05_2022_dog_breeds.ipynb

{
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   "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": "ae22b3fe-bbd6-44af-ab18-74177ee09c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the datasets:\n",
    "# breed_traits <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_traits.csv')\n",
    "# trait_description <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/trait_description.csv')\n",
    "# breed_rank_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_rank.csv')\n",
    "\n",
    "breed_traits = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_traits.csv')\n",
    "trait_description = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/trait_description.csv')\n",
    "breed_rank_all = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_rank.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "656b8a42-64bb-4d69-ad9b-c9f24fad8ff1",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Breed</th>\n",
       "      <th>Affectionate With Family</th>\n",
       "      <th>Good With Young Children</th>\n",
       "      <th>Good With Other Dogs</th>\n",
       "      <th>Shedding Level</th>\n",
       "      <th>Coat Grooming Frequency</th>\n",
       "      <th>Drooling Level</th>\n",
       "      <th>Coat Type</th>\n",
       "      <th>Coat Length</th>\n",
       "      <th>Openness To Strangers</th>\n",
       "      <th>Playfulness Level</th>\n",
       "      <th>Watchdog/Protective Nature</th>\n",
       "      <th>Adaptability Level</th>\n",
       "      <th>Trainability Level</th>\n",
       "      <th>Energy Level</th>\n",
       "      <th>Barking Level</th>\n",
       "      <th>Mental Stimulation Needs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Retrievers (Labrador)</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Double</td>\n",
       "      <td>Short</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Bulldogs</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Smooth</td>\n",
       "      <td>Short</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>German Shepherd Dogs</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Double</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Retrievers (Golden)</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Double</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bulldogs</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Smooth</td>\n",
       "      <td>Short</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "                   Breed  Affectionate With Family  Good With Young Children  \\\n",
       "0  Retrievers (Labrador)                         5                         5   \n",
       "1        French Bulldogs                         5                         5   \n",
       "2   German Shepherd Dogs                         5                         5   \n",
       "3    Retrievers (Golden)                         5                         5   \n",
       "4               Bulldogs                         4                         3   \n",
       "\n",
       "   Good With Other Dogs  Shedding Level  Coat Grooming Frequency  \\\n",
       "0                     5               4                        2   \n",
       "1                     4               3                        1   \n",
       "2                     3               4                        2   \n",
       "3                     5               4                        2   \n",
       "4                     3               3                        3   \n",
       "\n",
       "   Drooling Level Coat Type Coat Length  Openness To Strangers  \\\n",
       "0               2    Double       Short                      5   \n",
       "1               3    Smooth       Short                      5   \n",
       "2               2    Double      Medium                      3   \n",
       "3               2    Double      Medium                      5   \n",
       "4               3    Smooth       Short                      4   \n",
       "\n",
       "   Playfulness Level  Watchdog/Protective Nature  Adaptability Level  \\\n",
       "0                  5                           3                   5   \n",
       "1                  5                           3                   5   \n",
       "2                  4                           5                   5   \n",
       "3                  4                           3                   5   \n",
       "4                  4                           3                   3   \n",
       "\n",
       "   Trainability Level  Energy Level  Barking Level  Mental Stimulation Needs  \n",
       "0                   5             5              3                         4  \n",
       "1                   4             3              1                         3  \n",
       "2                   5             5              3                         5  \n",
       "3                   5             3              1                         4  \n",
       "4                   4             3              2                         3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breed_traits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40019c9c-dee6-4e35-9995-16ecefff2669",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Trait</th>\n",
       "      <th>Trait_1</th>\n",
       "      <th>Trait_5</th>\n",
       "      <th>Description</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Affectionate With Family</td>\n",
       "      <td>Independent</td>\n",
       "      <td>Lovey-Dovey</td>\n",
       "      <td>How affectionate a breed is likely to be with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good With Young Children</td>\n",
       "      <td>Not Recommended</td>\n",
       "      <td>Good With Children</td>\n",
       "      <td>A breed's level of tolerance and patience with...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good With Other Dogs</td>\n",
       "      <td>Not Recommended</td>\n",
       "      <td>Good With Other Dogs</td>\n",
       "      <td>How generally friendly a breed is towards othe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shedding Level</td>\n",
       "      <td>No Shedding</td>\n",
       "      <td>Hair Everywhere</td>\n",
       "      <td>How much fur and hair you can expect the breed...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coat Grooming Frequency</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Daily</td>\n",
       "      <td>How frequently a breed requires bathing, brush...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                      Trait          Trait_1               Trait_5  \\\n",
       "0  Affectionate With Family      Independent           Lovey-Dovey   \n",
       "1  Good With Young Children  Not Recommended    Good With Children   \n",
       "2      Good With Other Dogs  Not Recommended  Good With Other Dogs   \n",
       "3            Shedding Level      No Shedding       Hair Everywhere   \n",
       "4   Coat Grooming Frequency          Monthly                 Daily   \n",
       "\n",
       "                                         Description  \n",
       "0  How affectionate a breed is likely to be with ...  \n",
       "1  A breed's level of tolerance and patience with...  \n",
       "2  How generally friendly a breed is towards othe...  \n",
       "3  How much fur and hair you can expect the breed...  \n",
       "4  How frequently a breed requires bathing, brush...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trait_description.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb4ffc7d-b682-4f1e-aaac-696143d0b817",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Breed</th>\n",
       "      <th>2013 Rank</th>\n",
       "      <th>2014 Rank</th>\n",
       "      <th>2015 Rank</th>\n",
       "      <th>2016 Rank</th>\n",
       "      <th>2017 Rank</th>\n",
       "      <th>2018 Rank</th>\n",
       "      <th>2019 Rank</th>\n",
       "      <th>2020 Rank</th>\n",
       "      <th>links</th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Retrievers (Labrador)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.akc.org/dog-breeds/labrador-retrie...</td>\n",
       "      <td>https://www.akc.org/wp-content/uploads/2017/11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Bulldogs</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>https://www.akc.org/dog-breeds/french-bulldog/</td>\n",
       "      <td>https://www.akc.org/wp-content/uploads/2017/11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>German Shepherd Dogs</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>https://www.akc.org/dog-breeds/german-shepherd...</td>\n",
       "      <td>https://www.akc.org/wp-content/uploads/2017/11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Retrievers (Golden)</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>https://www.akc.org/dog-breeds/golden-retriever/</td>\n",
       "      <td>https://www.akc.org/wp-content/uploads/2017/11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bulldogs</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>https://www.akc.org/dog-breeds/bulldog/</td>\n",
       "      <td>https://www.akc.org/wp-content/uploads/2017/11...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Breed  2013 Rank  2014 Rank  2015 Rank  2016 Rank  \\\n",
       "0  Retrievers (Labrador)        1.0        1.0        1.0        1.0   \n",
       "1        French Bulldogs       11.0        9.0        6.0        6.0   \n",
       "2   German Shepherd Dogs        2.0        2.0        2.0        2.0   \n",
       "3    Retrievers (Golden)        3.0        3.0        3.0        3.0   \n",
       "4               Bulldogs        5.0        4.0        4.0        4.0   \n",
       "\n",
       "   2017 Rank  2018 Rank  2019 Rank  2020 Rank  \\\n",
       "0        1.0        1.0        1.0          1   \n",
       "1        4.0        4.0        4.0          2   \n",
       "2        2.0        2.0        2.0          3   \n",
       "3        3.0        3.0        3.0          4   \n",
       "4        5.0        5.0        5.0          5   \n",
       "\n",
       "                                               links  \\\n",
       "0  https://www.akc.org/dog-breeds/labrador-retrie...   \n",
       "1     https://www.akc.org/dog-breeds/french-bulldog/   \n",
       "2  https://www.akc.org/dog-breeds/german-shepherd...   \n",
       "3   https://www.akc.org/dog-breeds/golden-retriever/   \n",
       "4            https://www.akc.org/dog-breeds/bulldog/   \n",
       "\n",
       "                                               Image  \n",
       "0  https://www.akc.org/wp-content/uploads/2017/11...  \n",
       "1  https://www.akc.org/wp-content/uploads/2017/11...  \n",
       "2  https://www.akc.org/wp-content/uploads/2017/11...  \n",
       "3  https://www.akc.org/wp-content/uploads/2017/11...  \n",
       "4  https://www.akc.org/wp-content/uploads/2017/11...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breed_rank_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06278bf-4307-4380-a708-077579152b1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cfe1c87b-083c-4112-bba8-94817dd25c0f",
   "metadata": {},
   "source": [
    "Now, let's try to build a plot that will track the top 10 dogs by rank over the period the data is avaiable. Did they change places? In R this is a simple line plot with the seconday axis trick for readibility. First we prepare the data. Note that for the seconday axis we will use a separate dataframe. Also, I am doing an extra transformation here with creating a `year` variable of class `date`, as opposed to the `dbl` one. Not sure if it is going to be used though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "126c9a31-1385-45c6-a717-31d221964814",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Breed</th>\n",
       "      <th>year_rank</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Retrievers (Labrador)</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Bulldogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>German Shepherd Dogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Retrievers (Golden)</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bulldogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Poodles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Beagles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Rottweilers</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Pointers (German Shorthaired)</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Dachshunds</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Breed  year_rank  rank\n",
       "0           Retrievers (Labrador)  2013 Rank   1.0\n",
       "1                 French Bulldogs  2013 Rank  11.0\n",
       "2            German Shepherd Dogs  2013 Rank   2.0\n",
       "3             Retrievers (Golden)  2013 Rank   3.0\n",
       "4                        Bulldogs  2013 Rank   5.0\n",
       "..                            ...        ...   ...\n",
       "75                        Poodles  2020 Rank   6.0\n",
       "76                        Beagles  2020 Rank   7.0\n",
       "77                    Rottweilers  2020 Rank   8.0\n",
       "78  Pointers (German Shorthaired)  2020 Rank   9.0\n",
       "79                     Dachshunds  2020 Rank  10.0\n",
       "\n",
       "[80 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R: Prepere the data\n",
    "# top_rated_dogs_long <- top_rated_dogs %>% \n",
    "#  select(breed = Breed, ends_with(\"Rank\")) %>% \n",
    "#  tidyr::pivot_longer(\n",
    "#    cols = ends_with(\"Rank\"),\n",
    "#    names_to = \"year_rank\",\n",
    "#    values_to = \"rank\"\n",
    "#  ) %>% \n",
    "#  mutate(year_rank = readr::parse_number(year_rank)) %>% \n",
    "#  mutate(year = paste0(1,\"-\",1,\"-\",year_rank)) %>% \n",
    "#  mutate(year = lubridate::dmy(year)) \n",
    "\n",
    "#last_year <- top_rated_dogs_long %>% \n",
    "#  filter(year == as.Date(\"2020-01-01\")) \n",
    "\n",
    "top_rated_dogs = breed_rank_all.head(10)\n",
    "\n",
    "# apparently there are two ways to do the pivot in pandas\n",
    "# pandas.wide_to_long and pandas.melt\n",
    "# let's use melt; this reminds me of data.table.\n",
    "\n",
    "top_rated_dogs_long = pd.melt(top_rated_dogs, \n",
    "                              id_vars = ['Breed'],\n",
    "                              value_vars = [\"2013 Rank\" , \"2014 Rank\", \"2015 Rank\", \"2016 Rank\", \n",
    "                                            \"2017 Rank\", \"2018 Rank\", \"2019 Rank\", \"2020 Rank\"], \n",
    "                              var_name = \"year_rank\", \n",
    "                              value_name = \"rank\")\n",
    "\n",
    "top_rated_dogs_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "537ff2ba-fd92-49ac-b62b-cd252312ef5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# next we need the year as a number\n",
    "# here `assing` I think it is equivalent of `mutate`\n",
    "\n",
    "top_rated_dogs_long = top_rated_dogs_long.assign(year = top_rated_dogs_long.year_rank.str.rstrip(' Rank').astype('int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "890a4912-dad5-48e0-9caa-227fe58d3ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# and as date\n",
    "from datetime import datetime\n",
    "\n",
    "top_rated_dogs_long = top_rated_dogs_long.assign(year_d = top_rated_dogs_long.year.map(lambda y: datetime(y, 1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f917a65b-1e46-452c-ae8f-88738a9bbb89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        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>Breed</th>\n",
       "      <th>year_rank</th>\n",
       "      <th>rank</th>\n",
       "      <th>year</th>\n",
       "      <th>year_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Retrievers (Labrador)</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>French Bulldogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>German Shepherd Dogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Retrievers (Golden)</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bulldogs</td>\n",
       "      <td>2013 Rank</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Poodles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Beagles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Rottweilers</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Pointers (German Shorthaired)</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Dachshunds</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Breed  year_rank  rank  year     year_d\n",
       "0           Retrievers (Labrador)  2013 Rank   1.0  2013 2013-01-01\n",
       "1                 French Bulldogs  2013 Rank  11.0  2013 2013-01-01\n",
       "2            German Shepherd Dogs  2013 Rank   2.0  2013 2013-01-01\n",
       "3             Retrievers (Golden)  2013 Rank   3.0  2013 2013-01-01\n",
       "4                        Bulldogs  2013 Rank   5.0  2013 2013-01-01\n",
       "..                            ...        ...   ...   ...        ...\n",
       "75                        Poodles  2020 Rank   6.0  2020 2020-01-01\n",
       "76                        Beagles  2020 Rank   7.0  2020 2020-01-01\n",
       "77                    Rottweilers  2020 Rank   8.0  2020 2020-01-01\n",
       "78  Pointers (German Shorthaired)  2020 Rank   9.0  2020 2020-01-01\n",
       "79                     Dachshunds  2020 Rank  10.0  2020 2020-01-01\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_rated_dogs_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d60da19-9b9d-4c08-9cc6-2a7ce4406930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Breed                object\n",
       "year_rank            object\n",
       "rank                float64\n",
       "year                  int64\n",
       "year_d       datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_rated_dogs_long.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb53ab03-e1d2-476a-87a8-79c85ff08dfe",
   "metadata": {},
   "source": [
    "Seems we have the data in the needed format. I don't know if the same approach with secondary `y` axis is used in `matplotlib`, but let's make the same dataset as in `R`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93ed2635-3c26-416a-ae34-ad8850707ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Breed</th>\n",
       "      <th>year_rank</th>\n",
       "      <th>rank</th>\n",
       "      <th>year</th>\n",
       "      <th>year_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Retrievers (Labrador)</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>French Bulldogs</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>German Shepherd Dogs</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Retrievers (Golden)</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Bulldogs</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Poodles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Beagles</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Rottweilers</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Pointers (German Shorthaired)</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Dachshunds</td>\n",
       "      <td>2020 Rank</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Breed  year_rank  rank  year     year_d\n",
       "70          Retrievers (Labrador)  2020 Rank   1.0  2020 2020-01-01\n",
       "71                French Bulldogs  2020 Rank   2.0  2020 2020-01-01\n",
       "72           German Shepherd Dogs  2020 Rank   3.0  2020 2020-01-01\n",
       "73            Retrievers (Golden)  2020 Rank   4.0  2020 2020-01-01\n",
       "74                       Bulldogs  2020 Rank   5.0  2020 2020-01-01\n",
       "75                        Poodles  2020 Rank   6.0  2020 2020-01-01\n",
       "76                        Beagles  2020 Rank   7.0  2020 2020-01-01\n",
       "77                    Rottweilers  2020 Rank   8.0  2020 2020-01-01\n",
       "78  Pointers (German Shorthaired)  2020 Rank   9.0  2020 2020-01-01\n",
       "79                     Dachshunds  2020 Rank  10.0  2020 2020-01-01"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_year = top_rated_dogs_long.query('year == 2020')\n",
    "last_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411b8c2d-ca5d-4fe9-b0d5-3888c3a02128",
   "metadata": {},
   "source": [
    "Let's plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "32d820e6-e346-44c9-a9b4-8795bf2542a4",
   "metadata": {},
   "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": [
    "# R: ggplot(top_rated_dogs_long) +\n",
    "#  aes(x = year, y = rank, color = breed) +\n",
    "#  geom_line() +\n",
    "#  theme_minimal() +\n",
    "#  theme(legend.position = \"none\") +\n",
    "#  scale_x_date(expand = expansion(mult = 0)) +\n",
    "#  scale_y_continuous(limits = c(0, 15),\n",
    "#  sec.axis = dup_axis(\n",
    "#    breaks = last_year$rank,\n",
    "#    labels = last_year$breed,\n",
    "#    name = NULL)) +\n",
    "#  labs(x = \"Year\", y = \"Rank\", title = \"The best ranked dogs in USA\", \n",
    "#       caption = \"Tidy tuesday data for week 5 of 2022\")\n",
    "\n",
    "#import matplotx\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "top_rated_dogs_long.groupby(\"Breed\").plot(kind = \"line\", x = \"year\", y = \"rank\", ax = ax)\n",
    "ax.legend().set_visible(False)\n",
    "ax.set(title = \"The best ranked dogs in USA\", \n",
    "       xlabel = \"Year\",\n",
    "       ylabel = \"Rank\"\n",
    "      )\n",
    "txt=\"Tidy tuesday data for week 5 of 2022\"\n",
    "fig.text(0.80, 0.01, txt, ha='center')\n",
    "\n",
    "for i in range(10):\n",
    "    ax.text(x = 2021, y = last_year[\"rank\"].iloc[i], s= last_year[\"Breed\"].iloc[i]);\n",
    "\n",
    "# Not the best of plots, but this is one way to do 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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}