vbhupati.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1MqoAq-qMj8",
        "outputId": "aaa2f003-c57c-444a-d17b-e7e82a578689"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IPython version:      %6.6s 7.9.0\n"
          ]
        }
      ],
      "source": [
        "import IPython\n",
        "import json\n",
        "# Numpy is a library for working with Arrays\n",
        "import numpy as np\n",
        "# SciPy implements many different numerical algorithms\n",
        "import scipy as sp\n",
        "# Pandas is good with data tables\n",
        "import pandas as pd\n",
        "# Module for plotting\n",
        "import matplotlib\n",
        "#BeautifulSoup parses HTML documents (once you get them via requests)\n",
        "import bs4\n",
        "# Nltk helps with some natural language tasks, like stemming\n",
        "import nltk\n",
        "# Bson is a binary format of json to be stored in databases\n",
        "import bson\n",
        "# Mongo is one of common nosql databases \n",
        "# it stores/searches json documents natively\n",
        "import pymongo\n",
        "print (\"IPython version:      %6.6s\", IPython.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvKKCokFqMkI",
        "outputId": "cee59bdb-4f9d-4e70-d340-7bf5166b0545"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Make a 2 row x 3 column array of random numbers\n",
            "[[0.2332298  0.28393795 0.11584431]\n",
            " [0.66845718 0.14438696 0.68860014]]\n",
            "Add 5 to every element\n",
            "[[5.2332298  5.28393795 5.11584431]\n",
            " [5.66845718 5.14438696 5.68860014]]\n",
            "Get the first row\n",
            "[5.2332298  5.28393795 5.11584431]\n"
          ]
        }
      ],
      "source": [
        "#Here is what numpy can do\\n\",\n",
        "print (\"Make a 2 row x 3 column array of random numbers\")\n",
        "x = np.random.random((2, 3))\n",
        "print (x)\n",
        "\n",
        "#array operation (as in R)\n",
        "print (\"Add 5 to every element\")\n",
        "x = x + 5\n",
        "print (x)\n",
        "\n",
        "# get a slice (first row) (as in R)\n",
        "print (\"Get the first row\")\n",
        "print (x[0, :])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjrNcbdRqMkK",
        "outputId": "e7e6d24f-fafc-4887-956e-0a29c2b99e09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function ndarray.any>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# IPython is quite modern: just press <TAB> at the end of the unfinished statement to see the documentation\n",
        "#       on possible completions.\n",
        "#       In the code cell below, type x.<TAB>, to find built-in operations for x\n",
        "x.any"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "L7CbKMLmqMkM",
        "outputId": "3ecbe163-6ce0-410c-d2de-71056f79631c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "%matplotlib inline \n",
        "import matplotlib.pyplot as plt\n",
        "heads = np.random.binomial(500, .5, size=500)\n",
        "histogram = plt.hist(heads, bins=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "WSUF2aA5qMkN"
      },
      "source": [
        "# Task 1\n",
        "## write a program to produce Fibonacci numbers up to 1000000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNFFzO95qMkP",
        "outputId": "d8d08734-96e5-4936-abd6-40d61b1e931f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269]\n"
          ]
        }
      ],
      "source": [
        "fibnums = [0, 1, 1]\n",
        "\n",
        "while fibnums[-1] < 1000000:\n",
        "  fibnums.append(fibnums[-1] + fibnums[-2])\n",
        "\n",
        "print(fibnums)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ib3aXO_qMkQ"
      },
      "source": [
        "# Task 2\n",
        "## write a program to simulate 1000 tosses of a fair coin (use np.random.binomial)\n",
        "## Calculate the mean and standard deviation of that sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78x6qtxvqMkR",
        "outputId": "13d41cbd-ec10-4510-c60b-07bc5b9d2cfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Standard Deviation 0.4998789853554558\n",
            "Average 0.511\n"
          ]
        }
      ],
      "source": [
        "sim = np.random.binomial(1, 0.5, 1000)\n",
        "\n",
        "print(\"Standard Deviation\", np.std(sim))\n",
        "print(\"Average\", np.average(sim))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pL6BR0fiqMkT"
      },
      "source": [
        "# Task 3\n",
        "## Produce a scatterplot of y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1) distributions \n",
        "### use numpy.random.normal to generate gaussian distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WjIv5YkfqMkU",
        "outputId": "75dd18d1-bc53-493f-9757-05aa1f9be21b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x = np.random.normal(loc=0.0, scale=5.0, size=1000)\n",
        "e = np.random.normal(loc=0.0, scale=1.0, size=1000)\n",
        "y = []\n",
        "for xa, ea in zip(x, e):\n",
        "    val = 0.5*xa + ea\n",
        "    y.append(val)\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.scatter(x, y, s=0.2)\n",
        "\n",
        "ax.set_xlabel(r'gaussian (0, 5)', fontsize=15)\n",
        "ax.set_ylabel(r'0.5*x+e', fontsize=15)\n",
        "ax.set_title('y = 0.5*x+e where x has gaussian (0, 5) and e has gaussian (0, 1)')\n",
        "\n",
        "ax.grid(True)"
      ]
    }
  ],
  "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": "3.5.2"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}