NPS_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# == ***Project: Summer Academy NPS Data*** ==\n",
    "\n",
    "\n",
    "---\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![Image](nps.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Imports**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Data**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\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>Location</th>\n",
       "      <th>Track</th>\n",
       "      <th>Week</th>\n",
       "      <th>Rating (Num)</th>\n",
       "      <th>Schedule Pacing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>A little too fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Games</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>Way too slow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Week 2</td>\n",
       "      <td>4</td>\n",
       "      <td>A little too fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>350</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>1495</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>10</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1496</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>8</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1497</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>10</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1498</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>1</td>\n",
       "      <td>A little too slow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1499</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 5</td>\n",
       "      <td>8</td>\n",
       "      <td>Just right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1453 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID       Location           Track    Week Rating (Num)  \\\n",
       "0      134  San Francisco  Apps, Explorer  Week 1            3   \n",
       "1       36    Los Angeles            Apps  Week 1            4   \n",
       "2      117  San Francisco           Games  Week 1            4   \n",
       "3      253            NaN             NaN  Week 2            4   \n",
       "4      350  New York City  Apps, Explorer  Week 1            4   \n",
       "...    ...            ...             ...     ...          ...   \n",
       "1448  1495  New York City  Apps, Explorer  Week 7           10   \n",
       "1449  1496  New York City  Apps, Explorer  Week 7            8   \n",
       "1450  1497  New York City            Apps  Week 7           10   \n",
       "1451  1498  New York City  Apps, Explorer  Week 7            1   \n",
       "1452  1499  New York City            Apps  Week 5            8   \n",
       "\n",
       "        Schedule Pacing  \n",
       "0            Just right  \n",
       "1     A little too fast  \n",
       "2          Way too slow  \n",
       "3     A little too fast  \n",
       "4            Just right  \n",
       "...                 ...  \n",
       "1448         Just right  \n",
       "1449         Just right  \n",
       "1450         Just right  \n",
       "1451  A little too slow  \n",
       "1452         Just right  \n",
       "\n",
       "[1453 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv('Student-Feedback-2017.csv')\n",
    "data = pd.DataFrame(d)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Location', 'Track', 'Week', 'Rating (Num)', 'Schedule Pacing'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Cleaning**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Renaming_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Track</th>\n",
       "      <th>Week</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Pace</th>\n",
       "      <th>NPS Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>A little too fast</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>Games</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>Way too slow</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Week 2</td>\n",
       "      <td>4</td>\n",
       "      <td>A little too fast</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>350</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 1</td>\n",
       "      <td>4</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID       Location           Track    Week Rating               Pace  \\\n",
       "0  134  San Francisco  Apps, Explorer  Week 1      3         Just right   \n",
       "1   36    Los Angeles            Apps  Week 1      4  A little too fast   \n",
       "2  117  San Francisco           Games  Week 1      4       Way too slow   \n",
       "3  253            NaN             NaN  Week 2      4  A little too fast   \n",
       "4  350  New York City  Apps, Explorer  Week 1      4         Just right   \n",
       "\n",
       "  NPS Category  \n",
       "0     Promoter  \n",
       "1     Promoter  \n",
       "2     Promoter  \n",
       "3     Promoter  \n",
       "4     Promoter  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename columns\n",
    "# ---------------\n",
    "\n",
    "data = data.rename(columns={\"Rating (Num)\": \"Rating\", \"Schedule Pacing\": \"Pace\"})\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           int64\n",
       "Location    object\n",
       "Track       object\n",
       "Week        object\n",
       "Rating       int64\n",
       "Pace        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing rating datatype\n",
    "# -------------------------\n",
    "\n",
    "# data = data[data['Rating'] != '#ERROR!']\n",
    "# data['Rating'] = data['Rating'].astype(int)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _NPS categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Track</th>\n",
       "      <th>Week</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Pace</th>\n",
       "      <th>NPS Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>1495</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>10</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449</th>\n",
       "      <td>1496</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>8</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Passive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1497</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>10</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Promoter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1498</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps, Explorer</td>\n",
       "      <td>Week 7</td>\n",
       "      <td>1</td>\n",
       "      <td>A little too slow</td>\n",
       "      <td>Detractor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1499</td>\n",
       "      <td>New York City</td>\n",
       "      <td>Apps</td>\n",
       "      <td>Week 5</td>\n",
       "      <td>8</td>\n",
       "      <td>Just right</td>\n",
       "      <td>Passive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID       Location           Track    Week  Rating               Pace  \\\n",
       "1448  1495  New York City  Apps, Explorer  Week 7      10         Just right   \n",
       "1449  1496  New York City  Apps, Explorer  Week 7       8         Just right   \n",
       "1450  1497  New York City            Apps  Week 7      10         Just right   \n",
       "1451  1498  New York City  Apps, Explorer  Week 7       1  A little too slow   \n",
       "1452  1499  New York City            Apps  Week 5       8         Just right   \n",
       "\n",
       "     NPS Category  \n",
       "1448     Promoter  \n",
       "1449      Passive  \n",
       "1450     Promoter  \n",
       "1451    Detractor  \n",
       "1452      Passive  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def nps_category(rating):\n",
    "    if rating >= 9:\n",
    "        return \"Promoter\"\n",
    "    elif rating >= 7:\n",
    "        return \"Passive\"\n",
    "    else:\n",
    "        return \"Detractor\" \n",
    "    \n",
    "# data[\"NPS Category\"] = data.apply(lambda row: nps_category(row[\"Rating\"]), axis=1)\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **NPS**\n",
    "\n",
    "NPS = (Promoters - Detractors)  (Promoters + Passives + Detractors)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NPS is:  44.06896551724138 %\n"
     ]
    }
   ],
   "source": [
    "promoters_amt = len(data[data['NPS Category'].dropna() == 'Promoter'])\n",
    "detractors_amt = len(data[data['NPS Category'].dropna() == 'Detractor'])\n",
    "passive_amt = len(data[data['NPS Category'].dropna() == 'Passive'])\n",
    "\n",
    "NPS = (promoters_amt - detractors_amt)/(promoters_amt+detractors_amt+passive_amt) *100\n",
    "print('NPS is: ', NPS, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Questions**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "+ How many more promoters are there than detractors across our 2017 data?\n",
    "    - ...\n",
    "+ Which track boasts the best promoter-to-detractor ratio?\n",
    "    - ...\n",
    "+ Does the student experience get better the longer that they are enrolled at the Summer Academy?\n",
    "    - It stays about the same\n",
    "+ Does student satisfaction vary by location?\n",
    "    - ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _How many more promoters are there than detractors across our 2017 data?_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Which track boasts the best promoter-to-detractor ratio?_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Does the student experience get better the longer that they are enrolled at the Summer Academy?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews per week:  {'Week 1': 288, 'Week 2': 276, 'Week 3': 241, 'Week 4': 193, 'Week 5': 179, 'Week 6': 144, 'Week 7': 120, 'Week 8': 9} \n",
      "\n",
      "Averages Per Week:  [8.27, 8.35, 8.52, 8.33, 8.44, 8.54, 8.6, 9.11]\n"
     ]
    }
   ],
   "source": [
    "def find_average_per_week(data):\n",
    "    weeks = ['Week 1','Week 2','Week 3','Week 4','Week 5','Week 6','Week 7','Week 8']\n",
    "    week_averages = []\n",
    "    for i in weeks:\n",
    "        data_week = data[data['Week']== i]['Rating']\n",
    "        average = np.round(np.mean(data_week), 2)\n",
    "        week_averages.append(average)\n",
    "    return week_averages\n",
    "\n",
    "reviews_per_week = dict(data['Week'].value_counts())\n",
    "average_per_week = find_average_per_week(data)\n",
    "print(\"Number of reviews per week: \", reviews_per_week, '\\n')\n",
    "print(\"Averages Per Week: \", average_per_week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Week'].unique(), average_per_week)\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([5,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _How many reviews per location?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'San Francisco': 360, 'New York City': 330, 'Santa Clara': 201, 'Los Angeles': 106, 'Beijing': 78, 'Chicago': 70, 'Tokyo': 65, 'Redwood City': 58, 'Washington DC': 47, 'Oakland': 47, 'Atlanta': 33, 'Hong Kong': 11}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reviews_per_location = dict(data['Location'].value_counts())\n",
    "print(reviews_per_location)\n",
    "\n",
    "plt.bar(range(len(reviews_per_location)), list(reviews_per_location.values()), align='center')\n",
    "plt.xticks(range(len(reviews_per_location)), list(reviews_per_location.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Does student satisfaction vary by location?_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>Rating</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Atlanta</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beijing</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>38</td>\n",
       "      <td>98</td>\n",
       "      <td>75</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakland</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redwood City</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Clara</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>72</td>\n",
       "      <td>64</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington DC</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Rating         0   1   2   3   4   5   6   7   8   9    10\n",
       "Location                                                  \n",
       "Atlanta         0   0   0   0   0   0   0   1   8  10   14\n",
       "Beijing         0   0   0   0   0   0   0   6  19  26   27\n",
       "Chicago         0   0   0   0   0   0   4  12  17  20   17\n",
       "Hong Kong       0   0   0   0   0   1   2   3   4   1    0\n",
       "Los Angeles     0   0   0   0   1   1   3   8  25  33   35\n",
       "New York City   1   2   1   3   3   9  16  38  98  75   84\n",
       "Oakland         0   0   0   0   0   1   1   9   9  15   12\n",
       "Redwood City    0   0   0   0   0   3   2  10  13  18   12\n",
       "San Francisco   0   0   1   4   3   6  17  40  95  93  101\n",
       "Santa Clara     0   0   0   0   1   7   5  23  72  64   29\n",
       "Tokyo           0   0   0   1   3   2   3  10  13  11   22\n",
       "Washington DC   0   0   0   0   0   3   3   7  14  10   10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.crosstab(data['Location'], data['Rating']) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120e20a10>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(['Location'])['Rating'].value_counts().unstack().plot(kind='barh',stacked=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# **Visuals**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _NPS Category Amounts_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d991a10>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# chart of percentages of NPS categories\n",
    "\n",
    "data['NPS Category'].hist(bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Percentage of each Rating_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "Rating=%{value}<extra></extra>",
         "labels": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(data, values=data['Rating'], names=data['Rating'], title='Percentage of Rating')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "![image](pie.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     392\n",
       "9     384\n",
       "10    376\n",
       "7     177\n",
       "6      59\n",
       "5      35\n",
       "4      13\n",
       "3       8\n",
       "2       2\n",
       "1       2\n",
       "0       2\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27034482758620687"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[data['Rating']==8]) / len(data['Rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## _Count of each Rating_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1216dba90>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Bar Chart Example #1 (Simple): Categorical Variables Showing Counts\n",
    "sns.countplot(x=\"Rating\", palette=\"OrRd\", data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}