act_report_DIRK_KADIJK_v1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Insights and visalizations derived from wrangled/cleaned WeRateDogs data (Part 2/2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Introduction](#intro)\n",
    "2. [Insights and visualizations derived to answer the research questions](#insights)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This project focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook (`wrangle_act_v1.0_DIRK.ipynb`). This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. They’re Good Dogs Brent wrangles WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations. WeRateDogs has over 4 million followers and has received international media coverage.\n",
    "\n",
    "WeRateDogs downloaded their Twitter archive and sent it to Udacity via email exclusively for us to use in this project. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 5000+ of their tweets as they stood on August 1, 2017.\n",
    "\n",
    "The goal of this project is to wrangle the WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations. The challenge lies in the fact that the Twitter archive is great, but it only contains very basic tweet information that comes in JSON format. \n",
    "\n",
    "In part 1 of this project I gathered, assessed and cleaned the Twitter data as input dataset for a worthy analysis and visualization. This report comprises part 2 of the project and focuses on **communicating the gained insights and visualizations**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output report of this part 1 of the project is the `wrangle_report_DIRK_KADIJK.ipynb` file which describes the data wrangling/cleaning efforts and outputs. The output datafile of the data wrangling/cleaning part 1 is a `twitter_overall_table` pandas dataframe which is saved in the `twitter_archive_master.csv` file. This file is the input file for this report (see next table below).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "## 1.1. Data Wrangling/cleaning part 1 of the project\n",
    "\n",
    "In the wrangling/cleaning part (1/2) of the project we gathered 3 pieces of data and transformed that to the `twitter_overall_table`:\n",
    "\n",
    "### a. Enhanced Twitter Archive\n",
    "\n",
    "The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which I used to extract rating, dog name, and dog \"stage\" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive \"enhanced.\". We manually downloaded this twitter_archive_enhanced.csv file by clicking the following [link](https://d17h27t6h515a5.cloudfront.net/topher/2017/August/59a4e958_twitter-archive-enhanced/twitter-archive-enhanced.csv). \n",
    "\n",
    "### b. Image Predictions File\n",
    "\n",
    "The tweet image predictions, i.e., what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network. This file (image_predictions.tsv) hosted on Udacity's servers and we downloaded it programmatically using python Requests library AND the following URL of the file: https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv)\n",
    "\n",
    "### c. Twitter API\n",
    "\n",
    "Back to the basic-ness of Twitter archives: retweet count and favorite count are two of the notable column omissions. Fortunately, this additional data can be gathered by anyone from Twitter's API. Well, \"anyone\" who has access to data for the 3000 most recent tweets, at least. But we, because we have the WeRateDogs Twitter archive and specifically the tweet IDs within it, can gather this data for all 5000+.\n",
    "In this project, I'll be using Tweepy to query Twitter's API for data included in the WeRateDogs Twitter archive. This data will include retweet count and favorite count.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>The following Python packages are used in this project:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "jupyter": {
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import requests\n",
    "import tweepy\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "from tweepy import OAuthHandler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "The table below illustrates a subset of the following 10 most important columns in the `twitter_overall_table`:\n",
    "* **tweet_id**: identifier of WeRateDogs tweet\n",
    "* **rating_numerator**: score of dogs on a scale 10 - 15\n",
    "* **rating_denominator**: default reference norm with value 10\n",
    "* **name**: name of dog in tweet\n",
    "* **stage**: stage in development of dog based on a WeRateDogs classification scheme (value are: doggo, pupper, puppo, floofer)\n",
    "* **retweet_count**: count of retweet of this tweet\n",
    "* **favorite_count**: count of favorite of this tweet\n",
    "* **predicted_dograce1**: the best (#1) neural network algo prediction of dograce (dog breed) for the image in the tweet \n",
    "* **pred_confid1**: is how confident the algo is in its #1 prediction\n",
    "* **pred_dog1**: is wether or not the #1 prediction is a breed of dog\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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       "      <td>Zoey</td>\n",
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       "      <td>14.0</td>\n",
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       "      <td>Cassie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>7711</td>\n",
       "      <td>32467</td>\n",
       "      <td>Pembroke</td>\n",
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       "      <td>True</td>\n",
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      "text/plain": [
       "             tweet_id  rating_numerator  rating_denominator      name  stage  \\\n",
       "0  892420643555336193              13.0                  10   Phineas    NaN   \n",
       "1  892177421306343426              13.0                  10     Tilly    NaN   \n",
       "2  891815181378084864              12.0                  10    Archie    NaN   \n",
       "3  891689557279858688              13.0                  10     Darla    NaN   \n",
       "4  891327558926688256              12.0                  10  Franklin    NaN   \n",
       "5  891087950875897856              13.0                  10       NaN    NaN   \n",
       "6  890971913173991426              13.0                  10       Jax    NaN   \n",
       "7  890729181411237888              13.0                  10       NaN    NaN   \n",
       "8  890609185150312448              13.0                  10      Zoey    NaN   \n",
       "9  890240255349198849              14.0                  10    Cassie  doggo   \n",
       "\n",
       "   retweet_count  favorite_count        predicted_dograce1  pred_confid1  \\\n",
       "0           8853           39467                    Orange      0.097049   \n",
       "1           6514           33819                 Chihuahua      0.323581   \n",
       "2           4328           25461                 Chihuahua      0.716012   \n",
       "3           8964           42908               Paper_towel      0.170278   \n",
       "4           9774           41048                    Basset      0.555712   \n",
       "5           3261           20562  Chesapeake_bay_retriever      0.425595   \n",
       "6           2158           12041               Appenzeller      0.341703   \n",
       "7          16716           56848                Pomeranian      0.566142   \n",
       "8           4429           28226             Irish_terrier      0.487574   \n",
       "9           7711           32467                  Pembroke      0.511319   \n",
       "\n",
       "  predicted_dog1  \n",
       "0          False  \n",
       "1           True  \n",
       "2           True  \n",
       "3          False  \n",
       "4           True  \n",
       "5           True  \n",
       "6           True  \n",
       "7           True  \n",
       "8           True  \n",
       "9           True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table = pd.read_csv('twitter_archive_master.csv', encoding='utf-8')\n",
    "twitter_overall_table.iloc[:, np.r_[:1, 5:11, 13:16]].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "## 1.2. Research Questions\n",
    "\n",
    "This investigation of WeRateDogs data is focused on the following 3 research questions:\n",
    "* Research Question #1: To what extend is there a possitive correlation between a high rating_numerator at the one end, and a higher favorite_count (or retweet_count) at the other hand.\n",
    "\n",
    "* Research Question #2: Does filtering out of the \"non-dog\" predictions and the #1 predictions with probability <70% improve the correlation between rating_numerator and retweet_count / favorite_count? \n",
    "\n",
    "* Research Question #3: Which dograces (dog breeds) are top performing in terms of rating_numerator ?¶ \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='insights'></a>\n",
    "# 2. Insights and visualizations derived which answer the research questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. RESEARCH QUESTION #1: To what extend is there a possitive correlation between a high rating_numerator at the one end, and a higher favorite_count (or retweet_count) at the other hand?\n",
    "\n",
    "The summarized outcomes of the analysis are:\n",
    "\n",
    "* Only dograce #1 prediction has some predictive power: 50% of the dograce number #1 predictions have a confidence of less than 59%.\n",
    "* There is only a `moderate` positive correlation (0.3-0.4)  between rating_numerator at the one end, and a favorite_count (or retweet_count) at the other hand.\n",
    "* WeRateDogs score < 10 got never retweeted >10000 times.\n",
    "* WeRateDogs score < 10 got only favorite_count > 20000 in only 3 single cases, which can be explained with that 2 were not #1 predicted as dog while the third case has just a probability of 0.46. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>See below for specifics of the analysis and the visualizations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
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       "      <td>2.078000e+03</td>\n",
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       "      <td>2078.0</td>\n",
       "      <td>2078.000000</td>\n",
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       "      <td>1952.000000</td>\n",
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       "      <td>1.952000e+03</td>\n",
       "      <td>1.952000e+03</td>\n",
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       "      <th>mean</th>\n",
       "      <td>7.367280e+17</td>\n",
       "      <td>10.610577</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2842.781039</td>\n",
       "      <td>9004.178056</td>\n",
       "      <td>1.202357</td>\n",
       "      <td>0.594351</td>\n",
       "      <td>1.349446e-01</td>\n",
       "      <td>6.011246e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.726320e+16</td>\n",
       "      <td>2.149876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4718.678406</td>\n",
       "      <td>12237.183241</td>\n",
       "      <td>0.559650</td>\n",
       "      <td>0.272239</td>\n",
       "      <td>1.012024e-01</td>\n",
       "      <td>5.083426e-02</td>\n",
       "    </tr>\n",
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       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>81.000000</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>6.767853e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>641.500000</td>\n",
       "      <td>2041.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362775</td>\n",
       "      <td>5.352722e-02</td>\n",
       "      <td>1.595832e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.097095e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1402.500000</td>\n",
       "      <td>4199.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587635</td>\n",
       "      <td>1.178485e-01</td>\n",
       "      <td>4.941030e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.881228e+17</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3289.500000</td>\n",
       "      <td>11463.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.847062</td>\n",
       "      <td>1.963337e-01</td>\n",
       "      <td>9.157912e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.710420e-01</td>\n",
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      ],
      "text/plain": [
       "           tweet_id  rating_numerator  rating_denominator  retweet_count  \\\n",
       "count  2.078000e+03       2078.000000              2078.0    2078.000000   \n",
       "mean   7.367280e+17         10.610577                10.0    2842.781039   \n",
       "std    6.726320e+16          2.149876                 0.0    4718.678406   \n",
       "min    6.660209e+17          0.000000                10.0      16.000000   \n",
       "25%    6.767853e+17         10.000000                10.0     641.500000   \n",
       "50%    7.097095e+17         11.000000                10.0    1402.500000   \n",
       "75%    7.881228e+17         12.000000                10.0    3289.500000   \n",
       "max    8.924206e+17         14.000000                10.0   79515.000000   \n",
       "\n",
       "       favorite_count      img_num  pred_confid1  pred_confid2  pred_confid3  \n",
       "count     2078.000000  1952.000000   1952.000000  1.952000e+03  1.952000e+03  \n",
       "mean      9004.178056     1.202357      0.594351  1.349446e-01  6.011246e-02  \n",
       "std      12237.183241     0.559650      0.272239  1.012024e-01  5.083426e-02  \n",
       "min         81.000000     1.000000      0.044333  1.011300e-08  1.740170e-10  \n",
       "25%       2041.750000     1.000000      0.362775  5.352722e-02  1.595832e-02  \n",
       "50%       4199.500000     1.000000      0.587635  1.178485e-01  4.941030e-02  \n",
       "75%      11463.250000     1.000000      0.847062  1.963337e-01  9.157912e-02  \n",
       "max     132810.000000     4.000000      1.000000  4.880140e-01  2.710420e-01  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cel above tells that only dograce #1 prediction has some predictive power:\n",
    "- 50% of the dograce number #1 predictions have a confidence of less than 59%.\n",
    "- Note: there is not any #2 prediction with a confidence of more than 48% (and #3 predictions even less confident)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<br><br>\n",
    "Let us  now determine the level of correlation between rating_numerator, favorite_count and retweet_count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>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>0.313879</td>\n",
       "      <td>0.413777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>0.313879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>0.413777</td>\n",
       "      <td>0.911327</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rating_numerator  retweet_count  favorite_count\n",
       "rating_numerator          1.000000       0.313879        0.413777\n",
       "retweet_count             0.313879       1.000000        0.911327\n",
       "favorite_count            0.413777       0.911327        1.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table [[ 'rating_numerator','retweet_count', 'favorite_count']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one may expect there is a high (0.91) correlation between favorite_count and retweet_count.\n",
    "\n",
    "However it shows that the level of correlation between rating_numerator and resp. retweet_count and favorite_count is only `moderate` (score is resp. 0.3 and 0.4 which significantly less than the norm (>= 0.7) for 'high' correlation. <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now try to visualize the relationship between rating_numerator and `favorite_count`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "3c06ab2d-d93c-4cf0-a8b8-55df5753e24f",
    "_kg_hide-input": true,
    "_uuid": "391bd233403a29cc9be121f12afcaeae83a20952",
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter_overall_table.plot(x=['favorite_count'],y=['rating_numerator'],figsize=(12,6), title='Overall_rating vs Height', marker='o', kind='scatter');\n",
    "plt.ylabel('rating_numerator', fontsize=12)\n",
    "plt.xlabel('favorite_count', fontsize=12);\n",
    "plt.axvline(20000, c='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter diagram above makes clear that a rating_numerator lower than 10 correlates with a favorite_count of lower than 20000. 3 dogs are the exceptions of which 2 cases were not #1 predicted as dog while the third case had just a probability of 0.46.\n",
    "\n",
    "So the probability that a dog with low WeRateDogs-score (<10) gets a favorite_count > 20000 is quite low.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>The same finding do we get for the `retweet_count`. The diagram below tells that dogs with a \"WeRateDogs' score < 10 never got retweeted >10000 times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "3c06ab2d-d93c-4cf0-a8b8-55df5753e24f",
    "_kg_hide-input": true,
    "_uuid": "391bd233403a29cc9be121f12afcaeae83a20952",
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter_overall_table.plot(x=['retweet_count'],y=['rating_numerator'],figsize=(12,6), title='rating_numerator vs retweet_count', marker='o', kind='scatter');\n",
    "plt.ylabel('rating_numerator', fontsize=12)\n",
    "plt.xlabel('retweet_count', fontsize=12);\n",
    "plt.axvline(10000, c='green');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "## 2.2. Research Question #2: Does filtering out of the \"non-dog\" predictions and the #1 predictions with probability <70% improve the correlation between rating_numerator and retweet_count / favorite_count?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summarized outcomes of the analysis are:\n",
    "* Taking out observations of non_dogs does NOT or hardly improve the correlation rating_numerator and favorite_count/retweet_count: only +2-4%.\n",
    "* Taking out observations with #1 prediction probability < 70% does NOT improve the correlation between rating_numerator and retweet_count (even degrades from 0.34 to 0.33).\n",
    "* Visual inspections learns that taking out non-dog predictions and low probability #1 predictions is usefull to clean up outliers due to dirty data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>See below for specifics of the analysis and the visualizations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 25)\n",
    "twitter_overall_table_dog_only = twitter_overall_table[twitter_overall_table['predicted_dog1'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See first here again the correlation matrix with ALL rows for all observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>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>0.313879</td>\n",
       "      <td>0.413777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>0.313879</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.911327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>0.413777</td>\n",
       "      <td>0.911327</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rating_numerator  retweet_count  favorite_count\n",
       "rating_numerator          1.000000       0.313879        0.413777\n",
       "retweet_count             0.313879       1.000000        0.911327\n",
       "favorite_count            0.413777       0.911327        1.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table [[ 'rating_numerator','retweet_count', 'favorite_count']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here the correlation with the non-dog predictions taken out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>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>0.341517</td>\n",
       "      <td>0.456930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>0.341517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>0.456930</td>\n",
       "      <td>0.910982</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rating_numerator  retweet_count  favorite_count\n",
       "rating_numerator          1.000000       0.341517        0.456930\n",
       "retweet_count             0.341517       1.000000        0.910982\n",
       "favorite_count            0.456930       0.910982        1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "twitter_overall_table_dog_only [[ 'rating_numerator','retweet_count', 'favorite_count']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cel above tells that correlation hardly improves by taking out the non-dog predictions: only +2-4%.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now take out also the #1 predictions with probability <70% (besides the #1 non-dog predictions already taken out).\n",
    "\n",
    "Taking out observations with probability < 70% does NOT improve the correlation between rating_numerator and retweet_count (even degrades from 0.34 to 0.33)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "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>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>0.329381</td>\n",
       "      <td>0.457275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retweet_count</th>\n",
       "      <td>0.329381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favorite_count</th>\n",
       "      <td>0.457275</td>\n",
       "      <td>0.892012</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  rating_numerator  retweet_count  favorite_count\n",
       "rating_numerator          1.000000       0.329381        0.457275\n",
       "retweet_count             0.329381       1.000000        0.892012\n",
       "favorite_count            0.457275       0.892012        1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table_dog_confident = twitter_overall_table_dog_only[twitter_overall_table_dog_only['pred_confid1'] > 0.7]\n",
    "twitter_overall_table_dog_confident [[ 'rating_numerator','retweet_count', 'favorite_count']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also here do a visual check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "3c06ab2d-d93c-4cf0-a8b8-55df5753e24f",
    "_kg_hide-input": true,
    "_uuid": "391bd233403a29cc9be121f12afcaeae83a20952",
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter_overall_table_dog_confident.plot(x=['favorite_count'],y=['rating_numerator'],figsize=(12,6), title='rating_numerator vs retweet_count', marker='o', kind='scatter');\n",
    "\n",
    "plt.ylabel('rating_numerator', fontsize=12)\n",
    "plt.xlabel('favorite_count', fontsize=12);\n",
    "plt.axvline(20000, c='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual inspection learns that taking out non-dog predictions and low probability #1 predictions is usefull to clean up outliers due to dirty data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Research Question #3: Which dograces (dog breeds) are top performing in terms of rating_numerator ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg\" width=\"400\" height=\"400\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summarized outcomes of the analysis are:\n",
    "* The top 3 top performing dog breeds in terms of WeRateDog rating_numerator score (between 10-15) is:\n",
    "  1. Golden_retriever (see visual above this cel)\n",
    "  2. Pembroke\n",
    "  3. Labrador_retriever\n",
    "* Note: See below a plot with the top 10 performing dogs and the specific scores on y-axis\n",
    "* At the end you can also find a lookup table where you can find the top 'WeRateDogs' scores per dograce (dog breed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See below for specifics of the analysis and the visualizations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>We have the following top 20 rating of dograces (dog breeds), after correction for non-dog predictions and predictions with <70% probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golden_retriever      80\n",
       "Pembroke              50\n",
       "Labrador_retriever    44\n",
       "Pug                   28\n",
       "Chihuahua             25\n",
       "Samoyed               24\n",
       "Pomeranian            23\n",
       "Chow                  19\n",
       "Toy_poodle            14\n",
       "French_bulldog        14\n",
       "Name: predicted_dograce1, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table_dog_confident.query('rating_numerator >= 10')  ['predicted_dograce1'].value_counts().head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we focus on only the very high (>= 12) WeRateDogs scores we see that Samoyed climps to position 4 and Pug sinks to position 10 (so Pug had many 10 or 11 scores)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golden_retriever      58\n",
       "Pembroke              29\n",
       "Labrador_retriever    24\n",
       "Samoyed               15\n",
       "Chihuahua             14\n",
       "Chow                  12\n",
       "French_bulldog         9\n",
       "Pomeranian             8\n",
       "German_shepherd        7\n",
       "Toy_poodle             7\n",
       "Name: predicted_dograce1, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_overall_table_dog_confident.query('rating_numerator >= 12')  ['predicted_dograce1'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter_overall_table_dog_confident.query('rating_numerator >= 12')  ['predicted_dograce1'].value_counts().head(10).plot(kind='bar', figsize=(10,8));\n",
    "plt.ylabel('# Dogs with a high WeRateDogs rating')\n",
    "plt.title('Top 10 frequent dog names', size=15)\n",
    "plt.xlabel('Dog breed')\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "twitter_overall_table_dog_confident = twitter_overall_table_dog_confident[twitter_overall_table_dog_confident['rating_numerator'] >= 10 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally see here also a lookup table where you can look up the top 'WeRateDogs' scores per dograce:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "jupyter": {
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       "<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>rating_numerator</th>\n",
       "      <th>10.00</th>\n",
       "      <th>11.00</th>\n",
       "      <th>11.27</th>\n",
       "      <th>12.00</th>\n",
       "      <th>13.00</th>\n",
       "      <th>13.50</th>\n",
       "      <th>14.00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predicted_dograce1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghan_hound</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airedale</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American_staffordshire_terrier</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basenji</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basset</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beagle</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bernese_mountain_dog</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black-and-tan_coonhound</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blenheim_spaniel</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloodhound</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bluetick</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borzoi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston_bull</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boxer</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Briard</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittany_spaniel</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bull_mastiff</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardigan</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chesapeake_bay_retriever</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clumber</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cocker_spaniel</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Collie</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Curly-coated_retriever</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalmatian</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dandie_dinmont</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doberman</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English_springer</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat-coated_retriever</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French_bulldog</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German_shepherd</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German_short-haired_pointer</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Giant_schnauzer</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden_retriever</th>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon_setter</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great_dane</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great_pyrenees</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibizan_hound</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irish_terrier</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italian_greyhound</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Keeshond</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kelpie</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Komondor</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuvasz</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador_retriever</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakeland_terrier</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leonberg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malamute</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malinois</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maltese_dog</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexican_hairless</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miniature_pinscher</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miniature_poodle</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norwegian_elkhound</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old_english_sheepdog</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papillon</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pekinese</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pembroke</th>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pomeranian</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pug</th>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redbone</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhodesian_ridgeback</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rottweiler</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint_bernard</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoyed</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schipperke</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shetland_sheepdog</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shih-tzu</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siberian_husky</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Staffordshire_bullterrier</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibetan_mastiff</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy_poodle</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy_terrier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vizsla</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weimaraner</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Welsh_springer_spaniel</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West_highland_white_terrier</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whippet</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yorkshire_terrier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "rating_numerator           10.00  11.00  11.27  12.00  13.00  13.50  14.00\n",
       "predicted_dograce1                                                        \n",
       "Afghan_hound                 0.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Airedale                     1.0    2.0    0.0    1.0    0.0    0.0    0.0\n",
       "American_staffordshire...    2.0    1.0    0.0    1.0    1.0    0.0    0.0\n",
       "Basenji                      0.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Basset                       2.0    1.0    0.0    1.0    2.0    0.0    0.0\n",
       "Beagle                       0.0    1.0    0.0    1.0    1.0    0.0    0.0\n",
       "Bernese_mountain_dog         2.0    1.0    0.0    4.0    0.0    0.0    0.0\n",
       "Black-and-tan_coonhound      0.0    0.0    0.0    0.0    0.0    0.0    1.0\n",
       "Blenheim_spaniel             1.0    1.0    0.0    3.0    0.0    0.0    0.0\n",
       "Bloodhound                   0.0    0.0    0.0    0.0    0.0    0.0    1.0\n",
       "Bluetick                     0.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Borzoi                       0.0    1.0    0.0    0.0    1.0    0.0    0.0\n",
       "Boston_bull                  2.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Boxer                        3.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Briard                       0.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Brittany_spaniel             0.0    3.0    0.0    2.0    0.0    0.0    0.0\n",
       "Bull_mastiff                 2.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Cardigan                     1.0    1.0    0.0    2.0    2.0    0.0    0.0\n",
       "Chesapeake_bay_retriever     3.0    0.0    0.0    3.0    2.0    0.0    0.0\n",
       "Chihuahua                    6.0    5.0    0.0   10.0    3.0    0.0    1.0\n",
       "Chow                         1.0    6.0    0.0    8.0    4.0    0.0    0.0\n",
       "Clumber                      0.0    0.0    1.0    0.0    0.0    0.0    0.0\n",
       "Cocker_spaniel               2.0    4.0    0.0    2.0    1.0    0.0    0.0\n",
       "Collie                       2.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Curly-coated_retriever       0.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Dalmatian                    0.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Dandie_dinmont               1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Doberman                     1.0    3.0    0.0    0.0    1.0    0.0    0.0\n",
       "English_springer             1.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Flat-coated_retriever        0.0    1.0    0.0    2.0    0.0    0.0    0.0\n",
       "French_bulldog               0.0    5.0    0.0    5.0    2.0    0.0    2.0\n",
       "German_shepherd              4.0    0.0    0.0    5.0    2.0    0.0    0.0\n",
       "German_short-haired_po...    4.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Giant_schnauzer              1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Golden_retriever             9.0   13.0    0.0   36.0   20.0    1.0    1.0\n",
       "Gordon_setter                0.0    0.0    0.0    0.0    0.0    0.0    1.0\n",
       "Great_dane                   0.0    0.0    0.0    1.0    1.0    0.0    0.0\n",
       "Great_pyrenees               1.0    2.0    0.0    2.0    2.0    0.0    0.0\n",
       "Ibizan_hound                 1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Irish_terrier                1.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Italian_greyhound            1.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Keeshond                     0.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Kelpie                       0.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Komondor                     1.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Kuvasz                       1.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Labrador_retriever          10.0   10.0    0.0   13.0   11.0    0.0    0.0\n",
       "Lakeland_terrier             2.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Leonberg                     0.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Malamute                     2.0    4.0    0.0    1.0    2.0    0.0    0.0\n",
       "Malinois                     1.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Maltese_dog                  3.0    2.0    0.0    0.0    0.0    0.0    0.0\n",
       "Mexican_hairless             0.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Miniature_pinscher           1.0    1.0    0.0    4.0    0.0    0.0    0.0\n",
       "Miniature_poodle             0.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Norwegian_elkhound           1.0    0.0    0.0    1.0    1.0    0.0    0.0\n",
       "Old_english_sheepdog         1.0    1.0    0.0    1.0    1.0    0.0    0.0\n",
       "Papillon                     0.0    2.0    0.0    0.0    0.0    0.0    0.0\n",
       "Pekinese                     1.0    0.0    0.0    3.0    0.0    0.0    0.0\n",
       "Pembroke                     5.0   16.0    0.0   16.0   11.0    0.0    2.0\n",
       "Pomeranian                   6.0    9.0    0.0    7.0    0.0    0.0    1.0\n",
       "Pug                         14.0    7.0    0.0    4.0    3.0    0.0    0.0\n",
       "Redbone                      0.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Rhodesian_ridgeback          0.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "Rottweiler                   1.0    0.0    0.0    3.0    0.0    0.0    0.0\n",
       "Saint_bernard                0.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Samoyed                      1.0    8.0    0.0    6.0    9.0    0.0    0.0\n",
       "Schipperke                   1.0    1.0    0.0    1.0    0.0    0.0    0.0\n",
       "Shetland_sheepdog            2.0    1.0    0.0    2.0    1.0    0.0    0.0\n",
       "Shih-tzu                     2.0    3.0    0.0    0.0    0.0    0.0    0.0\n",
       "Siberian_husky               0.0    1.0    0.0    2.0    2.0    0.0    0.0\n",
       "Staffordshire_bullterrier    1.0    0.0    0.0    0.0    1.0    0.0    0.0\n",
       "Tibetan_mastiff              0.0    0.0    0.0    2.0    0.0    0.0    0.0\n",
       "Toy_poodle                   1.0    6.0    0.0    5.0    2.0    0.0    0.0\n",
       "Toy_terrier                  0.0    0.0    0.0    1.0    0.0    0.0    0.0\n",
       "Vizsla                       1.0    3.0    0.0    0.0    1.0    0.0    0.0\n",
       "Weimaraner                   2.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Welsh_springer_spaniel       0.0    1.0    0.0    0.0    0.0    0.0    0.0\n",
       "West_highland_white_te...    3.0    0.0    0.0    2.0    1.0    0.0    0.0\n",
       "Whippet                      2.0    0.0    0.0    0.0    0.0    0.0    0.0\n",
       "Yorkshire_terrier            0.0    0.0    0.0    1.0    0.0    0.0    0.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 3000)\n",
    "twitter_overall_table_dog_confident.groupby(['predicted_dograce1','rating_numerator']) ['tweet_id'].count()  .unstack().fillna(0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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