exercises/problems_1_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39186022-1b37-4164-8742-3b5620520730",
   "metadata": {},
   "source": [
    "# Solutions to Chapter 1 (DATA) problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65e54920-6c2f-4e4d-aa13-dac9064b1e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0393dc88-e3e6-4a7d-98ff-9e3fb76330c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "file_path = os.path.join(\"..\", \"datasets\", \"students.csv\")\n",
    "if os.path.exists(file_path):\n",
    "    data_file = open(file_path)\n",
    "else:\n",
    "    import io\n",
    "    data_file = io.StringIO(\"\"\"\n",
    "student_ID,background,curriculum,effort,score\n",
    "1,arts,debate,10.96,75\n",
    "2,science,lecture,8.69,75\n",
    "3,arts,debate,8.6,67\n",
    "4,arts,lecture,7.92,70.3\n",
    "5,science,debate,9.9,76.1\n",
    "6,business,debate,10.8,79.8\n",
    "7,science,lecture,7.81,72.7\n",
    "8,business,lecture,9.13,75.4\n",
    "9,business,lecture,5.21,57\n",
    "10,science,lecture,7.71,69\n",
    "11,business,debate,9.82,70.4\n",
    "12,arts,debate,11.53,96.2\n",
    "13,science,debate,7.1,62.9\n",
    "14,science,lecture,6.39,57.6\n",
    "15,arts,debate,12,84.3\n",
    "\"\"\")\n",
    "students = pd.read_csv(data_file, index_col=\"student_ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85477e9a-4522-4524-b47a-fd0ed687aa66",
   "metadata": {},
   "source": [
    "### Problem {prob:bar-chart_score-vs-curriculum}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8469747e-de88-4262-ba8c-65b69947be33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='curriculum', ylabel='score'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from statistics import mean\n",
    "sns.barplot(data=students, x=\"curriculum\", y=\"score\",\n",
    "            estimator=np.mean, errorbar=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "177ffc43-bc56-47e1-a2c8-e3126f6d339e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='curriculum', ylabel='score'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=students, x=\"curriculum\", y=\"score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b488c534-810f-4dfd-a092-3c5689beacbf",
   "metadata": {},
   "source": [
    "### Problem {prob:bar-chart_score-vs-curriculum-enhanced}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00c41443-0550-4a0f-97b8-d161a62a7c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='curriculum', ylabel='score'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "sns.barplot(data=students, x=\"curriculum\", y=\"score\",\n",
    "            estimator=np.mean, errorbar=\"sd\", capsize=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4315b6db-edae-40c1-8855-6facaa59cc75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f33c9c-1cd8-43e5-a41b-b558b8e6ebe6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}