03_2022_chocolate.ipynb

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   "cell_type": "markdown",
   "id": "c9ee6e81-17e4-4e49-825c-aea4caa2d7a0",
   "metadata": {},
   "source": [
    "This notebook is with the [dataset](https://github.com/rfordatascience/tidytuesday/blob/master/data/2022/2022-01-18/readme.md) of week 3 of 2022 from the Tidy Tuesday community."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f1042c-521f-4db8-8dcc-d075cdb516c7",
   "metadata": {},
   "source": [
    "I am trying to do some simple stuff in python, (maybe) equvalent to what I can do quickly with R. Suggestions are welcomed.\n",
    "I am writing comments in R as well, to be able to see / compare / learn between the two. A nice resource for comparing functions can be found [here](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_r.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8027f42c-38b4-4c8b-9f4f-6bfdd4f3bf03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the python toolkit for data analysis and visualistion. \n",
    "# more or less to doing library(tidyverse) (without going into details).\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd4c621e-35b2-45e3-805b-11beac100ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the dataset\n",
    "# R: chocolate <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv')\n",
    "chocolate = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a19037d0-6d4c-45c7-bbc7-ad5e7601149b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ref                                   int64\n",
       "company_manufacturer                 object\n",
       "company_location                     object\n",
       "review_date                           int64\n",
       "country_of_bean_origin               object\n",
       "specific_bean_origin_or_bar_name     object\n",
       "cocoa_percent                        object\n",
       "ingredients                          object\n",
       "most_memorable_characteristics       object\n",
       "rating                              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R: dplyr::glimpse(chocolate)\n",
    "chocolate.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab735478-c344-408a-afc7-d845a3961087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2530, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chocolate.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e551f0-333a-4763-8779-c7d99479cc2c",
   "metadata": {},
   "source": [
    "I want to start with something simple. I want to see if there is some kind of association between `cocoa_percent` and `rating`. A scatter plot would suit nicely. But `cocoa_percent` is `dtype object` (it's how pandas stores strings ([more here](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html)). We can veryfy that by looking at the data below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39a04520-81a6-4b89-9d14-d8d40afeaa4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ref</th>\n",
       "      <th>company_manufacturer</th>\n",
       "      <th>company_location</th>\n",
       "      <th>review_date</th>\n",
       "      <th>country_of_bean_origin</th>\n",
       "      <th>specific_bean_origin_or_bar_name</th>\n",
       "      <th>cocoa_percent</th>\n",
       "      <th>ingredients</th>\n",
       "      <th>most_memorable_characteristics</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2454</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2019</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>Kokoa Kamili, batch 1</td>\n",
       "      <td>76%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>rich cocoa, fatty, bready</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2458</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2019</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Zorzal, batch 1</td>\n",
       "      <td>76%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>cocoa, vegetal, savory</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2454</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2019</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Bejofo Estate, batch 1</td>\n",
       "      <td>76%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>cocoa, blackberry, full body</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2021</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>Matasawalevu, batch 1</td>\n",
       "      <td>68%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>chewy, off, rubbery</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2546</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2021</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Sur del Lago, batch 1</td>\n",
       "      <td>72%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>fatty, earthy, moss, nutty,chalky</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2546</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2021</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>Semuliki Forest, batch 1</td>\n",
       "      <td>80%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>mildly bitter, basic cocoa, fatty</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2542</td>\n",
       "      <td>5150</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2021</td>\n",
       "      <td>India</td>\n",
       "      <td>Anamalai, batch 1</td>\n",
       "      <td>68%</td>\n",
       "      <td>3- B,S,C</td>\n",
       "      <td>milk brownie, macadamia,chewy</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>797</td>\n",
       "      <td>A. Morin</td>\n",
       "      <td>France</td>\n",
       "      <td>2012</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>70%</td>\n",
       "      <td>4- B,S,C,L</td>\n",
       "      <td>vegetal, nutty</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>797</td>\n",
       "      <td>A. Morin</td>\n",
       "      <td>France</td>\n",
       "      <td>2012</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Peru</td>\n",
       "      <td>63%</td>\n",
       "      <td>4- B,S,C,L</td>\n",
       "      <td>fruity, melon, roasty</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1011</td>\n",
       "      <td>A. Morin</td>\n",
       "      <td>France</td>\n",
       "      <td>2013</td>\n",
       "      <td>Panama</td>\n",
       "      <td>Panama</td>\n",
       "      <td>70%</td>\n",
       "      <td>4- B,S,C,L</td>\n",
       "      <td>brief fruit note, earthy, nutty</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ref company_manufacturer company_location  review_date  \\\n",
       "0  2454                 5150           U.S.A.         2019   \n",
       "1  2458                 5150           U.S.A.         2019   \n",
       "2  2454                 5150           U.S.A.         2019   \n",
       "3  2542                 5150           U.S.A.         2021   \n",
       "4  2546                 5150           U.S.A.         2021   \n",
       "5  2546                 5150           U.S.A.         2021   \n",
       "6  2542                 5150           U.S.A.         2021   \n",
       "7   797             A. Morin           France         2012   \n",
       "8   797             A. Morin           France         2012   \n",
       "9  1011             A. Morin           France         2013   \n",
       "\n",
       "  country_of_bean_origin specific_bean_origin_or_bar_name cocoa_percent  \\\n",
       "0               Tanzania            Kokoa Kamili, batch 1           76%   \n",
       "1     Dominican Republic                  Zorzal, batch 1           76%   \n",
       "2             Madagascar           Bejofo Estate, batch 1           76%   \n",
       "3                   Fiji            Matasawalevu, batch 1           68%   \n",
       "4              Venezuela            Sur del Lago, batch 1           72%   \n",
       "5                 Uganda         Semuliki Forest, batch 1           80%   \n",
       "6                  India                Anamalai, batch 1           68%   \n",
       "7                Bolivia                          Bolivia           70%   \n",
       "8                   Peru                             Peru           63%   \n",
       "9                 Panama                           Panama           70%   \n",
       "\n",
       "  ingredients     most_memorable_characteristics  rating  \n",
       "0    3- B,S,C          rich cocoa, fatty, bready    3.25  \n",
       "1    3- B,S,C             cocoa, vegetal, savory    3.50  \n",
       "2    3- B,S,C       cocoa, blackberry, full body    3.75  \n",
       "3    3- B,S,C                chewy, off, rubbery    3.00  \n",
       "4    3- B,S,C  fatty, earthy, moss, nutty,chalky    3.00  \n",
       "5    3- B,S,C  mildly bitter, basic cocoa, fatty    3.25  \n",
       "6    3- B,S,C      milk brownie, macadamia,chewy    3.50  \n",
       "7  4- B,S,C,L                     vegetal, nutty    3.50  \n",
       "8  4- B,S,C,L              fruity, melon, roasty    3.75  \n",
       "9  4- B,S,C,L    brief fruit note, earthy, nutty    2.75  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# glimpse (more or less) provides this information but it is also possible to view head in R\n",
    "# R: head(chocolate, 10)\n",
    "chocolate.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cef1c72-3c4f-4e1b-a1b5-c2f9090e61c7",
   "metadata": {},
   "source": [
    "So the next step is to transform this variable for it be suitable for plotting in a scatter plot: from text to number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f9761d4-a41e-40c2-890d-6c2a687ba9c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# R: chocolate <- chocolate %>% \n",
    "#          dplyr::mutate(cocoa_pctg = readr::parse_number(cocoa_percent))\n",
    "# Should we divide or not by 100? It doesn't seem to important at this moment.\n",
    "chocolate[\"cocoa_pctg\"] = chocolate.cocoa_percent.str.rstrip('%').astype('float')# / 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f63a792-28fe-4a90-b9f2-5ab5884b4a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    76.0\n",
       "1    76.0\n",
       "2    76.0\n",
       "3    68.0\n",
       "4    72.0\n",
       "Name: cocoa_pctg, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify that we now have numbers.\n",
    "chocolate.cocoa_pctg.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9bc04f08-ba07-4395-9168-b8853c633ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any chocolates that are 100% cocoa? \n",
    "# R: max(chocolate$cocoa_pctg)\n",
    "chocolate.cocoa_pctg.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19fe92e4-db00-4e72-b24c-506c0ee1e3ac",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ref</th>\n",
       "      <th>company_manufacturer</th>\n",
       "      <th>company_location</th>\n",
       "      <th>review_date</th>\n",
       "      <th>country_of_bean_origin</th>\n",
       "      <th>specific_bean_origin_or_bar_name</th>\n",
       "      <th>cocoa_percent</th>\n",
       "      <th>ingredients</th>\n",
       "      <th>most_memorable_characteristics</th>\n",
       "      <th>rating</th>\n",
       "      <th>cocoa_pctg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>164</td>\n",
       "      <td>486</td>\n",
       "      <td>Artisan du Chocolat</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2010</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>very nutty, very bitter</td>\n",
       "      <td>1.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>293</td>\n",
       "      <td>81</td>\n",
       "      <td>Bonnat</td>\n",
       "      <td>France</td>\n",
       "      <td>2006</td>\n",
       "      <td>Blend</td>\n",
       "      <td>One Hundred</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>acidic, bitter, dry</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>323</td>\n",
       "      <td>341</td>\n",
       "      <td>Bouga Cacao (Tulicorp)</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2009</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>El Oro, Hacienda de Oro</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cardboard, very bitter, floral</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>435</td>\n",
       "      <td>1185</td>\n",
       "      <td>C-Amaro</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2013</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>fatty, controlled bitter, fruit</td>\n",
       "      <td>3.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>590</td>\n",
       "      <td>252</td>\n",
       "      <td>Claudio Corallo</td>\n",
       "      <td>Sao Tome</td>\n",
       "      <td>2008</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Principe, Sao Tome &amp; Principe</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chalky, musty, very bitter</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>626</td>\n",
       "      <td>817</td>\n",
       "      <td>Coppeneur</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2012</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Ecuador Puristique</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high intensity bitter</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>784</td>\n",
       "      <td>1109</td>\n",
       "      <td>Domori</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2013</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>IL100, H. San Jose, batch NDL3A01</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>smooth, fruit, coffee, bitter</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1106</td>\n",
       "      <td>1205</td>\n",
       "      <td>Habitual</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2014</td>\n",
       "      <td>Blend</td>\n",
       "      <td>one hundred</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unrefined, bitter, earthy</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1180</td>\n",
       "      <td>1113</td>\n",
       "      <td>Hotel Chocolat</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2013</td>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>Island Growers, 2012, 120hr c., batch 13080</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pastey, bitter, unfixable</td>\n",
       "      <td>1.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1197</td>\n",
       "      <td>1113</td>\n",
       "      <td>Hotel Chocolat (Coppeneur)</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2013</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Los Rios, H. Iara, 2012, 120hr c., batch 13092</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>candy,uncontrolled bitterness</td>\n",
       "      <td>2.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1198</td>\n",
       "      <td>1109</td>\n",
       "      <td>Hotel Chocolat (Coppeneur)</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2013</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Chanchamayo, Pichanadi, 2012, 60hr c., b.13092</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>spice,cocoa, tolerable bitter</td>\n",
       "      <td>2.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1199</td>\n",
       "      <td>1113</td>\n",
       "      <td>Hotel Chocolat (Coppeneur)</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2013</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Conacado,  2012, 120hr c.batch 13092</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>trop. Fruit, coffee, bitter</td>\n",
       "      <td>2.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1721</td>\n",
       "      <td>1760</td>\n",
       "      <td>Ocho</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Samoa</td>\n",
       "      <td>Sang Yum Coop</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>bitter, intense, nutty</td>\n",
       "      <td>2.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1730</td>\n",
       "      <td>2582</td>\n",
       "      <td>Odyssey</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2021</td>\n",
       "      <td>Blend</td>\n",
       "      <td>Elegant Asian Blend\", Vietnam, India</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>sharp fruit, intense, sl. Sour</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1776</td>\n",
       "      <td>266</td>\n",
       "      <td>Pacari</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2008</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Raw</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitter, earthy</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1892</td>\n",
       "      <td>202</td>\n",
       "      <td>Pralus</td>\n",
       "      <td>France</td>\n",
       "      <td>2008</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Sambirano Valley, Le 100%</td>\n",
       "      <td>100%</td>\n",
       "      <td>2- B,C</td>\n",
       "      <td>fatty, mild fruit, bitter, smoke</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017</td>\n",
       "      <td>615</td>\n",
       "      <td>S.A.I.D.</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2010</td>\n",
       "      <td>Blend</td>\n",
       "      <td>100 percent</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sticky, intense, very bitter</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2086</td>\n",
       "      <td>1502</td>\n",
       "      <td>Sirene</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2015</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Somia Plantation, 2012</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>creamy, tart, bitter</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2087</td>\n",
       "      <td>1506</td>\n",
       "      <td>Sirene</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2015</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Camino Verde P., Balao, Guayas, 2012</td>\n",
       "      <td>100%</td>\n",
       "      <td>1- B</td>\n",
       "      <td>floral, bourbon, bitter</td>\n",
       "      <td>3.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2424</td>\n",
       "      <td>153</td>\n",
       "      <td>Vintage Plantations (Tulicorp)</td>\n",
       "      <td>U.S.A.</td>\n",
       "      <td>2007</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Los Rios, Rancho Grande 2004/2007</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bland, mild fruit, strong bitter</td>\n",
       "      <td>2.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2457</td>\n",
       "      <td>1109</td>\n",
       "      <td>Willie's Cacao</td>\n",
       "      <td>U.K.</td>\n",
       "      <td>2013</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Java, Indonesian Black</td>\n",
       "      <td>100%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fruit, strong smoke, bitter</td>\n",
       "      <td>2.25</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index   ref            company_manufacturer company_location  review_date  \\\n",
       "0     164   486             Artisan du Chocolat             U.K.         2010   \n",
       "1     293    81                          Bonnat           France         2006   \n",
       "2     323   341          Bouga Cacao (Tulicorp)          Ecuador         2009   \n",
       "3     435  1185                         C-Amaro            Italy         2013   \n",
       "4     590   252                 Claudio Corallo         Sao Tome         2008   \n",
       "5     626   817                       Coppeneur          Germany         2012   \n",
       "6     784  1109                          Domori            Italy         2013   \n",
       "7    1106  1205                        Habitual           Canada         2014   \n",
       "8    1180  1113                  Hotel Chocolat             U.K.         2013   \n",
       "9    1197  1113      Hotel Chocolat (Coppeneur)             U.K.         2013   \n",
       "10   1198  1109      Hotel Chocolat (Coppeneur)             U.K.         2013   \n",
       "11   1199  1113      Hotel Chocolat (Coppeneur)             U.K.         2013   \n",
       "12   1721  1760                            Ocho      New Zealand         2016   \n",
       "13   1730  2582                         Odyssey           U.S.A.         2021   \n",
       "14   1776   266                          Pacari          Ecuador         2008   \n",
       "15   1892   202                          Pralus           France         2008   \n",
       "16   2017   615                        S.A.I.D.            Italy         2010   \n",
       "17   2086  1502                          Sirene           Canada         2015   \n",
       "18   2087  1506                          Sirene           Canada         2015   \n",
       "19   2424   153  Vintage Plantations (Tulicorp)           U.S.A.         2007   \n",
       "20   2457  1109                  Willie's Cacao             U.K.         2013   \n",
       "\n",
       "   country_of_bean_origin                specific_bean_origin_or_bar_name  \\\n",
       "0               Venezuela                                       Venezuela   \n",
       "1                   Blend                                     One Hundred   \n",
       "2                 Ecuador                         El Oro, Hacienda de Oro   \n",
       "3                 Ecuador                                         Ecuador   \n",
       "4                   Blend                   Principe, Sao Tome & Principe   \n",
       "5                 Ecuador                              Ecuador Puristique   \n",
       "6               Venezuela               IL100, H. San Jose, batch NDL3A01   \n",
       "7                   Blend                                     one hundred   \n",
       "8               St. Lucia     Island Growers, 2012, 120hr c., batch 13080   \n",
       "9                 Ecuador  Los Rios, H. Iara, 2012, 120hr c., batch 13092   \n",
       "10                   Peru  Chanchamayo, Pichanadi, 2012, 60hr c., b.13092   \n",
       "11     Dominican Republic            Conacado,  2012, 120hr c.batch 13092   \n",
       "12                  Samoa                                   Sang Yum Coop   \n",
       "13                  Blend            Elegant Asian Blend\", Vietnam, India   \n",
       "14                Ecuador                                             Raw   \n",
       "15             Madagascar                       Sambirano Valley, Le 100%   \n",
       "16                  Blend                                     100 percent   \n",
       "17             Madagascar                          Somia Plantation, 2012   \n",
       "18                Ecuador            Camino Verde P., Balao, Guayas, 2012   \n",
       "19                Ecuador               Los Rios, Rancho Grande 2004/2007   \n",
       "20              Indonesia                          Java, Indonesian Black   \n",
       "\n",
       "   cocoa_percent ingredients    most_memorable_characteristics  rating  \\\n",
       "0           100%         NaN           very nutty, very bitter    1.75   \n",
       "1           100%         NaN               acidic, bitter, dry    1.50   \n",
       "2           100%         NaN    cardboard, very bitter, floral    1.50   \n",
       "3           100%        1- B   fatty, controlled bitter, fruit    3.50   \n",
       "4           100%         NaN        chalky, musty, very bitter    1.00   \n",
       "5           100%         NaN             high intensity bitter    1.50   \n",
       "6           100%         NaN     smooth, fruit, coffee, bitter    3.00   \n",
       "7           100%         NaN         unrefined, bitter, earthy    2.00   \n",
       "8           100%         NaN         pastey, bitter, unfixable    1.75   \n",
       "9           100%        1- B     candy,uncontrolled bitterness    2.50   \n",
       "10          100%         NaN     spice,cocoa, tolerable bitter    2.75   \n",
       "11          100%         NaN       trop. Fruit, coffee, bitter    2.75   \n",
       "12          100%        1- B            bitter, intense, nutty    2.75   \n",
       "13          100%        1- B    sharp fruit, intense, sl. Sour    3.00   \n",
       "14          100%         NaN                    bitter, earthy    2.00   \n",
       "15          100%      2- B,C  fatty, mild fruit, bitter, smoke    3.00   \n",
       "16          100%         NaN      sticky, intense, very bitter    1.50   \n",
       "17          100%        1- B              creamy, tart, bitter    3.00   \n",
       "18          100%        1- B           floral, bourbon, bitter    3.00   \n",
       "19          100%         NaN  bland, mild fruit, strong bitter    2.00   \n",
       "20          100%         NaN       fruit, strong smoke, bitter    2.25   \n",
       "\n",
       "    cocoa_pctg  \n",
       "0        100.0  \n",
       "1        100.0  \n",
       "2        100.0  \n",
       "3        100.0  \n",
       "4        100.0  \n",
       "5        100.0  \n",
       "6        100.0  \n",
       "7        100.0  \n",
       "8        100.0  \n",
       "9        100.0  \n",
       "10       100.0  \n",
       "11       100.0  \n",
       "12       100.0  \n",
       "13       100.0  \n",
       "14       100.0  \n",
       "15       100.0  \n",
       "16       100.0  \n",
       "17       100.0  \n",
       "18       100.0  \n",
       "19       100.0  \n",
       "20       100.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R  chocolate %>% \n",
    "#  filter(cocoa_pctg == \"100\")\n",
    "# I am using reset_index() here to see the row count. I am not sure this is the right way.\n",
    "chocolate[chocolate[\"cocoa_percent\"] == \"100%\"].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32fb39a-8574-42d2-8298-c594275d9da3",
   "metadata": {},
   "source": [
    "Now we have the data in the desired format. Time for a plot. I am follwing mostly the explanation at [Effectively Using Matplotlib ](https://pbpython.com/effective-matplotlib.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88873af4-ec51-445a-9559-869431ed328d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# R: ggplot(chocolate) +\n",
    "#      aes(x = cocoa_pctg, y = rating ) +\n",
    "#      geom_point() +\n",
    "#      theme_light() +\n",
    "#      labs(title = \"Chocolate ratings by Flavors of Cacao\",\n",
    "#           x = \"Percent of cocoa\",\n",
    "#           y = \"Rating\")\n",
    "\n",
    "\n",
    "plt.style.use('seaborn') # can be 'ggplot' also, but let's see what else is there.\n",
    "fig, ax = plt.subplots()\n",
    "chocolate.plot(kind = \"scatter\", x = \"cocoa_pctg\", y = \"rating\", ax = ax)\n",
    "ax.set(title = \"Chocolate ratings by Flavors of Cacao\", \n",
    "       xlabel = \"Percent of cocoa\",\n",
    "       ylabel = \"Rating\"\n",
    "      ); #semicolon omits outpup of last object in cell which is the text, and we want it only in the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd45122-f2b6-499a-b60f-b32b3977e176",
   "metadata": {},
   "source": [
    "Seems like chocolate is good, regardless of how much cocoa is in it. :)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}