02_Matplotlib_Temel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-03-18T21:18:38.443032Z",
     "start_time": "2024-03-18T21:18:38.420856Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "file_path = r\"C:\\Users\\alica\\OneDrive\\Masaüstü\\study_performance.csv\"\n",
    "my_data = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Boxplot"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "44ed00961f12c3df"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x16bcfeb2df0>,\n  <matplotlib.lines.Line2D at 0x16bcfeb22e0>],\n 'caps': [<matplotlib.lines.Line2D at 0x16bcfea9790>,\n  <matplotlib.lines.Line2D at 0x16bcfea9e80>],\n 'boxes': [<matplotlib.lines.Line2D at 0x16bcfeb2d90>],\n 'medians': [<matplotlib.lines.Line2D at 0x16bcfea9670>],\n 'fliers': [<matplotlib.lines.Line2D at 0x16bcfea97f0>],\n 'means': []}"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot elimizdeki verilerde aykırı değerleri bulabileceğimiz bir kutu çizimidir.\n",
    "# Boxplot veri kümesindeki ortanca değerleri belirler ve bu değerlerin 1.5 katına kadar, alttan ve üstten uzayarak \"whis\" adlı çizgiler çeker. Bu çizgilerin dışında kalan kısımları \"fliers\" olarak adlandırır ve bunları aykırı değerlerler olarak işaretler. \n",
    "\n",
    "plt.yticks(range(0,101,10)) # yticks bir plot'da y etiketlerini spesifik olarak belirlemek için kullanılır.\n",
    "\n",
    "plt.boxplot(my_data[\"math_score\"]) # Zorunlu parametre olarak çizdirilecek bir dizi alır."
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-18T21:31:24.696806Z",
     "start_time": "2024-03-18T21:31:24.601449Z"
    }
   },
   "id": "bf33764cc303296f",
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "5ef42604431065a6"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}