econometrics 5th session.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b42ca7f",
   "metadata": {},
   "source": [
    "# 5TH SESSION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6605c9",
   "metadata": {},
   "source": [
    "## Review Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4744bba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "417cf14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(78.53981633974483, 31.41592653589793)\n",
      "Help on function circle in module __main__:\n",
      "\n",
      "circle(radius)\n",
      "    This function calculate the area and circumference of a circle\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def circle(radius):\n",
    "    '''This function calculate the area and circumference of a circle''' #adds help to the defined function\n",
    "    area = math.pi * radius ** 2 #local variable\n",
    "    circumference = 2 * math.pi * radius #local variable\n",
    "    return area, circumference\n",
    "\n",
    "print(circle(5))\n",
    "help(circle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd66659b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.53981633974483"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def circle(radius):\n",
    "    return math.pi * radius ** 2\n",
    "    return 2 * math.pi * radius #isn't stroed in the memory because there is no defined local variable\n",
    "circle(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80c0ed1f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9196091c",
   "metadata": {},
   "source": [
    "# Plotting part 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a22b5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21be105",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install matplotlib #jupter notebook\n",
    "# pip3 install matplotlib #vscode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "199c4082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 13,
     "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": [
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8961cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd273859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 5\n",
    "b = 6\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot(a , b, marker = \"*\", color = \"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b66372d",
   "metadata": {},
   "source": [
    "## Generating a Range of Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94998d1e",
   "metadata": {},
   "source": [
    "### Range function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "57dc8c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(10, 20)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(10, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "acee0556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "for i in range(10, 20):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8b4234b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f9828059",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 11, 12, 13, 14, 15, 16, 17, 18, 19)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(range(10, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4e84a54d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 12, 14, 16, 18]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(10, 20, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "731863a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9096\\1384772164.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#Can't support float\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "list(range(10.5, 20.5, 2)) #Can't support float"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab063abe",
   "metadata": {},
   "source": [
    "## Numpy library\n",
    "### np.arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb278a81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c8ee97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install numpy #jupter notebook\n",
    "# pip3 install numpy #vscode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6effb426",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8eca20b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0b1644a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0.5, 10.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2909265c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0.5, 10.5, dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03bfa1d4",
   "metadata": {},
   "source": [
    "### np.linspace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c05d4b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n",
       "        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n",
       "        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n",
       "        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n",
       "        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n",
       "        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n",
       "        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n",
       "        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n",
       "        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n",
       "        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10) #linspace produces 50 numbers by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "795d4ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  2.5,  5. ,  7.5, 10. ])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10, 5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "99a9db28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  5,  7, 10])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10, 5, dtype = int) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73fcbb22",
   "metadata": {},
   "source": [
    "# plotting part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dc4fa8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = np.linspace(1, 100, 100)\n",
    "B = np.linspace(1, 50, 100)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(A, B, color = 'magenta', marker = \"^\", label = \"Magenta\") #first step\n",
    "plt.title(\"A magenta colored line\") #second step\n",
    "plt.xlabel(\"A line\") #third step\n",
    "plt.ylabel(\"B line\") #forth step\n",
    "plt.xlim(1, 100)\n",
    "plt.ylim(1, 50)\n",
    "plt.grid(True) #fifth step\n",
    "plt.legend() #to get labels printed in plt.plot() #sixth step\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d1af2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4011820c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75c92fcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "581c6f74",
   "metadata": {},
   "source": [
    "# Condtional Statements and Control Flow Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4934aef",
   "metadata": {},
   "source": [
    "## If-Elif-Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f2e81749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = [1, 2, 3, 4]\n",
    "f = [1, 5, 3, 9]\n",
    "\n",
    "f == e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bdd85a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e[0] == f[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c3cd99fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the first items are equal\n"
     ]
    }
   ],
   "source": [
    "if e[0] == f[0]:\n",
    "    print(\"the first items are equal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dc77638d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the first items are equal\n"
     ]
    }
   ],
   "source": [
    "if e[0] == f[0]:\n",
    "    print(\"the first items are equal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31b2025",
   "metadata": {},
   "source": [
    "## Nested for loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "02b1a114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 equal items\n",
      "1 9 unequal items\n",
      "2 9 unequal items\n",
      "3 3 equal items\n",
      "3 9 unequal items\n",
      "4 9 unequal items\n"
     ]
    }
   ],
   "source": [
    "for i in e:\n",
    "    for j in f:\n",
    "        if i == j:\n",
    "            print(i, j, \"equal items\")\n",
    "    else: print(i, j, \"unequal items\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ccd156",
   "metadata": {},
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6449ffe4",
   "metadata": {},
   "source": [
    "### Modify the circle function: Update the circle function to also return the diameter of the circle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cf459a",
   "metadata": {},
   "source": [
    "# more to explore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a1489c",
   "metadata": {},
   "source": [
    " https://matplotlib.org/stable/users/explain/quick_start.html"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}