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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "# Getting Financial Data - Google Finance" |
| 8 | + ] |
| 9 | + }, |
| 10 | + { |
| 11 | + "cell_type": "markdown", |
| 12 | + "metadata": {}, |
| 13 | + "source": [ |
| 14 | + "### Introduction:\n", |
| 15 | + "\n", |
| 16 | + "This time you will get data from a website.\n", |
| 17 | + "\n", |
| 18 | + "\n", |
| 19 | + "### Step 1. Import the necessary libraries" |
| 20 | + ] |
| 21 | + }, |
| 22 | + { |
| 23 | + "cell_type": "code", |
| 24 | + "execution_count": null, |
| 25 | + "metadata": { |
| 26 | + "collapsed": false |
| 27 | + }, |
| 28 | + "outputs": [], |
| 29 | + "source": [] |
| 30 | + }, |
| 31 | + { |
| 32 | + "cell_type": "markdown", |
| 33 | + "metadata": {}, |
| 34 | + "source": [ |
| 35 | + "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)" |
| 36 | + ] |
| 37 | + }, |
| 38 | + { |
| 39 | + "cell_type": "code", |
| 40 | + "execution_count": null, |
| 41 | + "metadata": { |
| 42 | + "collapsed": false |
| 43 | + }, |
| 44 | + "outputs": [], |
| 45 | + "source": [] |
| 46 | + }, |
| 47 | + { |
| 48 | + "cell_type": "markdown", |
| 49 | + "metadata": {}, |
| 50 | + "source": [ |
| 51 | + "### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks" |
| 52 | + ] |
| 53 | + }, |
| 54 | + { |
| 55 | + "cell_type": "code", |
| 56 | + "execution_count": null, |
| 57 | + "metadata": { |
| 58 | + "collapsed": false |
| 59 | + }, |
| 60 | + "outputs": [], |
| 61 | + "source": [] |
| 62 | + }, |
| 63 | + { |
| 64 | + "cell_type": "markdown", |
| 65 | + "metadata": {}, |
| 66 | + "source": [ |
| 67 | + "### Step 4. Read the data from google, assign to df and print it" |
| 68 | + ] |
| 69 | + }, |
| 70 | + { |
| 71 | + "cell_type": "code", |
| 72 | + "execution_count": null, |
| 73 | + "metadata": { |
| 74 | + "collapsed": false |
| 75 | + }, |
| 76 | + "outputs": [], |
| 77 | + "source": [] |
| 78 | + }, |
| 79 | + { |
| 80 | + "cell_type": "markdown", |
| 81 | + "metadata": {}, |
| 82 | + "source": [ |
| 83 | + "### Step 5. What is the type of structure of df ?" |
| 84 | + ] |
| 85 | + }, |
| 86 | + { |
| 87 | + "cell_type": "code", |
| 88 | + "execution_count": null, |
| 89 | + "metadata": { |
| 90 | + "collapsed": false |
| 91 | + }, |
| 92 | + "outputs": [], |
| 93 | + "source": [] |
| 94 | + }, |
| 95 | + { |
| 96 | + "cell_type": "markdown", |
| 97 | + "metadata": {}, |
| 98 | + "source": [ |
| 99 | + "### Step 6. Print all the Items axis values\n", |
| 100 | + "#### To learn more about the Panel structure go to [documentation](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel) " |
| 101 | + ] |
| 102 | + }, |
| 103 | + { |
| 104 | + "cell_type": "code", |
| 105 | + "execution_count": null, |
| 106 | + "metadata": { |
| 107 | + "collapsed": false |
| 108 | + }, |
| 109 | + "outputs": [], |
| 110 | + "source": [] |
| 111 | + }, |
| 112 | + { |
| 113 | + "cell_type": "markdown", |
| 114 | + "metadata": {}, |
| 115 | + "source": [ |
| 116 | + "### Step 7. Good, now we know the data avaiable. Create a dataFrame called vol, with the Volume values." |
| 117 | + ] |
| 118 | + }, |
| 119 | + { |
| 120 | + "cell_type": "code", |
| 121 | + "execution_count": null, |
| 122 | + "metadata": { |
| 123 | + "collapsed": false |
| 124 | + }, |
| 125 | + "outputs": [], |
| 126 | + "source": [] |
| 127 | + }, |
| 128 | + { |
| 129 | + "cell_type": "markdown", |
| 130 | + "metadata": {}, |
| 131 | + "source": [ |
| 132 | + "### Step 8. Aggregate the data of Volume to weekly\n", |
| 133 | + "#### Hint: Be careful to not sum data from the same week of 2015 and other years." |
| 134 | + ] |
| 135 | + }, |
| 136 | + { |
| 137 | + "cell_type": "code", |
| 138 | + "execution_count": null, |
| 139 | + "metadata": { |
| 140 | + "collapsed": false, |
| 141 | + "scrolled": true |
| 142 | + }, |
| 143 | + "outputs": [], |
| 144 | + "source": [] |
| 145 | + }, |
| 146 | + { |
| 147 | + "cell_type": "markdown", |
| 148 | + "metadata": {}, |
| 149 | + "source": [ |
| 150 | + "### Step 9. Find all the volume traded in the year of 2015" |
| 151 | + ] |
| 152 | + }, |
| 153 | + { |
| 154 | + "cell_type": "code", |
| 155 | + "execution_count": null, |
| 156 | + "metadata": { |
| 157 | + "collapsed": false |
| 158 | + }, |
| 159 | + "outputs": [], |
| 160 | + "source": [] |
| 161 | + }, |
| 162 | + { |
| 163 | + "cell_type": "markdown", |
| 164 | + "metadata": {}, |
| 165 | + "source": [ |
| 166 | + "### BONUS: Create your own question and answer it." |
| 167 | + ] |
| 168 | + }, |
| 169 | + { |
| 170 | + "cell_type": "code", |
| 171 | + "execution_count": null, |
| 172 | + "metadata": { |
| 173 | + "collapsed": true |
| 174 | + }, |
| 175 | + "outputs": [], |
| 176 | + "source": [] |
| 177 | + } |
| 178 | + ], |
| 179 | + "metadata": { |
| 180 | + "kernelspec": { |
| 181 | + "display_name": "Python 2", |
| 182 | + "language": "python", |
| 183 | + "name": "python2" |
| 184 | + }, |
| 185 | + "language_info": { |
| 186 | + "codemirror_mode": { |
| 187 | + "name": "ipython", |
| 188 | + "version": 2 |
| 189 | + }, |
| 190 | + "file_extension": ".py", |
| 191 | + "mimetype": "text/x-python", |
| 192 | + "name": "python", |
| 193 | + "nbconvert_exporter": "python", |
| 194 | + "pygments_lexer": "ipython2", |
| 195 | + "version": "2.7.11" |
| 196 | + } |
| 197 | + }, |
| 198 | + "nbformat": 4, |
| 199 | + "nbformat_minor": 0 |
| 200 | +} |
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