NB1 Python warmup and Fisher Iris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic eqn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "815\n"
     ]
    }
   ],
   "source": [
    "# init x with value 345 and calculate 2x + 5^3\n",
    "def equation (x):\n",
    "    return 2 * x + 5 ** 3\n",
    "\n",
    "x = 345\n",
    "print(equation(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_wxb (w, x, b):\n",
    "    return w * x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "815\n"
     ]
    }
   ],
   "source": [
    "x = 345\n",
    "w = 2\n",
    "b = 5 ** 3\n",
    "\n",
    "print(f_wxb(w, x, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list with 5 short strings\n",
    "my_list = list()\n",
    "my_list = [\"hello\",\n",
    "           \"mango\",\n",
    "           \"salvo\",\n",
    "           \"geometric\",\n",
    "           \"cantor\"\n",
    "          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cantor\n",
      "geometric\n",
      "salvo\n",
      "mango\n",
      "hello\n"
     ]
    }
   ],
   "source": [
    "# loop to print elements of the list in reverse order\n",
    "for i in range(len(my_list)-1, -1, -1):\n",
    "    print(my_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# function to check if x = 100\n",
    "def equals_100 (x):\n",
    "    return x == 100\n",
    "\n",
    "print(equals_100(x = 99))\n",
    "print(equals_100(x = 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a dictionary\n",
    "my_dict = dict()\n",
    "my_dict = {\"data_name\": [\"Animal counts\"],\n",
    "           \"label\": [\"cat\", \"dog\", \"fish\"],\n",
    "           \"count\": [2, 5, 10]\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 10]\n"
     ]
    }
   ],
   "source": [
    "# content of *count* key\n",
    "print(my_dict[\"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animal name: cat, count: 5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Animal name: {my_dict['label'][0]}, count: {my_dict['count'][1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matrix ops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# randomized 2D array\n",
    "array_1 =  np.random.randint(0, 9, size=(2, 3))\n",
    "array_2 = np.random.randint(0, 9, size=(3, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matrix multiplication\n",
    "mul_array = np.dot(array_1, array_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36, 28, 80, 36],\n",
       "       [37, 23, 36, 50]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 37])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first column only\n",
    "mul_array[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher Iris Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load and print shape of iris data\n",
    "iris_data = np.load(\"Iris_data.npy\")\n",
    "iris_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "# plotting dataset with 2 features\n",
    "plt.scatter(iris_data[:, 1], iris_data[:, 3])\n",
    "plt.xlabel (\"sepal width\")\n",
    "plt.ylabel (\"petal width\")\n",
    "plt.title (\"Fisher Iris dataset\")\n",
    "plt.grid()\n",
    "plt.show() \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}