|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "<h1 align=\"center\"> Time Series Data Basics with Pandas Part 3: Price Variation from Pandas groupby </h1>" |
| 8 | + ] |
| 9 | + }, |
| 10 | + { |
| 11 | + "cell_type": "markdown", |
| 12 | + "metadata": {}, |
| 13 | + "source": [ |
| 14 | + "This code demonstrates how to \n", |
| 15 | + "\n", |
| 16 | + "**if this tutorial doesn't cover what you are looking for, please leave a comment below the youtube video and I will try to cover what you are interested in.**" |
| 17 | + ] |
| 18 | + }, |
| 19 | + { |
| 20 | + "cell_type": "markdown", |
| 21 | + "metadata": {}, |
| 22 | + "source": [ |
| 23 | + "<b> Part 1 </b>: Sampling, Rolling Mean (Smoothing), Linear Regression, Filtering, Join, plotting of a Time Series Pandas DataFrame <br>\n", |
| 24 | + "https://www.youtube.com/watch?v=OwnaUVt6VVE" |
| 25 | + ] |
| 26 | + }, |
| 27 | + { |
| 28 | + "cell_type": "markdown", |
| 29 | + "metadata": {}, |
| 30 | + "source": [ |
| 31 | + "<b> Part 2 </b>: Price Variation from Pandas GroupBy <br>\n", |
| 32 | + "https://www.youtube.com/watch?v=1S5UKLqe-gg" |
| 33 | + ] |
| 34 | + }, |
| 35 | + { |
| 36 | + "cell_type": "markdown", |
| 37 | + "metadata": {}, |
| 38 | + "source": [ |
| 39 | + "<h3 align='Left'> Importing Libraries</h3>" |
| 40 | + ] |
| 41 | + }, |
| 42 | + { |
| 43 | + "cell_type": "code", |
| 44 | + "execution_count": 1, |
| 45 | + "metadata": { |
| 46 | + "collapsed": false |
| 47 | + }, |
| 48 | + "outputs": [], |
| 49 | + "source": [ |
| 50 | + "import pandas as pd\n", |
| 51 | + "import pandas_datareader.data as web\n", |
| 52 | + "import numpy as np\n", |
| 53 | + "import matplotlib.pyplot as plt\n", |
| 54 | + "%matplotlib inline" |
| 55 | + ] |
| 56 | + }, |
| 57 | + { |
| 58 | + "cell_type": "markdown", |
| 59 | + "metadata": {}, |
| 60 | + "source": [ |
| 61 | + "<h3 align='Left'> Getting Data and Viewing with Pandas </h3>" |
| 62 | + ] |
| 63 | + }, |
| 64 | + { |
| 65 | + "cell_type": "code", |
| 66 | + "execution_count": null, |
| 67 | + "metadata": { |
| 68 | + "collapsed": true |
| 69 | + }, |
| 70 | + "outputs": [], |
| 71 | + "source": [] |
| 72 | + } |
| 73 | + ], |
| 74 | + "metadata": { |
| 75 | + "kernelspec": { |
| 76 | + "display_name": "Python 2", |
| 77 | + "language": "python", |
| 78 | + "name": "python2" |
| 79 | + }, |
| 80 | + "language_info": { |
| 81 | + "codemirror_mode": { |
| 82 | + "name": "ipython", |
| 83 | + "version": 2 |
| 84 | + }, |
| 85 | + "file_extension": ".py", |
| 86 | + "mimetype": "text/x-python", |
| 87 | + "name": "python", |
| 88 | + "nbconvert_exporter": "python", |
| 89 | + "pygments_lexer": "ipython2", |
| 90 | + "version": "2.7.12" |
| 91 | + } |
| 92 | + }, |
| 93 | + "nbformat": 4, |
| 94 | + "nbformat_minor": 0 |
| 95 | +} |
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