movielens_ratings2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "PATH_MOVIES  = 'Material/introducao-a-data-science-aula0/aula0/ml-latest-small/movies.csv'\n",
    "PATH_RATING= 'Material/introducao-a-data-science-aula0/aula0/ml-latest-small/ratings.csv'\n",
    "\n",
    "df_movies    = pd.read_csv(PATH_MOVIES)\n",
    "df_ratings   = pd.read_csv(PATH_RATING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\n",
       "1        2    Jumanji (1995)                   Adventure|Children|Fantasy"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toy Story rating mean -> 3.9209\n",
      "Jumanji   rating mean -> 3.4318\n"
     ]
    }
   ],
   "source": [
    "toy_story_rating = df_ratings.query('movieId == 1')\n",
    "jumanji_rating   = df_ratings.query('movieId == 2')\n",
    "\n",
    "print(f'Toy Story rating mean -> {toy_story_rating.rating.mean():.4f}\\nJumanji   rating mean -> {jumanji_rating.rating.mean():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Jumanji Ratings')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(7,3))\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "\n",
    "toy_story_rating['rating'].plot(kind='hist', ax=axes[0])\n",
    "jumanji_rating['rating'].plot(kind='hist', ax=axes[1])\n",
    "\n",
    "# Set x-axis labels for each subplot\n",
    "axes[0].set_xlabel('Toy Story Ratings')\n",
    "axes[1].set_xlabel('Jumanji Ratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='rating'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "import seaborn as sns\n",
    "\n",
    "sns.boxplot(x=toy_story_rating.rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x2619b5bef10>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5bf9d0>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5cecd0>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5cf5d0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2619b5cc410>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5aee90>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5cff50>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5dc810>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x2619b5be5d0>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5ce450>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2619b5cd290>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5dd090>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2619b5bee10>,\n",
       "  <matplotlib.lines.Line2D at 0x2619b5dd950>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.boxplot([toy_story_rating['rating'], jumanji_rating['rating']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='movieId', ylabel='rating'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAGwCAYAAACHJU4LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAeeklEQVR4nO3df3DX9X3A8deHoN8vSBIFCzMmsXSu2lah6LCH0urWjl3WUWV3zpqs9de6a4dUZXorbVel08XtXKxeHWVKhe7I3K499LSNPdsVmKMc8QdOr/6Yzh5QW+2FSQj2m0ry2R/OzCgghpDPO8njcfe9+34/3w/f7+ub+Emefj6fb75Znud5AAAkaELRAwAA7I9QAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkTSx6gEPR398fL7zwQlRXV0eWZUWPAwAchDzPY/fu3VFXVxcTJhx4n8moDpUXXnghGhoaih4DABiC7du3R319/QHXGdWhUl1dHRGvvdCampqCpwEADkZ3d3c0NDQM/B4/kFEdKq8f7qmpqREqADDKHMxpG06mBQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZBUaKtddd11kWTbocvLJJxc5EgCQkMI/6+cDH/hA/OAHPxi4PXFi4SMBAIkovAomTpwYv/Ebv1H0GGNanudRqVSKHuOQ5Hkevb29ERFRKpUO6oOsUlcul8fE66BYtu802b6HT+Gh8l//9V9RV1cX5XI55s2bF62trdHY2LjPdXt7ewf+Y4547WOieXuVSiWampqKHoM36ejoiEmTJhU9BqOc7TtNtu/hU+g5Kh/60Idi9erVcf/998eKFSvi+eefjw9/+MOxe/fufa7f2toatbW1A5eGhoYRnhgAGElZnud50UO87uWXX44TTjgh2tra4rLLLnvL/fvao9LQ0BC7du2KmpqakRx1VBkLu4YrlUosWrQoIiLWrVsX5XK54IkOnV3DDAfbd5ps3wfW3d0dtbW1B/X7u/BDP2909NFHx3vf+9549tln93l/qVSKUqk0wlONflmWjaldkOVyeUy9HjgUtm/GuqT+jkpPT08899xzcdxxxxU9CgCQgEJD5eqrr44NGzbET3/609i0aVMsWrQoqqqq4sILLyxyLAAgEYUe+tmxY0dceOGF0dXVFe9617ti/vz5sXnz5njXu95V5FgAQCIKDZW77rqryKcHABKX1DkqAABvJFQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEjWxKIHSFme51GpVIoeg4hB3wffk3SUy+XIsqzoMYAxTKgcQKVSiaampqLH4E0WLVpU9Aj8n46Ojpg0aVLRYwBjmEM/AECy7FE5SD0fvDDyCb5chcnziP69r12fMDHC4YbCZP17Y8rWfy56DGCc8Jv3IOUTJkZUHVH0GOPckUUPQETkRQ8AjCsO/QAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJCsZELlxhtvjCzL4sorryx6FAAgEUmESmdnZ6xcuTJmzZpV9CgAQEImFj1AT09PtLS0xO233x7XX3990eMMkuf5/9/oe7W4QSAlb9gWBm0jo0ye51GpVIoeg4hB3wffk3SUy+XIsqzoMYoPlcWLF8fHP/7x+NjHPva2odLb2xu9vb0Dt7u7uw/rbG98rurH7jqszwWjUW9vb0yePLnoMYakUqlEU1NT0WPwJosWLSp6BP5PR0dHTJo0qegxig2Vu+66Kx555JHo7Ow8qPVbW1tj+fLlh3kqACAVhYXK9u3b44orrogHHnggyuXyQf2bZcuWxdKlSwdud3d3R0NDw+EaMUql0sD13bM/GVF1xGF7Lhg1+l4d2MP4xm1kNPv6/J1Rqhq9h7FGuzyP+HX/a9ePnBCRwNGGcau3L4vLH5xa9BiDFBYqDz/8cLz00ktx2mmnDSzr6+uLjRs3xte//vXo7e2NqqqqQf+mVCqN6A/GQcfmqo4QKvAmKRy/Hg6lqjxKVW+/HofPwf3vKodfesFeWKh89KMfjccff3zQsksuuSROPvnk+Mu//Mu3RAoAMP4UFirV1dVxyimnDFp21FFHxbRp096yHAAYn5L4OyoAAPtS+NuT32j9+vVFjwAAJMQeFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkTSx6gNEi698bedFDjGd5HtG/97XrEyZGZFmx84xj2evfh1Euz/9/i+7tK3AQSMgbt4U3biNFEioHacrWfy56BGAY9fb2Dly//MFpBU4Caert7Y3JkycXPYZDPwBAuuxROYByuRwdHR1Fj0FEVCqVWLRoUURErFu3LsrlcsETERGj+vtQKpUGrn99fleUqgocBhLR2/f/exjfuI0USagcQJZlMWnSpKLH4E3K5bLvC4cse8N5TqWqECrwJlki5wI69AMAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyCg2VFStWxKxZs6KmpiZqampi3rx50dHRUeRIAEBCJhb55PX19XHjjTfGb/3Wb0We57FmzZo499xz49FHH40PfOADRY4GjCO9fVlE5EWPMW7lecSv+1+7fuSEiCwrdp7x7LVtIS2FhsrChQsH3b7hhhtixYoVsXnz5n2GSm9vb/T29g7c7u7uPuwzAmPf5Q9OLXoEYD+SOUelr68v7rrrrtizZ0/Mmzdvn+u0trZGbW3twKWhoWGEpwQARlKhe1QiIh5//PGYN29eVCqVmDJlSqxbty7e//7373PdZcuWxdKlSwdud3d3ixVgSMrlsnPiElGpVGLRokUREbFu3bool8sFT0REJPN9GFKo7O+QS5ZlUSqV4sgjjzzoxzrppJNi69atsWvXrvj2t78dF110UWzYsGGfsVIqlaJUKg1lZIBBsiyLSZMmFT0Gb1Iul31fGGRIoXL00UdHdoCznerr6+Piiy+Oa6+9NiZMOPDRpSOPPDJOPPHEiIg4/fTTo7OzM2655ZZYuXLlUEYDAMaQIYXK6tWr40tf+lJcfPHFccYZZ0RExJYtW2LNmjXx5S9/OX75y1/GTTfdFKVSKb74xS++o8fu7+8fdMIsADB+DSlU1qxZE3//938ff/zHfzywbOHChXHqqafGypUr44c//GE0NjbGDTfccMBQWbZsWTQ1NUVjY2Ps3r072tvbY/369fH9739/KGMBAGPMkEJl06ZN8Y1vfOMty+fMmRM//vGPIyJi/vz5sW3btgM+zksvvRSf/vSn4+c//3nU1tbGrFmz4vvf/3783u/93lDGAgDGmCGFSkNDQ6xatSpuvPHGQctXrVo18C6crq6uOOaYYw74OKtWrRrK0wMA48SQQuWmm26K888/Pzo6OmLu3LkREfHQQw/FU089Fd/+9rcjIqKzszMuuOCC4ZsUABh3hhQqn/jEJ+Kpp56KlStXxjPPPBMREU1NTXH33XfHu9/97oiI+NznPjdsQwIA49OQ/+DbzJkz33LoBwBgOA05VF5++eXYsmVLvPTSS9Hf3z/ovk9/+tOHPBgAwJBC5d57742Wlpbo6emJmpqaQX/8LcsyoQIADIshfSjhX/zFX8Sll14aPT098fLLL8f//M//DFx27tw53DMCAOPUkELlZz/7WXz+85+PyZMnD/c8AAADhhQqv//7vx8PPfTQcM8CADDIkM5R+fjHPx7XXHNN/OQnP4lTTz01jjjiiEH3f+ITnxiW4QCA8W1IofKZz3wmIiK++tWvvuW+LMuir6/v0KYCAIghhsqb344MAHA4DOkcFQCAkXDQe1RuvfXW+LM/+7Mol8tx6623HnDdz3/+84c8GADAQYfKzTffHC0tLVEul+Pmm2/e73pZlgkVAGBYHHSoPP/88/u8DgBwuAzpHJWvfvWr8corr7xl+a9+9at9vhMIAGAohhQqy5cvj56enrcsf+WVV2L58uWHPBQAQMQQQyXP80EfRPi6xx57LKZOnXrIQwEARLzDv6NyzDHHRJZlkWVZvPe97x0UK319fdHT0xOf/exnh31IAGB8ekeh8rWvfS3yPI9LL700li9fHrW1tQP3HXnkkfHud7875s2bN+xDAgDj0zsKlYsuuigiImbOnBlnnnnmWz7jBwBgOA3pT+ifffbZA9crlUr8+te/HnR/TU3NoU0FABBDPJn2lVdeicsvvzymT58eRx11VBxzzDGDLgAAw2FIoXLNNdfEv/3bv8WKFSuiVCrFHXfcEcuXL4+6urr41re+NdwzAgDj1JAO/dx7773xrW99K84555y45JJL4sMf/nCceOKJccIJJ8TatWujpaVluOcEAMahIe1R2blzZ7znPe+JiNfOR9m5c2dERMyfPz82btw4fNMBAOPakELlPe95z8Dn/Zx88snxr//6rxHx2p6Wo48+etiGAwDGtyGFyiWXXBKPPfZYRER84QtfiNtuuy3K5XJcddVVcc011wzrgADA+PWOz1F59dVX47777otvfOMbERHxsY99LJ566ql4+OGH48QTT4xZs2YN+5AAwPj0jkPliCOOiP/8z/8ctOyEE06IE044YdiGAgCIGOKhnz/5kz+JVatWDfcsAACDDOntyXv37o1vfvOb8YMf/CBOP/30OOqoowbd39bWNizDAQDj25BC5YknnojTTjstIiKeeeaZQfe98ROVAQAOxZBC5Uc/+tFwzwEA8BZDOkcFAGAkCBUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZBUaKq2trTF37tyorq6O6dOnx3nnnRdPP/10kSMBAAkpNFQ2bNgQixcvjs2bN8cDDzwQr776aixYsCD27NlT5FgAQCImFvnk999//6Dbq1evjunTp8fDDz8cH/nIRwqaauzJ8zwqlUrRYxySN84/2l/L68rlcmRZVvQYAEkrNFTebNeuXRERMXXq1H3e39vbG729vQO3u7u7R2Su0a5SqURTU1PRYwybRYsWFT3CsOjo6IhJkyYVPQZA0pI5mba/vz+uvPLKOOuss+KUU07Z5zqtra1RW1s7cGloaBjhKQGAkZTMHpXFixfHE088EQ8++OB+11m2bFksXbp04HZ3d7dYOQjlcjk6OjqKHuOQ5Hk+sDetVCqNiUMm5XK56BEAkpdEqFx++eVx3333xcaNG6O+vn6/65VKpSiVSiM42diQZdmYOMQwefLkokcAYIQVGip5nseSJUti3bp1sX79+pg5c2aR4wAAiSk0VBYvXhzt7e1xzz33RHV1dfziF7+IiIja2toxsQcAADg0hZ5Mu2LFiti1a1ecc845cdxxxw1c/uVf/qXIsQCARBR+6AcAYH+SeXsyAMCbCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRVGjU2bNsUFF1wQmzZtKnoUAEaIUGFUqFQq0dbWFi+++GK0tbVFpVIpeiQARoBQYVRYu3ZtdHV1RUREV1dXtLe3FzwRACNhYtEDwNvZsWNHtLe3R57nERGR53m0t7fHggULor6+vuDpoFh5no/6PYxvnH+0v5bXlcvlyLKs6DHGBKFC0vI8j1tuuWW/y//u7/7ODwPGtUqlEk1NTUWPMWwWLVpU9AjDoqOjIyZNmlT0GGOCQz8kbdu2bdHZ2Rl9fX2Dlvf19UVnZ2ds27atoMkAGAn2qJC0xsbGmDt3bjzyyCODYqWqqipOP/30aGxsLHA6KF65XI6Ojo6ixzgkeZ5Hb29vRESUSqUxsZe0XC4XPcKYIVRIWpZlccUVV8RFF120z+Vj4QcaHIosy8bEIYbJkycXPQKJcuiH5NXX10dzc/NAlGRZFs3NzXH88ccXPBkAh5tQYVRoaWmJadOmRUTEscceG83NzQVPBMBIECqMCuVyOZYuXRozZsyIq666yvFfgHHCOSqMGmeeeWaceeaZRY8BwAiyRwUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkiVUAIBkCRUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAKNymTZviggsuiE2bNhU9CokpNFQ2btwYCxcujLq6usiyLO6+++4ixwGgAJVKJdra2uLFF1+Mtra2qFQqRY9EQgoNlT179sTs2bPjtttuK3IMAAq0du3a6OrqioiIrq6uaG9vL3giUjKxyCdvamqKpqamIkcAoEA7duyI9vb2yPM8IiLyPI/29vZYsGBB1NfXFzwdKRhV56j09vZGd3f3oAsAo1Oe53HLLbfsd/nr8cL4NqpCpbW1NWprawcuDQ0NRY8EwBBt27YtOjs7o6+vb9Dyvr6+6OzsjG3bthU0GSkZVaGybNmy2LVr18Bl+/btRY8EwBA1NjbG3Llzo6qqatDyqqqqOOOMM6KxsbGgyUjJqAqVUqkUNTU1gy4AjE5ZlsUVV1yx3+VZlhUwFakZVaECwNhSX18fzc3NA1GSZVk0NzfH8ccfX/BkpKLQUOnp6YmtW7fG1q1bIyLi+eefj61btzouCTCOtLS0xLRp0yIi4thjj43m5uaCJyIlhYbKQw89FHPmzIk5c+ZERMTSpUtjzpw58ZWvfKXIsQAYQeVyOZYuXRozZsyIq666KsrlctEjkZAsH8Xv/+ru7o7a2trYtWuX81UAYJR4J7+/naMCACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJEuoAADJEioAQLKECgCQLKECACRLqDBqbNq0KS644ILYtGlT0aMAw8z2zf4IFUaFSqUSbW1t8eKLL0ZbW1tUKpWiRwKGie2bAxEqjApr166Nrq6uiIjo6uqK9vb2gicChovtmwMRKiRvx44d0d7eHnmeR0REnufR3t4eO3bsKHgy4FDZvnk7QoWk5Xket9xyy36Xv/7DDRh9bN8cDKFC0rZt2xadnZ3R19c3aHlfX190dnbGtm3bCpoMOFS2bw6GUCFpjY2NMXfu3Kiqqhq0vKqqKs4444xobGwsaDLgUNm+ORhChaRlWRZXXHHFfpdnWVbAVMBwsH1zMIQKyauvr4/m5uaBH1pZlkVzc3Mcf/zxBU8GHCrbN29HqDAqtLS0xLRp0yIi4thjj43m5uaCJwKGi+2bAxEqjArlcjmWLl0aM2bMiKuuuirK5XLRIwHDxPbNgWT5KH7/V3d3d9TW1sauXbuipqam6HEAgIPwTn5/26MCACRLqAAAyRIqAECyhAoAkCyhAgAkS6gAAMkSKgBAsoQKAJAsoQIAJGti0QMcitf/qG53d3fBkwAAB+v139sH88fxR3Wo7N69OyIiGhoaCp4EAHindu/eHbW1tQdcZ1R/1k9/f3+88MILUV1dPfAR4Yxd3d3d0dDQENu3b/fZTjDG2L7HlzzPY/fu3VFXVxcTJhz4LJRRvUdlwoQJUV9fX/QYjLCamho/yGCMsn2PH2+3J+V1TqYFAJIlVACAZAkVRo1SqRTXXnttlEqlokcBhpntm/0Z1SfTAgBjmz0qAECyhAoAkCyhAgAkS6gAAMkSKiRv48aNsXDhwqirq4ssy+Luu+8ueiRgmLS2tsbcuXOjuro6pk+fHuedd148/fTTRY9FQoQKyduzZ0/Mnj07brvttqJHAYbZhg0bYvHixbF58+Z44IEH4tVXX40FCxbEnj17ih6NRHh7MqNKlmWxbt26OO+884oeBTgMfvnLX8b06dNjw4YN8ZGPfKTocUiAPSoAJGPXrl0RETF16tSCJyEVQgWAJPT398eVV14ZZ511VpxyyilFj0MiRvWnJwMwdixevDieeOKJePDBB4sehYQIFQAKd/nll8d9990XGzdujPr6+qLHISFCBYDC5HkeS5YsiXXr1sX69etj5syZRY9EYoQKyevp6Ylnn3124Pbzzz8fW7dujalTp0ZjY2OBkwGHavHixdHe3h733HNPVFdXxy9+8YuIiKitrY1JkyYVPB0p8PZkkrd+/fr4nd/5nbcsv+iii2L16tUjPxAwbLIs2+fyO++8My6++OKRHYYkCRUAIFnengwAJEuoAADJEioAQLKECgCQLKECACRLqAAAyRIqAECyhAoAkCyhAowK1113XXzwgx8c1sf86U9/GlmWxdatW4f1cYHhI1SAUeHqq6+OH/7wh+/o32RZFnfffffhGQgYET6UEBgVpkyZElOmTCl6DGCE2aMCvGPnnHNOLFmyJK688so45phjYsaMGXH77bfHnj174pJLLonq6uo48cQTo6OjY+DfbNiwIc4444wolUpx3HHHxRe+8IXYu3dvRET84z/+Y9TV1UV/f/+g5zn33HPj0ksvjYh9H/q544474n3ve1+Uy+U4+eST4x/+4R8OOPeWLVtizpw5US6X47d/+7fj0UcfHYavBnA4CRVgSNasWRPHHntsbNmyJZYsWRKf+9zn4vzzz48zzzwzHnnkkViwYEF86lOfildeeSV+9rOfxR/8wR/E3Llz47HHHosVK1bEqlWr4vrrr4+IiPPPPz+6urriRz/60cDj79y5M+6///5oaWnZ5/OvXbs2vvKVr8QNN9wQTz75ZPzN3/xN/NVf/VWsWbNmn+v39PTEH/7hH8b73//+ePjhh+O6666Lq6++evi/MMDwygHeobPPPjufP3/+wO29e/fmRx11VP6pT31qYNnPf/7zPCLyH//4x/kXv/jF/KSTTsr7+/sH7r/tttvyKVOm5H19fXme5/m5556bX3rppQP3r1y5Mq+rqxu4/9prr81nz549cP9v/uZv5u3t7YPm+uu//ut83rx5A7cjIl+3bt3A402bNi3/1a9+NXD/ihUr8ojIH3300aF/MYDDyh4VYEhmzZo1cL2qqiqmTZsWp5566sCyGTNmRETESy+9FE8++WTMmzcvsiwbuP+ss86Knp6e2LFjR0REtLS0xHe+853o7e2NiNf2mHzyk5+MCRPe+mNqz5498dxzz8Vll102cO7KlClT4vrrr4/nnntun/M++eSTMWvWrCiXywPL5s2bdwhfAWAkOJkWGJIjjjhi0O0sywYtez1K3nzeyf4sXLgw8jyP7373uzF37tz493//97j55pv3uW5PT09ERNx+++3xoQ99aNB9VVVVB/0agPQJFeCwe9/73hff+c53Is/zgYD5j//4j6iuro76+vqIiCiXy/FHf/RHsXbt2nj22WfjpJNOitNOO22fjzdjxoyoq6uL//7v/97vOSz7muGf/umfolKpDOxV2bx58zC8OuBwcugHOOz+/M//PLZv3x5LliyJp556Ku6555649tprY+nSpYMO7bS0tMR3v/vd+OY3v/m2AbJ8+fJobW2NW2+9NZ555pl4/PHH484774y2trZ9rt/c3BxZlsVnPvOZ+MlPfhLf+9734qabbhrW1wkMP6ECHHbHH398fO9734stW7bE7Nmz47Of/Wxcdtll8eUvf3nQer/7u78bU6dOjaeffjqam5sP+Jh/+qd/GnfccUfceeedceqpp8bZZ58dq1evjpkzZ+5z/SlTpsS9994bjz/+eMyZMye+9KUvxd/+7d8O22sEDo8sz/O86CEAAPbFHhUAIFlCBQBIllABAJIlVACAZAkVACBZQgUASJZQAQCSJVQAgGQJFQAgWUIFAEiWUAEAkvW/zO+ZhiljK20AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='movieId', y='rating', data = df_ratings.query('movieId in [1,2]'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YqQYGBqoeYdnGxsais7Oz6jHWBadCSDc5ORkTExMxOzu7YPvs7GxMTEzE5ORkRZMB0G6OWJCur68vdu3aFS+99NKCuOjo6Igbb7wx+vr6KpwOqlWv12NsbKzqMZatLMtoNpsREVGr1db8Uch6vV71COuGsCBdURSxf//+uPvuuy+4fa3/AoLlKIpi3Rxy37JlS9UjsAo5FUJb9PT0xODg4HxEFEURg4ODcdVVV1U8GQDtJCxom71798a2bdsiImL79u0xODhY8UQAtJuwoG3q9XoMDQ3Fjh074oEHHnAOE2ADcI0FbXXzzTfHzTffXPUYAKwQRywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAgBIIywAgDTCAoAlGR8fjzvvvDPGx8erHoVVpOWwOHbsWNx+++2xc+fOKIoinnzyyTaMBcBq1mg0YmRkJE6fPh0jIyPRaDSqHolVouWwOHfuXFx//fXx8MMPt2MeANaAxx57LKampiIiYmpqKkZHRyueiNVic6vf0N/fH/39/e2YBYA14NSpUzE6OhplWUZERFmWMTo6Grfddlv09PRUPB1Va/s1Fs1mM2ZmZhbcAFibyrKMgwcPLrr93dhg42p7WAwPD0d3d/f8rbe3t927BKBNJicnY2JiImZnZxdsn52djYmJiZicnKxoMlaLtofFgQMHYnp6ev528uTJdu8SgDbp6+uLXbt2RUdHx4LtHR0dcdNNN0VfX19Fk7FatD0sarVadHV1LbgBsDYVRRH79+9fdHtRFBVMxWrifSwAaElPT08MDg7OR0RRFDE4OBhXXXVVxZOxGrQcFmfPno0TJ07EiRMnIiLi1VdfjRMnTjivBrCB7N27N7Zt2xYREdu3b4/BwcGKJ2K1aDksXnzxxbjhhhvihhtuiIiIoaGhuOGGG+J73/te+nAArE71ej2GhoZix44d8cADD0S9Xq96JFaJolzhvw2amZmJ7u7umJ6edr0FAKwR7/f12zUWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYQEApBEWAEAaYUFbjY+Px5133hnj4+NVjwIks765EGFB2zQajRgZGYnTp0/HyMhINBqNqkcCkljfLEZY0DaPPfZYTE1NRUTE1NRUjI6OVjwRkMX6ZjHCgrY4depUjI6ORlmWERFRlmWMjo7GqVOnKp4MWC7rm/ciLEhXlmUcPHhw0e3v/jIC1h7rm4sRFqSbnJyMiYmJmJ2dXbB9dnY2JiYmYnJysqLJgOWyvrkYYUG6vr6+2LVrV3R0dCzY3tHRETfddFP09fVVNBmwXNY3FyMsSFcURezfv3/R7UVRVDAVkMH65mKEBW3R09MTg4OD879kiqKIwcHBuOqqqyqeDFgu65v3Iixom71798a2bdsiImL79u0xODhY8URAFuubxQgL2qZer8fQ0FDs2LEjHnjggajX61WPBCSxvllMUa7w3wbNzMxEd3d3TE9PR1dX10ruGgBYovf7+u2IBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQRlgAAGmEBQCQZvNK7/DdN/qcmZlZ6V0DAEv07uv2xd6we8XD4syZMxER0dvbu9K7BgCW6cyZM9Hd3b3oP1/xzwqZm5uL1157LbZu3Tr/kbusXzMzM9Hb2xsnT5702TCwzljfG0tZlnHmzJnYuXNnbNq0+JUUK37EYtOmTdHT07PSu6ViXV1dfvHAOmV9bxzvdaTiXS7eBADSCAsAII2woK1qtVo8+OCDUavVqh4FSGZ9cyErfvEmALB+OWIBAKQRFgBAGmEBAKQRFgBAGmFB2zz88MPx0Y9+NOr1euzevTv+8Ic/VD0SkODYsWNx++23x86dO6MoinjyySerHolVRFjQFk888UQMDQ3Fgw8+GC+99FJcf/318dWvfjXeeOONqkcDluncuXNx/fXXx8MPP1z1KKxC/tyUtti9e3fs2rUrDh06FBFvf0ZMb29v3HffffHd73634umALEVRxJEjR+KOO+6oehRWCUcsSPfmm2/G8ePH49Zbb53ftmnTprj11lvj97//fYWTAdBuwoJ0//jHP2J2djZ27NixYPuOHTvi9ddfr2gqAFaCsAAA0ggL0m3fvj06Ojri9OnTC7afPn06rrjiioqmAmAlCAvSXXLJJXHjjTfGs88+O79tbm4unn322dizZ0+FkwHQbpurHoD1aWhoKO6+++74zGc+EzfddFP89Kc/jXPnzsU999xT9WjAMp09ezZeeeWV+fuvvvpqnDhxIi699NLo6+urcDJWA39uStscOnQofvKTn8Trr78en/rUp+JnP/tZ7N69u+qxgGU6evRofPGLX/yv7XfffXc8+uijKz8Qq4qwAADSuMYCAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANMICAEgjLACANP8fe0cTF8010a4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=[toy_story_rating.rating, jumanji_rating.rating])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8348591407114047 0.8817134921476455\n"
     ]
    }
   ],
   "source": [
    "print(toy_story_rating.rating.std(), jumanji_rating.rating.std())"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}