{ "cells": [ { "cell_type": "markdown", "id": "7c384478", "metadata": {}, "source": [ "# Título\n", "## Subtítulo\n", "### sub-subtítulo\n", "Esto es un párrafo en **markdow**. La *siguiente* ecuación $f(x)=x^3$ es evaluada en: \n", "- primer elemento\n", "- segundo elemento\n", "- tercer elemento\n", "\n", "1. Primer\n", "2. Segundo\n", "3. Tercer\n", " - Sub elemento\n", " - sub elemento" ] }, { "cell_type": "code", "execution_count": 6, "id": "461cd70f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "Hola\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "x = 5 \n", "print(x)\n", "x= \"Hola\"\n", "print(x)" ] }, { "cell_type": "markdown", "id": "dd0b1c6a", "metadata": {}, "source": [ "# The tips dataset\n", "This dataset comes from a restaurant and is used to teach EDA. Each row represents a bill (table) and registers the complete bill, tip, among other parameters during the service.\n", "\n", "- `total_bill`: Conplete amount without tip.\n", "- `tip`: The given tip.\n", "- `sex`: Sex identification (pay)\n", "- `smoker`: if there are smokers included in the table\n", "- `day`: day of the week\n", "- `time`: type of food(Lunch/Dinner)\n", "- `size`: Number of guessings" ] }, { "cell_type": "code", "execution_count": 8, "id": "a3367dd4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
........................
23929.035.92MaleNoSatDinner3
24027.182.00FemaleYesSatDinner2
24122.672.00MaleYesSatDinner2
24217.821.75MaleNoSatDinner2
24318.783.00FemaleNoThurDinner2
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244 rows × 7 columns

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" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4\n", ".. ... ... ... ... ... ... ...\n", "239 29.03 5.92 Male No Sat Dinner 3\n", "240 27.18 2.00 Female Yes Sat Dinner 2\n", "241 22.67 2.00 Male Yes Sat Dinner 2\n", "242 17.82 1.75 Male No Sat Dinner 2\n", "243 18.78 3.00 Female No Thur Dinner 2\n", "\n", "[244 rows x 7 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# importing the tips dataset\n", "import seaborn as sns\n", "df = sns.load_dataset(\"tips\")\n", "df" ] }, { "cell_type": "code", "execution_count": 11, "id": "ab96e102", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 244 entries, 0 to 243\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 total_bill 244 non-null float64 \n", " 1 tip 244 non-null float64 \n", " 2 sex 244 non-null category\n", " 3 smoker 244 non-null category\n", " 4 day 244 non-null category\n", " 5 time 244 non-null category\n", " 6 size 244 non-null int64 \n", "dtypes: category(4), float64(2), int64(1)\n", "memory usage: 7.4 KB\n" ] } ], "source": [ "df.head() # dataframe example \n", "df.info() " ] }, { "cell_type": "code", "execution_count": 13, "id": "ea342b45", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
count244.000000244.000000244244244244244.000000
uniqueNaNNaN2242NaN
topNaNNaNMaleNoSatDinnerNaN
freqNaNNaN15715187176NaN
mean19.7859432.998279NaNNaNNaNNaN2.569672
std8.9024121.383638NaNNaNNaNNaN0.951100
min3.0700001.000000NaNNaNNaNNaN1.000000
25%13.3475002.000000NaNNaNNaNNaN2.000000
50%17.7950002.900000NaNNaNNaNNaN2.000000
75%24.1275003.562500NaNNaNNaNNaN3.000000
max50.81000010.000000NaNNaNNaNNaN6.000000
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" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "count 244.000000 244.000000 244 244 244 244 244.000000\n", "unique NaN NaN 2 2 4 2 NaN\n", "top NaN NaN Male No Sat Dinner NaN\n", "freq NaN NaN 157 151 87 176 NaN\n", "mean 19.785943 2.998279 NaN NaN NaN NaN 2.569672\n", "std 8.902412 1.383638 NaN NaN NaN NaN 0.951100\n", "min 3.070000 1.000000 NaN NaN NaN NaN 1.000000\n", "25% 13.347500 2.000000 NaN NaN NaN NaN 2.000000\n", "50% 17.795000 2.900000 NaN NaN NaN NaN 2.000000\n", "75% 24.127500 3.562500 NaN NaN NaN NaN 3.000000\n", "max 50.810000 10.000000 NaN NaN NaN NaN 6.000000" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 15, "id": "ebdbde94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "total_bill 0\n", "tip 0\n", "sex 0\n", "smoker 0\n", "day 0\n", "time 0\n", "size 0\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().sum()" ] }, { "cell_type": "code", "execution_count": 24, "id": "27cf57bb", "metadata": {}, "outputs": [ { "data": { "image/png": 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tRESEz/73339fqlevLi1btpQJEybI2bNn8zxHRkaGpKWl+WwAAKBkCmiLjLfs7GwZPXq0dOjQwQQWt/vuu0/q168vtWvXll27dsn48ePNOJpPPvkkz3E3cXFxxVhyAAAQKEEul8slDvDII4/ImjVr5Msvv5Q6derkedznn38unTt3lqSkJLnqqqtybZHRzU1bZOrWrWtae6pUqVJk5UfRavDU6hJZxQen9wx0EQDAkfT7Ozw8/JLf345okRk5cqSsWrVKtmzZkm+IUbGxseY2ryATHBxsNgAAUPIFNMhoY9CoUaNk2bJlsnnzZmnYsOEln7Nz505zW6tWrWIoIQAAcLKABhmder1o0SJZsWKFWUvm2LFjZr82JYWGhsr+/fvN4z169JBq1aqZMTJjxowxM5patWoVyKIDAIDSHmTmzp3rWfTO24IFC2Tw4MFSoUIF+eyzz2TWrFlmbRkd69KvXz955plnAlRiAADgJAHvWsqPBhddNA8AAMDx68gAAAD8GQQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQIaZKZNmyZt2rSRypUrS40aNaRPnz6SmJjoc8z58+dlxIgRUq1aNQkLC5N+/frJ8ePHA1ZmAADgHAENMvHx8SakbN26VTZs2CBZWVnStWtXSU9P9xwzZswYWblypSxZssQcf+TIEenbt28giw0AAByiXCBffO3atT73Fy5caFpmduzYIbfccoukpqbK22+/LYsWLZJOnTqZYxYsWCDNmjUz4addu3YBKjkAAHACR42R0eCiIiIizK0GGm2l6dKli+eY6OhoqVevniQkJOR6joyMDElLS/PZAABAyeSYIJOdnS2jR4+WDh06SMuWLc2+Y8eOSYUKFaRq1ao+x9asWdM8lte4m/DwcM9Wt27dYik/AAAoxUFGx8rs3r1bFi9efFnnmTBhgmnZcW/Jycl+KyMAAHCWgI6RcRs5cqSsWrVKtmzZInXq1PHsj4qKkszMTElJSfFpldFZS/pYboKDg80GAABKvoC2yLhcLhNili1bJp9//rk0bNjQ5/HWrVtL+fLlZePGjZ59Oj370KFD0r59+wCUGAAAOEm5QHcn6YykFStWmLVk3ONedGxLaGiouR0yZIiMHTvWDACuUqWKjBo1yoQYZiwBAICABpm5c+ea244dO/rs1ynWgwcPNj/PnDlTypQpYxbC0xlJ3bp1k9deey0g5QUAAM5SLtBdS5cSEhIic+bMMRsAAIAjZy0BAAD8WQQZAABgLYIMAACwFkEGAABYiyADAACs5YiVfVFyNXhqdaCLUCr4q54PTu/pl/MAQHGhRQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArMUlCpArLi0AALABLTIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAoHQFmU6dOklKSspF+9PS0sxjAAAAjg0ymzdvlszMzIv2nz9/Xr744gt/lAsAAMC/11ratWuX5+effvpJjh075rl/4cIFWbt2rVx55ZV/5pQAAADFE2SuvfZaCQoKMltuXUihoaHyyiuvFL40AAAARRVkDhw4IC6XSxo1aiTbtm2TyMhIz2MVKlSQGjVqSNmyZf/MKQEAAIonyNSvX9/cZmdnF/4VAQAAAhFkvO3bt082bdokJ06cuCjYTJo0yR9lAwAA8H+QefPNN+WRRx6R6tWrS1RUlBkz46Y/E2QAAIBjg8zUqVPlueeek/Hjx/u/RAAAAEW5jszvv/8ud999d2GeCgAAENggoyFm/fr1/isFAABAcXUtNW7cWCZOnChbt26Va665RsqXL+/z+KOPPlqY0wIAABR9kHnjjTckLCxM4uPjzeZNB/sSZAAAgGODjC6MBwAAYOUYGQAAAGtbZB588MF8H58/f35hywMAAFC0QUanX3vLysqS3bt3S0pKSq4XkwQAAHBMkFm2bNlF+/QyBbra71VXXeWPcgEAABTfGJkyZcrI2LFjZebMmf46JQAAQPEN9t2/f7/88ccf/jwlAACAf7uWtOXFm8vlkqNHj8rq1atl0KBBhTklAABA8bTIfP/99z7brl27zP4XX3xRZs2aVeDzbNmyRXr16iW1a9c2C+ktX77c5/HBgweb/d7bHXfcUZgiAwCAEqhQLTKbNm3yy4unp6dLTEyMmc7dt2/fXI/R4LJgwQLP/eDgYL+8NgAAKKVBxu3kyZOSmJhofm7atKlERkb+qed3797dbPnR4BIVFXU5xQQAACVUmcK2pGgrSq1ateSWW24xm3YPDRkyRM6ePevXAm7evFlq1KhhgpJO7z516pRfzw8AAEpZkNHBvnqxyJUrV5pF8HRbsWKF2ff444/7rXDarfTuu+/Kxo0b5fnnnzfn1xacCxcu5PmcjIwMSUtL89kAAEDJVKiupY8//liWLl0qHTt29Ozr0aOHhIaGyj333CNz5871S+Huvfdez8/XXHONtGrVyiy4p600nTt3zvU506ZNk7i4OL+8PlDUGjy1mkoGgOJukdHuo5o1a160X7uA/N215K1Ro0ZSvXp1SUpKyvOYCRMmSGpqqmdLTk4usvIAAAALg0z79u1l8uTJcv78ec++c+fOmZYQfayoHD582IyR0bE5+Q0OrlKlis8GAABKpkJ1LelaMTp+pU6dOmb6tPrPf/5jQsT69esLfJ4zZ874tK4cOHBAdu7cKREREWbTYNSvXz8za0lXDX7yySelcePG0q1bt8IUGwAAlDCFCjI6XmXfvn3y/vvvy969e82+AQMGyMCBA804mYLavn273HbbbRetGKyrA+s4G11o75133jGDiXVWVNeuXeUf//gHa8kAAIDCBxkdUKtjZIYOHeqzf/78+WZtmfHjxxfoPDpYWC9vkJd169YVpngAAKCUKNQYmddff12io6Mv2t+iRQuZN2+eP8oFAABQNEHm2LFjuQ641ZV99eKRAAAAjg0ydevWla+++uqi/bpPx7IAAAA4doyMjo0ZPXq0ZGVlSadOncw+XX1XZxX5c2VfAAAAvweZJ554wqznMnz4cMnMzDT7QkJCzCBfXZAOAACgOAS58ps2VIB1YPbs2WOmXDdp0sSR06L1Wkvh4eFmlV8Wxys4ls7H5Tg4vScVCKBYvr8L1SLjFhYWJm3atLmcUwAAABTvYF8AAAAnIMgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGtd1iUKAKAor9XFNZsAXAotMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1gpokNmyZYv06tVLateuLUFBQbJ8+XKfx10ul0yaNElq1aoloaGh0qVLF9m3b1/AygsAAJwloEEmPT1dYmJiZM6cObk+PmPGDHn55Zdl3rx58s0330ilSpWkW7ducv78+WIvKwAAcJ5ygXzx7t27my032hoza9YseeaZZ6R3795m37vvvis1a9Y0LTf33ntvMZcWAAA4jWPHyBw4cECOHTtmupPcwsPDJTY2VhISEvJ8XkZGhqSlpflsAACgZApoi0x+NMQobYHxpvfdj+Vm2rRpEhcXJ6VVg6dWB7oIAAAUG8e2yBTWhAkTJDU11bMlJycHukgAAKC0BZmoqChze/z4cZ/9et/9WG6Cg4OlSpUqPhsAACiZHBtkGjZsaALLxo0bPft0vIvOXmrfvn1AywYAAJwhoGNkzpw5I0lJST4DfHfu3CkRERFSr149GT16tEydOlWaNGligs3EiRPNmjN9+vQJZLEBAIBDBDTIbN++XW677TbP/bFjx5rbQYMGycKFC+XJJ580a80MGzZMUlJS5KabbpK1a9dKSEhIAEsNAACcIsilC7aUYNodpdO2deBvaRgvw6wllCQHp/cMdBEAOPz727FjZAAAAC6FIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYq1ygCwAAeWnw1Gq/VM7B6T1LZHkA0CIDAAAsRtcSAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWlygAUOL569ICAJyHFhkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWItrLQFAKb+G1MHpPf1yHiAQaJEBAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFjL0UFmypQpEhQU5LNFR0cHulgAAMAhHL8gXosWLeSzzz7z3C9XzvFFBgAAxcTxqUCDS1RUVKCLAQAAHMjRXUtq3759Urt2bWnUqJEMHDhQDh06lO/xGRkZkpaW5rMBAICSydEtMrGxsbJw4UJp2rSpHD16VOLi4uTmm2+W3bt3S+XKlXN9zrRp08xxAFDSr5EEQCTI5XK5bKmIlJQUqV+/vrz00ksyZMiQPFtkdHPTFpm6detKamqqVKlSRUo6/kAC+LO4aCScSL+/w8PDL/n97egWmZyqVq0qV199tSQlJeV5THBwsNkAAEDJ5/gxMt7OnDkj+/fvl1q1agW6KAAAwAEcHWTGjRsn8fHxcvDgQfn666/lzjvvlLJly8qAAQMCXTQAAOAAju5aOnz4sAktp06dksjISLnppptk69at5mcAAABHB5nFixcHuggAAMDBHN21BAAAkB+CDAAAsBZBBgAAWIsgAwAArEWQAQAA1nL0rCUAQOnjr0utcOmF0oEWGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFpcoAAA46tICKJ56Pji9Z4moalpkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1gpyuVwuKcHS0tIkPDxcUlNTpUqVKn49d4OnVvv1fAAA2Obg9J4B/f6mRQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFpWBJk5c+ZIgwYNJCQkRGJjY2Xbtm2BLhIAAHAAxweZDz/8UMaOHSuTJ0+W7777TmJiYqRbt25y4sSJQBcNAAAEmOODzEsvvSRDhw6VBx54QJo3by7z5s2TihUryvz58wNdNAAAEGCODjKZmZmyY8cO6dKli2dfmTJlzP2EhISAlg0AAAReOXGwX3/9VS5cuCA1a9b02a/39+7dm+tzMjIyzOamSxu7lzr2t+yMs34/JwAANkkrgu9X7/Ne6kpKjg4yhTFt2jSJi4u7aH/dunUDUh4AAEqy8FlFe/7Tp0+bay5ZGWSqV68uZcuWlePHj/vs1/tRUVG5PmfChAlmcLBbdna2/Pbbb1KtWjUJCgoq8jKXNpqYNSQmJyf7/aKcoN6diM889V7apAXo77y2xGiIqV27dr7HOTrIVKhQQVq3bi0bN26UPn36eIKJ3h85cmSuzwkODjabt6pVqxZLeUsz/XATZKj30oTPPPVe2lQJwN/5/FpirAgySltXBg0aJDfccIO0bdtWZs2aJenp6WYWEwAAKN0cH2T69+8vJ0+elEmTJsmxY8fk2muvlbVr1140ABgAAJQ+jg8ySruR8upKQmBpN54uVpizOw/Ue0nFZ556L22c/pkPcl1qXhMAAIBDOXpBPAAAgPwQZAAAgLUIMgAAwFoEGQAAYC2CDApky5Yt0qtXL7PCoq6QvHz5cp/Hdcy4TpGvVauWhIaGmgt77tu3j9r1wyU32rRpI5UrV5YaNWqYhSETExN9jjl//ryMGDHCrF4dFhYm/fr1u2g1bPw5c+fOlVatWnkWAGvfvr2sWbOGOi9m06dPN39vRo8eTd0XsSlTppi69t6io6OtqHeCDApEFyGMiYmROXPm5Pr4jBkz5OWXX5Z58+bJN998I5UqVZJu3bqZDz8KLz4+3vzx2Lp1q2zYsEGysrKka9eu5vfhNmbMGFm5cqUsWbLEHH/kyBHp27cv1X4Z6tSpY75Ed+zYIdu3b5dOnTpJ79695ccff6TOi8m3334rr7/+ugmU3vi8F50WLVrI0aNHPduXX35pR73r9Gvgz9CPzbJlyzz3s7OzXVFRUa4XXnjBsy8lJcUVHBzs+uCDD6hcPzpx4oSp//j4eE89ly9f3rVkyRLPMXv27DHHJCQkUPd+dMUVV7jeeust6rwYnD592tWkSRPXhg0bXLfeeqvrscceM/v5vBedyZMnu2JiYnJ9zOn1TosMLtuBAwfMqsvaneR9fYzY2FhJSEighv0oNTXV3EZERJhbbTHQVhrvutfm4Hr16lH3fnLhwgVZvHixaQXTLibqvOhpK2TPnj19PteKui9a+/btM8MHGjVqJAMHDpRDhw5ZUe9WrOwLZ9MQo3JeNkLvux/D5dMLpupYgQ4dOkjLli09da8XV815YVTq/vL98MMPJrho96iOCVi2bJk0b95cdu7cSZ0XIQ2N3333nelayonPe9GJjY2VhQsXStOmTU23UlxcnNx8882ye/dux9c7QQaw6F+p+kfFu98aRUf/oGto0VawpUuXmovX6tgAFJ3k5GR57LHHzHiwkJAQqroYde/e3fOzjkvSYFO/fn356KOPzAQOJ6NrCZctKirK3OYcwa733Y/h8ui1xlatWiWbNm0yA1G96z4zM1NSUlKoez/Tf4E2btxYWrdubWaP6WD32bNnU+dFSLswTpw4Iddff72UK1fObBoedSKB/qwtAHzei0fVqlXl6quvlqSkJMd/5gkyuGwNGzY0H+aNGzd69qWlpZnZS9o0j8LTsdUaYrRb4/PPPzd17U2/ZMuXL+9T9zo9W/u2qXv/d+1lZGRQ50Woc+fOpktPW8Lc2w033GDGa7h/5vNePM6cOSP79+83S2o4/e8MXUso8Idak7n3AF/9w6KDTnXAl47dmDp1qjRp0sR82U6cONEMGtN1T3B53UmLFi2SFStWmLVk3P3ROpham3v1dsiQITJ27Fjzu9A1T0aNGmX+uLRr146qL6QJEyaYpnb9bJ8+fdr8DjZv3izr1q2jzouQfsbd47/cdCkHXbvEvZ/Pe9EYN26cWStMu5N0arVe7bps2bIyYMAA53/mAz1tCnbYtGmTmWqXcxs0aJBnCvbEiRNdNWvWNNOuO3fu7EpMTAx0sa2XW53rtmDBAs8x586dcw0fPtxMD65YsaLrzjvvdB09ejSg5bbdgw8+6Kpfv76rQoUKrsjISPN5Xr9+vedx6rz4eE+/pu6LTv/+/V21atUyn/krr7zS3E9KSrKi3oP0P4EOUwAAAIXBGBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgCsNXjw4AKvHt2xY0ezAnV+GjRoILNmzfLcDwoKkuXLl5ufDx48aO7ritYAnIMgA8CvChIY/PGcovDtt9/KsGHDAl0MAH8C11oCgP8vMjKSugAsQ4sMAL929cTHx8vs2bNNN4xu2iWj+9q2bSvBwcHmarpPPfWU/PHHH/k+58KFC+ZCdXoRUr1AZtOmTc0xl0NfU68mrhfBq169urm4qfdVWnJ2LQFwPlpkAPiNBo2ff/7ZXKn42WefNfs0kPTo0cMElnfffVf27t0rQ4cOlZCQEJkyZUquz9GWkezsbKlTp44sWbLEXP3466+/Nt0+GoTuueeeQpXvnXfeMeFo27Ztsn37dnM+vcK1lgeAnQgyAPxGWzoqVKggFStWlKioKLPv6aeflrp168qrr75qWluio6PlyJEjMn78eJk0aVKuz1Fly5aVuLg4z31tmUlISJCPPvqo0EFGyzFz5kxTDm3h+eGHH8x9ggxgL7qWABSpPXv2SPv27U14cOvQoYOcOXNGDh8+nO9z58yZI61btzYtNGFhYfLGG2/IoUOHCl2Wdu3a+ZRDy7Vv3z7TagTATgQZAI60ePFiGTdunOkKWr9+vZn2/MADD0hmZmagiwbAQehaAuBX2k3k3cLRrFkz+fjjj82gWndryFdffSWVK1c2Y2Bye477mBtvvFGGDx/u2bd///7LKts333zjc3/r1q3SpEkT040FwE60yADwK535o4FBZx79+uuvJogkJyfLqFGjzEDfFStWyOTJk2Xs2LFSpkyZXJ+jA301YOiA3HXr1pnBwDrDSNd5uRzaLaWvm5iYKB988IG88sor8thjj/npnQMIBIIMAL/S7iBt4WjevLkZ25KVlSWffvqpmSkUExMjDz/8sOkueuaZZ/J8jgaOhx56SPr27Sv9+/eX2NhYOXXqlE/rTGHcf//9cu7cOTMVfMSIESbEsAAeYLcgl/ciCgAAABahRQYAAFiLIAPAetoVpdOz89ouZ8o2AGejawmA9fTSAzpQOC86mLhcOSZpAiURQQYAAFiLriUAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAQGz1/wDPmEId2o8QWAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tb = df['total_bill']\n", "plt.figure()\n", "plt.hist(tb, bins=25)\n", "plt.title(\"Histogram: total_bill\")\n", "plt.xlabel(\"total_bill\")\n", "plt.ylabel(\"count\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "id": "03a4ee9a", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.boxplot(tb, vert=False)\n", "plt.title(\"Boxplot: total_bill\")\n", "plt.ylabel(\"total_bill\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "55a01dd2", "metadata": {}, "outputs": [], "source": [ "# Task 1: Nmerically state the quartile values (IQR)\n", "# Task 2: Scatter plot total_bill vs tip\n", "# Task 3: Scatter plot tip vs size\n", "# Task 4: \n", " # -What does the data represent?\n", "\t#- What are typical ranges?\n", "\t#- Any suspicious values? Why?\n", "\t#- One conclusion in plain language." ] }, { "cell_type": "code", "execution_count": null, "id": "5b6f2d01", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.14.2)", "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": "3.14.2" } }, "nbformat": 4, "nbformat_minor": 5 }