example_eb_bill.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "import numpy\n",
    "import numpy as np\n",
    "from numpy import zeros\n",
    "from numpy import zeros, array, ones, zeros"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "a = list(range(10))\n",
    "for i in range(10):\n",
    "    a[i] += 1"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "numpy.array(list(range(10))) + 1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "* numpy - numerical computation\n",
    "* matplotlib - to plot"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "* List - []\n",
    "* arrays - numpy array\n",
    "* dictionary - {key     : value}\n",
    "* tuple - ()\n",
    "* set - {}"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "source": [
    "len((1, 2 , 3))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "metadata": {},
     "execution_count": 111
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "Person = {'name': 'Sathish',\n",
    "     'Age': 29,\n",
    "     'Living in': 'Netherlands'}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "Person.items()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_items([('name', 'Sathish'), ('Age', 29), ('Living in', 'Netherlands')])"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "source": [
    "A = [1, 2, 3]\n",
    "B = (3, 4, 5, 6)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "source": [
    "C[0] = C[0] + 1"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "source": [
    "C, A"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "([2, 2], [2, 2])"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "source": [
    "A[0] = A[0] + 1"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "source": [
    "C = {3, 5, 6}\n",
    "D = {3, 5, 7, 8, 9}"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "units_consumed_per_month = [100, 110, 96, 105, 140, 135, 130, 128, 120, 115, 108, 90]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "# Plotting units consumed per month\n",
    "plt.figure()\n",
    "plt.plot(range(1, 13), np.array(units_consumed_per_month)) \n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Units');\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "* Store your data in a variable either as list or array or dictionary.\n",
    "* Do some calculations\n",
    "* Analyze the results by plotting it - using matplotlib"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Methods to install python packages:\n",
    "\n",
    "1. pip install package_name\n",
    "2. conda install package_name"
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.6.8",
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    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit ('base': conda)"
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  "interpreter": {
   "hash": "c9590d7032ea884dfefbafe07253313ce5154fbe651f8bce5ea637ccc15b006b"
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 },
 "nbformat": 4,
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}