Plots_Data_Distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "739\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "data = []\n",
    "with open('train_dic_with_sem.pkl') as f:\n",
    "    data = pickle.load(f)\n",
    "\n",
    "print len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'dict'>\n"
     ]
    }
   ],
   "source": [
    "print type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 {'grade': 10, 'semester': 1}\n"
     ]
    }
   ],
   "source": [
    "for k,v in data.iteritems():\n",
    "    print k,v['COM101']\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_courses = {}\n",
    "for k,v in data.iteritems():\n",
    "    for key,val in v.iteritems():\n",
    "        sem_val = int(val['semester'])\n",
    "        if sem_val > 0 and sem_val < 4:\n",
    "            pre_courses[key] = 0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_courses['CSE131']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "course_student_dic = {}\n",
    "course_grade_dic = {}\n",
    "\n",
    "for k,v in data.iteritems():\n",
    "    for key, value in v.iteritems():\n",
    "        sem_val = int(value['semester'])\n",
    "        if (sem_val>4) and (key not in pre_courses):\n",
    "#             print key,sem_val\n",
    "            if key not in course_student_dic:\n",
    "                course_student_dic[key] = 0\n",
    "            course_student_dic[key] += 1\n",
    "            grade_val = int(value['grade'])\n",
    "            if grade_val>0:\n",
    "                if key not in course_grade_dic:\n",
    "                    course_grade_dic[key] = 0\n",
    "                course_grade_dic[key]+=grade_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "226"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(course_grade_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "244"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(course_student_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "\n",
    "sorted_student = sorted(course_student_dic.items(), key=operator.itemgetter(1),reverse=True)\n",
    "sorted_grade = sorted(course_grade_dic.items(), key=operator.itemgetter(1),reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('CSE506', 396)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_student[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('CSE345', 2956)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_grade[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('plot_student.json','wb') as f:\n",
    "    f.write(json.dumps(sorted_student))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('plot_grade.json','wb') as f:\n",
    "    f.write(json.dumps(sorted_grade))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                                               \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "top = []\n",
    "for i in range(5):\n",
    "    top.append(sorted_student[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "labels, ys = zip(*top)\n",
    "xs = np.arange(len(labels)) \n",
    "width = 1\n",
    "\n",
    "plt.bar(xs, ys, width, align='center')\n",
    "\n",
    "plt.xticks(xs, labels) #Replace default x-ticks with xs, then replace xs with labels\n",
    "plt.yticks(ys)\n",
    "\n",
    "plt.savefig('netscore.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "for i in range(len(top)):\n",
    "    x.append(top[i][0])\n",
    "    y.append(top[i][1])\n",
    "revx = x[::-1]\n",
    "revy = y[::-1]\n",
    "x = revx\n",
    "y = revy\n",
    "\n",
    "# def plot_bar_x():\n",
    "#     # this is for plotting purpose\n",
    "#     index = np.arange(len(label))\n",
    "#     plt.bar(index, value)\n",
    "#     plt.xlabel('Course', fontsize=10)\n",
    "#     plt.ylabel('No. of students', fontsize=10)\n",
    "#     plt.xticks(index, label, fontsize=10, rotation=30)\n",
    "#     plt.title('Course vs No. of students')\n",
    "#     plt.show()\n",
    "\n",
    "# plot_bar_x()\n",
    "\n",
    "fig, ax = plt.subplots()    \n",
    "width = 0.75 # the width of the bars \n",
    "ind = np.arange(len(y))  # the x locations for the groups\n",
    "for i, v in enumerate(y):\n",
    "    ax.text(v + 3, i + .25, str(v), color='blue', fontweight='bold')\n",
    "ax.barh(ind, y, width, color=\"blue\")\n",
    "ax.set_yticks(ind+width/2)\n",
    "ax.set_yticklabels(x, minor=False)\n",
    "plt.title('Courses vs No. of students')\n",
    "plt.xlabel('No. of students')\n",
    "plt.ylabel('Courses')      \n",
    "plt.savefig('courses_vs_students')\n",
    "#plt.show()\n",
    "\n",
    "# https://stackoverflow.com/questions/30228069/how-to-display-the-value-of-the-bar-on-each-bar-with-pyplot-barh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---Pie charts time----\n",
    "\n",
    "cse535_dic = {}\n",
    "cse506_dic = {}\n",
    "cse345_dic = {}\n",
    "cse300_dic = {}\n",
    "fin401_dic = {}\n",
    "for k,v in data.iteritems():\n",
    "    for key,val in v.iteritems():\n",
    "        if key == 'CSE535':\n",
    "            grade_val = val['grade']\n",
    "            if grade_val not in cse535_dic:\n",
    "                cse535_dic[grade_val] = 0\n",
    "            cse535_dic[grade_val]+=1\n",
    "        if key == 'CSE506':\n",
    "            grade_val = val['grade']\n",
    "            if grade_val not in cse506_dic:\n",
    "                cse506_dic[grade_val] = 0\n",
    "            cse506_dic[grade_val]+=1\n",
    "        if key == 'CSE345':\n",
    "            grade_val = val['grade']\n",
    "            if grade_val not in cse345_dic:\n",
    "                cse345_dic[grade_val] = 0\n",
    "            cse345_dic[grade_val]+=1\n",
    "        if key == 'CSE300':\n",
    "            grade_val = val['grade']\n",
    "            if grade_val not in cse300_dic:\n",
    "                cse300_dic[grade_val] = 0\n",
    "            cse300_dic[grade_val]+=1\n",
    "        if key == 'FIN401':\n",
    "            grade_val = val['grade']\n",
    "            if grade_val not in fin401_dic:\n",
    "                fin401_dic[grade_val] = 0\n",
    "            fin401_dic[grade_val]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "9\n",
      "9\n",
      "9\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print len(cse535_dic)\n",
    "print len(cse506_dic)\n",
    "print len(cse345_dic)\n",
    "print len(cse300_dic)\n",
    "print len(fin401_dic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "del cse535_dic[0]\n",
    "del cse506_dic[0]\n",
    "del cse345_dic[0]\n",
    "del cse300_dic[0]\n",
    "del fin401_dic[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data to plot\n",
    "\n",
    "labels = []\n",
    "sizes = []\n",
    "for k,v in fin401_dic.iteritems():\n",
    "    labels.append(str(k))\n",
    "    sizes.append(int(v))\n",
    "\n",
    "# labels = ['Python', 'C++', 'Ruby', 'Java']\n",
    "# sizes = [215, 130, 245, 210]\n",
    "colors = ['red', 'yellowgreen', 'lightcoral', 'lightskyblue','blue','orange','green','gold']\n",
    "# explode = (0.1, 0, 0, 0)  # explode 1st slice\n",
    " \n",
    "# Plot\n",
    "plt.pie(sizes, labels=labels,colors=colors,\n",
    "    autopct='%1.1f%%', shadow=True, startangle=140)\n",
    "plt.title('FIN401')\n",
    " \n",
    "plt.axis('equal')\n",
    "plt.savefig('FIN401')\n",
    "plt.show()\n",
    "# https://pythonspot.com/matplotlib-pie-chart/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}