ulrik.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./data/cats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>type</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>size</th>\n",
       "      <th>coat</th>\n",
       "      <th>breed</th>\n",
       "      <th>photos</th>\n",
       "      <th>med_photos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>46744842</td>\n",
       "      <td>https://www.petfinder.com/cat/arianna-46744842...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>46717321</td>\n",
       "      <td>https://www.petfinder.com/cat/ozzy-46717321/oh...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Male</td>\n",
       "      <td>Extra Large</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>46626338</td>\n",
       "      <td>https://www.petfinder.com/cat/zena-46626338/ca...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>46620170</td>\n",
       "      <td>https://www.petfinder.com/cat/mika-46620170/ca...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Baby</td>\n",
       "      <td>Female</td>\n",
       "      <td>Small</td>\n",
       "      <td>Short</td>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>46582751</td>\n",
       "      <td>https://www.petfinder.com/cat/ruby-46582751/fl...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Young</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>494</td>\n",
       "      <td>38142502</td>\n",
       "      <td>https://www.petfinder.com/cat/bobbie-38142502/...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Young</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>495</td>\n",
       "      <td>38101900</td>\n",
       "      <td>https://www.petfinder.com/cat/sheba-38101900/u...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Long</td>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>496</td>\n",
       "      <td>38042354</td>\n",
       "      <td>https://www.petfinder.com/cat/bobby-38042354/g...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Young</td>\n",
       "      <td>Male</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>497</td>\n",
       "      <td>38024353</td>\n",
       "      <td>https://www.petfinder.com/cat/cami-38024353/pa...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Female</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Short</td>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>498</td>\n",
       "      <td>38018266</td>\n",
       "      <td>https://www.petfinder.com/cat/bobbie-38018266/...</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Female</td>\n",
       "      <td>Small</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>[{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...</td>\n",
       "      <td>['https://dl5zpyw5k3jeb.cloudfront.net/photos/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        id                                                url  \\\n",
       "0             0  46744842  https://www.petfinder.com/cat/arianna-46744842...   \n",
       "1             1  46717321  https://www.petfinder.com/cat/ozzy-46717321/oh...   \n",
       "2             2  46626338  https://www.petfinder.com/cat/zena-46626338/ca...   \n",
       "3             3  46620170  https://www.petfinder.com/cat/mika-46620170/ca...   \n",
       "4             4  46582751  https://www.petfinder.com/cat/ruby-46582751/fl...   \n",
       "..          ...       ...                                                ...   \n",
       "494         494  38142502  https://www.petfinder.com/cat/bobbie-38142502/...   \n",
       "495         495  38101900  https://www.petfinder.com/cat/sheba-38101900/u...   \n",
       "496         496  38042354  https://www.petfinder.com/cat/bobby-38042354/g...   \n",
       "497         497  38024353  https://www.petfinder.com/cat/cami-38024353/pa...   \n",
       "498         498  38018266  https://www.petfinder.com/cat/bobbie-38018266/...   \n",
       "\n",
       "    type     age  gender         size   coat             breed  \\\n",
       "0    Cat   Adult  Female       Medium    NaN        Abyssinian   \n",
       "1    Cat   Adult    Male  Extra Large    NaN        Abyssinian   \n",
       "2    Cat  Senior  Female       Medium    NaN        Abyssinian   \n",
       "3    Cat    Baby  Female        Small  Short        Abyssinian   \n",
       "4    Cat   Young  Female       Medium    NaN        Abyssinian   \n",
       "..   ...     ...     ...          ...    ...               ...   \n",
       "494  Cat   Young  Female       Medium    NaN  American Bobtail   \n",
       "495  Cat   Adult  Female       Medium   Long  American Bobtail   \n",
       "496  Cat   Young    Male        Small    NaN  American Bobtail   \n",
       "497  Cat   Adult  Female       Medium  Short  American Bobtail   \n",
       "498  Cat   Adult  Female        Small    NaN  American Bobtail   \n",
       "\n",
       "                                                photos  \\\n",
       "0    [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "1    [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "2    [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "3    [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "4    [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "..                                                 ...   \n",
       "494  [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "495  [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "496  [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "497  [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "498  [{'small': 'https://dl5zpyw5k3jeb.cloudfront.n...   \n",
       "\n",
       "                                            med_photos  \n",
       "0    ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "1    ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "2    ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "3    ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "4    ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "..                                                 ...  \n",
       "494  ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "495  ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "496  ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "497  ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "498  ['https://dl5zpyw5k3jeb.cloudfront.net/photos/...  \n",
       "\n",
       "[499 rows x 11 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"age\"].value_counts().plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>age_mode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abyssinian</th>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Bobtail</th>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  age_mode\n",
       "breed                     \n",
       "Abyssinian             253\n",
       "American Bobtail       246"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"breed\").agg(age_mode=(\"age\", \"count\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abyssinian</td>\n",
       "      <td>Adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>American Bobtail</td>\n",
       "      <td>Baby</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              breed    age\n",
       "0        Abyssinian  Adult\n",
       "0  American Bobtail   Baby"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breeds = df[\"breed\"].unique().tolist()\n",
    "rows = []\n",
    "for breed in breeds:\n",
    "    row = df[df[\"breed\"] == breed][[\"breed\", \"age\"]].mode()\n",
    "    rows.append(row)\n",
    "\n",
    "df_new = pd.concat(rows)\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Abyssinian', 'American Bobtail'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"breed\"].unique()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}