1. Python Programming Language Basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# few basic libraries we need to use for programming in python\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10  +  20  =  30\n"
     ]
    }
   ],
   "source": [
    "# variable creation and manipulation\n",
    "a = 10\n",
    "b = 20\n",
    "c = a + b\n",
    "print(a, \" + \", b, \" = \",c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a is 10\n"
     ]
    }
   ],
   "source": [
    "# if else condtion\n",
    "if a == 10:\n",
    "    print('a is 10')\n",
    "else:\n",
    "    print('a is not 10')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# loops \n",
    "for count in range(4):\n",
    "    print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "14\n",
      "18\n",
      "22\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "# loops : start at 10 increment with steps of 2 till 30 (30 is not included)\n",
    "for count in range(10,30,4):\n",
    "    print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 6, 2, -2, -6, -10, -14, -18]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "12\n",
      "4\n",
      "-4\n",
      "-12\n",
      "-20\n",
      "-28\n",
      "-36\n"
     ]
    }
   ],
   "source": [
    "# list creation and manipulation\n",
    "my_list = []\n",
    "for count in range(10,-20,-4):\n",
    "    my_list.append(count)\n",
    "\n",
    "# print the list\n",
    "print(my_list)\n",
    "\n",
    "# plot the list\n",
    "plt.plot(my_list)\n",
    "plt.show()\n",
    "\n",
    "# use the elements in a list iteratively\n",
    "for item in my_list:\n",
    "    print(item*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[ 10   6   2  -2  -6 -10 -14 -18]\n",
      "[ 4  5  7 -2 -1] \n",
      "\n",
      "arr3 data type: int64\n",
      "arr4 data type: float64\n"
     ]
    }
   ],
   "source": [
    "# numpy arrays\n",
    "arr = np.arange(4)\n",
    "print(arr)\n",
    "\n",
    "# use the existing list to create a numpy array\n",
    "arr2 = np.array(my_list)\n",
    "print(arr2)\n",
    "\n",
    "# create a random numpy array by passing the list as an argument to np.array()\n",
    "arr3 = np.array([4, 5, 7, -2, -1])\n",
    "print(arr3, '\\n')\n",
    "# check the variable type of the array\n",
    "print(\"arr3 data type:\", arr3.dtype)\n",
    "\n",
    "# floating type array\n",
    "arr4 = np.array([1.2, 4.5])\n",
    "print(\"arr4 data type:\", arr4.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Dimensional arrays "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n",
      "float64 \n",
      "\n",
      "[[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "int32 \n",
      "\n",
      "[[1 1 1 1 1]\n",
      " [1 1 1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "# create a matrix of zeros with 3 rows and 4 columns\n",
    "mat1 = np.zeros([3,4])\n",
    "print(mat1)\n",
    "print(mat1.dtype, '\\n')\n",
    "\n",
    "# change the data type of the matrix\n",
    "mat2 = mat1.astype('int32')\n",
    "print(mat2)\n",
    "print(mat2.dtype, '\\n')\n",
    "\n",
    "# create a matrix of ones with rows 2 and 5 columns\n",
    "mat3 = np.ones([2, 5], dtype = 'int32')\n",
    "print(mat3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore more at https://docs.scipy.org/doc/numpy/user/quickstart.html"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}