Untitled (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "14a5f55e",
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>combined scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>63</td>\n",
       "      <td>65.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>40</td>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "      <td>51.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>some college</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>59</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>56.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group B</td>\n",
       "      <td>high school</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>68</td>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group E</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>78</td>\n",
       "      <td>73</td>\n",
       "      <td>68</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0    male        group A                 high school      standard   \n",
       "1  female        group D            some high school  free/reduced   \n",
       "2    male        group E                some college  free/reduced   \n",
       "3    male        group B                 high school      standard   \n",
       "4    male        group E          associate's degree      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \\\n",
       "0               completed          67             67             63   \n",
       "1                    none          40             59             55   \n",
       "2                    none          59             60             50   \n",
       "3                    none          77             78             68   \n",
       "4               completed          78             73             68   \n",
       "\n",
       "   combined scores  \n",
       "0        65.666667  \n",
       "1        51.333333  \n",
       "2        56.333333  \n",
       "3        74.333333  \n",
       "4        73.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Does being female mean that the average exam score will be higher or lower? Does doing a test prep course get you a higher score\n",
    "import pandas\n",
    "df = pd.read_csv(\"exams.csv\")\n",
    "df[\"combined scores\"] = ((df[\"math score\"] + df[\"reading score\"] + df[\"writing score\"])/300)*100\n",
    "df.head(5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f65f3130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'combined scores')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "The data above shows exam scores of students allong with their parent's level of education, test prep, gender race/ethnicity\n",
    "and lunch status. In the first graph we want to see the mean test scores of students who have completed\n",
    "a test prep course compared to a student who has not completed one.\n",
    "We see that the students who have completed a test prep course have a higher mean compared to the students who have not.\n",
    "\n",
    "'''\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df1 = df.groupby('test preparation course')[['combined scores']].mean()\n",
    "df1.values[0,0:2]\n",
    "df1\n",
    "\n",
    "sns.barplot(data=df1,y=df1.values[0:2,0],x=df1.index)\n",
    "plt.ylabel('combined scores')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "58a2f92f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        math score  reading score  writing score\n",
      "gender                                          \n",
      "female   63.196687      71.888199      71.708075\n",
      "male     69.384913      66.305609      64.029014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "'''\n",
    "In the second graph we want to see the mean test scores of each subject based on gender. First we group by gender.\n",
    "We then want to see the mean score of each exam so we combine gender with each score of the exams. \n",
    "The graph shows Malestudents have a higher math score while females have higher reading and writing scores\n",
    "'''\n",
    "\n",
    "df4=df.groupby('gender')[['math score','reading score','writing score']].mean()\n",
    "print(df4)\n",
    "df4.plot(kind='bar',grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eff70ebb",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}