",
    "df_clean = df_clean[df_clean.in_reply_to_user_id.isnull()]\n",
    "df_clean = df_clean[df_clean.in_reply_to_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                      2097\n",
       "in_reply_to_status_id            0\n",
       "in_reply_to_user_id              0\n",
       "timestamp                     2097\n",
       "source                           4\n",
       "text                          2097\n",
       "retweeted_status_id              0\n",
       "retweeted_status_user_id         0\n",
       "retweeted_status_timestamp       0\n",
       "expanded_urls                 2093\n",
       "rating_numerator                33\n",
       "rating_denominator              14\n",
       "name                           955\n",
       "doggo                            2\n",
       "floofer                          2\n",
       "pupper                           2\n",
       "puppo                            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: \n",
    "Data type for the timestamp column is object, instead of datetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Convert the data type of the timestamp column from object to datetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.timestamp = pd.to_datetime(arg = df_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime64[ns, UTC]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.timestamp.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #4: \n",
    "The four possible values for the source column not only indicate the source of the tweet but includes the HTML tags and attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Remove the HTML tags and attributes from the values of the source column by mapping these values to their corresponding sources defined in a dictionary object.\n",
    "- Convert the data type of the source column from string (object) to category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# empty dictionary for storing mapping of sources\n",
    "source_dict = {}\n",
    "\n",
    "for source in df_clean.source.unique():\n",
    "    source_dict[source] = source[source.find('>')+1:source.find('</a>')]\n",
    "    # source.find('>')+1: starting position of source name\n",
    "    # source.find('</a>'): ending position + 1 of source name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map sources to the source names defined in the above dictionary\n",
    "def map_source(dataframe):\n",
    "    if dataframe['source'] in source_dict.keys():\n",
    "        return source_dict[dataframe['source']]\n",
    "\n",
    "df_clean['source'] = df_clean.apply(map_source, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert data type to category\n",
    "df_clean.source = df_clean.source.astype(dtype = 'category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "After the values of the source column were shortened by removing the HTML tags and attributes, the data type of the column was successfully converted to category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Twitter for iPhone\n",
      "2 Twitter Web Client\n",
      "3 Vine - Make a Scene\n",
      "4 TweetDeck\n"
     ]
    }
   ],
   "source": [
    "# unique values for source column\n",
    "for i, source in enumerate(df_clean.source.unique()):\n",
    "    print(i+1, source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['TweetDeck', 'Twitter Web Client', 'Twitter for iPhone',\n",
       "                  'Vine - Make a Scene'],\n",
       ", ordered=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.source.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #5: \n",
    "181 Tweets were created by re-tweeting existing tweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "1.Add a new column retweet with the value True for retweets and the value False for non-retweets.\n",
    "\n",
    "2.Drop all three columns, retweeted_status_id, retweeted_status_user_id, and retweeted_status_timestamp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a new column retweet\n",
    "df_clean['retweet'] = df_clean.retweeted_status_id.notna()\n",
    "df_clean.drop(columns = ['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp','in_reply_to_user_id', 'in_reply_to_status_id'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "- The retweet column shows 2097 tweets were non-retweets.\n",
    "- The columns, retweeted_status_id, retweeted_status_user_id, and retweeted_status_timestamp,in_reply_to_user_id, in_reply_to_status_id were successfully removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2097\n",
       "Name: retweet, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numbers of retweets and non-retweets\n",
    "df_clean.retweet.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',\n",
       "       'pupper', 'puppo', 'retweet'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remaining columns\n",
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #6: \n",
    "The expanded_urls column does not show meaningful information besides the tweet id which is already listed under the tweet_id column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Remove the expanded_urls column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.drop(columns = 'expanded_urls', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The expanded_urls column was successfully removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo',\n",
       "       'retweet'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #7: \n",
    "23 dog ratings extracted from the tweet are inaccurate in that these ratings have values for the rating_denominator column other than 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Convert all NaN values under the rating column of the dataframe object df_ratings to the string 0/0.\n",
    "- Correct the rating_numerator and rating_denominator columns in the original dataframe based on the values under the rating column of the dataframe object df_ratings.\n",
    "    - Split the values under the rating column by the separator /.\n",
    "    - Assign the first of the two splitted values to rating_numerator and the second to rating_denominator.\n",
    "    - Convert the data type of the splitted values from string to integer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2080\n",
       "50        3\n",
       "11        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "150       1\n",
       "170       1\n",
       "20        1\n",
       "90        1\n",
       "40        1\n",
       "110       1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter the wrong rating_denominator values using mask\n",
    "\n",
    "mask= df_clean['rating_denominator'] == 10\n",
    "df_clean[mask]\n",
    "df_clean=df_clean[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The only remaining rows with values for the rating_denominator column besides 10 are those for which no rating is available in the text of the original tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    2080\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #8:\n",
    "109 names of dogs in the name column are inaccurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replace the erroneous names in the name column with the values under the new_name column of the dataframe object df_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       592\n",
       "a           54\n",
       "Charlie     11\n",
       "Lucy        11\n",
       "Oliver      10\n",
       "Cooper      10\n",
       "Tucker       9\n",
       "Penny        9\n",
       "the          8\n",
       "Winston      8\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['name'].value_counts().head(10)\n",
    "# it shows that  it has lots of missing values in name and \"a\" is even not a name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the most frequent incorrect dog name and set them as None\n",
    "not_name_list = ['a','the','an']\n",
    "for i in not_name_list:\n",
    "        df_clean['name'].replace(i, 'None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['name'].str.islower().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['name'] = df_clean.name.str.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "Erroneous names in the name column were successfully replaced with the correct names of the dogs. If the corresponding tweet did not introduce the name of the dog, then NaN was assigned, thus still replacing any erroneous names which were ordinary words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       659\n",
       "Lucy        11\n",
       "Charlie     11\n",
       "Cooper      10\n",
       "Oliver      10\n",
       "Tucker       9\n",
       "Penny        9\n",
       "Lola         8\n",
       "Sadie        8\n",
       "Winston      8\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['name'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #9:\n",
    "The five columns, name, doggo, floofer, pupper, and puppo, use \"None\" instead of NaN for missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replace the all instances of \"None\" in the five columns with NaN."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 659\n",
      "doggo 1997\n",
      "floofer 2070\n",
      "pupper 1850\n",
      "puppo 2056\n"
     ]
    }
   ],
   "source": [
    "# columns to be corrected\n",
    "column_list = ['name', 'doggo', 'floofer', 'pupper','puppo']\n",
    "\n",
    "# number of instances of None in each column\n",
    "for column in column_list:\n",
    "    print(column, df_clean[df_clean[column] == \"None\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace None with NaN\n",
    "for column in column_list:\n",
    "    df_clean.loc[df_clean[df_clean[column] == \"None\"].index, column] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "No instance of \"None\" was found in any of the five columns after the replacement with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 0\n",
      "doggo 0\n",
      "floofer 0\n",
      "pupper 0\n",
      "puppo 0\n"
     ]
    }
   ],
   "source": [
    "# number of instances of None remaining in each column\n",
    "for column in column_list:\n",
    "    print(column, df_clean[df_clean[column] == \"None\"].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #10:\n",
    "Four possible dog \"stages\", doggo, floofer, pupper, and puppo, are shown as column headers although these \"stages\" are not variable names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Extract the stage from each row across the four columns doggo, floofer, pupper, and puppo.\n",
    "- Assign the stage to a new column labeled as stage.\n",
    "- Drop the four columns doggo, floofer, pupper, and puppo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doggo       83\n",
       "floofer     10\n",
       "pupper     230\n",
       "puppo       24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of instances of each stage\n",
    "df_clean.loc[:, ['doggo', 'floofer', 'pupper', 'puppo']].notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sub-dataframe of columns representing stages\n",
    "df_stages = df_clean.loc[:, ['doggo', 'floofer', 'pupper', 'puppo']]\n",
    "\n",
    "# extract stage from the four columns in each row\n",
    "df_clean['stage'] = df_stages.apply(lambda x: ','.join(x.dropna()), axis = 1)\n",
    "\n",
    "# replace empty values with NaN\n",
    "df_clean['stage'].replace(to_replace = '', value = np.nan, inplace = True)\n",
    "\n",
    "# drop the four columns\n",
    "df_clean.drop(columns = ['doggo', 'floofer', 'pupper', 'puppo'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "- The four columns doggo, floofer, pupper, and puppo were successfully removed from the dataframe.\n",
    "   - For 14 tweets, more than one stage were observed.\n",
    "   - Some instances of this case occurred because the image appears to include pictures of more than one dog.\n",
    "   - In other instances, the stages were not specifically referring to a dog in that the image was not a picture of a dog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'retweet', 'stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remaining columns\n",
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper           221\n",
       "doggo             72\n",
       "puppo             23\n",
       "floofer            9\n",
       "doggo,pupper       9\n",
       "doggo,puppo        1\n",
       "doggo,floofer      1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of instances of each stage\n",
    "df_clean['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel</td>\n",
       "      <td>doggo,puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk</td>\n",
       "      <td>doggo,floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Meet Maggie &amp;amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda</td>\n",
       "      <td>doggo,pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                      text  \\\n",
       "191         Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel   \n",
       "200   At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk   \n",
       "460   This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7   \n",
       "531                       Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho   \n",
       "575                       This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj   \n",
       "705   This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd   \n",
       "733                                                                                                             Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u   \n",
       "889                       Meet Maggie &amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll   \n",
       "956                               Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8   \n",
       "1063                                                                           This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC   \n",
       "1113                                                                                            Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda   \n",
       "\n",
       "              stage  \n",
       "191     doggo,puppo  \n",
       "200   doggo,floofer  \n",
       "460    doggo,pupper  \n",
       "531    doggo,pupper  \n",
       "575    doggo,pupper  \n",
       "705    doggo,pupper  \n",
       "733    doggo,pupper  \n",
       "889    doggo,pupper  \n",
       "956    doggo,pupper  \n",
       "1063   doggo,pupper  \n",
       "1113   doggo,pupper  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list-object containing unique stages which include multiple stages\n",
    "multiple_stages = [stage_ for stage_ in df_clean.stage.dropna().unique() if stage_.find(',') != -1]\n",
    "\n",
    "# compare stages mentioned in the tweet with the extracted stages\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "df_clean.loc[df_clean.query('stage in @multiple_stages').index, ['text', 'stage']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'doggo', 'puppo', 'pupper', 'floofer', 'doggo,puppo',\n",
       "       'doggo,floofer', 'doggo,pupper'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.stage.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Replace all instances of multiple stages with NaN due to the multiple variations of this case.\n",
    "- Convert the data type of the stage column from string to category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace multiple stages with NaN\n",
    "df_clean.loc[df_clean.query('stage in @multiple_stages').index, 'stage'] = np.nan\n",
    "\n",
    "# convert data type to category\n",
    "df_clean.stage = df_clean.stage.astype(dtype = 'category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The stage column was successfully converted to a categorical variable with four possible values: pupper, doggo, puppo, and floofer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     221\n",
       "doggo       72\n",
       "puppo       23\n",
       "floofer      9\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of instances of each stage\n",
    "df_clean.stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['doggo', 'floofer', 'pupper', 'puppo'], ordered=False)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data type of stage column\n",
    "df_clean.stage.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Image Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #1:\n",
    "Data type for the tweet_id column is integer, instead of string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Convert the data type of the tweet_id column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean.tweet_id = df1_clean.tweet_id.astype(dtype = 'str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The data type of the tweet_id column was successfully converted to string (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2:\n",
    "Three sets of three columns are shown as column headers although these are not variable names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Rename the columns p1, p2, and p3 as first_prediction, second_prediction and third_prediction\n",
    "- Rename the columns p1_conf, p2_conf and p3_conf as first_confidence, second_confidence and third_confidence\n",
    "- Rename the columns p1_dog, p2_dog and p3_dog as first_dog, second_dog and third_dog<BR>\n",
    "\n",
    "    Capitalize them afterwards\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the column names\n",
    "df1_clean.rename(columns={'p1':'first_prediction', 'p1_conf': 'first_confidence', 'p1_dog': 'first_dog',\n",
    "                                  'p2': 'second_prediction', 'p2_conf': 'second_confidence', 'p2_dog': 'second_dog',\n",
    "                                  'p3': 'third_prediction', 'p3_conf': 'third_confidence', 'p3_dog': 'third_dog'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['first_prediction'] = df1_clean.first_prediction.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['second_prediction'] = df1_clean.second_prediction.str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['third_prediction'] = df1_clean.third_prediction.str.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "Three sets of three columns were successfullyrenamed for easy readability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>first_prediction</th>\n",
       "      <th>first_confidence</th>\n",
       "      <th>first_dog</th>\n",
       "      <th>second_prediction</th>\n",
       "      <th>second_confidence</th>\n",
       "      <th>second_dog</th>\n",
       "      <th>third_prediction</th>\n",
       "      <th>third_confidence</th>\n",
       "      <th>third_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>Collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>Malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>Bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.651137</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.263788</td>\n",
       "      <td>True</td>\n",
       "      <td>Greater_swiss_mountain_dog</td>\n",
       "      <td>0.016199</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Box_turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>Mud_turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>Terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chow</td>\n",
       "      <td>0.692517</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.058279</td>\n",
       "      <td>True</td>\n",
       "      <td>Fur_coat</td>\n",
       "      <td>0.054449</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Shopping_cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "      <td>Shopping_basket</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>False</td>\n",
       "      <td>Golden_retriever</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Miniature_poodle</td>\n",
       "      <td>0.201493</td>\n",
       "      <td>True</td>\n",
       "      <td>Komondor</td>\n",
       "      <td>0.192305</td>\n",
       "      <td>True</td>\n",
       "      <td>Soft-coated_wheaten_terrier</td>\n",
       "      <td>0.082086</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Golden_retriever</td>\n",
       "      <td>0.775930</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.093718</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.072427</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Gordon_setter</td>\n",
       "      <td>0.503672</td>\n",
       "      <td>True</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.174201</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.109454</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Walker_hound</td>\n",
       "      <td>0.260857</td>\n",
       "      <td>True</td>\n",
       "      <td>English_foxhound</td>\n",
       "      <td>0.175382</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pug</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>True</td>\n",
       "      <td>Bull_mastiff</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Bloodhound</td>\n",
       "      <td>0.195217</td>\n",
       "      <td>True</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>True</td>\n",
       "      <td>Malinois</td>\n",
       "      <td>0.075628</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Lhasa</td>\n",
       "      <td>0.582330</td>\n",
       "      <td>True</td>\n",
       "      <td>Shih-tzu</td>\n",
       "      <td>0.166192</td>\n",
       "      <td>True</td>\n",
       "      <td>Dandie_dinmont</td>\n",
       "      <td>0.089688</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.298617</td>\n",
       "      <td>True</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.149842</td>\n",
       "      <td>True</td>\n",
       "      <td>Borzoi</td>\n",
       "      <td>0.133649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Hen</td>\n",
       "      <td>0.965932</td>\n",
       "      <td>False</td>\n",
       "      <td>Cock</td>\n",
       "      <td>0.033919</td>\n",
       "      <td>False</td>\n",
       "      <td>Partridge</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Desktop_computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>Desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>Bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.176053</td>\n",
       "      <td>True</td>\n",
       "      <td>Toy_terrier</td>\n",
       "      <td>0.111884</td>\n",
       "      <td>True</td>\n",
       "      <td>Basenji</td>\n",
       "      <td>0.111152</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3   666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4   666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "5   666050758794694657  https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "6   666051853826850816  https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "7   666055525042405380  https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg   \n",
       "8   666057090499244032  https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg   \n",
       "9   666058600524156928  https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg   \n",
       "10  666063827256086533  https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg   \n",
       "11  666071193221509120  https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg   \n",
       "12  666073100786774016  https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg   \n",
       "13  666082916733198337  https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg   \n",
       "14  666094000022159362  https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg   \n",
       "15  666099513787052032  https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg   \n",
       "16  666102155909144576  https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg   \n",
       "17  666104133288665088  https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18  666268910803644416  https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "19  666273097616637952  https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg   \n",
       "\n",
       "    img_num        first_prediction  first_confidence  first_dog  \\\n",
       "0         1  Welsh_springer_spaniel          0.465074       True   \n",
       "1         1                 Redbone          0.506826       True   \n",
       "2         1         German_shepherd          0.596461       True   \n",
       "3         1     Rhodesian_ridgeback          0.408143       True   \n",
       "4         1      Miniature_pinscher          0.560311       True   \n",
       "5         1    Bernese_mountain_dog          0.651137       True   \n",
       "6         1              Box_turtle          0.933012      False   \n",
       "7         1                    Chow          0.692517       True   \n",
       "8         1           Shopping_cart          0.962465      False   \n",
       "9         1        Miniature_poodle          0.201493       True   \n",
       "10        1        Golden_retriever          0.775930       True   \n",
       "11        1           Gordon_setter          0.503672       True   \n",
       "12        1            Walker_hound          0.260857       True   \n",
       "13        1                     Pug          0.489814       True   \n",
       "14        1              Bloodhound          0.195217       True   \n",
       "15        1                   Lhasa          0.582330       True   \n",
       "16        1          English_setter          0.298617       True   \n",
       "17        1                     Hen          0.965932      False   \n",
       "18        1        Desktop_computer          0.086502      False   \n",
       "19        1       Italian_greyhound          0.176053       True   \n",
       "\n",
       "     second_prediction  second_confidence  second_dog  \\\n",
       "0               Collie           0.156665        True   \n",
       "1   Miniature_pinscher           0.074192        True   \n",
       "2             Malinois           0.138584        True   \n",
       "3              Redbone           0.360687        True   \n",
       "4           Rottweiler           0.243682        True   \n",
       "5     English_springer           0.263788        True   \n",
       "6           Mud_turtle           0.045885       False   \n",
       "7      Tibetan_mastiff           0.058279        True   \n",
       "8      Shopping_basket           0.014594       False   \n",
       "9             Komondor           0.192305        True   \n",
       "10     Tibetan_mastiff           0.093718        True   \n",
       "11   Yorkshire_terrier           0.174201        True   \n",
       "12    English_foxhound           0.175382        True   \n",
       "13        Bull_mastiff           0.404722        True   \n",
       "14     German_shepherd           0.078260        True   \n",
       "15            Shih-tzu           0.166192        True   \n",
       "16        Newfoundland           0.149842        True   \n",
       "17                Cock           0.033919       False   \n",
       "18                Desk           0.085547       False   \n",
       "19         Toy_terrier           0.111884        True   \n",
       "\n",
       "               third_prediction  third_confidence  third_dog  \n",
       "0             Shetland_sheepdog          0.061428       True  \n",
       "1           Rhodesian_ridgeback          0.072010       True  \n",
       "2                    Bloodhound          0.116197       True  \n",
       "3            Miniature_pinscher          0.222752       True  \n",
       "4                      Doberman          0.154629       True  \n",
       "5    Greater_swiss_mountain_dog          0.016199       True  \n",
       "6                      Terrapin          0.017885      False  \n",
       "7                      Fur_coat          0.054449      False  \n",
       "8              Golden_retriever          0.007959       True  \n",
       "9   Soft-coated_wheaten_terrier          0.082086       True  \n",
       "10           Labrador_retriever          0.072427       True  \n",
       "11                     Pekinese          0.109454       True  \n",
       "12                 Ibizan_hound          0.097471       True  \n",
       "13               French_bulldog          0.048960       True  \n",
       "14                     Malinois          0.075628       True  \n",
       "15               Dandie_dinmont          0.089688       True  \n",
       "16                       Borzoi          0.133649       True  \n",
       "17                    Partridge          0.000052      False  \n",
       "18                     Bookcase          0.079480      False  \n",
       "19                      Basenji          0.111152       True  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Additional Tweet Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #1:\n",
    "Data type for the id column is integer, instead of string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Convert the data type of the id column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean.id = df2_clean.id.astype(dtype = 'str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The data type of the id column was successfully converted to string (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2:\n",
    "Unlike the two previous datasets, the name of the column for tweet IDs in this dataset is id instead of tweet_id."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Change the name of the id column to tweet_id."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean.rename(columns = {'id':'tweet_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n",
    "The name of the id column was successfully changed to tweet_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id          object\n",
       "retweet_count      int64\n",
       "favorite_count     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine two datasets\n",
    "df_archive_master1 = df_clean.merge(right = df2_clean\n",
    "                                           , on = 'tweet_id'\n",
    "                                           , how = 'inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the combined  df_clean and df2_clean datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet</th>\n",
       "      <th>stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7009</td>\n",
       "      <td>33810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5301</td>\n",
       "      <td>29327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3481</td>\n",
       "      <td>22049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7225</td>\n",
       "      <td>36935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7760</td>\n",
       "      <td>35308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp              source  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1  892177421306343426 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "2  891815181378084864 2017-07-31 00:18:03+00:00  Twitter for iPhone   \n",
       "3  891689557279858688 2017-07-30 15:58:51+00:00  Twitter for iPhone   \n",
       "4  891327558926688256 2017-07-29 16:00:24+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "   rating_numerator  rating_denominator      name  retweet stage  \\\n",
       "0                13                  10   Phineas    False   NaN   \n",
       "1                13                  10     Tilly    False   NaN   \n",
       "2                12                  10    Archie    False   NaN   \n",
       "3                13                  10     Darla    False   NaN   \n",
       "4                12                  10  Franklin    False   NaN   \n",
       "\n",
       "   retweet_count  favorite_count  \n",
       "0           7009           33810  \n",
       "1           5301           29327  \n",
       "2           3481           22049  \n",
       "3           7225           36935  \n",
       "4           7760           35308  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_master1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2072, 11)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_master1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2072 entries, 0 to 2071\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2072 non-null   object             \n",
      " 1   timestamp           2072 non-null   datetime64[ns, UTC]\n",
      " 2   source              2072 non-null   category           \n",
      " 3   text                2072 non-null   object             \n",
      " 4   rating_numerator    2072 non-null   int64              \n",
      " 5   rating_denominator  2072 non-null   int64              \n",
      " 6   name                1414 non-null   object             \n",
      " 7   retweet             2072 non-null   bool               \n",
      " 8   stage               324 non-null    category           \n",
      " 9   retweet_count       2072 non-null   int64              \n",
      " 10  favorite_count      2072 non-null   int64              \n",
      "dtypes: bool(1), category(2), datetime64[ns, UTC](1), int64(4), object(3)\n",
      "memory usage: 152.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_master1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the df1_clean dataset before final join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>first_prediction</th>\n",
       "      <th>first_confidence</th>\n",
       "      <th>first_dog</th>\n",
       "      <th>second_prediction</th>\n",
       "      <th>second_confidence</th>\n",
       "      <th>second_dog</th>\n",
       "      <th>third_prediction</th>\n",
       "      <th>third_confidence</th>\n",
       "      <th>third_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>Collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>Malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>Bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num        first_prediction  first_confidence  first_dog  \\\n",
       "0        1  Welsh_springer_spaniel          0.465074       True   \n",
       "1        1                 Redbone          0.506826       True   \n",
       "2        1         German_shepherd          0.596461       True   \n",
       "3        1     Rhodesian_ridgeback          0.408143       True   \n",
       "4        1      Miniature_pinscher          0.560311       True   \n",
       "\n",
       "    second_prediction  second_confidence  second_dog     third_prediction  \\\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",
       "\n",
       "   third_confidence  third_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  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2075, 12)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   tweet_id           2075 non-null   object \n",
      " 1   jpg_url            2075 non-null   object \n",
      " 2   img_num            2075 non-null   int64  \n",
      " 3   first_prediction   2075 non-null   object \n",
      " 4   first_confidence   2075 non-null   float64\n",
      " 5   first_dog          2075 non-null   bool   \n",
      " 6   second_prediction  2075 non-null   object \n",
      " 7   second_confidence  2075 non-null   float64\n",
      " 8   second_dog         2075 non-null   bool   \n",
      " 9   third_prediction   2075 non-null   object \n",
      " 10  third_confidence   2075 non-null   float64\n",
      " 11  third_dog          2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitterArchive_df_clean = pd.merge(df_archive_master1,df1_clean[['tweet_id']],on=\"tweet_id\", how= 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # clean twitter archive data w/ retweet and favorite counts\n",
    "twitterArchive_df_clean.to_csv('data/twitter_archive_master.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read .csv files containing cleaned data\n",
    "df_master = pd.read_csv('data/twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Dataframe: twitter_archive_master.csv\n",
      "  - tweet_id: from \"int64\" to \"object\"\n",
      "  - timestamp: from \"object\" to \"datetime64[ns, UTC]\"\n"
     ]
    }
   ],
   "source": [
    "# correct data types of selected columns\n",
    "df_list = [df_master]\n",
    "df_name = ['twitter_archive_master.csv']\n",
    "\n",
    "for i in range(len(df_list)):\n",
    "    # columns with data types reverted back to those from uncleaned version\n",
    "    col1 = df_list[i].columns[0] # tweet_id\n",
    "    col2 = df_list[i].columns[1] # timestamp\n",
    "    \n",
    "    # data type of the column from uncleaned version\n",
    "    col1_type = df_list[i][col1].dtype\n",
    "    col2_type = df_list[i][col2].dtype\n",
    "    \n",
    "    # .csv file name\n",
    "    print('{}. Dataframe: {}'.format(i+1, df_name[i]))\n",
    "    \n",
    "    # change data type (of tweet_id) to string\n",
    "    df_list[i][col1] = df_list[i][col1].astype(dtype = 'str')\n",
    "    \n",
    "    # column name: data type before/after\n",
    "    print('  - {}: from \"{}\" to \"{}\"'.format(col1, col1_type, df_list[i][col1].dtype))\n",
    "\n",
    "    if i != 2: # df_image does not have timestamp column\n",
    "        # change data type (of timestamp) to string\n",
    "        df_list[i][col2] = pd.to_datetime(arg = df_list[i][col2])\n",
    "\n",
    "        # column name: data type before/after\n",
    "        print('  - {}: from \"{}\" to \"{}\"'.format(col2, col2_type, df_list[i][col2].dtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id              2072\n",
       "timestamp             2072\n",
       "source                   4\n",
       "text                  2072\n",
       "rating_numerator        20\n",
       "rating_denominator       1\n",
       "name                   946\n",
       "retweet                  1\n",
       "stage                    4\n",
       "retweet_count         1599\n",
       "favorite_count        1903\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assessing the master dataframe\n",
    "df_master.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2072, 11)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assessing the master dataframe\n",
    "df_master.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2072 entries, 0 to 2071\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2072 non-null   object             \n",
      " 1   timestamp           2072 non-null   datetime64[ns, UTC]\n",
      " 2   source              2072 non-null   object             \n",
      " 3   text                2072 non-null   object             \n",
      " 4   rating_numerator    2072 non-null   int64              \n",
      " 5   rating_denominator  2072 non-null   int64              \n",
      " 6   name                1414 non-null   object             \n",
      " 7   retweet             2072 non-null   bool               \n",
      " 8   stage               324 non-null    object             \n",
      " 9   retweet_count       2072 non-null   int64              \n",
      " 10  favorite_count      2072 non-null   int64              \n",
      "dtypes: bool(1), datetime64[ns, UTC](1), int64(4), object(5)\n",
      "memory usage: 164.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#assessing the master dataframe\n",
    "df_master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2072.000000</td>\n",
       "      <td>2072.0</td>\n",
       "      <td>2072.000000</td>\n",
       "      <td>2072.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.696911</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2302.565637</td>\n",
       "      <td>7785.951255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.893628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4038.882960</td>\n",
       "      <td>11341.336936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>1710.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1112.000000</td>\n",
       "      <td>3543.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2628.250000</td>\n",
       "      <td>9716.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1776.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70732.000000</td>\n",
       "      <td>144872.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator  retweet_count  favorite_count\n",
       "count       2072.000000              2072.0    2072.000000     2072.000000\n",
       "mean          11.696911                10.0    2302.565637     7785.951255\n",
       "std           39.893628                 0.0    4038.882960    11341.336936\n",
       "min            0.000000                10.0      11.000000       66.000000\n",
       "25%           10.000000                10.0     508.000000     1710.250000\n",
       "50%           11.000000                10.0    1112.000000     3543.000000\n",
       "75%           12.000000                10.0    2628.250000     9716.000000\n",
       "max         1776.000000                10.0   70732.000000   144872.000000"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_6784\\3151183234.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_master.groupby(['stage'])['rating_numerator','retweet_count','favorite_count'].min()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>doggo</th>\n",
       "      <td>8</td>\n",
       "      <td>568</td>\n",
       "      <td>2162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floofer</th>\n",
       "      <td>10</td>\n",
       "      <td>397</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pupper</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puppo</th>\n",
       "      <td>9</td>\n",
       "      <td>569</td>\n",
       "      <td>2750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating_numerator  retweet_count  favorite_count\n",
       "stage                                                   \n",
       "doggo                   8            568            2162\n",
       "floofer                10            397            1349\n",
       "pupper                  3             24             222\n",
       "puppo                   9            569            2750"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.groupby(['stage'])['rating_numerator','retweet_count','favorite_count'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Windows\\Temp\\ipykernel_6784\\2123420858.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_master.groupby([\"stage\"])['rating_numerator','retweet_count','favorite_count'].max()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>doggo</th>\n",
       "      <td>14</td>\n",
       "      <td>70732</td>\n",
       "      <td>144872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floofer</th>\n",
       "      <td>13</td>\n",
       "      <td>14739</td>\n",
       "      <td>28111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pupper</th>\n",
       "      <td>27</td>\n",
       "      <td>30210</td>\n",
       "      <td>108903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puppo</th>\n",
       "      <td>14</td>\n",
       "      <td>39921</td>\n",
       "      <td>124110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating_numerator  retweet_count  favorite_count\n",
       "stage                                                   \n",
       "doggo                  14          70732          144872\n",
       "floofer                13          14739           28111\n",
       "pupper                 27          30210          108903\n",
       "puppo                  14          39921          124110"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.groupby([\"stage\"])['rating_numerator','retweet_count','favorite_count'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cooper        10\n",
       "Charlie       10\n",
       "Lucy          10\n",
       "Oliver        10\n",
       "Tucker         9\n",
       "              ..\n",
       "Wishes         1\n",
       "Rose           1\n",
       "Theo           1\n",
       "Fido           1\n",
       "Christoper     1\n",
       "Name: name, Length: 946, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>retweet</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rating_numerator</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.023013</td>\n",
       "      <td>0.022026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating_denominator</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>0.023013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>0.022026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.925636</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    rating_numerator  rating_denominator  retweet  \\\n",
       "rating_numerator            1.000000                 NaN      NaN   \n",
       "rating_denominator               NaN                 NaN      NaN   \n",
       "retweet                          NaN                 NaN      NaN   \n",
       "retweet_count               0.023013                 NaN      NaN   \n",
       "favorite_count              0.022026                 NaN      NaN   \n",
       "\n",
       "                    retweet_count  favorite_count  \n",
       "rating_numerator         0.023013        0.022026  \n",
       "rating_denominator            NaN             NaN  \n",
       "retweet                       NaN             NaN  \n",
       "retweet_count            1.000000        0.925636  \n",
       "favorite_count           0.925636        1.000000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.corr(method= 'pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_numerator      1.000000\n",
       "rating_denominator         NaN\n",
       "retweet                    NaN\n",
       "retweet_count         0.023013\n",
       "favorite_count        0.022026\n",
       "Name: rating_numerator, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations= df_master.corr(method= 'pearson')\n",
    "(correlations['rating_numerator'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1.The dog stages: \"pupper\" and \"doggo\" have the lowest \"rating numerator\" of 3 and 8, according to the last two tables.<br>\n",
    "2.The highest \"rating numerator\" for all dog stages, is (27), and that represents \"pupper\" stages.<br>\n",
    "3.We can infer from the pivot table above that @dogrates primarily used an iPhone to make most if its tweet.<br>\n",
    "4.From the above correlation table, we can see that the strongest and most valuable correlation exists between 'favorite_count' and \"retweet_count\". Their correlation value is 0.925636, which is classified as a very strong correlation.<br>\n",
    "5.Cooper, Charlie, Lucy, and Oliver are the most common dog names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Relationship between Retweet Count and Fovourite Count')]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NVnZMTzGYAzIMw5iiRBNOrSeZrvyYnmIwB2QYhjHFSGeUzoE44Vh1qtsKxRyQYRjGFCIcT9EVTlRFJIOJYg7IMAxjCpBKZ+gcSDAQn9q1Hi/mgAzDMKqcvliSrnCiqsf0FIPNB2QYhlGlJNMZ9vVG6egv74DSVCbD3p5o2cofCasBGYZhVCG9kSRdkUTZB5Q+8Wo319+3jdqgn7s/dxIB/+TVS8wBGYZhVBHxVJqOcIJ4Mj32xhNgf2+MG+/fzgOvdAAgAo/t6ubEFXPKul8v5oAMwzCqAFWlO5Kkt8xhdKLJNLc/+hp3PtZGMu3s5w2LmrnmA2/kDYtmlW2/+TAHZBiGUWFiyTQH+ss7oFRVue+lA9x0/w4OhOMAzG0McdG6FZx69HwOn9dYtn2PhDkgwzCMCpHJKF2RBH1lDqOzrT3Mtzdu49k9vQAE/cJfrlnCX71lKXVBP84kA5OPOSDDMIwKEEmk6Ogv74DS3kiSWx7cyf89s4/sXHQnrZzLp9+xnENb6sq230IxB2QYhjGJpDNKZzhOuIwDStMZ5VdP7eXWB3cN7uewOfVcsn4Fa5bNLtt+x4s5IMMwjEmiP5akayBR1qmxn3itmxvu287OjgEAGmr8nP/WZZz554dOqsS6EMwBGYZhlJlUOkNHOFHWqbH398b47v3b2ZyVVQN/8cZD+MTbltFSHyrbfieCOSDDMIwy0htN0j1QvjA6sWSaOx5t444tbYOT0b3h0GY+e8pKjlzQVJZ9lgpzQIZhGGUgkcrQEY4TK9OAUlVl00sHuGnzDtr7h8uqT1k1r2LKtvFgDsgwDKOEqKpT64mUb0Dp9vYw375vG8/szi+rniqYAzIMwygRsaQzQ2m2KazU9EaTfP9Pu7j7mb2Dsuq3rZzDZ96xoipk1ePFHJBhGMYEUVW6BhL0lmlAaTqjbHjakVX3u7OgHja7notPXsGbq0hWPV7MARmGYUyAaMKp9ZQrjE4+WfV5Jy7jrGNLI6sO+n3MbqiMSs4ckGEYRhFkMkrnQIL+WHlqPflk1e/5s4VccNLhtJZAVh3w+WhpCNJcG5xwWUXbULE9G4ZhTFEG4ik6w+UJo5NPVr360GY+VyJZtU+Elvogs+qCFVfKmQMyDMMokFQ6Q+dAgoEyhNFRVe5/+QDfvX9IVj2nMcSn1y3nlFXzJ+wsRITm2gAt9SH8vuqQaJsDMgzDKIC+WJKucHkGlG5vD3P9fdt4OldWfcJS6kITl1U31gZorQ8RtFA8hmEYU4dk2hlQGk2UfkBpuWXV9aEArQ1BagLVOTbIHJBhGMYI9EaSdEUSJR9QWm5ZdU3Qz+z6UElqT+XEHJBhGEYO8VSajnCCeBnC6Dz5WjfXe2XVIT/nvbU0suqg30drQ4jGmqnxaJ8aVhqGYUwCqkpPJElPtPRhdMopq/b7hJb6EM21gYor28aDOSDDMAwc+fOB/tIPKC2nrNonwqw6R1LtqxJl23gwB2QYxowmk1G6Ign6ShxGZyRZ9UXrlvPOCcqqRYQmV9lWLZLqYjAHZBjGjCWSSNHRX/oBpflk1R86fjEfecthExYGNNYEaG2oPkl1MZgDMgxjxpHOKJ0DccKx0g4ozSurXjGHT69fwaIJyqrrQn5a60PUTqHpFsbCHJBhGDOKcDxFZzhOOlM6kUE5ZdWhgI85DTVVL6kuBnNAhmHMCFLpDB3hBJFEaWs9B8mqSxSteqpJqoth+h6ZYRiGS280SfdAacPo7O9zZdUvl1ZW7fcJLXUhmuumlqS6GMwBGYYxbUmknDA6sRIOKI0l09zxWBt3PDYkqz7mkGYufefEZNU+EZrrgrRMUUl1MZgDMgxj2qGqTq0nUroBpY6suoPv3r+9pLJqEXGUbfXBkkwwN5Uo69GKyC0i0i4iz3nSrhKRPSLylPt5r2fdFSKyTUReEpHTPOnHi8iz7rrrxP2nRaRGRO500x8RkWWePOeJyCvu57xyHqdhGNVDLJlmT0+UroHSxXDbfiDMF376NFff/QLt/XGCfuHcE5Zw21+fwKlHLyja+TTUBFjUUse8ppoZ53yg/DWgW4Hrgdty0v9TVf/dmyAixwDnAKuBQ4Hfi8iRqpoGbgQuBB4Gfg2cDtwDXAB0q+pKETkHuBb4sIjMBr4CrAEUeFxENqhqd3kO0zCMSqOqdA0k6C3hgNLeaJJb/7SL//XIqt+6Yg6fmaCsujboZ3bD9JJUF0NZHZCqbvbWSsbgTOAOVY0DO0VkG3CCiOwCmlX1IQARuQ04C8cBnQlc5eb/GXC9Wzs6DbhXVbvcPPfiOK3bS3BYhmFUGdFEmo5w6cLopDPK3c/s5ft/2kWfK6teOrueSyYoqw4FfMxuCFEfst4PqFwf0GdF5OPAFuALbs1kEU4NJ8tuNy3pLuem4363AahqSkR6gTne9Dx5DMOYJmQySudAgv5Y6Wo9T7X1cP3GbezwRKv++FuX8f4JyKoDPh+tDUGaaoMls3M6UAkHdCPwVZymsa8C/wF8AkfFmIuOkk6ReYYhIhfiNO+xdOnS0ew2DKOKGIin6AyXLozO/r4YN92/g/tfPgC4suo3LOSCtxcvq55JkupimHQHpKqvZ5dF5L+Au92fu4Elnk0XA3vd9MV50r15dotIAJgFdLnp63PybBrBnpuBmwHWrFlT+rl2DcMoKemM0hmOE46XZkBp3JVV354jq/7cKSs5amFxsmpxo1TPJEl1MUy6AxKRQ1R1n/vz/UBWIbcB+ImIfANHhHAE8KiqpkWkX0TWAo8AHwe+7clzHvAQcDawUVVVRH4L/IuItLrbvRu4otzHZhhGeemPJekaSJQkjI6qsvmVDm7c5JFVN4S4cN1yTj26eFl1U21wSkmqN21t56bNO2jrjrCktZ6L1i1n/ar5k7LvsjogEbkdpyYyV0R24yjT1ovIsThNYruAiwBU9XkRuQt4AUgBl7gKOIDP4Cjq6nDEB/e46d8DfugKFrpwVHSoapeIfBV4zN3u6qwgwTCMqUcy7QwojSZKM6B0xwEnWvVTbUPRqs8+fjEfecvSogUCDTXO9AihwNRwPOA4nys3PE/QL7TUBWnvj3Hlhue5GibFCUmpZ/2byqxZs0a3bNlSaTMMw/DQG0nSHSlNGJ2+aJLvP7iL/326dLLqqSypPvfmh2nvjw1zupFEivlNtdx+4dqCyxGRx1V1zXj3b1pAwzCqkngqTUc4QbwEYXTyyaqXtNbx2VNWFi2rDvodSXXDFA4W2tYdoaVuuDKvLuhnd3dkUvY/dc+cYRjTElWlJ5KkJ1qaMDpPt/Xw7fu2seOAR1Z94mGc9aZFRU3qFvD5aGkI0jwNJNVLWusPqgFFk2kWt9ZPyv7NARmGUTXEkmkO9JdmQOnrfTG+m0dW/YmTDmd2w/hl1T4RWuqDzKoLThtJ9UXrlnPlhueJJFLUBf1Ek2mSaeWidcsnZf/mgAzDqDiZjNIVSdBXgjA6cU+06rhHVv3ZU1awamHzuMsTEZprA7TUh/BPM0n1+lXzuRq4afMOdndHWDydVHCGYRhjEUk4A0onWuvJyqq/e/92Xu8bklV/ypVV+4qotTTWBphdH5oykupiWL9q/qQ5nFzMARmGURHSGaVzIE44NvEBpflk1R88bjEfXVucrLo+FKC1IUhNYOop26YS5oAMw5h0wvEUneH4hAeU5pNVn7h8DhevX8Gi1vHLqmuCfuZMUUn1VKQgByQin1fVb42VZhiGMRqpdIbOgQQDEwyjM5Ks+pKTV3LC4eOXVU8HSfVUpNCzfR6Q62zOz5NmGIaRl75Ykq7wxAeU5sqq60N+zitSVp2VVDfVWLDQSjCqAxKRc4G/Ag4XkQ2eVU1AZzkNMwxjepBIOWF0YhMcUPq6G616kyurBjdadRGy6ukoqZ6KjFUDehDYB8zFmTYhSz/wTLmMMgxj6qOq9EaTdEcmNqA0nkxz55Y2bn/UK6tu4rOnrBy3rHo6S6qnIqM6IFV9FXgVOHFyzDEMYzoQTzkDSrPTGxRDqWXVjbVOsNBioh8Y5aFQEcIHgGuB+TgDigVQVR3/qC7DMKYtqkp3JEnvBMPoOLLq7TzV1gNMTFZtkurqpdB/8l+B96nqi+U0xjCMqUspwuj0RZPc+uAuNpRAVl0T9DO7PkRdyBxPtVKoA3rdnI9hGPnIZJTOgQT9seLD6Diy6n18/087JyyrDvp9tDaEaDRJddVT6D+0RUTuBH4JxLOJqvo/5TDKMIypQSSRoqM/QSpTfK3n6d09XL9xG9snKKv2+4SW+hDNtSapnioU6oCagQjO1NZZFDAHZBgzkHRG6QzHCU9gQGk+WfXpqxfyybePT1btE2FWnSOp9pmybUpRkANS1b8utyGGYZSfTVvbuWnzDtq6IywpMvJxfyxJ10Ci6DA6pZJViwhNrrLNJNVTk0JVcN/HqfEMQ1U/UXKLDMMoC5u2tnPlhucJ+oWWuiDt/TGu3PA8V0NBTiiZztAZThBJFFfrUVUeeKWDGz2y6tkNIS58++GcesyCccmqG2sCtDaYpHqqU2gT3N2e5Vrg/cDe0ptjGEa5uGnzDoJ+GZQx14cCRBIpbtq8Y0wH1BtN0j1QfBidnR0DXH/fNp58rQeAgE84+/jxy6rrQn5a6y1Y6HSh0Ca4n3t/i8jtwO/LYpFhGGWhrTtCS93waaTrgn52d0dGzJNIZTgQjhMvMoxOPln12uWzuXj9inFN+xwKOMFCi5lawaheiv03jwCWltIQwzDKy5LWetr7Y8Me4tFkOq8jUFV6Ikl6ihxQms4o//fsPm7545CsenFrHZecvIK3HD6n4HKCfh8t9UGaaoNjb2xMOQrtA+rH6QMS93s/8MUy2mUYRom5aN1yrtzwPJFEirqgn2gyTTKtXLRu+bDtJjqgNJ+s+mNrD+MDxxUuq/b7hJa6EM11JqmezhTaBNdUbkMMwygv61fN52qcvqDd3REW56jgMhmlO5KgN1rcgNL2vhg3bd7BfS8NyapPW72AT719ecGyanEl1S0mqZ4RFNwEJyJnAOvcn5tU9e7RtjcMo/pYv2p+XsFBNJGmI1xcrSefrProQ5r47MkrOfqQwmTVIuIo2+qDBEzZNmMotAnuGuDNwI/dpM+LyNtU9YqyWWYYRtlJZ5TOgTjh2Pil1arKA9s6uHHTxGTVDTXOWJ5QwBzPTKPQGtB7gWNVNQMgIj8AngTMARnGFCUcT9EZjhc1oLQUsuraoJ/ZDSapnsmMRwXXAnS5y7NKb4phGJNBKp2hcyDBQBFhdEohqzZJtZGl0Cvg68CTInIfjhJuHVb7MYwpR18sSVd4/ANKSyGrDvh8tDaYpNoYolAV3O0isgmnH0iAL6rq/nIaZhhG6UimM3SE40QT4x9QOlFZdbkk1aWIa2dUlkJFCO8HNqrqBvd3i4icpaq/LKdxhmFMnJ5Igu7I+AeUTlRWXU5J9UTj2hnVQaFNcF9R1V9kf6hqj4h8BWd+IMMwqL438ngqTUc4Me4wOvFkmru27OYnj742KKtetbCJz51SuKy6qTZYVkn1ROLaGdVDoQ4o31VkPYiG4VJNb+SqSnckSe84w+hkZdXf3bSD/X0xwJFVf+rth/OuAmXVkyWpLiaunVF9jGdG1G8AN+CE4vkc8HjZrDKMKUa1vJEXG0ZnZ8cAN9y3jSdyZNUfectSGgqY2nqyJdUjxbVrCPk59+aHq6YWaoxOoQ7oc8CXgTvd378DvlQWiwxjClLpN/JMRumKJOgbZxid/liSWx98lV89tWeYrPoz71jBktljy6qDfkdSXYiTKiX54tr1RpMIkMxoxWuhRmEUqoIbAC4fab2IfFtVP1cyqwxjijGeSNOlJpJI0dGfIJUpvNaTzii/fnYf38uRVV+8fgVrl48tqw74fLQ0BGmukKQ6X1y7kN9HIp2peC3UKJxSvba8rUTlGMaUpNBI06UknVE6w3HC4xxQ+szuHq7fuJ1tB8LA+GTVPhFa6oPMqgtWPEp1bly7k67daP1CUwwTEhhGCRgr0nSp6Y8l6RpIjCuMzkRk1SJCc22AlvoQ/iqNUl3JWqhRHOaADKNEjBRpupSk0hk6wgkiicJrPVlZ9e2PvkasCFl1Y22A2fWhqo9SXYlaqDExSnVF5X0lEpFbRKRdRJ7zpM0WkXtF5BX3u9Wz7goR2SYiL4nIaZ7040XkWXfddeLW/UWkRkTudNMfEZFlnjznuft4RUTOK9FxGkbF6I0m2d0dLdj5qCqbXznAX9+6he8/uItYKsPshhBfPP0orv+rN43pfOpDARa11jG/qbbqnQ+4tdAzVjO/qZbeaJL5TbVcfcZq6/+pYsZVAxKRBleQkMu3RshyK3A9cJsn7XLgD6p6jYhc7v7+oogcA5wDrAYOBX4vIkeqahq4EbgQeBj4NXA6cA9wAdCtqitF5BzgWuDDIjIb+AqwBkc2/riIbFDV7vEcr2FUA4mUE0YnNo4BpfmiVX/wuEV8dO1hYyrWaoJ+5kzRKNWTUQs1SkehoXjeCvw30AgsFZE/By5S1YsBVPXWfPlUdbO3VuJyJrDeXf4BsAlneu8zgTtUNQ7sFJFtwAkisgtoVtWHXFtuA87CcUBnAle5Zf0MuN6tHZ0G3KuqXW6ee3Gc1u2FHK9hVAOqSm80Oa4wOv2xJD948FV+6ZFVv+VwJ1r1WLLqSkmqjZlLoVfaf+I81DcAqOrTIrJu9CwjskBV97nl7BOR7OvKIpwaTpbdblrSXc5Nz+Zpc8tKiUgvMMebniePYVQ9saQzQ2kiVZi0eiKy6qykuqmmtMFCDWMsCn7VUdW2nItz/GF1Ryffla+jpBebZ/hORS7Ead5j6dKlY1tpGGVEVekaSNA7jgGl+WTVH117GB8cQ1ZdTZJqY2ZSqANqc5vhVERCwKXAi0Xu83UROcSt/RwCtLvpu4Elnu0WA3vd9MV50r15dotIAGeivC43fX1Onk35jFHVm4GbAdasWTP+qSENo0REE06tp9AwOsXKqqeCpNqYGRTqgD6NIzRYhPNw/x1wcZH73ACcB1zjfv/Kk/4TN+bcocARwKOqmhaRfhFZCzwCfBz4dk5ZDwFn40wZoSLyW+BfPAq7d2MT6BlVSiajdA4k6I8VVuuJJ9Pc9fhubn9k/LLqxpoArQ2hgubxMYxyU6gDOkpVP+JNEJG3AX8aLZOI3I5TE5krIrtxlGnXAHeJyAXAa8CHAFT1eRG5C3gBSAGXuAo4gM/gKOrqcMQH97jp3wN+6AoWunBUdKhql4h8FXjM3e7qrCDBMKqJgXiKznBhYXRUlT9u6+TGTdsHo1W31ge5cN3yMaNV14WcYKE1gamnbDOmL1KIukZEnlDV48ZKm+qsWbNGt2zZUmkzjBlAKp2hcyDBQIFhdPJFq/7AcYv4mCurfnRHF3c81sa+viiHNNdxzpuXcMLy2dQE/cyuD1EXMsdjlA8ReVxV14w336g1IBE5EXgrME9E/tazqhmwK9owiqAvlqQrnCBTwMtfIbLqR3d08a2NrxDwOX07nQNxrtv4Cl+uP4b3/Nkh5TwUw5gQYzXBhXDG/gSAJk96H06fi2EYBZJMOwNKo4mxBaTpjHLPc/v43h93DSriRpJV3/FYGwGfUBf0gzizkcaSKW576FVzQEZVM6oDUtX7gftF5FZVfXWSbDKMaUdvJElXJFHQgNJnd/fy7fu2sa3dkVXXBf187MSRZdX7+qI01wbx+wS/TxBxJsazKNBGtTNWE9w3VfUynAgDB905qnpGuQwzjOlAPJWmI5wgXkAYnQP9cW7avIONW9sH005bvYBPnnQ4cxpr8uYREZa01tM1EKc2ODQVgUWBNqYCYzXB/dD9/vdyG2IY0wlVpTuSpDc6dhidRCrDXVva+Mk4ZdVZSfVnT15pUaCNKclYTXCPi4gf+JSqfnSSbDKMKU0smeZA/9gDSrOy6u/ev519vUOy6k+9fTnvXj2yrLou5Ke1fihY6GTPRWQYpWLMcUDuQNB5IhJS1cRkGGUYU5FMRumKJOgrIIzOrs4Bbti4jcddWbXfJ3zgTYv4+IkjR6sOBZxgod4J17JYFGhjKlLoQNRdwJ9EZAMwOB2Dqn6jHEYZxlQjkkjR0T/2gNJwLMWtD+3il08OyapPcGXVS0eIVh30+2ipD9JUG8y73jCmKoU6oL3ux8dwObZhzGjSGaUzHCc8xoBSR1a9n+/9ceeYsuosfp/QUheiuc6iVBvTk4IckKr+M4CINDk/NVxWqwxjChCOp+gMx0lnRhcZPLenl+s25siq1y7lA8ctJhQ4WFb96M4ufvb4bvb1Rlk6u8H6c4xpS6ET0r0BRxE32/3dAXxcVZ8vo22GUZWk0hk6wokxp8Y+0B/n5s07+EOBsmoR4Zm2Hm64bxuhgI/W+hDt/TGu3PA8V4M5IWPaUWgT3M3A36rqfQAish74L5wwPYYx5dm0tZ2bNu+grTvCklFUZL3RJN0Do4fRySerPmphE5eOIqtuqAnQWh/iH//nWUIB36DQoD4UIJJIcdPmHeaAjGlHoQ6oIet8AFR1k4g0lMkmw5hUNm1t58oNzxP0Cy11wby1jkTKCaMTG2VAqaryp22d3DgOWXVt0IlSnZVUt3VHaKkbLjaoC/otqoExLSnUAe0QkS8zNDD1o8DO8phkGJPLTZt3EPRL3lrHO46a59R6IqMPKN3VOcAN923n8Ve7gSFZ9cdOPIzGPLLqkSTVS1rrae+PDUu3qAbGdKVQB/QJ4J+B/8GZ7vp+4K/LZZRhTCYj1TraugbY0xMlkRpZWp1XVr2slYtPXplXVh3w+WhtGFlSfdG65RbVwJgxFOqAlqnqpWW1xDAqRG6tQ1UJx1PMbawd0flkZdW3/HEnPa6selFLVlY9+yDZdKGSaotqYMwkCnVA3xCRQ4CfAneY+s2YTnhrHTV+H+FEimRaOefNS/Ju/9yeXr69cRuvFCCrFhFm1QVpqQvi8xU2lseiGhgzhULHAZ0sIguBvwRuFpFm4E5V/VpZrTOMAilUxZaP9avmc5UqN9y3nT09ERZ6ZhT1kk9W/e5jFvCpt+eXVTfVBmmtDxLIM4WCYRgFTsk9LIPInwH/AHxYVUNlsapC2JTcUxOvis3bb3L1GasLckID8RSd4ZHD6CRSGX76eBs/fuQ1Yklnm4BPOHxOAxecdPhBjiorqc43yNQwpiNlmZLbU/jRwIdxZkHtBO4AvjDenRlGORhNxTaaA0pnlI5wnIERwujkk1X7BGbVBZnTECSWSvOtja/weY7ghOWzD5JUG4YxOoX2AX0fuB14t6ruLaM9hjFuihk70x9L0jWQGDGMTm60anBqPc21fua6zW11QUcifeeWNt537KEjRrH2MpGmQsOYbhTaB7S23IYYRrGMZ+xMMu0MKI0m8g8ozSerrg34WNBcQ1t3lL5YitpgisYaR83WWBOgIxwv2PmMNeDVMGYShTbBHQF8HTgGqM2mq6oNTjAOYrLf8gsdO9MbSdIVSeQdUJovWnXI76O5NkBrfRARoSbgI5nO0B1JMKs+iF9kXINEi20qNIzpSqG9pN8HbgRSwMnAbQxFRTCMQbJv+e39sWFv+Zs8yrFSs37VfK4+YzXzm2rpjSaZ31Q7TIAQT6XZ0xOlcyCe1/k8t6eXi3/8BN+492V6o0nqgn4+9fbDaakP0NoQHBy3M7shBOqIErLOZzyDRNu6I9Tl9A9ZmB1jJlNoH1Cdqv5BRERVXwWuEpEHgK+U0TZjiuCt8fRFkzTU+JlV51SUJ/KWP56aVL6xM6pKTyRJTzR/GJ18sup3ubLquY01PLazm86BOHVBPz6f0FofIqPKQDxNbzQ57kGiFmbHMIZTqAOKiYgPeEVEPgvsAazNwDioX2N/b4xoIk1NwD8YbqaYt/yJ9pfEkmkO9MdJpg+WVueTVR+1oInPnbKSYw4dilZ9zpuXcN19r5DKZKgPBIgm0wT9fq47541FNZlZmB3DGM6oDkhEfqiqHwN+BdQDlwJfBU4Bziu/eUa1k9uvURPwkUhnONAfH3RAxbzlF9tfkskoXZEEfW4/jpeRolV/8qTDOe0NC4dFq64J+jnzTYtYOKu2ZGFxLMyOYQxnrBrQ8SJyGPARnPl/Itj4H8NDrgR6bmMNe3ujxFJpVJVoMk1fNEnQJ5x07caCRQnFSKsjiRQd/fkHlBYarTro99HaEBpMK3VYHAuzYxhDjOWAvgv8BlgOPI4TCVs939Z2MMPx9mv0RZN0uFNUiwj7e6PMa6pFgWRGx9WUNp7+knRG6RyIE44dPKA0HEvxg4d28QuPrPrNy1q5ZP1Kls4ZKsvvE1rqQzTXjh4s1DCM0jGqCk5Vr1PVo4FbVHW5qh7u/Z4kG40q5qJ1y0mmlQP9Mfb2RkmkHYXY/KYQ9TVBVJVZdUHqQ86DvT4UIOgXbtq8o6ByI4kUqs53vv6ScDzF7u7IQc4nnVH+75l9fPyWR/n5E47zObSllq+dtZprPvBng87HJ464YElrPbPqguZ8DGMSKXQg6mfKbYgxNVm/aj5n7+7hWxu3kc4oPoF5jTXMbawlkkixszPCEfMbh+UpRJQwVn9JKp2hI5wgkji41pMbrbo26ONjaw/jg55o1SJCU60Ts81fYJRqwzBKS6EqOMPIy6at7fzsiT0IUBMQQOiOJKkL+Qf7UaLJdFHS45H6S3qjSboHEmRypNUH+uP81wM7+P2LQ7LqU4+ez4Xrlg+GzwForAnQ2hAiaFGqDaOimAMyJkRWrVYT8JFKKz6fkEE50B/H7xOWz21gIJEuifQ4kXLC6MSS6YPS88mqP3vKClYfOmtwu7qQEyy0JmDBQg2jGjAHZBzEeAaAZtVqWfUbGUCUWCpDMq18+S9WAROTHquqU+uJDB9Qqqo8uL2T72waLqu+4KTDOd0jqw4FfMxpqKEuZI7HMKoJc0DGMMY7ADSrVmt2JdOv98WIpRQRqA86TVwTkR7HU86A0typsV/tHOD6MWTVuZJqwzCqC7szjWGMdwCod3Q/KKmMEvAJi1pqSWa06GjPqkp3JElPJDEsPSur/uVTewenUsiVVZuk2jCmBuaAjGGMdwCoV632xGvdBPzCgqZamuuC9EWTtPfHuOhHj3Pc0taCm97yhdHJqPKb5/bz3w/spMeNcnBoSy0Xr1/BicvnICL4RGiuC9JSF8RnyjbDqHrMARnDKCZgZraJ7aRrN9LijqXpiybZ2xtFcJxHIQNQMxmlcyBBf2x4GJ3n9vRy/X3bePn1/LLq7Lw8rfVBAqZsM4wpgzkgYxgTCZjpdV4d4Tg+BARq/L4xm/IG4ik6w8PD6HSEnWjVo8mqG2qcsTzZ8T2GYUwdzAEZw5hIwEyv80qkMwiACvOaslNYH9yUl84oneE44fjQgNJEKsPPHt/Njx55dVBWfeSCRj53yspBWXVt0JFU1wZN2WYYU5WKOSAR2QX0A2kgpaprRGQ2cCewDNgF/KWqdrvbXwFc4G5/qar+1k0/HrgVqAN+DXxeVVVEanAmzjse6AQ+rKq7JunwpjTFqNay0u1IIuUo1hR8PmHhrNoRo2L3x5J0DSQGxQRZWfWN929nb09+WXXQ72NOY2hYE6FhGFOTSt/FJ6tqh+f35cAfVPUaEbnc/f1FETkGOAdYDRwK/F5EjlTVNM5MrRcCD+M4oNOBe3CcVbeqrhSRc4BrgQ9P1oHNJLLS7UQqTTSRJp7KIAIhv+D3yWBU7GxTXjLtDCiNJoYGlL7qRqveMoKsOuDz0dIQpLk2OJIZhmFMMSrtgHI5E1jvLv8A2AR80U2/Q1XjwE4R2Qac4NaimlX1IQARuQ04C8cBnQlc5Zb1M+B6d0bXg6fGNCbETZt3kEil6RxI4EMI+IS0KtFUhqBPhs0e+qalrezpjg6G0QnHU9z20C5+8eSQrHrNYa1ccvIKDpvTgE+ElvpgSQOFjmegrWEY5aOSDkiB34mIAjep6s3AAlXdB6Cq+0Qk+1RYhFPDybLbTUu6y7np2TxtblkpEekF5gDeGhciciFODYqlS5eW7uhmEG3dEfpjKXzIoPzZL5BMZ2htqOE3f7OWRCrDgXCczoE44Cjjfvvcfv77jzvpjjiqt0Nm1XLJyY6s2ufz0VwboGWUYKHFOJKJzrRqGEbpqKQDepuq7nWdzL0isnWUbfM9gXSU9NHyDE9wHN/NAGvWrLHaEeN/sC9prWd/b4yAx1GoOuq3tq4BugcS9ESHwujkk1V/9C2Hcfbxjqy6sTbA7PrQqJLqYh3JWANtrXZkGJNHxbSrqrrX/W4HfgGcALwuIocAuN9Z/e1uYIkn+2Jgr5u+OE/6sDwiEgBmAV3lOJbpRPbB3t4fG/Zg37S1fcQ8F61bjt9tdlOUjCqq0FwXYF5TLd2RBKpKRzjO1+/ZyqV3PDXofE49ej4/+OsT+Ku3LKWlPsSi1jrmN9WOOZ7H60jGM89QW3eEuhzlXFadV8yxG4ZRPBVxQCLSICJN2WXg3cBzwAbgPHez84BfucsbgHNEpEZEDgeOAB51m+v6RWStOB0EH8/Jky3rbGDjTOv/2bS1nXNvfpiTrt3IuTc/XNCDtJgH+/pV87lk/Qp8IiTTGXwoLfUBRHx8eM0SEqkMP3nkNT5+y6Pc+8LrgCOrvu6cY/nH9x7N4tn1HDKrjoWzaguOVD2aIxmNJa31RHOiaWfVecU6NcMwiqNSTXALgF+4ncoB4Ceq+hsReQy4S0QuAF4DPgSgqs+LyF3AC0AKuMRVwAF8hiEZ9j3uB+B7wA9dwUIXjopuxlBsE9VIoXheae/n3JsfHrFp6tJTj+SNi1v4zqbttHUNsKC5jg+vWUxKM3ziB48Nyqpb6oK8c9V8trWH+Zdfv8jS2Q1cvH5FQX033qaxxpC/qHmGRhto+6VfPTeuMESGYUwMmWGVglFZs2aNbtmypdJmTIjsg/qJ17oRGDYOJ5JIMb+pltsvXDti/nNvfvigUDwd4RhdA0kWt9YNe2iffdwiHtrRxSvt/cSTaQI+4bA5jZzz5iW098f4rwd20u8OMPUJfOC4RaxeOIub/7iDmoCPhpB/cNqGq89YPaIT8jrT7P77okkUmFUXHGbTaOXknqPcgbb5jr2Qc2YYMx0ReVxV14w7nzmgIaa6A/I+qF/rirgqDOHQFscJ9UUT7O+L01jjJ5lWQgEfR8xvGlabyfew390dpbU+yLym2sF9HeiP0R1JMrshyIG++KDko7k2wEAiQzxn+oSADz629jCe3dNH10CchpqhmsZYD/mRHEPI76OlPlT0PEOjnb/xOjXDmMkU64CqbRyQMQEGx+OEU6TSzouF3+dMVQ2wpyeGoPTFnHXRRJpdneFhTXP5QvH0RBLDprQG6I+lSKYz9ESSiE8QnLA6XZHUsO384tR+FPjxI6/RVBvgkFl1w7bxNnPlU6GN1CzYG01yz2XrSnPymFgYIsMwxo/VgDxM1RpQ9qH9yM5OVJ2Hvgi4YdTwixNZIOE6JcGZrE0EAm64nPHWQF7Y20vQL6QyCgLp9MEa94AwpGZzxwU1hALMa6rJ28yV7Z/JrYE0hPwk0hlrGjOMKqXYGpCFEJ7ieKXDqo4TSCmkM0MDodIKibTidxMUxxmoKol0ZsyOdid8jhJJpEhnMvRFE/h8Qn3IjyqkPM7HL+BOhIrfJ4g4n+y4oFDAN1iWqvOdFQGMpEJT1RHzGIYxdTEHNAUYTU597W+20t4X47Wu4Q4kw8E1EnGbw7IkM0rI7ytovp9/ft8xtNaH6BpIMKsuxBsXzaI7kiLt2YlfYHZDiFl1QYJ+cW0YGhc0qz7IEfObuPqM1cxvqqU3mmR+U+1gH0tbd4RUOsOOA2G27u9jx4EwqXSGgUR6xDyGYUxdrA+oyhlNTg3wcnsYvzBY+/Hic5veUmnF73NqRX4fZLLbuoNFx6pNxFNpjljYxLUf/DMe2tHJjZt2sKcnCjhNeKGAD1GlJuhjpStqeGZ3Dzds2k4ynaHG72NWQ5Cg3z/Yp5LPeTSG/Gw7MIBfBL84du/pibFyXkNREboNw6huzAFVOaOFjgEI+oVkKjOsJpJFBAShJiBOLcQHNQEfKU8U6oF4mk+etDTvw11V6Y4k+d3z+7n1T7vY0REe7Efy+4T3v+lQzn/rMpa0NtBcFxgWLHT9qvm8cXHLuDr0BxJpUhklheIT8LnllSoIqWEY1YU5oCpnJAXY7u4I0WSaTDq/8wGnxrNkTi2ReIoD4YTTP5TRwXbXYMBHJJHmhk3bAWcwaZZYMs2B/jh/eOF1/u13LzHgcVqhgI/Prl/BR09cRktdcDAAaS7jqbVs2trO3t4YAbeGllHHAc5rDA2brM4wjOmD9QFVOU01Aba1D/WJ9MeSRJNpGmsC9EQSg0q3kXi9N0K763wA4qkMGXAUCuo0oWVUuWHTdjZtbSeTUQ70x9ndHeGb977MVXe/MNz5+IXmGj8Pbu9kdkNoROczXrI1Pb/48IsMSrc7BhI0hGzWU8OYjpgDqmKu+/3LvNLeTyyVIZVW4ilnUGhfNEl/NEE6kye8NxDy+wbFBtERKg/qzlgq4kwal8pk+M6m7ezujvLwjg7Ov+Uxfv7knoPyJdJKx0CSLa92lTRIZ1t3hAVNNSQzGZIZxZ0aiIxC50DCAoIaxjTEmuCqlE1b27lh03ZEhKBPSSmkMiAoHZ4aTT4c5dno5XtXZzJKyCfs7Ahz2R1P8kRbz5j2pTJwwW1bOHJ+I188fdWoYXQKmd5gSWs97f0xgn6fM6U3gDjS7ea64OB0CYZhTB/MAVUpTlSDzEGOppBhw+mxvM/gds6DPpNRgkEfHQMJDoQTg+v9woj9S9n9vLi/n0/e9hiHzKpDYZiT8Sr4/AJPtnWP6LSyg1DTGSUUEMAZO7RwVq0FBDWMaYo1wVUpz+3pKcjZ5KNA/0MirSTSSgYYSGScZjmBBU011AWEgM+JljAWqQy0dUc50BcbDO2TrfkE/Y6cem9vDM04Tm1nx8BB8+ysXzWfq89YTX3ITyqjBHxDMewKiXJtGMbUwxxQlRIZS11QQrIOq8YvrJjXyLymGuY315FhKHpCISQySmc4STKdHmx2qwv66QjHB6fr9rkT1+WbZ2f9qvlcd86bWNRSz8JZtTTWBCzqgWFMY6wJrsrI1hwKbUYrFUEfpFXZ1REGEXwiaEZHbYI7CHXGHvVGkuz2RQb7dRLpDH63KqXqiCRGalazgKCGMXMwB1QhvJ3zTTUB+qMJ9vcnBpufJpt0xnEeaQWfKEkdClzqKrbH7BMSNwhqPJ0ZdBxXbngev0/IZNQpR2FeU82ozWoW9cAwZgbWBFcBvAFE+6NJXtzfz+7euBNZGga/J5MMjnNRhpyM4DiUUMBHwB2XE/QLPoYCnXrxi5DOKAGfb7DWcvUZq1k2u560Og7okFk1+H1izWqGYVgNqBJkO+c7wwl6Y9U1yj9b2wn6wO/zEUtlnGkb/D7Sqqxa2Iyqsr83yrymWl5uD+ND8fl8pDOKX4RLPFNsZ2sz3llIs1MvWC3HMGY25oAqQFt3BL9ATzQ5qfsN+qC1PkS7R2p90DZ+H8l0hpSCz1XFqQJu3w1ANJnmiAXN3H7h2hGnt87FmtUMw8jFHNAks2lrO10DCSKe8DblJqtkCwX8LJhVx0AiPSgMiLtjjbJNas4kdc5kdmlV5jQE6XZnOV3YWHOQKs0ci2EYxWIOaBLZtLWdz9/55KQ5H8GZlTQjzqRusVSavmiCZDpDMq2I3+nfyagiCK31QcLxFLEM1If8LG11nNUR82tRVQYSaYI+IeT38aVfPceSzaZQMwyjeMwBTSLX/mYrvSMFZysDIhAI+MlkFHXnBtrdEyXo8zG/MUhfLEU8lSHgF+Y0BpnTUENjrTM/UL4J30abm8ickGEY48Uc0CSyrT08qfvLqDOtQlZTV+/3Mb+phrmNtQAsmAWRRIqgT2htqBmzH2e0uYnMARmGMV7MAZWJ3CCcC5tDJCsgr87uUYBYIk0ylaEm4Kep1pljqC7opzea5Dd/s3bMskabm8gwDGO8mAMqA7lNVS/t7+OhHZOreMsl64jS6sz3k3VA44mzlo1skK0BjTe/YRiGFxuIWga8TVXheIruSGWdTxbFbZZLpVHVccdZu2jdcpJpJ18x+Q3DMLyYAyoD2SCcAAf640VHtS4XIb+P3miS+U21ecUGI5GNbDC/qbao/IZhGF6sCa4MeJuqBiZxvI+XkF9IeAK3ZSMcCLBsTgP3XLauqHJt3I9hGKXCHFCJ2bS1nZ5Igl2dEYLjmcughPh9zmRuXrLOZ26jM9bHMAyj0lgTXAnJig8S6QyLW2pJpiZvTh8vQb9w1MImQq4D9Ak0hPzMawzRH0vT3h/n3JsfHjYhnGEYxmRjDqiEeMUH0UR6fHPplJAFTbWICIe21BHwCaGAj7mNIboiSVIZZWFzzeAgUnNChmFUCnNAJSQrPuiLJkcN+Flumt2xOk21QRa11KIK+/viBPzCopY6mutC1IcCeWclNQzDmCysD6iEZMUH+3ujFbNhdv3wgaIBv4/jlrYODiIVGeqXskGkhmFUEqsBlZCL1i2nN5okXoG2NwFOPLyVxtpg3nE6S1rriSaHK/JsEKlhGJXEakAlprMCTW+rFjRy+XuOPmjit9y4bldueJ5IIkVd0E80mbZBpIZhVBRzQCXkC3c9OSmDTgVH2Rbw+7hk/QouPfXIwXUjjdNZv2o+V0NBk8cZhmFMBuaASsTf3PEEnZHSj68J+QAR0gqHNNfQVOuM4ynGgdggUsMwqglzQCXgut+/zC+e2lfSMv3ACcvnWG3FMIxpizmgEvCN379S8jI/f+oRw5rWDMMwphvTXgUnIqeLyEsisk1ELi91+af/56aSlifAktY6cz6GYUx7pnUNSET8wA3Au4DdwGMiskFVXyjVPra+PjDhMvw+WNxSR8DvG5wO2zAMY7oz3WtAJwDbVHWHqiaAO4AzK2zTIEcvbOLW89/MCcvmkFFsegPDMGYU07oGBCwC2jy/dwNvqZAtw5jXGOKLp68yZZphGDOW6e6A8s2HMGyojohcCFwIsHTp0rIbNLcxxBHzm0zVZhjGjGe6O6DdwBLP78XAXu8GqnozcDPAmjVryjaOdNc1f1Guog3DMKYk070P6DHgCBE5XERCwDnAhlLu4Nbz3zzmenM+hmEYBzOta0CqmhKRzwK/xRnbeYuqPl/KfaxfNZ9bz3+zhbgxDMMYJ9PaAQGo6q+BX5dzHyYkMAzDGD/TvQnOMAzDqFLMARmGYRgVwRyQYRiGURHMARmGYRgVQVQnf/roakVEDgCvTqCIuUBHicwpN2ZreTBby4PZWh5KZethqjpvvJnMAZUQEdmiqmsqbUchmK3lwWwtD2Zreai0rdYEZxiGYVQEc0CGYRhGRTAHVFpurrQB48BsLQ9ma3kwW8tDRW21PiDDMAyjIlgNyDAMw6gI5oAMwzCMimAOqASIyOki8pKIbBORyydxv7eISLuIPOdJmy0i94rIK+53q2fdFa6NL4nIaZ7040XkWXfddSIibnqNiNzppj8iIsuKtHOJiNwnIi+KyPMi8vkqtrVWRB4VkaddW/+5Wm317McvIk+KyN1TwNZd7n6eEpEt1WyviLSIyM9EZKt77Z5YjbaKyFHu+cx++kTksmq09SBU1T4T+OBM87AdWA6EgKeBYyZp3+uA44DnPGn/ClzuLl8OXOsuH+PaVgMc7trsd9c9CpyIM4PsPcB73PSLge+6y+cAdxZp5yHAce5yE/Cya0812ipAo7scBB4B1lajrR6b/xb4CXB3tV4DHlt3AXNz0qrSXuAHwCfd5RDQUq22emz2A/uBw6rdVlU1B1SCP/xE4Lee31cAV0zi/pcx3AG9BBziLh8CvJTPLpw5kk50t9nqST8XuMm7jbscwBkxLSWw+VfAu6rdVqAeeAJ4S7XaijPL7x+AUxhyQFVpq1vGLg52QFVnL9AM7MzNW4225tj3buBPU8FWVbUmuBKwCGjz/N7tplWKBaq6D8D9zk5UNJKdi9zl3PRheVQ1BfQCcyZinFt1fxNOzaIqbXWbtJ4C2oF7VbVqbQW+CfwDkPGkVautAAr8TkQeF5ELq9je5cAB4Ptu8+Z/i0hDldrq5Rzgdne52m01B1QCJE9aNWrbR7JzNPtLemwi0gj8HLhMVftG23SE/U6KraqaVtVjcWoXJ4jIG0bZvGK2isj/A9pV9fFCs4yw30m7BoC3qepxwHuAS0Rk3SjbVtLeAE7z9o2q+iZgAKcZayQqfm5FJAScAfx0rE1H2O9kXgeAOaBSsBtY4vm9GNhbIVsAXheRQwDc73Y3fSQ7d7vLuenD8ohIAJgFdBVjlIgEcZzPj1X1f6rZ1iyq2gNsAk6vUlvfBpwhIruAO4BTRORHVWorAKq61/1uB34BnFCl9u4Gdru1X4Cf4TikarQ1y3uAJ1T1dfd3NdsKmAMqBY8BR4jI4e4byDnAhgraswE4z10+D6e/JZt+jqtmORw4AnjUrZr3i8haV/Hy8Zw82bLOBjaq2wg8Htxyvwe8qKrfqHJb54lIi7tcB5wKbK1GW1X1ClVdrKrLcK67jar60Wq0FUBEGkSkKbuM01/xXDXaq6r7gTYROcpNeifwQjXa6uFchprfcsuvNlsdJtqJZB8FeC+Osms78E+TuN/bgX1AEucN5QKcdtk/AK+437M92/+Ta+NLuOoWN30NzoNgO3A9QxEyanGq89tw1DHLi7TzJJzq+jPAU+7nvVVq6xuBJ11bnwOudNOrztYcu9czJEKoSltx+lWedj/PZ++VKrb3WGCLey38EmitYlvrgU5glietKm31fiwUj2EYhlERrAnOMAzDqAjmgAzDMIyKYA7IMAzDqAjmgAzDMIyKYA7IMCaIOEErLy7zPi4Tkfpy7qMAG84XkUMraYMxvTAHZBgFIg757pkWnGCN5eQyHKltJTkfMAdklAxzQIYxCiKyTJxQ/N/BCUz6ZRF5TESeEXeqBuAaYIUbCv/fROQ7InKGm/8XInKLu3yBiHzNXf6oONM+PCUiN4mI301/t4g8JCJPiMhPRaRRRC7FefDfJyL3jWLr6W6+p0XkD27abBH5pWvvwyLyRjf9KhH5O0/e59xjzR7vf4kzHcXvRKRORM7GGSPyY9fmutKeaWMmYg7IMMbmKOA24Is4QRlPwBmkeLw4scwuB7ar6rGq+vfAZuDtbt5FOOHvwRmQ+4CIHA18GCcu2rFAGviIiMwFvgScqk68tC3A36rqdTghUU5W1ZPzGSgi84D/Aj6oqn8OfMhd9c/Ak6r6RuAf3eMYiyOAG1R1NdDjlvkz156PuMcZLaAcwxiVQKUNMIwpwKuq+rCI/DtO+Jgn3fRGnIf1aznbPwBcJiLH4IRvaXVjcZ0IXIoT0uR44DEn4gl1OHG61uI4qz+56SHgoQJtXAtsVtWdAKqajdN1EvBBN22jiMwRkVljlLVTVZ9ylx/HmfLDMEqOOSDDGJsB91uAr6vqTd6VkjM7pKruEWf2ydNxakOzgb8Ewqra78bZ+oGqXpFTzvtwpn84twgbhfzRiUeKYpxieAtIrWc57llO4zhIwyg51gRnGIXzW+AT4kwrgYgsEpH5QD/OTK9eHsIRDmzGqRH9nfsNTlyus9282X6aw4CHgbeJyEo3vV5EjnTz5NtH7v7e4QaXRERmu+mbgY+4aeuBDnWmwtiFE90ZETkOZ2bMsRjLBsMYF+aADKNAVPV3OFNfPyQiz+KE6G9S1U6cZrPnROTf3M0fAAKqug1HvDDbTUNVX8Dp6/mdiDwD3Iszc+UBHKXZ7W76w8Aqt7ybgXtGEiG4eS8E/kdEngbudFddBaxxy7uGoYjGPwdmizPx3mdwgumOxa3Ad02EYJQKC0ZqGIZhVASrARmGYRgVwUQIhjHFEJFHgJqc5I+p6rOVsMcwisWa4AzDMIyKYE1whmEYRkUwB2QYhmFUBHNAhmEYRkUwB2QYhmFUBHNAhmEYRkUwB2QYhmFUhP8fN1HsaSLuFeMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"retweet_count\", y=\"favorite_count\", data=df_master).set(title= 'Relationship between Retweet Count and Fovourite Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This sctterplot above confirms a strong correlation between retweet_count' vs 'favorite_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Twitter for iPhone     1940\n",
      "Vine - Make a Scene      91\n",
      "Twitter Web Client       31\n",
      "TweetDeck                10\n",
      "Name: source, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Distribution of Source')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_source = df_master['source'].value_counts().index\n",
    "print(df_master['source'].value_counts())\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(data = df_master, y = 'source', order = sorted_source)\n",
    "plt.xticks(rotation = 360)\n",
    "plt.xlabel('Count', fontsize=14)\n",
    "plt.ylabel('Source', fontsize=14)\n",
    "plt.title('The Distribution of Source',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see from the pivot table able that jigzaw puzzle has the highest prediction amongst the dog breeds with confidence level of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The Distribution of Dog Stages')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_stage = df_master['stage'].value_counts().head(3).index\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.countplot(data = df_master, x = 'stage', order = sorted_stage, orient = 'h')\n",
    "plt.xticks(rotation = 360)\n",
    "plt.xlabel('Count', fontsize=14)\n",
    "plt.ylabel('Dog Stages', fontsize=14)\n",
    "plt.title('The Distribution of Dog Stages',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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