├── .gitignore ├── 0_Getting_Started.ipynb ├── 1_Python_Data_Tools.ipynb ├── 2_Storing_Data_Module.ipynb ├── 3_Data_Collection.ipynb ├── 4_Exploring_Data.ipynb ├── 5_Data_Analysis.ipynb ├── 6_Data_Cleaning.ipynb ├── 7_Web_Apps.ipynb ├── Appendix.ipynb ├── Exercise Solutions.ipynb ├── Projects.ipynb ├── README.md ├── artist_metrics_small.pkl ├── artists.csv ├── artists.tsv ├── artists_no_column_names.csv ├── artists_updated.tsv ├── checkout_times.txt ├── ep_data.json ├── height_weight.csv ├── height_weight.txt ├── nbs.db └── nbs_top_artists_app.py /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ 2 | *.pyc 3 | .DS_Store 4 | -------------------------------------------------------------------------------- /0_Getting_Started.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Introduction to Data Science\n", 8 | "===\n", 9 | "Welcome to our Introduction to Data Science. I've broken the material up into 7 modules that are inteded to be executed sequentially. I expect that people will come to this material with different backgrounds and different levels of experience, so feel free to skip any of the modules for the material that you're already comfortable with.\n", 10 | "\n", 11 | "There's a table of contents at the bottom of this notebook for those who want to skip ahead and browse what's available.\n", 12 | "\n", 13 | "Python\n", 14 | "-----\n", 15 | "Throughout this course we'll be putting together a \"data science toolbox\", a set of resouces that you'll use to collect, clean, store, explore, visualize, and analyze data. There are several good platforms for doing these tasks, and in this tutorial we'll be using the Python programming language for almost all of them.\n", 16 | "\n", 17 | "Python's major advantages are that\n", 18 | "1. it's free\n", 19 | "2. it's easy to learn and use\n", 20 | "3. there's a Python library that solves almost every problem we'll come up against\n", 21 | "\n", 22 | "Python comes with a core set of libraries, but a number of the data tools we'll need are not included. You could download the libraries for these tools one-by-one, but there are a number of dependencies that make this process a little bit tedious. Instead, I recommend downloading the Anaconda distribution of Python provided by Continuum Analytics [https://docs.continuum.io/anaconda/install]. It comes with almost all of the libraries you'll ever need *and* it takes care of all of the dependency issues that would otherwise make the process a huge pain in the ass.\n", 23 | "\n", 24 | "There aren't many pre-requisites for this course, but you should have at least a basic familiarity with Python. If you don't have much experience using Python there are a few good online tutorials, including:\n", 25 | "1. https://docs.python.org/2/tutorial/index.html\n", 26 | "2. http://www.codecademy.com/en/tracks/python\n", 27 | "\n", 28 | "The first comes directly from python, and, if you can get through it, is probably the best option. If the first option is a little over your head, the second tutorial from code academy is a little bit easier to get started with. It even allows you to complete all of the exercises right in your browser.\n", 29 | "\n", 30 | "If you have other recommendations for python tutorials, please let me know!\n", 31 | "\n", 32 | "\n", 33 | "The Command Line\n", 34 | "--\n", 35 | "One great feature of python is that it comes with a command line tool that allows you to run python code one line at a time and print the results to your screen. Getting to the command line will be slightly different depending on whether you're operating system is Mac, Windows, or Linux.\n", 36 | "\n", 37 | "On Mac you'll use the Terminal app. On Windows you'll open the Command Prompt program. If you're using Linux, I'll assume you've already skipped this section. Regardless of whether you're on Mac, Windows, or Linux, I'll be referring to your command line tool as a \"terminal\".\n", 38 | "\n", 39 | "\n", 40 | "Github\n", 41 | "--\n", 42 | "All of the modules and supporting material for this course are hosted on github here: https://github.com/adamhajari/Introduction-to-Data-Science. If you don't already have one, setup a github account, fork this repo [https://help.github.com/articles/fork-a-repo/], and clone your forked copy of the repo to whatever computers you'll be working from.\n", 43 | "\n", 44 | "\n", 45 | "IPython and Jupyter Notebook\n", 46 | "--\n", 47 | "IPython is an alternative command line tool to the one that comes with python. It's part of the Anaconda python distribution, so if you've installed Anaconda you can open an ipython shell just by typing \n", 48 | "\n", 49 | "```\n", 50 | "$ ipython\n", 51 | "```\n", 52 | "\n", 53 | "in your terminal window.\n", 54 | "\n", 55 | "The ipython shell has all of the functionality of the regular python shell, plus several handy extras. The two features that you'll probably find most helpful are tab completion and built in object descriptions [http://ipython.org/ipython-doc/stable/interactive/tutorial.html#tab-completion].\n", 56 | "\n", 57 | "Anaconda also comes with an interactive browser-based editor called Jupyter that combines code execution, plots, and text formatting. It's a great tool for both exploring data, sharing your results, and learning how to use new python tools. Jupyter notebooks are essentially running ipython in your browser so all the features available in ipython are also available in jupyter notebooks. All of the modules for this course are written in jupyter notebooks which allows you to execute all of the examples in your browser and explore the results yourself.\n", 58 | "\n", 59 | "To start exploring the jupyter notbooks included in this module, open a terminal window, go to the directory where you've cloned the course material and run:\n", 60 | "\n", 61 | "```\n", 62 | "$ jupyter notebook\n", 63 | "```\n", 64 | "\n", 65 | "A new window should open up in your browser with a list of all of the available modules. Clicking on any of them will open that module in a new tab. If you put your cursor in a cell with python code, pressing shift+enter will execute that code. You can add to that code to modify and/or print the results, or you can create a new cell with the plus button at the top left corner of the jupyter notebook.\n", 66 | "\n", 67 | "Don't stop at reading the material and executing the code that's written for you. Use ipython's ? feature to bring up help pages and it's tab completion feature to explore what options are available to you with different methods.\n", 68 | "\n", 69 | "The exercise at the end of this module will show you a few ways you can use the ipython notebook.\n", 70 | "\n", 71 | "Table of Contents\n", 72 | "---\n", 73 | " 0. Getting Started
\n", 74 | " 1. Python Data Tools
\n", 75 | " 2. Data Storage
\n", 76 | " 3. Data Collection
\n", 77 | " 4. Exploring Data
\n", 78 | " 5. Statistical Methods
\n", 79 | " 6. Data Cleaning
\n", 80 | " 7. Web Apps with Spyre
" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "Exercise: Exploring python with jupyter notebooks\n", 88 | "---\n", 89 | "let's start by creating a list. The next block is python code so you can execute it by putting your cursor in the cell and either pressing the play button at the top of the notebook or by pressing shift+return on your keyboard." 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 1, 95 | "metadata": { 96 | "collapsed": false 97 | }, 98 | "outputs": [], 99 | "source": [ 100 | "list_of_fruits = ['apple','pear','orange']" 101 | ] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "When you press execute the cell above (either by pressing shift+return or by pressing the play button at the top of the notebook), you create a new variable named `list_of_fruits`. `list_of_fruits` is a python list object, which has a few methods such as `append` and `count`. Type \n", 108 | "````\n", 109 | "list_of_fruits.\n", 110 | "````\n", 111 | "and press tab to see a full list of all methods and properties available to the list object. For any of these methods you can put a `?` at the beginning of the line to get a short description of what the method does and to see what parameters need to be included. For instance, you can see that there's a `reverse` method. It's probably clear what this method should do, but you may not know whether it creates a *new* list object or does an \"in place\" reversal to the existing object. To find out execute:\n", 112 | "````\n", 113 | "?list_of_fruits.reverse()\n", 114 | "````\n", 115 | "in the cell below. This should bring up a short description of the `reverse` method making it a little more clear how it works." 116 | ] 117 | }, 118 | { 119 | "cell_type": "markdown", 120 | "metadata": {}, 121 | "source": [ 122 | "Now let's do something to this list. Add a \"watermelon\" item to the `list_of_fruits` object and then reverse the list. You can print the results with the line:\n", 123 | "````\n", 124 | "print(list_of_fruits)\n", 125 | "````\n", 126 | "Finally, remove the \"orange\" from the list. Your final result should look like:\n", 127 | "````\n", 128 | "['watermelon', 'pear', 'apple']\n", 129 | "````" 130 | ] 131 | } 132 | ], 133 | "metadata": { 134 | "kernelspec": { 135 | "display_name": "Python 2", 136 | "language": "python", 137 | "name": "python2" 138 | }, 139 | "language_info": { 140 | "codemirror_mode": { 141 | "name": "ipython", 142 | "version": 2 143 | }, 144 | "file_extension": ".py", 145 | "mimetype": "text/x-python", 146 | "name": "python", 147 | "nbconvert_exporter": "python", 148 | "pygments_lexer": "ipython2", 149 | "version": "2.7.12" 150 | } 151 | }, 152 | "nbformat": 4, 153 | "nbformat_minor": 0 154 | } 155 | -------------------------------------------------------------------------------- /2_Storing_Data_Module.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Data Storage" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "#### Text Files" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "The simplest way to store data is with a text file. [Reading and writing to a text file from Python is simple](http://www.pythonforbeginners.com/files/reading-and-writing-files-in-python), and those files can easily be opened with any text editor. A text file can take any form, but your file needs to be parsable. That means when fetching data from the file your python script needs to know where one data point ends and the next begins. The standard convention for storing data is by comma sparated value (or CSVs). CSVs store data the same way an excel spreadsheet does, with rows and columns. Let's say you had three rows and three columns of integer values to store. You would store that data in a CSV as:\n", 22 | "````\n", 23 | "1,2,2\n", 24 | "3,2,1\n", 25 | "4,4,3\n", 26 | "````\n", 27 | "Rows are separated by a new line, and columns by commas. Knowing this simple convention makes it easy for a computer to parse the file so that it can then do something with the data.\n", 28 | "\n", 29 | "However, in some cases CSVs are actually a bad choice for storing data. Suppose that one of the fields contains names of bands:\n", 30 | "````\n", 31 | "ACDC,2,1\n", 32 | "Lady Gaga,4,3\n", 33 | "Peter,Paul and Marry,1,3\n", 34 | "````\n", 35 | "Are \"Peter\" and \"Paul and Marry\" two different columns? It's obvious to most people that they're not, but not at all obvious to a computer. If you're storing strings and there's chance that you may have commas in those strings, CSVs aren't going to work. Luckily, there are plenty of other more obscure characters that can be used to separatate values. If commas aren't a good choice, then you may choose to go with tabs (represented by \\t):\n", 36 | "````\n", 37 | "ACDC\\t2\\t1\n", 38 | "Lady Gaga\\t4\\t3\n", 39 | "Peter, Paul and Marry\\t1\\t3\n", 40 | "````\n", 41 | "\n", 42 | "You could write your own CSV Python parser, but a number of Python tools already exist to do this for you. Let's look at how you'd do this with pandas." 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 1, 48 | "metadata": { 49 | "collapsed": false 50 | }, 51 | "outputs": [], 52 | "source": [ 53 | "import pandas as pd\n", 54 | "file_path = 'artists.csv'\n", 55 | "artists_df = pd.read_csv(file_path)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 2, 61 | "metadata": { 62 | "collapsed": false 63 | }, 64 | "outputs": [ 65 | { 66 | "data": { 67 | "text/html": [ 68 | "
\n", 69 | "\n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | "
namemusicbrainz_idid
0The Scenica8e347d8-29d8-459c-a444-68febeb99c6b500
1Our Family Namefc93c97f-1b9b-49af-8370-7b36e29ad22e710
2KhamelienNaN744
3Death in the Park000c60eb-a2c4-46be-9097-e603fd8795c6786
4enter the collectorf50c0834-e2a7-4241-bd32-061d67560c41818
\n", 111 | "
" 112 | ], 113 | "text/plain": [ 114 | " name musicbrainz_id id\n", 115 | "0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b 500\n", 116 | "1 Our Family Name fc93c97f-1b9b-49af-8370-7b36e29ad22e 710\n", 117 | "2 Khamelien NaN 744\n", 118 | "3 Death in the Park 000c60eb-a2c4-46be-9097-e603fd8795c6 786\n", 119 | "4 enter the collector f50c0834-e2a7-4241-bd32-061d67560c41 818" 120 | ] 121 | }, 122 | "execution_count": 2, 123 | "metadata": {}, 124 | "output_type": "execute_result" 125 | } 126 | ], 127 | "source": [ 128 | "artists_df.head()" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "If you open artists.csv in a text editor, the first few lines look like this:\n", 136 | "````\n", 137 | "name,musicbrainz_id,id\n", 138 | "The Scenic,a8e347d8-29d8-459c-a444-68febeb99c6b,500\n", 139 | "Our Family Name,fc93c97f-1b9b-49af-8370-7b36e29ad22e,710\n", 140 | "Khamelien,,744\n", 141 | "````\n", 142 | "There are two things worth noting here:\n", 143 | "\n", 144 | "1. Khamelien doesn't have a musicbrainz id, but we still need to leave a space for it in the csv. Every row must have the same number of columns, and if we leave a field blank, Pandas will fill it in with the special NaN (Not a Number) value to denote that it's missing.\n", 145 | "2. By default, pandas assumes that the first line of a CSV contains the column names. If your csv does not have column names in the first line, you can set them yourself with the `names` parameter in the read_csv method:" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 3, 151 | "metadata": { 152 | "collapsed": false 153 | }, 154 | "outputs": [ 155 | { 156 | "data": { 157 | "text/html": [ 158 | "
\n", 159 | "\n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | "
namemusicbrainz_idNBS_id
0The Scenica8e347d8-29d8-459c-a444-68febeb99c6b500
1Our Family Namefc93c97f-1b9b-49af-8370-7b36e29ad22e710
2KhamelienNaN744
3Death in the Park000c60eb-a2c4-46be-9097-e603fd8795c6786
4enter the collectorf50c0834-e2a7-4241-bd32-061d67560c41818
\n", 201 | "
" 202 | ], 203 | "text/plain": [ 204 | " name musicbrainz_id NBS_id\n", 205 | "0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b 500\n", 206 | "1 Our Family Name fc93c97f-1b9b-49af-8370-7b36e29ad22e 710\n", 207 | "2 Khamelien NaN 744\n", 208 | "3 Death in the Park 000c60eb-a2c4-46be-9097-e603fd8795c6 786\n", 209 | "4 enter the collector f50c0834-e2a7-4241-bd32-061d67560c41 818" 210 | ] 211 | }, 212 | "execution_count": 3, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "artists_df = pd.read_csv(\n", 219 | " 'artists_no_column_names.csv',\n", 220 | " header=None,\n", 221 | " names=['name','musicbrainz_id','NBS_id']\n", 222 | ")\n", 223 | "artists_df.head()" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "As mentioned before, separating strings with commas can be problematic. It is better to use tabs (\\t). We can use the same `read_csv` pandas method to open tab separated files, but in this case we need to specify that the separaters are tabs using the `sep` parameter." 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 4, 236 | "metadata": { 237 | "collapsed": false 238 | }, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/html": [ 243 | "
\n", 244 | "\n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | "
namemusicbrainz_idid
0The Scenica8e347d8-29d8-459c-a444-68febeb99c6b500
1Our Family Namefc93c97f-1b9b-49af-8370-7b36e29ad22e710
2KhamelienNaN744
3Death in the Park000c60eb-a2c4-46be-9097-e603fd8795c6786
4enter the collectorf50c0834-e2a7-4241-bd32-061d67560c41818
\n", 286 | "
" 287 | ], 288 | "text/plain": [ 289 | " name musicbrainz_id id\n", 290 | "0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b 500\n", 291 | "1 Our Family Name fc93c97f-1b9b-49af-8370-7b36e29ad22e 710\n", 292 | "2 Khamelien NaN 744\n", 293 | "3 Death in the Park 000c60eb-a2c4-46be-9097-e603fd8795c6 786\n", 294 | "4 enter the collector f50c0834-e2a7-4241-bd32-061d67560c41 818" 295 | ] 296 | }, 297 | "execution_count": 4, 298 | "metadata": {}, 299 | "output_type": "execute_result" 300 | } 301 | ], 302 | "source": [ 303 | "artists_df = pd.read_csv('artists.tsv', sep='\\t')\n", 304 | "artists_df.head()" 305 | ] 306 | }, 307 | { 308 | "cell_type": "markdown", 309 | "metadata": {}, 310 | "source": [ 311 | "Now let's suppose we've made some updates to the data, and now we want to save our results to a new csv." 312 | ] 313 | }, 314 | { 315 | "cell_type": "code", 316 | "execution_count": 5, 317 | "metadata": { 318 | "collapsed": false 319 | }, 320 | "outputs": [ 321 | { 322 | "data": { 323 | "text/html": [ 324 | "
\n", 325 | "\n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | "
namemusicbrainz_idid
0The Scenica8e347d8-29d8-459c-a444-68febeb99c6b500
1Our Family Namefc93c97f-1b9b-49af-8370-7b36e29ad22e710
2KhamelienNaN744
3Death in the Park000c60eb-a2c4-46be-9097-e603fd8795c6786
4Enter the Collectorf50c0834-e2a7-4241-bd32-061d67560c41818
\n", 367 | "
" 368 | ], 369 | "text/plain": [ 370 | " name musicbrainz_id id\n", 371 | "0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b 500\n", 372 | "1 Our Family Name fc93c97f-1b9b-49af-8370-7b36e29ad22e 710\n", 373 | "2 Khamelien NaN 744\n", 374 | "3 Death in the Park 000c60eb-a2c4-46be-9097-e603fd8795c6 786\n", 375 | "4 Enter the Collector f50c0834-e2a7-4241-bd32-061d67560c41 818" 376 | ] 377 | }, 378 | "execution_count": 5, 379 | "metadata": {}, 380 | "output_type": "execute_result" 381 | } 382 | ], 383 | "source": [ 384 | "artists_df.loc[4,'name'] = 'Enter the Collector'\n", 385 | "artists_df.head()" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": 6, 391 | "metadata": { 392 | "collapsed": false 393 | }, 394 | "outputs": [], 395 | "source": [ 396 | "artists_df.to_csv('artists_updated.tsv', sep='\\t')" 397 | ] 398 | }, 399 | { 400 | "cell_type": "markdown", 401 | "metadata": {}, 402 | "source": [ 403 | "Using the `to_csv` method, we write our results to the file `artists_updated.tsv` which can be opened with the `read_csv` method (using the sep='t' paramater) or with any text editor. Note: if we write to a file that already exists, `to_csv` will overwrite the old file with the new." 404 | ] 405 | }, 406 | { 407 | "cell_type": "markdown", 408 | "metadata": {}, 409 | "source": [ 410 | "#### SQL" 411 | ] 412 | }, 413 | { 414 | "cell_type": "markdown", 415 | "metadata": {}, 416 | "source": [ 417 | "Text files work well for simple, relatively small, datasets that can live in one or two independant tables and don't need to be updated often. However when the data you're working with is either really big or you have multiple tables that are related to eachother, you might want to to start looking into other solutions. \n", 418 | "\n", 419 | "The most common tabular step up from CSVs are realtional databases that use SQL (Structured Query Language). Three advantages of a SQL database over text files are that:\n", 420 | "\n", 421 | "1. It's much easier to join two or more tables that share common columns (we'll see an example in a second)\n", 422 | "2. SQL is very efficient at searching through large data sets and pulling data from multiple tables matching a specific query criteria.\n", 423 | "3. SQL allows for easy inserting new rows or updating existing rows without needing to overwrite an entire dataset.\n", 424 | "\n", 425 | "There are several different implementations of SQL, and at most companies there is a person (or team of people) who manage the setup and maintenance of a SQL databse. Fortunately, the query languages across all implementations of SQL are very similar, so you don't necessarily need to know the nuts and bolts of how the data is stored to get the data you need.\n", 426 | "\n", 427 | "If you're new to SQL and don't have the resources to manage a production-level relational database, there's a free and easy to use SQL implementation called sqlite. It requires no setup and comes pre-installed with Python. sqlite is a standalone application that you can access [from the command line](https://sqlite.org/quickstart.html) by typing `sqlite3 /path/to/database` (where `/path/to/database` is the path to a database file. We'll be using the `nbs.db` database that's included in this repo in this tutorial. In your terminal window, change directories [cd] to where the `nbs.db` file is located and run\n", 428 | "````\n", 429 | "$ sqlite3 nbs.db\n", 430 | "````" 431 | ] 432 | }, 433 | { 434 | "cell_type": "markdown", 435 | "metadata": {}, 436 | "source": [ 437 | "You should see a command prompt letting you know that you're using the sqlite command-line program. To see what tables are included in the nbs database use the `.tables` command:\n", 438 | "````\n", 439 | "sqlite> .tables\n", 440 | "artists networks\n", 441 | "endpoints x_artists_endpoints\n", 442 | "````\n", 443 | "and to see the columns and types for a table use the `.schema` command:\n", 444 | "````\n", 445 | "sqlite> .schema artists\n", 446 | "CREATE TABLE \"artists\" (\n", 447 | "\"index\" INTEGER,\n", 448 | " \"name\" TEXT,\n", 449 | " \"musicbrainz_id\" TEXT,\n", 450 | " \"created_at\" TIMESTAMP,\n", 451 | " \"updated_at\" TIMESTAMP,\n", 452 | " \"deleted_at\" TIMESTAMP,\n", 453 | " \"id\" INTEGER\n", 454 | ");\n", 455 | "CREATE INDEX \"ix_artists_index\"ON \"artists\" (\"index\");\n", 456 | "````\n", 457 | "Now let's query some data. To get the first five rows from the `artists` table, run the following from the command line:\n", 458 | "````\n", 459 | "sqlite> SELECT * FROM artists LIMIT 5;\n", 460 | "````\n", 461 | "The results should look like this:\n", 462 | "````\n", 463 | "The Scenic|a8e347d8-29d8-459c-a444-68febeb99c6b|2009-06-16 07:58:58|2015-05-19 12:28:17||500\n", 464 | "Our Family Name|fc93c97f-1b9b-49af-8370-7b36e29ad22e|2009-06-16 08:04:18|2014-08-27 09:04:37||710\n", 465 | "Khamelien||2009-06-16 08:05:08|2014-08-27 09:04:36||744\n", 466 | "Death in the Park|000c60eb-a2c4-46be-9097-e603fd8795c6|2009-06-16 08:06:10|2014-08-27 09:04:37||786\n", 467 | "enter the collector|f50c0834-e2a7-4241-bd32-061d67560c41|2009-06-16 08:06:54|2014-08-27 09:04:34||818\n", 468 | "````\n", 469 | "To get the first five rows that don't have a musicbrainz id:\n", 470 | "````\n", 471 | "sqlite> SELECT * FROM artists WHERE musicbrainz_id IS NULL LIMIT 5;\n", 472 | "Khamelien||2009-06-16 08:05:08|2014-08-27 09:04:36||744\n", 473 | "Shorelines End||2009-06-16 08:10:11|2014-08-27 09:04:32||952\n", 474 | "Dearth||2009-06-16 08:36:24|2014-08-27 09:51:29||1978\n", 475 | "Redink||2009-06-16 19:45:19|2014-08-27 09:59:37||4297\n", 476 | "The Atomic Ballroom Calamity||2009-06-16 19:47:00|2014-08-27 09:59:45||4359\n", 477 | "````\n", 478 | "and to find an artist named \"Redink\":\n", 479 | "````\n", 480 | "sqlite> SELECT * FROM artists WHERE name='The Good Ship';\n", 481 | "The Good Ship|efe7fd43-c3bb-4bb1-b306-8a92af4348c0|2009-07-09 05:56:18|2014-08-27 09:17:04||208860\n", 482 | "````\n", 483 | "\n", 484 | "In addition to the sqlite command-line tool, we can also query a sqlite database directly from Python. As with CSVs, there are a couple of tools for doing this, and we'll use a tool built directly into pandas. Because pandas is tabular, pulling data from sql and putting it directly into a dataframe is fairly simple.\n", 485 | "\n", 486 | "(Note that in the below query you will have to update the url to reflect the path to where nbs.db is stored on your computer.)" 487 | ] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": 7, 492 | "metadata": { 493 | "collapsed": false 494 | }, 495 | "outputs": [ 496 | { 497 | "name": "stdout", 498 | "output_type": "stream", 499 | "text": [ 500 | "there are 4349 rows in the artists table\n" 501 | ] 502 | }, 503 | { 504 | "data": { 505 | "text/html": [ 506 | "
\n", 507 | "\n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | "
namemusicbrainz_idcreated_atupdated_atdeleted_atid
0The Scenica8e347d8-29d8-459c-a444-68febeb99c6b2009-06-16 07:58:582015-05-19 12:28:17None500
1Our Family Namefc93c97f-1b9b-49af-8370-7b36e29ad22e2009-06-16 08:04:182014-08-27 09:04:37None710
2KhamelienNone2009-06-16 08:05:082014-08-27 09:04:36None744
3Death in the Park000c60eb-a2c4-46be-9097-e603fd8795c62009-06-16 08:06:102014-08-27 09:04:37None786
4enter the collectorf50c0834-e2a7-4241-bd32-061d67560c412009-06-16 08:06:542014-08-27 09:04:34None818
\n", 567 | "
" 568 | ], 569 | "text/plain": [ 570 | " name musicbrainz_id \\\n", 571 | "0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b \n", 572 | "1 Our Family Name fc93c97f-1b9b-49af-8370-7b36e29ad22e \n", 573 | "2 Khamelien None \n", 574 | "3 Death in the Park 000c60eb-a2c4-46be-9097-e603fd8795c6 \n", 575 | "4 enter the collector f50c0834-e2a7-4241-bd32-061d67560c41 \n", 576 | "\n", 577 | " created_at updated_at deleted_at id \n", 578 | "0 2009-06-16 07:58:58 2015-05-19 12:28:17 None 500 \n", 579 | "1 2009-06-16 08:04:18 2014-08-27 09:04:37 None 710 \n", 580 | "2 2009-06-16 08:05:08 2014-08-27 09:04:36 None 744 \n", 581 | "3 2009-06-16 08:06:10 2014-08-27 09:04:37 None 786 \n", 582 | "4 2009-06-16 08:06:54 2014-08-27 09:04:34 None 818 " 583 | ] 584 | }, 585 | "execution_count": 7, 586 | "metadata": {}, 587 | "output_type": "execute_result" 588 | } 589 | ], 590 | "source": [ 591 | "import sqlite3 as lite\n", 592 | "import pandas as pd\n", 593 | "con = lite.connect('nbs.db')\n", 594 | "query = 'select * from artists'\n", 595 | "artists_df = pd.read_sql(query,con)\n", 596 | "con.close()\n", 597 | "print('there are %s rows in the artists table' % len(artists_df))\n", 598 | "artists_df.head()\n" 599 | ] 600 | }, 601 | { 602 | "cell_type": "markdown", 603 | "metadata": {}, 604 | "source": [ 605 | "Here we've created a connection to the sqlite database using the sqlite3 library, and then passed a sql query and our sqlite connection object to the read_sql method to get the query results as a dataframe." 606 | ] 607 | }, 608 | { 609 | "cell_type": "markdown", 610 | "metadata": {}, 611 | "source": [ 612 | "**Exercise:** Try writing your own function to turn a sql query into a pandas dataframe. Your function will take a query string and a sqlite connection object and return a pandas dataframe. You can use any methods from the sqlite module, but use only the pandas dataframe constructor from the pandas library (pd.DataFrame()). \n", 613 | "\n", 614 | "*Hint: Here's one way to get column names from from a sqlite query: http://stackoverflow.com/questions/7831371/is-there-a-way-to-get-a-list-of-column-names-in-sqlite*" 615 | ] 616 | }, 617 | { 618 | "cell_type": "markdown", 619 | "metadata": {}, 620 | "source": [ 621 | "#### Non-tabular Data" 622 | ] 623 | }, 624 | { 625 | "cell_type": "markdown", 626 | "metadata": {}, 627 | "source": [ 628 | "So far, all of the ways we've looked at for storing data have assumed that our data is tabular, but sometimes data doesn't fit neatly into rows and columns. Suppose for instance that you had a list of arists and you wanted to store all of their EPs and all of the tracks on those EPs. It is *possible* to store this data is a relational database, but that's not necessarily the best or most intuitive way to do so. \n", 629 | "\n", 630 | "An alternative data structure uses key-value mappings. Storing EP data for the bands Green Day and Alkaline Trio in a key-value map might look like this:" 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": 8, 636 | "metadata": { 637 | "collapsed": false 638 | }, 639 | "outputs": [], 640 | "source": [ 641 | "ep_data = {\"Green Day\":{\n", 642 | " \"EPs\":{\n", 643 | " \"1,000 Hours\":{\n", 644 | " \"release_year\":1989,\n", 645 | " \"tracks\":[\"1,000 Hours\",\"1,000 Hours\",\"Only of You\",\"The One I Want\"]},\n", 646 | " \"Slappy\":{\n", 647 | " \"release_year\":1990,\n", 648 | " \"tracks\":[\"Paper Lanterns\",\"Why do you Want Him?\",\"409 in Your Coffeemaker\",\"Knowledge\"]},\n", 649 | " \"Tune In, Tokyo...\":{\n", 650 | " \"release_year\":2001,\n", 651 | " \"location\":\"Japan\"}\n", 652 | " },\n", 653 | " \"artist_id\":1},\n", 654 | " \"Alkaline Trio\":{\n", 655 | " \"EPs\":{\n", 656 | " \"For Your Lungs Only\":{\n", 657 | " \"release_year\":1998,\n", 658 | " \"tracks\":[\"Snake Oil Tanker\",\"Southern Rock\",\"Cooking Wine\",\"For Your Lungs Only\"]},\n", 659 | " \"I Lied My Face Off\":{\n", 660 | " \"release_year\":1999,\n", 661 | " \"tracks\":[\"Goodbye Forever\",\"This Is Getting Over You\",\"Bleeder\",\"I Lied My Face Off\"]},\n", 662 | " \"Broken Wing\":{\n", 663 | " \"release_year\":2013,\n", 664 | " \"tracks\":[\"Balanced On A Shelf\",\"Pocket Knife\",\"Broken Wing\",\"Sun Burns\"]}\n", 665 | " },\n", 666 | " \"artist_id\":2}}\n" 667 | ] 668 | }, 669 | { 670 | "cell_type": "markdown", 671 | "metadata": {}, 672 | "source": [ 673 | "The key-value pair data structure in Python is called a dictionary (and known as a JSON object in javascript, and goes by other names in other programming languages). Python dictionaries are very flexible. The key can be either a string or a number, and the value can be almost anything.\n", 674 | "\n", 675 | "If we want all of the metadata available for the Alkaline Trio album \"For Your Lungs Only\" we just pass in the keys for the nested set of dictionaries:" 676 | ] 677 | }, 678 | { 679 | "cell_type": "code", 680 | "execution_count": 9, 681 | "metadata": { 682 | "collapsed": false 683 | }, 684 | "outputs": [ 685 | { 686 | "data": { 687 | "text/plain": [ 688 | "{'release_year': 1998,\n", 689 | " 'tracks': ['Snake Oil Tanker',\n", 690 | " 'Southern Rock',\n", 691 | " 'Cooking Wine',\n", 692 | " 'For Your Lungs Only']}" 693 | ] 694 | }, 695 | "execution_count": 9, 696 | "metadata": {}, 697 | "output_type": "execute_result" 698 | } 699 | ], 700 | "source": [ 701 | "ep_data['Alkaline Trio']['EPs']['For Your Lungs Only']" 702 | ] 703 | }, 704 | { 705 | "cell_type": "markdown", 706 | "metadata": {}, 707 | "source": [ 708 | "and to get just the release year for that album:" 709 | ] 710 | }, 711 | { 712 | "cell_type": "code", 713 | "execution_count": 10, 714 | "metadata": { 715 | "collapsed": false 716 | }, 717 | "outputs": [ 718 | { 719 | "data": { 720 | "text/plain": [ 721 | "1998" 722 | ] 723 | }, 724 | "execution_count": 10, 725 | "metadata": {}, 726 | "output_type": "execute_result" 727 | } 728 | ], 729 | "source": [ 730 | "ep_data['Alkaline Trio']['EPs']['For Your Lungs Only']['release_year']" 731 | ] 732 | }, 733 | { 734 | "cell_type": "markdown", 735 | "metadata": {}, 736 | "source": [ 737 | "To store a Python dictionary, we can convert to a JSON string and save to a text file:" 738 | ] 739 | }, 740 | { 741 | "cell_type": "code", 742 | "execution_count": 11, 743 | "metadata": { 744 | "collapsed": false 745 | }, 746 | "outputs": [], 747 | "source": [ 748 | "import json\n", 749 | "ep_data_str = json.dumps(ep_data)\n", 750 | "f = open('ep_data.json', 'w')\n", 751 | "f.write(ep_data_str)\n", 752 | "f.close()" 753 | ] 754 | }, 755 | { 756 | "cell_type": "markdown", 757 | "metadata": {}, 758 | "source": [ 759 | "After converting the dictionary to a string (using `json.dumps`), the above code block writes that string to the file `ep_data.json` (the last three lines)." 760 | ] 761 | }, 762 | { 763 | "cell_type": "markdown", 764 | "metadata": {}, 765 | "source": [ 766 | "#### MongoDB" 767 | ] 768 | }, 769 | { 770 | "cell_type": "markdown", 771 | "metadata": {}, 772 | "source": [ 773 | "Storing key-value data as json files is fine for small data sets (less than a few Mb), but once your data grows larger than a 100 Mb, you may want to consider moving it into a queryable storage engine like MongoDB.\n", 774 | "\n", 775 | "MongoDB has a Python library and a good tutorial for getting started [link: http://api.mongodb.org/python/current/tutorial.html]. " 776 | ] 777 | } 778 | ], 779 | "metadata": { 780 | "kernelspec": { 781 | "display_name": "Python 2", 782 | "language": "python", 783 | "name": "python2" 784 | }, 785 | "language_info": { 786 | "codemirror_mode": { 787 | "name": "ipython", 788 | "version": 2 789 | }, 790 | "file_extension": ".py", 791 | "mimetype": "text/x-python", 792 | "name": "python", 793 | "nbconvert_exporter": "python", 794 | "pygments_lexer": "ipython2", 795 | "version": "2.7.12" 796 | } 797 | }, 798 | "nbformat": 4, 799 | "nbformat_minor": 0 800 | } 801 | -------------------------------------------------------------------------------- /3_Data_Collection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Data Collection\n", 8 | "===\n", 9 | "Up until now, the data that you've been working with has been given to in the form of a text file, a database, or in a python pickle. In this module, we'll learn a few techniques for writing our own python scripts for collecting data from the internet. \n", 10 | "\n", 11 | "Collecting data from the internet can be done in one of two ways:\n", 12 | "1. through an API (Application Program Interface)\n", 13 | "2. by parsing html\n", 14 | "\n", 15 | "The API route is always preferable as it is (usually) sactioned by whomever is hosting the data, which allows them to dictate terms of use and monitor and restrict how their service is used. An access token (or \"key\") is typically required to access data from an official API.\n", 16 | "\n", 17 | "Not all websites provide a public API for accessing data. In those cases, you may have to write a custom HTML parser. HTML is the language that the static components of webpages are written in. This code is usually well structured and it is often times not difficult to write a short script to find the elements of a interest in the HTML.\n", 18 | "\n", 19 | "Getting Data via an API\n", 20 | "---\n", 21 | "Each API is different as is the process getting an API key. Usually getting an API key is as simple as creating an account and registering for a key. Almost all companies that offer a public API, also offer documentation on how to use their API and how to get a key. Here's Goodreads' Developer's page: https://www.goodreads.com/api\n", 22 | "\n", 23 | "We will want to retrieve a Goodreads API key. In order to do so, create an account with Goodreads, and then head to the developer page and apply for an API key. \n", 24 | "\n", 25 | "When filling out the form to register your client you will only need to fill in an application name and company name. Feel free to leave the other fields blank. Agree to the terms of service listed below and apply. Goodreads will instantly provide you with an API key and a client secret. \n" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "In order to get data from Goodreads, we'll be making \"requests\" to their API. We'll be making our requests through python, although you could also make a request directly from your browser. Your browser is in fact making dozens or even hundreds of requests every time it loads a web page. In the cases where your browser loads a web page, it's converting HTML into well formatted human-readable content. When we make requests to an API we'll usually get back content that was designed for a computer to read. Paste the following url (replacing YOUR-API-KEY with your API key) into your browser.\n", 33 | "\n", 34 | "````\n", 35 | "https://www.goodreads.com/book/review_counts.json?isbns=0141439602&key=YOUR-API-KEY\n", 36 | "\n", 37 | "````\n", 38 | "\n", 39 | "What you get back is a JSON object that contains review counts for the book `A Tale of Two Cities`. An ISBN is essentially a serial number for a book. In order to know which book the ISBN refers to, we used the 'book.show' method listed in the Goodreads API documentation. Goodreads lists all available methods here. Replacing the book 'id' in the sample URL returns metadata around the book in question, including the title. \n", 40 | "\n", 41 | "We briefly saw JSON objects in the previous module. They're identical in structure to python dictionaries, and python comes with a library for converting JSON strings into a dictionary.\n", 42 | "\n", 43 | "Let's make that same API request, now using python's `requests` library. Create a variable using your API key, so that we can easily substitute in the code. " 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 1, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [ 53 | { 54 | "data": { 55 | "text/plain": [ 56 | "u'{\"books\":[{\"id\":1953,\"isbn\":\"0141439602\",\"isbn13\":\"9780141439600\",\"ratings_count\":642940,\"reviews_count\":1063067,\"text_reviews_count\":10290,\"work_ratings_count\":684009,\"work_reviews_count\":1175567,\"work_text_reviews_count\":13291,\"average_rating\":\"3.81\"}]}'" 57 | ] 58 | }, 59 | "execution_count": 1, 60 | "metadata": {}, 61 | "output_type": "execute_result" 62 | } 63 | ], 64 | "source": [ 65 | "import requests\n", 66 | "my_key = \"PUT YOUR API KEY HERE\"\n", 67 | "url = \"https://www.goodreads.com/book/review_counts.json?isbns=0141439602&key=%s\" % my_key\n", 68 | "response = requests.get(url)\n", 69 | "response.text" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "The response object has other metadata about the response (success/error codes, headers, etc.), but we're only interested in the content (or \"text\") of the response. As we saw before, the response text is a JSON string. We'll use python's `json` library to turn that string into a dictionary" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 2, 82 | "metadata": { 83 | "collapsed": false 84 | }, 85 | "outputs": [ 86 | { 87 | "data": { 88 | "text/plain": [ 89 | "{u'books': [{u'average_rating': u'3.81',\n", 90 | " u'id': 1953,\n", 91 | " u'isbn': u'0141439602',\n", 92 | " u'isbn13': u'9780141439600',\n", 93 | " u'ratings_count': 642940,\n", 94 | " u'reviews_count': 1063067,\n", 95 | " u'text_reviews_count': 10290,\n", 96 | " u'work_ratings_count': 684009,\n", 97 | " u'work_reviews_count': 1175567,\n", 98 | " u'work_text_reviews_count': 13291}]}" 99 | ] 100 | }, 101 | "execution_count": 2, 102 | "metadata": {}, 103 | "output_type": "execute_result" 104 | } 105 | ], 106 | "source": [ 107 | "import json\n", 108 | "response_data = json.loads(response.text)\n", 109 | "response_data" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "which we saw how to work with in the previous module:" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 3, 122 | "metadata": { 123 | "collapsed": false 124 | }, 125 | "outputs": [ 126 | { 127 | "data": { 128 | "text/plain": [ 129 | "u'3.81'" 130 | ] 131 | }, 132 | "execution_count": 3, 133 | "metadata": {}, 134 | "output_type": "execute_result" 135 | } 136 | ], 137 | "source": [ 138 | "ave_rating = response_data['books'][0]['average_rating']\n", 139 | "ave_rating" 140 | ] 141 | }, 142 | { 143 | "cell_type": "markdown", 144 | "metadata": {}, 145 | "source": [ 146 | ">**Note:** The \"books\" value is actually a list of dictionaries (with only one element), we need to specify that we want the first element in that list. \n", 147 | "\n", 148 | "This is the basic workflow for using python to get data from the internet:\n", 149 | "1. Make a request and get the content\n", 150 | "2. convert the content into a parsable python object (like a dictionary)\n", 151 | "3. get the data of interest and do something with it\n", 152 | "\n", 153 | "\n", 154 | "### API Parameters\n", 155 | "API parameters are passed in at the end of the url. The first parameter is preceded with a `?` and all subsequent parameters are separated by an `&`. All requests to Goodread's API require at least one parameter (the API key). Goodread's `review_count` endpoint also take an `isbns` parameter which is a comma separated list of isbns that you want review counts for. For instance, the isbns for the first 3 Harry Potter books are 0747532699, 0747538492, and 0747542155, and the URL used to request the review counts for those 3 books would be\n", 156 | "\n", 157 | "````\n", 158 | "https://www.goodreads.com/book/review_counts.json?isbns=0747532699,0747538492,0747542155&key=YOUR-API-KEY\n", 159 | "````\n", 160 | "\n", 161 | "You can paste this url in your browser to see the results (be sure to replace YOUR-API-KEY with the API key you got from goodreads).\n", 162 | "\n", 163 | ">**PRO-TIP:** There are browser extensions that make it much easier to view json in your browser (everything is well-formatted and you can collapse values). For those of you who use the chrome web browser, [here's the json formatting extentsion that I use](https://chrome.google.com/webstore/detail/jsonview/chklaanhfefbnpoihckbnefhakgolnmc?hl=en). You may have to restart your to start the extension.\n", 164 | "\n", 165 | "**Exercise 3.1:**\n", 166 | "Write a function that takes a list of goodreads ISBNs and returns the goodreads Ids.\n", 167 | "\n", 168 | "\n", 169 | "### Rate Limits\n", 170 | "One important consideration when building a crawler that will make lots of API requests is that it adheres to the rate limits (i.e. number of requests per hour) [specified in the documentation](https://www.goodreads.com/api/terms). You can do this by putting a pause between each request. Goodreads has a 1 request per second rate limit. Use the `sleep` function from python's `time` library to implement these pauses:" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 4, 176 | "metadata": { 177 | "collapsed": false 178 | }, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/plain": [ 183 | "200" 184 | ] 185 | }, 186 | "execution_count": 4, 187 | "metadata": {}, 188 | "output_type": "execute_result" 189 | } 190 | ], 191 | "source": [ 192 | "import time\n", 193 | "response = requests.get(url) # make a request\n", 194 | "time.sleep(1) # sleep for 1 second\n", 195 | "response = requests.get(url) # make the next request request\n", 196 | "\n", 197 | "# code should be 200 if all is well and 429 if we've exceeded our rate limit\n", 198 | "response.status_code" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "Exceeding the rate limit isn't the only thing that can go wrong with an API call and there's a [different status code for each issue you might have](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes). For instance, if we make a request to a non-exising endpoint, we get a 404 (Not Found) status code." 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 5, 211 | "metadata": { 212 | "collapsed": false, 213 | "scrolled": false 214 | }, 215 | "outputs": [ 216 | { 217 | "data": { 218 | "text/plain": [ 219 | "404" 220 | ] 221 | }, 222 | "execution_count": 5, 223 | "metadata": {}, 224 | "output_type": "execute_result" 225 | } 226 | ], 227 | "source": [ 228 | "# -1 is not a valid ISBN\n", 229 | "url = \"https://www.goodreads.com/book/review_counts.json?isbns=-1&key=%s\" % my_key\n", 230 | "response = requests.get(url)\n", 231 | "response.status_code" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "Parsing HTML for data\n", 239 | "---\n", 240 | "An official API is always the best way to get data from a host, but sometimes a website either doesn't have an API or, if they do have an API, some data of interest may not be available via an API endpoint. In those cases you may be able to get the raw HTML and use an HTML parsing library to extract the data of interest.\n", 241 | "\n", 242 | "We can get the raw HTML, the same we we got API data, with the `requests` library. Let's grab the HTML from the wikipedia page on Conan episodes from 2014." 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 6, 248 | "metadata": { 249 | "collapsed": false 250 | }, 251 | "outputs": [], 252 | "source": [ 253 | "url = \"https://en.wikipedia.org/wiki/List_of_Conan_episodes_(2014)\"\n", 254 | "response = requests.get(url)\n", 255 | "html_raw = response.text" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "The response text is raw html which is difficult to work with, so we'll use the [beautiful soup](http://www.crummy.com/software/BeautifulSoup/bs4/doc/) library to help us parse out the data we're interested in." 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": 8, 268 | "metadata": { 269 | "collapsed": false 270 | }, 271 | "outputs": [], 272 | "source": [ 273 | "from bs4 import BeautifulSoup # To get everything\n", 274 | "html_soup = BeautifulSoup(html_raw)" 275 | ] 276 | }, 277 | { 278 | "cell_type": "markdown", 279 | "metadata": {}, 280 | "source": [ 281 | "HTML is a nested set of opening and closing tags which define the page layout and properties. Everything is typically wrapped in html open (<html>) and close (</html>) tags. Between <html></html> there's usually a <head></head> section where metadata and javascript code lives, and a <body></body> section where the page content lives. Once you've soupified your html, You can access nested elements as though they were attributes of their parent object. For instance, to get the `head` section of the page you would do:" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 9, 287 | "metadata": { 288 | "collapsed": false 289 | }, 290 | "outputs": [], 291 | "source": [ 292 | "head = html_soup.html.head" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "and to get the title element within the `head` section:" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": 10, 305 | "metadata": { 306 | "collapsed": false 307 | }, 308 | "outputs": [ 309 | { 310 | "data": { 311 | "text/plain": [ 312 | "List of Conan episodes (2014) - Wikipedia" 313 | ] 314 | }, 315 | "execution_count": 10, 316 | "metadata": {}, 317 | "output_type": "execute_result" 318 | } 319 | ], 320 | "source": [ 321 | "head.title" 322 | ] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": {}, 327 | "source": [ 328 | "You can also perform a search on a beautiful soup object. Let's say you want to get the Table of Contents element from this wikipedia page. Suppose you know that the Table of Contents HTML element has an `id` attribute with value \"toc\"." 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": 11, 334 | "metadata": { 335 | "collapsed": false 336 | }, 337 | "outputs": [], 338 | "source": [ 339 | "table_of_contents = html_soup.find(id='toc')" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "Beautiful soup objects have a few useful methods and attributes. The `text` attribute is a string of all text within the element that would be displayed in a browser, and the `attrs` attibute is a dictionary of html attributes that are a part of the tag (like the `id` or `class`)." 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": 12, 352 | "metadata": { 353 | "collapsed": false 354 | }, 355 | "outputs": [ 356 | { 357 | "data": { 358 | "text/plain": [ 359 | "{'class': ['toc'], 'id': 'toc'}" 360 | ] 361 | }, 362 | "execution_count": 12, 363 | "metadata": {}, 364 | "output_type": "execute_result" 365 | } 366 | ], 367 | "source": [ 368 | "table_of_contents.attrs # the table of contents element has 2 attributes, a class (equal to ['toc']) and an 'id' (equal to 'toc')" 369 | ] 370 | }, 371 | { 372 | "cell_type": "code", 373 | "execution_count": 13, 374 | "metadata": { 375 | "collapsed": false 376 | }, 377 | "outputs": [ 378 | { 379 | "name": "stdout", 380 | "output_type": "stream", 381 | "text": [ 382 | "\n", 383 | "\n", 384 | "Contents\n", 385 | "\n", 386 | "\n", 387 | "1 2014\n", 388 | "\n", 389 | "1.1 January\n", 390 | "1.2 February\n", 391 | "1.3 March\n", 392 | "1.4 April\n", 393 | "1.5 May\n", 394 | "1.6 June\n", 395 | "1.7 July\n", 396 | "1.8 August\n", 397 | "1.9 September\n", 398 | "1.10 October\n", 399 | "1.11 November\n", 400 | "1.12 December\n", 401 | "\n", 402 | "\n", 403 | "2 References\n", 404 | "\n", 405 | "\n" 406 | ] 407 | } 408 | ], 409 | "source": [ 410 | "print table_of_contents.text" 411 | ] 412 | }, 413 | { 414 | "cell_type": "markdown", 415 | "metadata": {}, 416 | "source": [ 417 | "If we want to find *all* instances of an element meeting our search criteria we can instead use the `findAll` method. Let's get a list of all months included in the Table of Contents. If you print the `table_of_contents` object, you'll see that each month is wrapped in a list tag (<li>) with class attribute 'toclevel-2'." 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": 14, 423 | "metadata": { 424 | "collapsed": false 425 | }, 426 | "outputs": [ 427 | { 428 | "data": { 429 | "text/plain": [ 430 | "12" 431 | ] 432 | }, 433 | "execution_count": 14, 434 | "metadata": {}, 435 | "output_type": "execute_result" 436 | } 437 | ], 438 | "source": [ 439 | "month_elements = table_of_contents.findAll('li', {'class':'toclevel-2'})\n", 440 | "len(month_elements)" 441 | ] 442 | }, 443 | { 444 | "cell_type": "markdown", 445 | "metadata": {}, 446 | "source": [ 447 | "As expected, there are 12 month_elements. Let's see what the first month_element looks like:" 448 | ] 449 | }, 450 | { 451 | "cell_type": "code", 452 | "execution_count": 15, 453 | "metadata": { 454 | "collapsed": false 455 | }, 456 | "outputs": [ 457 | { 458 | "data": { 459 | "text/plain": [ 460 | "
  • 1.1 January
    • " 461 | ] 462 | }, 463 | "execution_count": 15, 464 | "metadata": {}, 465 | "output_type": "execute_result" 466 | } 467 | ], 468 | "source": [ 469 | "month_elements[0]" 470 | ] 471 | }, 472 | { 473 | "cell_type": "markdown", 474 | "metadata": {}, 475 | "source": [ 476 | "Each month element has a tag (a link to somewhere else in the page in this case) and two spans. The text of the spans are the table of content number and month. Let's create a lists for the href, number, and month and put the results in a dataframe." 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": 16, 482 | "metadata": { 483 | "collapsed": false 484 | }, 485 | "outputs": [ 486 | { 487 | "data": { 488 | "text/html": [ 489 | "
    \n", 490 | "\n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | "
    hrefmonth
    1.1#JanuaryJanuary
    1.2#FebruaryFebruary
    1.3#MarchMarch
    1.4#AprilApril
    1.5#MayMay
    1.6#JuneJune
    1.7#JulyJuly
    1.8#AugustAugust
    1.9#SeptemberSeptember
    1.10#OctoberOctober
    1.11#NovemberNovember
    1.12#DecemberDecember
    \n", 561 | "
    " 562 | ], 563 | "text/plain": [ 564 | " href month\n", 565 | "1.1 #January January\n", 566 | "1.2 #February February\n", 567 | "1.3 #March March\n", 568 | "1.4 #April April\n", 569 | "1.5 #May May\n", 570 | "1.6 #June June\n", 571 | "1.7 #July July\n", 572 | "1.8 #August August\n", 573 | "1.9 #September September\n", 574 | "1.10 #October October\n", 575 | "1.11 #November November\n", 576 | "1.12 #December December" 577 | ] 578 | }, 579 | "execution_count": 16, 580 | "metadata": {}, 581 | "output_type": "execute_result" 582 | } 583 | ], 584 | "source": [ 585 | "hrefs = []\n", 586 | "toc_numbers = []\n", 587 | "months = []\n", 588 | "for element in month_elements:\n", 589 | " a_tag = element.a # a tags in html are used for links within a page or too another page\n", 590 | " hrefs.append(a_tag.attrs['href']) # the href attribute determines where the link goes\n", 591 | " toc_number = element.find('span',{'class':'tocnumber'})\n", 592 | " toc_numbers.append(toc_number.text)\n", 593 | " month = element.find('span',{'class':'toctext'})\n", 594 | " months.append(month.text)\n", 595 | "\n", 596 | "import pandas as pd\n", 597 | "pd.DataFrame({'month':months, 'href':hrefs}, index=toc_numbers)" 598 | ] 599 | }, 600 | { 601 | "cell_type": "markdown", 602 | "metadata": {}, 603 | "source": [ 604 | "Notice that with the `find` and `findAll` methods, we can specified both the tag type (a, span, div, etc.) as well as attribute values. It's worth looking over the [beautiful soup Quick Start](http://www.crummy.com/software/BeautifulSoup/bs4/doc/#quick-start) documentation to see all of the available methods. If you're not very familiar with HTML you might also consider looking over [this tutorial](http://www.w3schools.com/html/) or taking the [codeacademy course](https://www.codecademy.com/learn/web) in HTML/CSS for a deeper dive.\n", 605 | "\n", 606 | ">**Pro-Tip:** Most web browsers have their own built-in developer tools for investegating all the HTML and other elements that go into displaying a web page. In Chrome you can right click on any element on a page and click on \"Inspect Element\" to look at it's HTML. Use the arrows on the left of each element to expand or collapse in order to show/hide all of its children.\n", 607 | "\n", 608 | "**Exercise 3.2:** Use beautiful soup to get the show number (\"No.\"), air date, lists of guests, and list of entertainment guests for every Conan show from 2014. Put the results in a dataframe. **Bonus:** make the date a datetime object." 609 | ] 610 | } 611 | ], 612 | "metadata": { 613 | "kernelspec": { 614 | "display_name": "Python 2", 615 | "language": "python", 616 | "name": "python2" 617 | }, 618 | "language_info": { 619 | "codemirror_mode": { 620 | "name": "ipython", 621 | "version": 2 622 | }, 623 | "file_extension": ".py", 624 | "mimetype": "text/x-python", 625 | "name": "python", 626 | "nbconvert_exporter": "python", 627 | "pygments_lexer": "ipython2", 628 | "version": "2.7.12" 629 | } 630 | }, 631 | "nbformat": 4, 632 | "nbformat_minor": 0 633 | } 634 | -------------------------------------------------------------------------------- /7_Web_Apps.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Web Apps with Spyre\n", 8 | "---\n", 9 | "\n", 10 | "Once you've collected data and done some analysis you'll probably want to present it to others. As we've already seen, Jupyter notebooks are a great way to do this, but if we wanted to make our results more interactive we could put the data in a web application.\n", 11 | "\n", 12 | "Spyre is a python library that is used to quickly build data focussed web applications. Spyre does not come pre-installed with the Anaconda distribution of Python so checkout the [Spyre github page and README](https://github.com/adamhajari/spyre) for instruction on how to install it. The README also contains a couple of short examples that you should try yourself before continuing with this module.\n", 13 | "\n", 14 | "Once you've successfully gotten the examples for the spyre README to work, let's again turn to the `nbs.db` sqlite database to make a new app. We'll be pulling in data from two tables and joining the results in pandas. Start by querying all rows from the `artists` and `social_data` tables. Let's work on the components of the app here and then put them into a separate script." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/html": [ 27 | "
    \n", 28 | "\n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | "
    idname
    0500The Scenic
    1710Our Family Name
    2744Khamelien
    3786Death in the Park
    4818enter the collector
    \n", 64 | "
    " 65 | ], 66 | "text/plain": [ 67 | " id name\n", 68 | "0 500 The Scenic\n", 69 | "1 710 Our Family Name\n", 70 | "2 744 Khamelien\n", 71 | "3 786 Death in the Park\n", 72 | "4 818 enter the collector" 73 | ] 74 | }, 75 | "execution_count": 1, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "import sqlite3 as lite\n", 82 | "import pandas as pd\n", 83 | "con = lite.connect('nbs.db')\n", 84 | "artists_query = \"select id, name from artists\"\n", 85 | "artists_df = pd.read_sql(artists_query, con)\n", 86 | "\n", 87 | "socail_query = \"select * from social_data\"\n", 88 | "social_df = pd.read_sql(socail_query, con)\n", 89 | "artists_df.head()" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 2, 95 | "metadata": { 96 | "collapsed": false 97 | }, 98 | "outputs": [ 99 | { 100 | "data": { 101 | "text/html": [ 102 | "
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    artist_idFaceBookPageLikesTwitterFollowersYouTubeViewsInstagramLikesInstagramFollowers
    05004797.01395.0NaNNaNNaN
    1744NaN224.044287.0NaNNaN
    29524142.064.0130279.0NaNNaN
    31017NaNNaN2966.0NaNNaN
    4108628980.043405.0402054.056832.07653.0
    \n", 163 | "
    " 164 | ], 165 | "text/plain": [ 166 | " artist_id FaceBookPageLikes TwitterFollowers YouTubeViews \\\n", 167 | "0 500 4797.0 1395.0 NaN \n", 168 | "1 744 NaN 224.0 44287.0 \n", 169 | "2 952 4142.0 64.0 130279.0 \n", 170 | "3 1017 NaN NaN 2966.0 \n", 171 | "4 1086 28980.0 43405.0 402054.0 \n", 172 | "\n", 173 | " InstagramLikes InstagramFollowers \n", 174 | "0 NaN NaN \n", 175 | "1 NaN NaN \n", 176 | "2 NaN NaN \n", 177 | "3 NaN NaN \n", 178 | "4 56832.0 7653.0 " 179 | ] 180 | }, 181 | "execution_count": 2, 182 | "metadata": {}, 183 | "output_type": "execute_result" 184 | } 185 | ], 186 | "source": [ 187 | "social_df.head()" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "We can now use pandas' `merge` function to merge these two dataframes so we have the social data and the artists' names in one place. The `artist_id`s from social_df match the `id`s from artist_df so we'll merge on those two columns" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 3, 200 | "metadata": { 201 | "collapsed": false 202 | }, 203 | "outputs": [ 204 | { 205 | "data": { 206 | "text/html": [ 207 | "
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    idnameartist_idFaceBookPageLikesTwitterFollowersYouTubeViewsInstagramLikesInstagramFollowers
    0500The Scenic5004797.01395.0NaNNaNNaN
    1744Khamelien744NaN224.044287.0NaNNaN
    2952Shorelines End9524142.064.0130279.0NaNNaN
    31017Hollywood Ave1017NaNNaN2966.0NaNNaN
    41086Play for keeps108628980.043405.0402054.056832.07653.0
    \n", 280 | "
    " 281 | ], 282 | "text/plain": [ 283 | " id name artist_id FaceBookPageLikes TwitterFollowers \\\n", 284 | "0 500 The Scenic 500 4797.0 1395.0 \n", 285 | "1 744 Khamelien 744 NaN 224.0 \n", 286 | "2 952 Shorelines End 952 4142.0 64.0 \n", 287 | "3 1017 Hollywood Ave 1017 NaN NaN \n", 288 | "4 1086 Play for keeps 1086 28980.0 43405.0 \n", 289 | "\n", 290 | " YouTubeViews InstagramLikes InstagramFollowers \n", 291 | "0 NaN NaN NaN \n", 292 | "1 44287.0 NaN NaN \n", 293 | "2 130279.0 NaN NaN \n", 294 | "3 2966.0 NaN NaN \n", 295 | "4 402054.0 56832.0 7653.0 " 296 | ] 297 | }, 298 | "execution_count": 3, 299 | "metadata": {}, 300 | "output_type": "execute_result" 301 | } 302 | ], 303 | "source": [ 304 | "df_merged = pd.merge(artists_df, social_df, left_on='id', right_on='artist_id')\n", 305 | "df_merged.head()" 306 | ] 307 | }, 308 | { 309 | "cell_type": "markdown", 310 | "metadata": {}, 311 | "source": [ 312 | "Let's put these steps in a function so that we can use them again later." 313 | ] 314 | }, 315 | { 316 | "cell_type": "code", 317 | "execution_count": 4, 318 | "metadata": { 319 | "collapsed": true 320 | }, 321 | "outputs": [], 322 | "source": [ 323 | "import sqlite3 as lite\n", 324 | "import pandas as pd\n", 325 | "\n", 326 | "def get_social_data():\n", 327 | " con = lite.connect('nbs.db')\n", 328 | " artists_query = \"select id, name from artists\"\n", 329 | " artists_df = pd.read_sql(artists_query, con)\n", 330 | "\n", 331 | " socail_query = \"select * from social_data\"\n", 332 | " social_df = pd.read_sql(socail_query, con)\n", 333 | " df_merged = pd.merge(artists_df, social_df, left_on='id', right_on='artist_id')\n", 334 | " return df_merged.drop(['id', 'artist_id'], axis=1)" 335 | ] 336 | }, 337 | { 338 | "cell_type": "markdown", 339 | "metadata": {}, 340 | "source": [ 341 | "Now let's create a second function that takes the merged results, sorts them by one of the featuers, and returns the top artist limited by a paramater n." 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 5, 347 | "metadata": { 348 | "collapsed": false 349 | }, 350 | "outputs": [ 351 | { 352 | "data": { 353 | "text/html": [ 354 | "
    \n", 355 | "\n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | "
    nameTwitterFollowers
    1881The Amp Jackers97933
    1049DaveRose95106
    2043Dez Duron89117
    649Pauly Shore84642
    1525Doyin aka Lyrical80197
    3571Chantelle Lee78375
    3425Billi. B71055
    \n", 401 | "
    " 402 | ], 403 | "text/plain": [ 404 | " name TwitterFollowers\n", 405 | "1881 The Amp Jackers 97933\n", 406 | "1049 DaveRose 95106\n", 407 | "2043 Dez Duron 89117\n", 408 | "649 Pauly Shore 84642\n", 409 | "1525 Doyin aka Lyrical 80197\n", 410 | "3571 Chantelle Lee 78375\n", 411 | "3425 Billi. B 71055" 412 | ] 413 | }, 414 | "execution_count": 5, 415 | "metadata": {}, 416 | "output_type": "execute_result" 417 | } 418 | ], 419 | "source": [ 420 | "def top_artists(network, n=10):\n", 421 | " \"\"\"return the top n artists from network\"\"\"\n", 422 | " df_social = get_social_data() # get the data from sql\n", 423 | " \n", 424 | " # sort the result by the given network in descending order\n", 425 | " df_sorted = df_social.sort_values(by=network, ascending=False)\n", 426 | " \n", 427 | " # limit the results to the top n artists\n", 428 | " df_limited = df_sorted.head(n).copy()\n", 429 | " \n", 430 | " # convert the values to integers so they look nicer\n", 431 | " df_limited.loc[:, network] = df_limited.loc[:, network].astype('int')\n", 432 | " \n", 433 | " # return the results but just include the values for the network of interest\n", 434 | " return df_limited.loc[:, ['name', network]]\n", 435 | "\n", 436 | "top_artists('TwitterFollowers', n=7)" 437 | ] 438 | }, 439 | { 440 | "cell_type": "markdown", 441 | "metadata": {}, 442 | "source": [ 443 | "**Exercise:**\n", 444 | "Now try to add these two functions to a spyre app that has a drop down menu for selecting a feature type and a slider that allows you to limit your results to some values between 0 and 100.\n", 445 | "\n", 446 | "If you get stuck check out `nbs_top_artists_app.py` in this repo. You should be able to run the app from a terminal window by running \n", 447 | "\n", 448 | "```\n", 449 | "$ python nbs_top_artists_app.py\n", 450 | "```\n", 451 | "\n", 452 | "Paste `http://127.0.0.1:8080` into your browser url box to try the app out." 453 | ] 454 | } 455 | ], 456 | "metadata": { 457 | "kernelspec": { 458 | "display_name": "Python 2", 459 | "language": "python", 460 | "name": "python2" 461 | }, 462 | "language_info": { 463 | "codemirror_mode": { 464 | "name": "ipython", 465 | "version": 2 466 | }, 467 | "file_extension": ".py", 468 | "mimetype": "text/x-python", 469 | "name": "python", 470 | "nbconvert_exporter": "python", 471 | "pygments_lexer": "ipython2", 472 | "version": "2.7.12" 473 | } 474 | }, 475 | "nbformat": 4, 476 | "nbformat_minor": 0 477 | } 478 | -------------------------------------------------------------------------------- /Appendix.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Appendix" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# 1. Install Necessary Packages\n", 15 | "\n", 16 | "Anaconda offers a suite of packages for Python for science-related modules. By installing Anaconda, you will no longer have to install each module individually (e.g. matplotlib, ipython, pandas etc.) These are the basic packages necessary for data science in python. In the event there are additional modules necessary, these are possible to install through conda install * module name” in terminal. Do not use pip install. \n", 17 | "\n", 18 | "Download anaconda through this page: http://continuum.io/downloads, select install for me only. \n", 19 | "\n", 20 | "Once you have installed anaconda, check conda list, it should list all the packages you have. If it returns: “command not valid” you may have issues with your environment path variable. If so type “export PATH=~/anaconda/bin:$PATH” into terminal in order to rectify this. Check conda list again. You should now be able to view all the packages. You may also be prompted to upgrade certain packages. If so type “conda update pip”, and select Y. " 21 | ] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 2", 27 | "language": "python", 28 | "name": "python2" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 2 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython2", 40 | "version": "2.7.10" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 0 45 | } 46 | -------------------------------------------------------------------------------- /Projects.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Projects" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": { 14 | "collapsed": false 15 | }, 16 | "outputs": [], 17 | "source": [] 18 | } 19 | ], 20 | "metadata": { 21 | "kernelspec": { 22 | "display_name": "Python 2", 23 | "language": "python", 24 | "name": "python2" 25 | }, 26 | "language_info": { 27 | "codemirror_mode": { 28 | "name": "ipython", 29 | "version": 2 30 | }, 31 | "file_extension": ".py", 32 | "mimetype": "text/x-python", 33 | "name": "python", 34 | "nbconvert_exporter": "python", 35 | "pygments_lexer": "ipython2", 36 | "version": "2.7.12" 37 | } 38 | }, 39 | "nbformat": 4, 40 | "nbformat_minor": 0 41 | } 42 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Introduction to Data Science 2 | ==== 3 | Welcome to my Introduction to Data Science. I've broken the material into 7 modules that are inteded to be executed sequentially. I expect that people will come to this material with different backgrounds and different levels of experience, so feel free to skip any of the modules for the material that you're already comfortable with. 4 | 5 | There's a table of contents at the bottom of this notebook for those who want to skip ahead and browse what's available here. 6 | 7 | Python 8 | ----- 9 | Throughout this course we'll be putting together a "data science toolbox", a set of resouces that you'll use to collect, clean, store, explore, visualize, and analyze data. There are several good platforms for doing these tasks, and in this tutorial we'll be using the Python programming language for almost all of them. 10 | 11 | Python's major advantages are that 12 | 13 | 1. it's free 14 | 2. it's easy to learn and use 15 | 3. there's a Python library that solves almost every problem we'll come up against 16 | 17 | Python comes with a core set of libraries, but a number of the data tools we'll need are not included. You could download the libraries for these tools one-by-one, but there are a number of dependencies that make this process a little bit tedious. Instead, I recommend downloading the [Anaconda distribution of Python provided by Continuum Analytics](https://docs.continuum.io/anaconda/install). It comes with almost all of the libraries you'll ever need *and* it takes care of all of the dependency issues that would otherwise make you hate your life. 18 | 19 | There aren't many pre-requisites for this course, but basic familiarity of Python is one of them. There are two online tutorials that I recommend: 20 | 21 | 1. https://docs.python.org/2/tutorial/index.html 22 | 2. http://www.codecademy.com/en/tracks/python 23 | 24 | The first comes directly from python, and, if you can get through it, is probably the best option. If option one feels a little over your head, the second tutorial from code academy is a little bit easier to get started with. It even allows you to complete all of the exercises right in your browser. 25 | 26 | If you have other recommendations for python tutorials, please let me know! 27 | 28 | The Command Line 29 | -- 30 | One of the great features of python is that it comes with a command line tool that allows you to run python code one line at a time and print the results to your screen (this is not the case with languages like C++ or Java). This is one (of the many) features that makes python so easy to learn. 31 | 32 | Getting to the command line will be slightly different depending on whether you're operating system is Mac, Windows, or Linux. 33 | 34 | On Mac you'll use the Terminal app. On Windows you'll open the Command Prompt program. If you're using Linux, I'll assume you've already skipped this section. Regardless of whether you'r on Mac, Windows, or Linux, I'll be referring to your command line tool as a "terminal". 35 | 36 | IPython and Jupyter Notebooks 37 | -- 38 | IPython is an alternative command line tool to the one that comes with python. It's part of the Anaconda python distribution, so if you've installed Anaconda you can open an ipython shell just by typing ipython in your terminal window. 39 | 40 | The ipython shell has all of the functionality of the regular python shell, plus several handy extras. The two features that you'll probably find most helpful are tab completion and built in object descriptions [http://ipython.org/ipython-doc/stable/interactive/tutorial.html#tab-completion]. 41 | 42 | Anaconda also comes with an interactive browser-based editor called Jupyter that combines code execution, plots, and text formatting. It's a great tool for both exploring data, sharing your results, and learning how to use new python tools. Jupyter notebooks are essentially running ipython in your browser so all the features available in ipython are also available in jupyter notebooks. All of the modules for this course are written in jupyter notebooks which allows you to execute all of the examples in your browser and explore the results yourself. 43 | 44 | 45 | Github 46 | -- 47 | All of the modules and supporting material for this course are hosted on github here: [https://github.com/adamhajari/Introduction-to-Data-Science]. If you don't already have one, setup a github account, fork this repo [https://help.github.com/articles/fork-a-repo/], and clone your forked copy of the repo to whatever computers you'll be working from. 48 | 49 | Table of Contents 50 | --- 51 | - [0.Getting Started](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/0_Getting_Started.ipynb) 52 | - [1.Python Data Tools](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/1_Python_Data_Tools.ipynb) 53 | - [2.Data Storage](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/2_Storing_Data_Module.ipynb) 54 | - [3.Data Collection](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/3_Data_Collection.ipynb) 55 | - [4.Exploring Data](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/4_Exploring_Data.ipynb) 56 | - [5.Data Analysis](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/5_Data_Analysis.ipynb) 57 | - [6.Data Cleaning](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/6_Data_Cleaning.ipynb) 58 | - [7.Web Apps with Spyre](https://github.com/adamhajari/Introduction-to-Data-Science/blob/master/7_Web_Apps.ipynb) 59 | -------------------------------------------------------------------------------- /artist_metrics_small.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adamhajari/Introduction-to-Data-Science/d2b14506ddcb81ae5d2b7ce8e95e7a15cf4c0108/artist_metrics_small.pkl -------------------------------------------------------------------------------- /artists.csv: -------------------------------------------------------------------------------- 1 | name,musicbrainz_id,id 2 | The Scenic,a8e347d8-29d8-459c-a444-68febeb99c6b,500 3 | Our Family Name,fc93c97f-1b9b-49af-8370-7b36e29ad22e,710 4 | Khamelien,,744 5 | Death in the Park,000c60eb-a2c4-46be-9097-e603fd8795c6,786 6 | enter the collector,f50c0834-e2a7-4241-bd32-061d67560c41,818 7 | Shorelines End,,952 8 | Hollywood Ave,2e258b9f-88bd-42a8-8bec-d977a1254546,1017 9 | Play for keeps,276f57a2-7ba9-41e7-bb11-6d56729bb1f7,1086 10 | Pan Trees,d4cbfbd0-3b75-4430-9722-57c174901c83,1242 11 | Dearth,,1978 12 | Dead Reprise,e9e4e778-fdd3-46c8-8c03-36e31a014b54,2005 13 | Crime Lab,7b0cbb25-c0e3-4e47-9abd-e7378a01945a,2138 14 | Beat Union,79edf7bc-0b58-4c52-92f0-37a0faa57038,2311 15 | Big City Kids,e3dda876-0e6f-4996-bf01-7e8798ded368,2337 16 | Idle Sons,bdd52c2d-2bf1-426e-a992-94616aa1d46b,2982 17 | Spanish Gamble,6d574d88-b5d6-469f-943d-bcb501a9612e,3103 18 | Demiricous,f9974a34-a5ed-4e1c-8059-8225e113a43a,3326 19 | The Mystic Underground,d6cb1345-d8af-4c63-bf47-e577a625c4aa,3606 20 | The Students,ab4c5827-9332-4c4b-b080-d53b05b7183e,3675 21 | Redink,,4297 22 | The Atomic Ballroom Calamity,,4359 23 | aderbat,,4970 24 | Donnie Sloan,c464c26d-fa5e-4085-9239-388f3c6157ae,5153 25 | The American Life,3c472548-0391-481b-8e61-0d3eef8f9a2d,5246 26 | Stan Ipcus,3e0aba46-36f9-4a72-8f1a-b876204b49b0,5673 27 | Seconds to Breathe,747819a3-1f05-4be3-a2ed-b23c33d127cd,5954 28 | Guy Chambers & Sophie Hunter,229d2f2b-a4ce-4f6a-bde5-152804593304,6039 29 | Hey Dakota,,6345 30 | All's Quiet,,6528 31 | Ever Ready Records,3dbddfe3-8d66-4012-b963-b11ee3f2ad2d,6744 32 | Storm The Beaches,,7034 33 | We Are The Fury,c98d1841-f2bf-406b-931d-84516fc2e50b,7061 34 | Eric Scot Porter,5c55ab4f-0b41-4537-8035-7c90d52df5f0,7163 35 | Epollo,,7165 36 | Honesty Eyes,c5118628-0c47-4a8a-86f9-a0e638be697e,7173 37 | Gone By Daylight,921e530c-5c29-489a-aa46-fd9d8cc04c2a,7627 38 | The OverUnder,c8394169-6e07-4a4c-8adc-7055b4421279,8325 39 | Olde York,94c126da-78de-414d-8d52-2a37069eb518,8756 40 | rebecca correia,081c95a4-b3bf-4992-b240-85d0f2a58750,9115 41 | Dremnt the End,5d3850e2-2798-4ed8-95e9-0be87665c326,9166 42 | Young twinn,223ce0b2-865b-4039-b67c-f59894d413de,9343 43 | The Erection Kids,82afc7d5-b773-48bc-a511-8678485a9f23,9809 44 | MoeRoc,,10008 45 | Beight,c1fe023a-d869-4404-a927-b46bdf348875,10606 46 | slowEarth,7077f68e-ae63-4883-8524-29ed8816ec68,10889 47 | Luna Angel,b8b9ec90-126b-4f29-b380-cdc451653007,11073 48 | Dallas Superstars,2284f210-0229-4f19-a64f-a811a7f114c9,11384 49 | Not Sorry,7657ca7f-bac5-4671-a937-8a7160b3fb78,12343 50 | Majic ship,f73d124a-6ac5-4d5c-9df3-8425727ee327,12411 51 | Hello Nurse,289a6156-00d3-4201-9540-1798e2c01e61,12415 52 | Dirty Bird,544f63c1-33f0-4e41-829c-74488e763851,12681 53 | Quincy Coleman,b6dab43e-a42f-4795-b6eb-5b44c4f03f3d,13316 54 | Jayson Belt,893b9756-3be9-4a11-91d7-193c6738125b,13644 55 | Metal Kites,72f153e9-364c-4a40-8306-0657ac36b817,13993 56 | Jelly Joe,588e04c6-cfd8-4f12-ac2c-035fe33c6b85,14124 57 | DJ Impact,1caaafed-6995-4395-9621-63bf63162c56,14307 58 | Buen Chico,2a6c8647-6178-4e83-9b9c-5314f7e23bce,14628 59 | Fast Heart Mart,337818dd-e77c-4184-b706-b47589a7ce9a,14807 60 | Luc Skyz,6e9ba48a-5972-4cf1-bd9f-9d35248c73bc,15039 61 | Greg Howe & Ritchie Kotzen,c5583f98-458e-4de7-8176-00e8e0d3ebea,15075 62 | Willie Isz,23cd5cb7-50f7-419d-a4c9-f758ea68e030,15134 63 | Midevil,,15299 64 | Eli Bolin,,15412 65 | Raina Rose,399f28a0-6837-4014-8092-d5bced4f6b41,15672 66 | Felina,6f393ff8-1f77-40f5-8515-2f72b0c28cf2,15683 67 | Boss Volenti,52980b00-2ac6-4e91-89a4-b8dc6cc2287e,16764 68 | Buddy Leezle,06c9a271-c45f-48f0-8f8a-39f861e7f8bd,17038 69 | Calmer,38acdf39-6586-452e-b92d-b569a1333b2c,17172 70 | Canabasse,,17247 71 | LoudLion,e8e779e1-edec-45bc-ba43-e0d28f48400c,17322 72 | Circus Diablo,877f5179-607d-4a27-8361-439e4e851504,17403 73 | Onion Flavored Rings,2b34c270-cfed-418a-9ee3-ae6125d841c8,17672 74 | Otis Jackson Jr. Trio,10e2c144-8782-4fd5-a0ba-3a7125266695,17698 75 | Pretty Boy Thorson & The Falling Angels,21f3822b-7e97-41a2-bdc2-347d9fe1177c,18278 76 | Shot Baker,9c825e0b-4d4e-4155-a3f0-263c62d6d195,18358 77 | RU36,c44cedbc-9a92-4572-b357-15bbd23d0798,18486 78 | Qurage,,18489 79 | Blackbyrd McKnight,c94a1d00-739c-464b-8984-2e01367fbce3,18751 80 | Ova Looven,cfce497d-d5a6-4634-a232-032030daebd2,18904 81 | Fitter,,19243 82 | Yovee,,19267 83 | Annie Bethancourt,e3b26c0e-1aee-4203-a361-dd6216812232,19288 84 | Sammy Blaze,d564168f-eefb-4841-afa1-0e84074d53e8,19361 85 | The Give It Ups,36597a25-8cfc-47a9-8d83-a4cf70f301d4,19362 86 | Severance,e817449b-a4fd-4e96-974b-8593d07758f6,19432 87 | LaMaW,,19598 88 | Dan Padilla,7dd62fa4-d408-446e-818d-21548b041559,19689 89 | Freestyle Evolution,db16ea37-11b2-4b9f-9a2c-0dabcd86aa26,19806 90 | The Fatal Flaw,94a65125-70ec-4316-950e-f82c9f23f12e,20412 91 | Now That's What I Call Music,5c5b2c0d-c221-42d3-93db-01744062dede,21091 92 | NOTIIBELIKESOMEONE,,21113 93 | Delroy Washington,d443ef12-46a8-487c-aab2-5810e411c251,21148 94 | Tyshawn Sorey,25bb66e5-5010-44de-ac1f-7c431e124eeb,21327 95 | Our Mercury,89116de7-0aa7-47f5-a36c-c7f7cadd7013,21603 96 | John X,de6c91e4-bbc9-4ec6-9142-ba47654a7fa9,21624 97 | Little Sammy Davis,bb0475ad-f101-4a2a-83b7-d06eae8f7b21,21986 98 | Live by the fist,,22128 99 | Lukas Sherfey,61337ef9-3e4d-4228-870d-895e3caa1c6a,22141 100 | Jann Wilde & Rose Avenue,4c457892-7f68-4c93-8bc4-df590b29cb3a,22288 101 | Tidemouth,,22541 102 | The Bridge Band,aa31b48f-f6d8-4d30-9a92-4f16ec0fdde7,23052 103 | Fernando Kabusacki,be28510c-8263-40e7-9613-573af995c6c4,24072 104 | The Voids,feca7cbb-d3ad-4b38-9399-a00aa9f9625f,24256 105 | Great Bloomers,1ae2f002-0bea-4bff-8e18-fd70f96a2395,24918 106 | Outer Spacist,fed64c4b-4ccd-4ea4-b717-93cc5d6e7086,24937 107 | Kid Meets Cougar,992f1e3f-86a3-4b6c-b164-2fdfbe83531f,25631 108 | Evan Brass,ca16ed4f-79b0-4369-b5bd-968cfaaaf7e7,25700 109 | Zenia,a4f4769a-edaa-4490-a1aa-d35deb4f07e7,25741 110 | m t h . E l e c t r o,902286c2-e7d1-4ee8-adb6-216d6a71a05c,25946 111 | BMW (Music),,26058 112 | 77Klash,,26170 113 | Clairmont,,26207 114 | Marc Decoca,72139ae8-cc45-40e9-8a36-d29ea8b66847,26675 115 | The Gertrudes,c8394169-6e07-4a4c-8adc-7055b4421279,26708 116 | Bicycle Records,69ba1791-7b83-4c23-9995-6a47fde9c01f,26966 117 | J Laine,,27084 118 | provincial parks,7a6f9b53-8dd4-4372-b900-22988c252da4,27431 119 | Le Rug,3917de50-0ac1-4a1d-b680-c903f07c6ea2,27700 120 | Ketch Harbour Wolves,0f05d2c9-f90c-47c1-87a9-d9c99aa0be20,27745 121 | Peter Friestedt,0a8fe595-a14e-42b0-ad24-d587a491d54b,28555 122 | Fuck Montreal,87cf6aa6-a005-445b-8920-1c5b3fdfbfaa,28561 123 | Ed in the Refridgerators,e8de09ea-2bfd-43c2-851b-0ccd2109e319,28731 124 | Uncle Monsterface,33a32e1e-9857-4743-9532-344fb6f0c4eb,28738 125 | Peter Murray,6ab4a566-a279-441a-af5f-4d47459e1c18,28932 126 | With Horses In Her Eyes,e026d5d7-f08d-4807-941c-88765e78b02f,29315 127 | Arlequin,1d811df7-e264-4cab-9d12-38ad1af5e415,29347 128 | SIR 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JP,9f4688cf-c8c7-4a5c-8375-88f425230c39,34414 147 | Daniel Bernstein,f00ecbc4-d45d-4d8b-beed-dda037a5d732,34620 148 | Julie Neumark,609ef5a8-7109-4a30-9205-5428a029e34b,34965 149 | Mazinga Phaser,596cca00-9095-4546-a242-77bc2faf38da,35080 150 | Brandon White,,35228 151 | B.G. and the Chopper City Boyz,8fa26ab1-d57b-4fc5-957c-1884fd512a66,35393 152 | Amanda Martin & Lyle Goodman,3862daf7-7fd0-4236-b58a-4c311d9839a2,35984 153 | Jenn Cristy,,36006 154 | AKP,2b524c77-87e7-43bc-b23b-c5967873aed0,36185 155 | Dave Anthony,17c5dce8-1c29-46ba-94f0-70a443c0c94e,36419 156 | Pat McCurdy,6651fa24-38bc-44e4-a1c1-f11003997b8e,36507 157 | Woogie,dae27553-2819-482f-b4eb-2033d534335f,36554 158 | JaGoFF,,36654 159 | Thom Shepherd,,36740 160 | Istvan Szekely,361e711a-a5c0-4065-a27b-c62e261b0afc,36761 161 | Babaryan,,37068 162 | Phil & Daniel Rosenthal (Holiday),19f89a7c-008b-4433-91d7-1a1a68d1591d,37124 163 | Jeff Caylor,bbbdb644-50b2-4777-8e14-64a267c5cd58,37340 164 | Austins 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Isz,23cd5cb7-50f7-419d-a4c9-f758ea68e030,15134 62 | Midevil,,15299 63 | Eli Bolin,,15412 64 | Raina Rose,399f28a0-6837-4014-8092-d5bced4f6b41,15672 65 | Felina,6f393ff8-1f77-40f5-8515-2f72b0c28cf2,15683 66 | Boss Volenti,52980b00-2ac6-4e91-89a4-b8dc6cc2287e,16764 67 | Buddy Leezle,06c9a271-c45f-48f0-8f8a-39f861e7f8bd,17038 68 | Calmer,38acdf39-6586-452e-b92d-b569a1333b2c,17172 69 | Canabasse,,17247 70 | LoudLion,e8e779e1-edec-45bc-ba43-e0d28f48400c,17322 71 | Circus Diablo,877f5179-607d-4a27-8361-439e4e851504,17403 72 | Onion Flavored Rings,2b34c270-cfed-418a-9ee3-ae6125d841c8,17672 73 | Otis Jackson Jr. Trio,10e2c144-8782-4fd5-a0ba-3a7125266695,17698 74 | Pretty Boy Thorson & The Falling Angels,21f3822b-7e97-41a2-bdc2-347d9fe1177c,18278 75 | Shot Baker,9c825e0b-4d4e-4155-a3f0-263c62d6d195,18358 76 | RU36,c44cedbc-9a92-4572-b357-15bbd23d0798,18486 77 | Qurage,,18489 78 | Blackbyrd McKnight,c94a1d00-739c-464b-8984-2e01367fbce3,18751 79 | Ova Looven,cfce497d-d5a6-4634-a232-032030daebd2,18904 80 | Fitter,,19243 81 | Yovee,,19267 82 | Annie Bethancourt,e3b26c0e-1aee-4203-a361-dd6216812232,19288 83 | Sammy Blaze,d564168f-eefb-4841-afa1-0e84074d53e8,19361 84 | The Give It Ups,36597a25-8cfc-47a9-8d83-a4cf70f301d4,19362 85 | Severance,e817449b-a4fd-4e96-974b-8593d07758f6,19432 86 | LaMaW,,19598 87 | Dan Padilla,7dd62fa4-d408-446e-818d-21548b041559,19689 88 | Freestyle Evolution,db16ea37-11b2-4b9f-9a2c-0dabcd86aa26,19806 89 | The Fatal Flaw,94a65125-70ec-4316-950e-f82c9f23f12e,20412 90 | Now That's What I Call Music,5c5b2c0d-c221-42d3-93db-01744062dede,21091 91 | NOTIIBELIKESOMEONE,,21113 92 | Delroy Washington,d443ef12-46a8-487c-aab2-5810e411c251,21148 93 | Tyshawn Sorey,25bb66e5-5010-44de-ac1f-7c431e124eeb,21327 94 | Our Mercury,89116de7-0aa7-47f5-a36c-c7f7cadd7013,21603 95 | John X,de6c91e4-bbc9-4ec6-9142-ba47654a7fa9,21624 96 | Little Sammy Davis,bb0475ad-f101-4a2a-83b7-d06eae8f7b21,21986 97 | Live by the fist,,22128 98 | Lukas Sherfey,61337ef9-3e4d-4228-870d-895e3caa1c6a,22141 99 | Jann Wilde & Rose Avenue,4c457892-7f68-4c93-8bc4-df590b29cb3a,22288 100 | Tidemouth,,22541 101 | The Bridge Band,aa31b48f-f6d8-4d30-9a92-4f16ec0fdde7,23052 102 | Fernando Kabusacki,be28510c-8263-40e7-9613-573af995c6c4,24072 103 | The Voids,feca7cbb-d3ad-4b38-9399-a00aa9f9625f,24256 104 | Great Bloomers,1ae2f002-0bea-4bff-8e18-fd70f96a2395,24918 105 | Outer Spacist,fed64c4b-4ccd-4ea4-b717-93cc5d6e7086,24937 106 | Kid Meets Cougar,992f1e3f-86a3-4b6c-b164-2fdfbe83531f,25631 107 | Evan Brass,ca16ed4f-79b0-4369-b5bd-968cfaaaf7e7,25700 108 | Zenia,a4f4769a-edaa-4490-a1aa-d35deb4f07e7,25741 109 | m t h . E l e c t r o,902286c2-e7d1-4ee8-adb6-216d6a71a05c,25946 110 | BMW (Music),,26058 111 | 77Klash,,26170 112 | Clairmont,,26207 113 | Marc Decoca,72139ae8-cc45-40e9-8a36-d29ea8b66847,26675 114 | The Gertrudes,c8394169-6e07-4a4c-8adc-7055b4421279,26708 115 | Bicycle Records,69ba1791-7b83-4c23-9995-6a47fde9c01f,26966 116 | J Laine,,27084 117 | provincial parks,7a6f9b53-8dd4-4372-b900-22988c252da4,27431 118 | Le Rug,3917de50-0ac1-4a1d-b680-c903f07c6ea2,27700 119 | Ketch Harbour Wolves,0f05d2c9-f90c-47c1-87a9-d9c99aa0be20,27745 120 | Peter Friestedt,0a8fe595-a14e-42b0-ad24-d587a491d54b,28555 121 | Fuck Montreal,87cf6aa6-a005-445b-8920-1c5b3fdfbfaa,28561 122 | Ed in the Refridgerators,e8de09ea-2bfd-43c2-851b-0ccd2109e319,28731 123 | Uncle Monsterface,33a32e1e-9857-4743-9532-344fb6f0c4eb,28738 124 | Peter Murray,6ab4a566-a279-441a-af5f-4d47459e1c18,28932 125 | With Horses In Her Eyes,e026d5d7-f08d-4807-941c-88765e78b02f,29315 126 | Arlequin,1d811df7-e264-4cab-9d12-38ad1af5e415,29347 127 | SIR PSYCH,,29358 128 | Roy Young,e36b3991-b6c3-43ca-a526-271c59aead1b,29461 129 | Utopium,c9125ab5-3bfd-4813-bb0a-4fa7855f4de9,29810 130 | The Bowery Riots,6bb85a2a-c3fa-4d33-92a3-aa89ff291525,29822 131 | Moment Trigger,e8f3f51e-97bd-4994-b3ac-c8cf6d446f53,30110 132 | Falling Starr,85ca9b7c-9763-477a-bc60-e6f83437aade,30226 133 | Tuff Luvs,,30963 134 | SlowMotionNoise,00864544-3bdf-4599-baed-8a7ca9f792bf,31047 135 | allroh,,31236 136 | Owen Lyons,f1d6b892-56a8-4d58-9581-593cf260c6f3,31592 137 | No More Lies,332a07ad-ba98-42bb-a03c-0a4d2deac15a,31697 138 | Carlos Campos & Benjamin Franklin,ef7e0813-ec6c-404c-a91a-70465fe6141b,31895 139 | Drew Nelson,05848221-5b7d-4378-b7f4-e535f9aa9aa9,32261 140 | Williams Riley,e39a8d90-3ae8-4e24-a145-1940a247fe0d,32935 141 | William Susman,,33054 142 | Evyn Charles,687a094a-bd5c-4d90-af53-d3a39407a2d9,33365 143 | The Redlands Palomino Co.,,33497 144 | Bobby Donaldson,ed79aaf4-2b64-4d29-b769-60aff85eb65f,33770 145 | DJ JP,9f4688cf-c8c7-4a5c-8375-88f425230c39,34414 146 | Daniel Bernstein,f00ecbc4-d45d-4d8b-beed-dda037a5d732,34620 147 | Julie Neumark,609ef5a8-7109-4a30-9205-5428a029e34b,34965 148 | Mazinga Phaser,596cca00-9095-4546-a242-77bc2faf38da,35080 149 | Brandon White,,35228 150 | B.G. and the Chopper City Boyz,8fa26ab1-d57b-4fc5-957c-1884fd512a66,35393 151 | Amanda Martin & Lyle Goodman,3862daf7-7fd0-4236-b58a-4c311d9839a2,35984 152 | Jenn Cristy,,36006 153 | AKP,2b524c77-87e7-43bc-b23b-c5967873aed0,36185 154 | Dave Anthony,17c5dce8-1c29-46ba-94f0-70a443c0c94e,36419 155 | Pat McCurdy,6651fa24-38bc-44e4-a1c1-f11003997b8e,36507 156 | Woogie,dae27553-2819-482f-b4eb-2033d534335f,36554 157 | JaGoFF,,36654 158 | Thom Shepherd,,36740 159 | Istvan Szekely,361e711a-a5c0-4065-a27b-c62e261b0afc,36761 160 | Babaryan,,37068 161 | Phil & Daniel Rosenthal (Holiday),19f89a7c-008b-4433-91d7-1a1a68d1591d,37124 162 | Jeff Caylor,bbbdb644-50b2-4777-8e14-64a267c5cd58,37340 163 | Austins 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180 | the german measles,436502a9-004f-41d2-967f-6c8f80d54749,41955 181 | Jonathan Kingham,17638589-dd20-44e7-98fe-acb21e8b6784,42632 182 | Home Items,c3a3ed7e-3d35-459a-92a9-4af20d3656bd,42735 183 | Mark Sinnis,4ba5c621-1d0e-4534-bbbe-680d105ba7ab,43038 184 | Arkadelphia,,43692 185 | David Condos,b5f0a9b2-c577-44bb-83da-fa428de81dbc,43761 186 | Standing Small,,43965 187 | Cath and Phil Tyler,5a50967c-913e-4aae-bf94-edac8b529264,44404 188 | Benjamin Faugloire Project,90750bbc-1b5d-446e-ba81-ed1a2bc30856,44405 189 | Leaf Language,e799dadd-239e-4d39-abc8-70e874dccbf5,44716 190 | NSP,b5ce8b5d-592f-435c-b1bc-b952b59478f0,46399 191 | Round Sky Music,,46580 192 | wezzy,,47360 193 | Three Years Hiding,c8962afc-9c56-4c3b-b393-73830f720f9e,47498 194 | Depressor,d74f2314-51b8-481a-baf0-6771f84a97b0,47947 195 | Spokinn Movement,,48026 196 | Nomade,9cbcac00-5b04-4121-9b7c-ae5f39f37400,48220 197 | Sammy G,,48270 198 | The Pirates Charles,,48311 199 | Bored Of Music,3c3cf8b1-4a65-4f24-a870-36c7487e4869,48558 200 | 3 Minute Warning,b9db348e-6744-408a-b012-7caca1435970,48668 201 | Daniel Gun,6ceae164-46a9-4bc7-a63e-99694da8a758,48814 202 | Doin' It For The Money,,48954 203 | Tony Thomas,c44cae2a-44b3-4828-87a9-99c092769826,49039 204 | Beat the red light,,49079 205 | Devotos,e02933be-dfa0-43ad-b44a-9fb109a881a5,49195 206 | Linh Nguyen,6d04f922-7e1f-4788-a76e-3ff4dafd21e3,49204 207 | Suely Mesquita,9c158c73-0627-492f-9a57-227126bd2a40,49429 208 | Kassiano,,49872 209 | The Black Apples,a697464c-69b1-4bbc-88cc-570c025a25e5,50281 210 | The Mahoney Brothers,833f996c-8703-424e-9307-284d95864783,50560 211 | Fantastadon,c5cbbefb-de04-43d4-9ffc-a5b9cd85b2ef,50660 212 | Machan,,51292 213 | Bentli,,51799 214 | The Sneaks,5ec358f5-4e9b-4fe6-bd75-7f087c050bc9,51997 215 | No Man's Band,32b36a3a-d456-4a49-bb1b-5423782ba7c6,52054 216 | Sars Flannery,a4bb5010-e604-4412-b05b-4dc67fcc846c,52372 217 | Unique Chique,d51531a7-58b2-4cb4-a72f-a7fc801f826a,52427 218 | Mokai,3ad6e69a-0361-4e94-9180-336cb82fcf51,52517 219 | "Bono, Edge, Chris Martin (Coldplay) & Brian Eno",15699b51-b8aa-452d-98ed-9da5e8b9c37a,53275 220 | Guthrie Kennard,1d437a9b-6c1b-4aee-9c43-cec71f1d4050,53329 221 | Only for the sake of aching,c11abfdb-1b4c-4828-b9a2-46040f2d729d,53537 222 | Pine Mountain Railroad,1852587c-74e2-4edf-b609-a2208a1ed71f,53626 223 | Deleted Waveform Gatherings,ef50caa4-c3ba-43a3-95c2-35e3a53efd9a,53663 224 | Die Daily,,53769 225 | Superbutt,638d29dd-fb25-4b30-975a-a9b431042dac,54195 226 | Jack Green,ada2b416-890a-4c12-a4a9-b47447dec780,54667 227 | sTuRcH,,54842 228 | Takeo Toyama,b2d731d0-252d-4842-9707-4f5c5247ee34,55341 229 | Jeff Wootton,,55357 230 | Ian Shaw,7d926e97-df9b-4d3a-b7cd-b8a937a8a5df,55432 231 | Glenn Kaiser,7a6817e4-c0ca-4021-bb40-accd80181461,55500 232 | Jack Brass Band,73701a01-07e2-44f7-8935-e094bdc22892,55597 233 | Estrela Guia,abe427c8-70a4-44dc-b75f-297859473926,55633 234 | Sacrificial Blood,c5d2441a-a659-4e82-a9f1-fd65cffdaa8f,55701 235 | Nothingful,,55963 236 | Ginferno,2707a688-60fc-4884-acb7-f590dc0c247e,56049 237 | Senderos,,56063 238 | Pepe California,303b1a76-bcb1-4cbc-9dc8-9883492c1143,56683 239 | Andrew Gouche,73d347ca-db2c-4ea7-bbfa-76c3f67bf5a7,57014 240 | push pins,1b9e6350-f7f8-483f-9917-a8c9fcf52e99,57027 241 | Queyi,,57326 242 | Black Thunder,,57511 243 | Searing Solace,c14b9e99-73d3-4601-9e1f-85d9c99a086d,57792 244 | DJ Amir,5dd0c8e8-68aa-4c4e-b686-3f77c574602a,57928 245 | Rock gegen rechts,ac6ee694-5f12-44d4-82c7-b8088f7b6661,57962 246 | Unabombers,b0eb9a8f-367c-4bd6-b046-f0a2dd449b20,58129 247 | Scrapy,f7583439-caf4-4573-a00e-e6ccc25658c1,58132 248 | Waco Jesus,133b0b58-3b06-4e96-bdc2-2758918c088d,58271 249 | Man Made Hell,f70b7464-131c-4ca3-a695-5558c3498d7c,58323 250 | Kulin Ban,3cc6c090-e81b-4b20-9704-576f6df28898,58494 251 | The Best of the Worst,4c9eddc3-5bc9-4320-849a-3c3513e3c000,58627 252 | Smoke Like A Fish,8314e9f5-d104-4e28-9db5-ec122ee0672c,58678 253 | Minus Pilots,d407ae0a-d53b-4f69-98ba-bbe9ed04d000,59382 254 | Kp9000,,60095 255 | Mads Tolling,27e47c3e-af28-4d1f-89b6-fc2c7a583310,60138 256 | Rituel,ff0fe9ca-f931-40a4-a3e5-c242b4f3a59b,60198 257 | Broken Yoke,58df6cc0-6251-42ea-9ec0-103818916e65,60466 258 | Do it Yourself,,60792 259 | Pistol For Ringo,d47caa02-e74f-427d-8d45-d533a83ccb74,60929 260 | Dash Jacket,9e28db02-657d-4d6c-970c-a77454248123,61588 261 | Never Turn Back,,61625 262 | The Spooky Men's Chorale,c6fa941e-d403-445f-9cde-5ca0336d7196,61710 263 | Your Enemies Friends,818389a4-36d2-4f9c-9f88-45d595b27781,61712 264 | The Mayhaws,c8394169-6e07-4a4c-8adc-7055b4421279,61779 265 | Nathan Daughtrey,9bf32cee-257e-45ab-8a47-bc910d611dad,62101 266 | propergol y colargol,30986faa-d591-44bc-aff9-47cbdf823e56,62138 267 | Side On Sound,53a96d99-95eb-4b9f-ab27-abf5d3ff7403,62795 268 | druques,,62837 269 | Mortal Agony,3ac7cefa-6281-4384-ac2c-0f052c73f950,63071 270 | SteddyBeats,,63190 271 | Exit State,,63934 272 | Zero Hero,cf3154b9-024e-405d-b581-5f5f7e92ed63,63958 273 | Andy George,85d9152c-cfba-4bcc-8f49-55e3d5d54db2,64037 274 | The Aquamarines,f2bd3a27-965d-47d3-8e12-450e6e939b65,64240 275 | cem tuncer,86f48ea5-dfa2-4f71-b312-19c1f6156c6b,64366 276 | Slashed Seat Affair,e7e0423b-1201-4222-9135-01fcf871f595,64460 277 | Muldoon,,64608 278 | Guzzler,,64672 279 | The Inclined Plane,2d94782f-90f2-4fff-b3bc-1b2e410f43ab,64677 280 | Jahtari Riddim Force,ac6c9805-4e13-45fb-9a3d-c3de7e138fa9,64787 281 | Balbino Medellin,db29c0fa-f015-4cf2-9149-8c3bb073adea,64816 282 | Concrete DJ'z,9ba906bc-092b-4de7-b9bc-16c8a3e6251e,64884 283 | Russian Rullet,,64905 284 | Shagghie,,65056 285 | Standeg,a29ab6cb-cf36-412e-bba1-9dccd358694a,65348 286 | Bad News,9dec8cb1-0b16-43d5-b7f1-b9e24997a240,65364 287 | Sogar,4c02afbe-784d-4589-874c-f6ed951e5b11,65443 288 | Lara Korona,,65548 289 | Sinwar,7d1d2620-afa4-4565-aaf0-8441e7415fba,65694 290 | Albert Sanz,6d5993a7-ef89-41cd-88c8-9ac4d0b39b0c,65713 291 | Wesley Dysart,be4edada-661f-4856-a588-2664de750ee7,65766 292 | Dancing on Debris,,65903 293 | Mikrofisch,27047e70-a833-4627-b589-9888eb7cf6e4,66112 294 | DJ IQ,2ae29e41-cf82-4358-a26a-de253d482023,67022 295 | Dancing with the Enemy,0a02e70e-db2f-4cfe-a646-e8ced413f7d5,67477 296 | CORN HEAD,e9aa5ac8-e65d-4446-890c-d1c2192e3c78,67638 297 | OG Daddy V,15e8741b-474e-4ec9-9ffb-a89db700efb5,67713 298 | Christopher G,5efe021b-61c8-45ed-9c89-ab808a892c96,67894 299 | Ryan Kauffman,e1ab965e-5f2d-499f-9969-a503487f7d7c,68106 300 | Hotel Mama,e31fbea1-a167-4aca-a644-554bddcf97a3,68174 301 | Christian Reichert,1a78d4ba-b927-49ea-bd1e-9e44dcc7eb4e,68230 302 | -------------------------------------------------------------------------------- /artists_updated.tsv: -------------------------------------------------------------------------------- 1 | name musicbrainz_id id 2 | 0 The Scenic a8e347d8-29d8-459c-a444-68febeb99c6b 500 3 | 1 Our Family Name 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c6fa941e-d403-445f-9cde-5ca0336d7196 61710 264 | 262 Your Enemies Friends 818389a4-36d2-4f9c-9f88-45d595b27781 61712 265 | 263 The Mayhaws c8394169-6e07-4a4c-8adc-7055b4421279 61779 266 | 264 Nathan Daughtrey 9bf32cee-257e-45ab-8a47-bc910d611dad 62101 267 | 265 propergol y colargol 30986faa-d591-44bc-aff9-47cbdf823e56 62138 268 | 266 Side On Sound 53a96d99-95eb-4b9f-ab27-abf5d3ff7403 62795 269 | 267 druques 62837 270 | 268 Mortal Agony 3ac7cefa-6281-4384-ac2c-0f052c73f950 63071 271 | 269 SteddyBeats 63190 272 | 270 Exit State 63934 273 | 271 Zero Hero cf3154b9-024e-405d-b581-5f5f7e92ed63 63958 274 | 272 Andy George 85d9152c-cfba-4bcc-8f49-55e3d5d54db2 64037 275 | 273 The Aquamarines f2bd3a27-965d-47d3-8e12-450e6e939b65 64240 276 | 274 cem tuncer 86f48ea5-dfa2-4f71-b312-19c1f6156c6b 64366 277 | 275 Slashed Seat Affair e7e0423b-1201-4222-9135-01fcf871f595 64460 278 | 276 Muldoon 64608 279 | 277 Guzzler 64672 280 | 278 The Inclined Plane 2d94782f-90f2-4fff-b3bc-1b2e410f43ab 64677 281 | 279 Jahtari Riddim Force ac6c9805-4e13-45fb-9a3d-c3de7e138fa9 64787 282 | 280 Balbino Medellin db29c0fa-f015-4cf2-9149-8c3bb073adea 64816 283 | 281 Concrete DJ'z 9ba906bc-092b-4de7-b9bc-16c8a3e6251e 64884 284 | 282 Russian Rullet 64905 285 | 283 Shagghie 65056 286 | 284 Standeg a29ab6cb-cf36-412e-bba1-9dccd358694a 65348 287 | 285 Bad News 9dec8cb1-0b16-43d5-b7f1-b9e24997a240 65364 288 | 286 Sogar 4c02afbe-784d-4589-874c-f6ed951e5b11 65443 289 | 287 Lara Korona 65548 290 | 288 Sinwar 7d1d2620-afa4-4565-aaf0-8441e7415fba 65694 291 | 289 Albert Sanz 6d5993a7-ef89-41cd-88c8-9ac4d0b39b0c 65713 292 | 290 Wesley Dysart be4edada-661f-4856-a588-2664de750ee7 65766 293 | 291 Dancing on Debris 65903 294 | 292 Mikrofisch 27047e70-a833-4627-b589-9888eb7cf6e4 66112 295 | 293 DJ IQ 2ae29e41-cf82-4358-a26a-de253d482023 67022 296 | 294 Dancing with the Enemy 0a02e70e-db2f-4cfe-a646-e8ced413f7d5 67477 297 | 295 CORN HEAD e9aa5ac8-e65d-4446-890c-d1c2192e3c78 67638 298 | 296 OG Daddy V 15e8741b-474e-4ec9-9ffb-a89db700efb5 67713 299 | 297 Christopher G 5efe021b-61c8-45ed-9c89-ab808a892c96 67894 300 | 298 Ryan Kauffman e1ab965e-5f2d-499f-9969-a503487f7d7c 68106 301 | 299 Hotel Mama e31fbea1-a167-4aca-a644-554bddcf97a3 68174 302 | 300 Christian Reichert 1a78d4ba-b927-49ea-bd1e-9e44dcc7eb4e 68230 303 | -------------------------------------------------------------------------------- /checkout_times.txt: -------------------------------------------------------------------------------- 1 | U-Scan Employee_Checkout 2 | 341 169 3 | 450 164 4 | 199 243 5 | 57 53 6 | 33 51 7 | 88 82 8 | 51 46 9 | 78 55 10 | 107 104 11 | 232 44 12 | 117 47 13 | 131 53 14 | 135 62 15 | 99 127 16 | 126 86 17 | 70 94 18 | 85 232 19 | 348 134 20 | 100 206 21 | 97 169 22 | 83 210 23 | 146 117 24 | 182 124 25 | 171 259 -------------------------------------------------------------------------------- /ep_data.json: -------------------------------------------------------------------------------- 1 | {"Alkaline Trio": {"artist_id": 2, "EPs": {"I Lied My Face Off": {"tracks": ["Goodbye Forever", "This Is Getting Over You", "Bleeder", "I Lied My Face Off"], "release_year": 1999}, "Broken Wing": {"tracks": ["Balanced On A Shelf", "Pocket Knife", "Broken Wing", "Sun Burns"], "release_year": 2013}, "For Your Lungs Only": {"tracks": ["Snake Oil Tanker", "Southern Rock", "Cooking Wine", "For Your Lungs Only"], "release_year": 1998}}}, "Green Day": {"artist_id": 1, "EPs": {"Tune In, Tokyo...": {"release_year": 2001, "location": "Japan"}, "Slappy": {"tracks": ["Paper Lanterns", "Why do you Want Him?", "409 in Your Coffeemaker", "Knowledge"], "release_year": 1990}, "1,000 Hours": {"tracks": ["1,000 Hours", "1,000 Hours", "Only of You", "The One I Want"], "release_year": 1989}}}} -------------------------------------------------------------------------------- /height_weight.csv: -------------------------------------------------------------------------------- 1 | sex,height,weight,age 2 | 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791 | 1,71.91199025,134,28 792 | 2,64.8431586655,140,69 793 | 1,72.6474652337,185,65 794 | 2,63.0016122581,200,51 795 | 1,70.8524535543,130,19 796 | 2,68.357340581,160,50 797 | 2,62.1873090174,178,66 798 | 1,70.6178842833,224,49 799 | 2,64.6235588843,245,47 800 | 2,63.3810085976,167,37 801 | 1,67.9103442021,174,62 802 | -------------------------------------------------------------------------------- /height_weight.txt: -------------------------------------------------------------------------------- 1 | height;weight;age;male 2 | 151.765;47.8256065;63;1 3 | 139.7;36.4858065;63;0 4 | 136.525;31.864838;65;0 5 | 156.845;53.0419145;41;1 6 | 145.415;41.276872;51;0 7 | 163.83;62.992589;35;1 8 | 149.225;38.2434755;32;0 9 | 168.91;55.4799715;27;1 10 | 147.955;34.869885;19;0 11 | 165.1;54.487739;54;1 12 | 154.305;49.89512;47;0 13 | 151.13;41.220173;66;1 14 | 144.78;36.0322145;73;0 15 | 149.9;47.7;20;0 16 | 150.495;33.849303;65.3;0 17 | 163.195;48.5626935;36;1 18 | 157.48;42.3258035;44;1 19 | 143.9418;38.3568735;31;0 20 | 121.92;19.617854;12;1 21 | 105.41;13.947954;8;0 22 | 86.36;10.489315;6.5;0 23 | 161.29;48.987936;39;1 24 | 156.21;42.7226965;29;0 25 | 129.54;23.586784;13;1 26 | 109.22;15.989118;7;0 27 | 146.4;35.493574;56;1 28 | 148.59;37.9032815;45;0 29 | 147.32;35.4652245;19;0 30 | 137.16;27.328918;17;1 31 | 125.73;22.6796;16;0 32 | 114.3;17.860185;11;1 33 | 147.955;40.312989;29;1 34 | 161.925;55.111428;30;1 35 | 146.05;37.5063885;24;0 36 | 146.05;38.498621;35;0 37 | 152.7048;46.606578;33;0 38 | 142.875;38.838815;27;0 39 | 142.875;35.5786225;32;0 40 | 147.955;47.400364;36;0 41 | 160.655;47.8823055;24;1 42 | 151.765;49.4131785;30;1 43 | 162.8648;49.384829;24;1 44 | 171.45;56.5572525;52;1 45 | 147.32;39.12231;42;0 46 | 147.955;49.89512;19;0 47 | 144.78;28.803092;17;0 48 | 121.92;20.41164;8;1 49 | 128.905;23.359988;12;0 50 | 97.79;13.267566;5;0 51 | 154.305;41.2485225;55;1 52 | 143.51;38.55532;43;0 53 | 146.7;42.4;20;1 54 | 157.48;44.6504625;18;1 55 | 127;22.0105518;13;1 56 | 110.49;15.422128;9;0 57 | 97.79;12.757275;5;0 58 | 165.735;58.5984165;42;1 59 | 152.4;46.719976;44;0 60 | 141.605;44.22522;60;0 61 | 158.8;50.9;20;0 62 | 155.575;54.317642;37;0 63 | 164.465;45.8978405;50;1 64 | 151.765;48.024053;50;0 65 | 161.29;52.219779;31;1 66 | 154.305;47.62716;25;0 67 | 145.415;45.642695;23;0 68 | 145.415;42.410852;52;0 69 | 152.4;36.4858065;79.3;1 70 | 163.83;55.9335635;35;1 71 | 144.145;37.194544;27;0 72 | 129.54;24.550667;13;1 73 | 129.54;25.627948;14;0 74 | 153.67;48.307548;38;1 75 | 142.875;37.3362915;39;0 76 | 146.05;29.596878;12;0 77 | 167.005;47.173568;30;1 78 | 158.4198;47.286966;24;0 79 | 91.44;12.927372;0.599999999999909;1 80 | 165.735;57.549485;51;1 81 | 149.86;37.931631;46;0 82 | 147.955;41.900561;17;0 83 | 137.795;27.5840635;12;0 84 | 154.94;47.2019175;22;0 85 | 160.9598;43.204638;29;1 86 | 161.925;50.2636635;38;1 87 | 147.955;39.3774555;30;0 88 | 113.665;17.463292;6;1 89 | 159.385;50.689;45;1 90 | 148.59;39.4341545;47;0 91 | 136.525;36.28736;79;0 92 | 158.115;46.266384;45;1 93 | 144.78;42.2691045;54;0 94 | 156.845;47.62716;31;1 95 | 179.07;55.7067675;23;1 96 | 118.745;18.824068;9;0 97 | 170.18;48.5626935;41;1 98 | 146.05;42.807745;23;0 99 | 147.32;35.0683315;36;0 100 | 113.03;17.8885345;5;1 101 | 162.56;56.755699;30;0 102 | 133.985;27.442316;12;1 103 | 152.4;51.255896;34;0 104 | 160.02;47.230267;44;1 105 | 149.86;40.936678;43;0 106 | 142.875;32.715323;73.3;0 107 | 167.005;57.0675435;38;1 108 | 159.385;42.977842;43;1 109 | 154.94;39.9444455;33;0 110 | 148.59;32.4601775;16;0 111 | 111.125;17.123098;11;1 112 | 111.76;16.499409;6;1 113 | 162.56;45.9545395;35;1 114 | 152.4;41.106775;29;0 115 | 124.46;18.257078;12;0 116 | 111.76;15.081934;9;1 117 | 86.36;11.4815475;7.59999999999991;1 118 | 170.18;47.5988105;58;1 119 | 146.05;37.5063885;53;0 120 | 159.385;45.019006;51;1 121 | 151.13;42.2691045;48;0 122 | 160.655;54.8562825;29;1 123 | 169.545;53.523856;41;1 124 | 158.75;52.1914295;81.75;1 125 | 74.295;9.752228;1;1 126 | 149.86;42.410852;35;0 127 | 153.035;49.5832755;46;0 128 | 96.52;13.097469;5;1 129 | 161.925;41.730464;29;1 130 | 162.56;56.018612;42;1 131 | 149.225;42.1557065;27;0 132 | 116.84;19.391058;8;0 133 | 100.076;15.081934;6;1 134 | 163.195;53.0986135;22;1 135 | 161.925;50.235314;43;1 136 | 145.415;42.52425;53;0 137 | 163.195;49.101334;43;1 138 | 151.13;38.498621;41;0 139 | 150.495;49.8100715;50;0 140 | 141.605;29.313383;15;1 141 | 170.815;59.760746;33;1 142 | 91.44;11.7083435;3;0 143 | 157.48;47.9390045;62;1 144 | 152.4;39.292407;49;0 145 | 149.225;38.1300775;17;1 146 | 129.54;21.999212;12;0 147 | 147.32;36.8826995;22;0 148 | 145.415;42.127357;29;0 149 | 121.92;19.787951;8;0 150 | 113.665;16.782904;5;1 151 | 157.48;44.565414;33;1 152 | 154.305;47.853956;34;0 153 | 120.65;21.1770765;12;0 154 | 115.6;18.9;7;1 155 | 167.005;55.1964765;42;1 156 | 142.875;32.998818;40;0 157 | 152.4;40.879979;27;0 158 | 96.52;13.267566;3;0 159 | 160;51.2;25;1 160 | 159.385;49.044635;29;1 161 | 149.86;53.4388075;45;0 162 | 160.655;54.090846;26;1 163 | 160.655;55.3665735;45;1 164 | 149.225;42.240755;45;0 165 | 125.095;22.3677555;11;0 166 | 140.97;40.936678;85.5999999999999;0 167 | 154.94;49.6966735;26;1 168 | 141.605;44.338618;24;0 169 | 160.02;45.9545395;57;1 170 | 150.1648;41.95726;22;0 171 | 155.575;51.482692;24;0 172 | 103.505;12.757275;6;0 173 | 94.615;13.0124205;4;0 174 | 156.21;44.111822;21;0 175 | 153.035;32.205032;79;0 176 | 167.005;56.755699;50;1 177 | 149.86;52.673371;40;0 178 | 147.955;36.4858065;64;0 179 | 159.385;48.8461885;32;1 180 | 161.925;56.9541455;38.7;1 181 | 155.575;42.0990075;26;0 182 | 159.385;50.178615;63;1 183 | 146.685;46.549879;62;0 184 | 172.72;61.80191;22;1 185 | 166.37;48.987936;41;1 186 | 141.605;31.524644;19;1 187 | 142.875;32.205032;17;0 188 | 133.35;23.756881;14;0 189 | 127.635;24.4089195;9;1 190 | 119.38;21.5172705;7;1 191 | 151.765;35.2951275;74;0 192 | 156.845;45.642695;41;1 193 | 148.59;43.885026;33;0 194 | 157.48;45.5576465;53;0 195 | 149.86;39.008912;18;0 196 | 147.955;41.163474;37;0 197 | 102.235;13.1258185;6;0 198 | 153.035;45.245802;61;0 199 | 160.655;53.637254;44;1 200 | 149.225;52.3048275;35;0 201 | 114.3;18.3421265;7;1 202 | 100.965;13.7495075;4;1 203 | 138.43;39.0939605;23;0 204 | 91.44;12.530479;4;1 205 | 162.56;45.699394;55;1 206 | 149.225;40.3980375;53;0 207 | 158.75;51.482692;59;1 208 | 149.86;38.668718;57;0 209 | 158.115;39.235708;35;1 210 | 156.21;44.338618;29;0 211 | 148.59;39.519203;62;1 212 | 143.51;31.071052;18;0 213 | 154.305;46.776675;51;0 214 | 131.445;22.509503;14;0 215 | 157.48;40.6248335;19;1 216 | 157.48;50.178615;42;1 217 | 154.305;41.276872;25;0 218 | 107.95;17.57669;6;1 219 | 168.275;54.6;41;1 220 | 145.415;44.9906565;37;0 221 | 147.955;44.735511;16;0 222 | 100.965;14.401546;5;1 223 | 113.03;19.050864;9;1 224 | 149.225;35.8054185;82;1 225 | 154.94;45.2174525;28;1 226 | 162.56;48.1091015;50;1 227 | 156.845;45.6710445;43;0 228 | 123.19;20.808533;8;1 229 | 161.0106;48.420946;31;1 230 | 144.78;41.1918235;67;0 231 | 143.51;38.4135725;39;0 232 | 149.225;42.127357;18;0 233 | 110.49;17.6617385;11;0 234 | 149.86;38.2434755;48;0 235 | 165.735;48.3358975;30;1 236 | 144.145;38.9238635;64;0 237 | 157.48;40.029494;72;1 238 | 154.305;50.2069645;68;0 239 | 163.83;54.2892925;44;1 240 | 156.21;45.6;43;0 241 | 153.67;40.766581;16;0 242 | 134.62;27.1304715;13;0 243 | 144.145;39.4341545;34;0 244 | 114.3;20.4966885;10;0 245 | 162.56;43.204638;62;1 246 | 146.05;31.864838;44;0 247 | 120.65;20.8935815;11;1 248 | 154.94;45.4442485;31;1 249 | 144.78;38.045029;29;0 250 | 106.68;15.989118;8;0 251 | 146.685;36.0889135;62;0 252 | 152.4;40.879979;67;0 253 | 163.83;47.910655;57;1 254 | 165.735;47.7122085;32;1 255 | 156.21;46.379782;24;0 256 | 152.4;41.163474;77;1 257 | 140.335;36.5992045;62;0 258 | 158.115;43.09124;17;1 259 | 163.195;48.137451;67;1 260 | 151.13;36.7126025;70;0 261 | 171.1198;56.5572525;37;1 262 | 149.86;38.6970675;58;0 263 | 163.83;47.4854125;35;1 264 | 141.605;36.2023115;30;0 265 | 93.98;14.288148;5;0 266 | 149.225;41.276872;26;0 267 | 105.41;15.2236815;5;0 268 | 146.05;44.7638605;21;0 269 | 161.29;50.4337605;41;1 270 | 162.56;55.281525;46;1 271 | 145.415;37.931631;49;0 272 | 145.415;35.493574;15;1 273 | 170.815;58.456669;28;1 274 | 127;21.488921;12;0 275 | 159.385;44.4236665;83;0 276 | 159.4;44.4;54;1 277 | 153.67;44.565414;54;0 278 | 160.02;44.622113;68;1 279 | 150.495;40.483086;68;0 280 | 149.225;44.0834725;56;0 281 | 127;24.4089195;15;0 282 | 142.875;34.416293;57;0 283 | 142.113;32.772022;22;0 284 | 147.32;35.947166;40;0 285 | 162.56;49.5549;19;1 286 | 164.465;53.183662;41;1 287 | 160.02;37.081146;75.9000000000001;1 288 | 153.67;40.5114355;73.9000000000001;0 289 | 167.005;50.6038575;49;1 290 | 151.13;43.9700745;26;1 291 | 147.955;33.792604;17;0 292 | 125.3998;21.375523;13;0 293 | 111.125;16.669506;8;0 294 | 153.035;49.89;88;1 295 | 139.065;33.5941575;68;0 296 | 152.4;43.8566765;33;1 297 | 154.94;48.137451;26;0 298 | 147.955;42.751046;56;0 299 | 143.51;34.8415355;16;1 300 | 117.983;24.097075;13;0 301 | 144.145;33.906002;34;0 302 | 92.71;12.076887;5;0 303 | 147.955;41.276872;17;0 304 | 155.575;39.7176495;74;1 305 | 150.495;35.947166;69;0 306 | 155.575;50.915702;50;1 307 | 154.305;45.756093;44;0 308 | 130.6068;25.2594045;15;0 309 | 101.6;15.3370795;5;0 310 | 157.48;49.214732;18;0 311 | 168.91;58.8252125;41;1 312 | 150.495;43.4597835;27;0 313 | 111.76;17.8318355;8.90000000000009;1 314 | 160.02;51.9646335;38;1 315 | 167.64;50.688906;57;1 316 | 144.145;34.246196;64.5;0 317 | 145.415;39.3774555;42;0 318 | 160.02;59.5622995;24;1 319 | 147.32;40.312989;16;1 320 | 164.465;52.16308;71;1 321 | 153.035;39.972795;49.5;0 322 | 149.225;43.941725;33;1 323 | 160.02;54.601137;28;0 324 | 149.225;45.075705;47;0 325 | 85.09;11.453198;3;1 326 | 84.455;11.7650425;1;1 327 | 59.6138;5.896696;1;0 328 | 92.71;12.1052365;3;1 329 | 111.125;18.313777;6;0 330 | 90.805;11.3681495;5;0 331 | 153.67;41.333571;27;0 332 | 99.695;16.2442635;5;0 333 | 62.484;6.80388;1;0 334 | 81.915;11.8784405;2;1 335 | 96.52;14.968536;2;0 336 | 80.01;9.865626;1;1 337 | 150.495;41.900561;55;0 338 | 151.765;42.524;83.4000000000001;1 339 | 140.6398;28.859791;12;1 340 | 88.265;12.7856245;2;0 341 | 158.115;43.147939;63;1 342 | 149.225;40.82328;52;0 343 | 151.765;42.864444;49;1 344 | 154.94;46.209685;31;0 345 | 123.825;20.581737;9;0 346 | 104.14;15.87572;6;0 347 | 161.29;47.853956;35;1 348 | 148.59;42.52425;35;0 349 | 97.155;17.066399;7;0 350 | 93.345;13.1825175;5;1 351 | 160.655;48.5059945;24;1 352 | 157.48;45.869491;41;1 353 | 167.005;52.900167;32;1 354 | 157.48;47.570461;43;1 355 | 91.44;12.927372;6;0 356 | 60.452;5.6699;1;1 357 | 137.16;28.91649;15;1 358 | 152.4;43.544832;63;0 359 | 152.4;43.431434;21;0 360 | 81.28;11.509897;1;1 361 | 109.22;11.7083435;2;0 362 | 71.12;7.540967;1;1 363 | 89.2048;12.700576;3;0 364 | 67.31;7.200773;1;0 365 | 85.09;12.360382;1;1 366 | 69.85;7.7961125;1;0 367 | 161.925;53.2120115;55;0 368 | 152.4;44.678812;38;0 369 | 88.9;12.5588285;3;1 370 | 90.17;12.700576;3;1 371 | 71.755;7.37087;1;0 372 | 83.82;9.2135875;1;0 373 | 159.385;47.2019175;28;1 374 | 142.24;28.632995;16;0 375 | 142.24;31.6663915;36;0 376 | 168.91;56.4438545;38;1 377 | 123.19;20.014747;12;1 378 | 74.93;8.50485;1;1 379 | 74.295;8.3064035;1;0 380 | 90.805;11.623295;3;0 381 | 160.02;55.791816;48;1 382 | 67.945;7.9662095;1;0 383 | 135.89;27.21552;15;0 384 | 158.115;47.4854125;45;1 385 | 85.09;10.8011595;3;1 386 | 93.345;14.004653;3;0 387 | 152.4;45.1607535;38;0 388 | 155.575;45.529297;21;0 389 | 154.305;48.874538;50;0 390 | 156.845;46.5782285;41;1 391 | 120.015;20.128145;13;0 392 | 114.3;18.14368;8;1 393 | 83.82;10.9145575;3;1 394 | 156.21;43.885026;30;0 395 | 137.16;27.158821;12;1 396 | 114.3;19.050864;7;1 397 | 93.98;13.834556;4;0 398 | 168.275;56.0469615;21;1 399 | 147.955;40.086193;38;0 400 | 139.7;26.5634815;15;1 401 | 157.48;50.802304;19;0 402 | 76.2;9.2135875;1;1 403 | 66.04;7.5693165;1;1 404 | 160.7;46.3;31;1 405 | 114.3;19.4194075;8;0 406 | 146.05;37.9032815;16;1 407 | 161.29;49.3564795;21;1 408 | 69.85;7.314171;0;0 409 | 133.985;28.1510535;13;1 410 | 67.945;7.824462;0;1 411 | 150.495;44.111822;50;0 412 | 163.195;51.0291;39;1 413 | 148.59;40.766581;44;1 414 | 148.59;37.5630875;36;0 415 | 161.925;51.59609;36;1 416 | 153.67;44.8205595;18;0 417 | 68.58;8.0229085;0;0 418 | 151.13;43.4030845;58;0 419 | 163.83;46.719976;58;1 420 | 153.035;39.5475525;33;0 421 | 151.765;34.7848365;21.5;0 422 | 132.08;22.792998;11;1 423 | 156.21;39.292407;26;1 424 | 140.335;37.4496895;22;0 425 | 158.75;48.6760915;28;1 426 | 142.875;35.606972;42;0 427 | 84.455;9.3836845;2;1 428 | 151.9428;43.714929;21;1 429 | 161.29;48.19415;19;1 430 | 127.9906;29.8520235;13;1 431 | 160.9852;50.972401;48;1 432 | 144.78;43.998424;46;0 433 | 132.08;28.292801;11;1 434 | 117.983;20.354941;8;1 435 | 160.02;48.19415;25;1 436 | 154.94;39.179009;16;1 437 | 160.9852;46.6916265;51;1 438 | 165.989;56.415505;25;1 439 | 157.988;48.591043;28;1 440 | 154.94;48.2224995;26;0 441 | 97.9932;13.2959155;5;1 442 | 64.135;6.6621325;1;0 443 | 160.655;47.4854125;54;1 444 | 147.32;35.550273;66;0 445 | 146.7;36.6;20;0 446 | 147.32;48.9595865;25;0 447 | 172.9994;51.255896;38;1 448 | 158.115;46.5215295;51;1 449 | 147.32;36.967748;48;0 450 | 124.9934;25.117657;13;1 451 | 106.045;16.272613;6;1 452 | 165.989;48.647742;27;1 453 | 149.86;38.045029;22;0 454 | 76.2;8.50485;1;0 455 | 161.925;47.286966;60;1 456 | 140.0048;28.3495;15;0 457 | 66.675;8.1363065;0;0 458 | 62.865;7.200773;0;1 459 | 163.83;55.394923;43;1 460 | 147.955;32.488527;12;1 461 | 160.02;54.204244;27;1 462 | 154.94;48.477645;30;1 463 | 152.4;43.0628905;29;0 464 | 62.23;7.257472;0;0 465 | 146.05;34.189497;23;0 466 | 151.9936;49.951819;30;0 467 | 157.48;41.3052215;17;1 468 | 55.88;4.8477645;0;0 469 | 60.96;6.23689;0;1 470 | 151.765;44.338618;41;0 471 | 144.78;33.45241;42;0 472 | 118.11;16.896302;7;0 473 | 78.105;8.221355;3;0 474 | 160.655;47.286966;43;1 475 | 151.13;46.1246365;35;0 476 | 121.92;20.184844;10;0 477 | 92.71;12.757275;3;1 478 | 153.67;47.400364;75.5;1 479 | 147.32;40.8516295;64;0 480 | 139.7;50.348712;38;1 481 | 157.48;45.132404;24.2;0 482 | 91.44;11.623295;4;0 483 | 154.94;42.240755;26;1 484 | 143.51;41.6454155;19;0 485 | 83.185;9.1568885;2;1 486 | 158.115;45.2174525;43;1 487 | 147.32;51.255896;38;0 488 | 123.825;21.205426;10;1 489 | 88.9;11.5949455;3;1 490 | 160.02;49.271431;23;1 491 | 137.16;27.952607;16;0 492 | 165.1;51.199197;49;1 493 | 154.94;43.8566765;41;0 494 | 111.125;17.690088;6;1 495 | 153.67;35.5219235;23;0 496 | 145.415;34.246196;14;0 497 | 141.605;42.88542;43;0 498 | 144.78;32.545226;15;0 499 | 163.83;46.776675;21;1 500 | 161.29;41.8722115;24;1 501 | 154.9;38.2;20;1 502 | 161.3;43.3;20;1 503 | 170.18;53.637254;34;1 504 | 149.86;42.977842;29;0 505 | 123.825;21.54562;11;1 506 | 85.09;11.4248485;3;0 507 | 160.655;39.7743485;65;1 508 | 154.94;43.3463855;46;0 509 | 106.045;15.478827;8;0 510 | 126.365;21.9141635;15;1 511 | 166.37;52.673371;43;1 512 | 148.2852;38.441922;39;0 513 | 124.46;19.27766;12;0 514 | 89.535;11.113004;3;1 515 | 101.6;13.494362;4;0 516 | 151.765;42.807745;43;0 517 | 148.59;35.890467;70;0 518 | 153.67;44.22522;26;0 519 | 53.975;4.252425;0;0 520 | 146.685;38.0733785;48;0 521 | 56.515;5.159609;0;0 522 | 100.965;14.3164975;5;1 523 | 121.92;23.2182405;8;1 524 | 81.5848;10.659412;3;0 525 | 154.94;44.111822;44;1 526 | 156.21;44.0267735;33;0 527 | 132.715;24.9759095;15;1 528 | 125.095;22.5945515;12;0 529 | 101.6;14.344847;5;0 530 | 160.655;47.8823055;41;1 531 | 146.05;39.405805;37.4;0 532 | 132.715;24.777463;13;0 533 | 87.63;10.659412;6;0 534 | 156.21;41.050076;53;1 535 | 152.4;40.82328;49;0 536 | 162.56;47.0318205;27;0 537 | 114.935;17.519991;7;1 538 | 67.945;7.2291225;1;0 539 | 142.875;34.246196;31;0 540 | 76.835;8.0229085;1;1 541 | 145.415;31.127751;17;1 542 | 162.56;52.16308;31;1 543 | 156.21;54.0624965;21;0 544 | 71.12;8.051258;0;1 545 | 158.75;52.5316235;68;1 -------------------------------------------------------------------------------- /nbs.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adamhajari/Introduction-to-Data-Science/d2b14506ddcb81ae5d2b7ce8e95e7a15cf4c0108/nbs.db -------------------------------------------------------------------------------- /nbs_top_artists_app.py: -------------------------------------------------------------------------------- 1 | from spyre import server 2 | 3 | import sqlite3 as lite 4 | import pandas as pd 5 | 6 | 7 | class TopArtistsApp(server.App): 8 | title = "Top Artists" 9 | inputs = [ 10 | { 11 | 'label': 'Network', 12 | 'type': 'dropdown', 13 | 'options': [ 14 | {'label': 'Facebook Page Likes', 'value': 'FaceBookPageLikes'}, 15 | {'label': 'Twitter Followers', 'value': 'TwitterFollowers'}, 16 | {'label': 'Youtube Views', 'value': 'YouTubeViews'}, 17 | {'label': 'Instagram Followers', 'value': 'InstagramFollowers'}, 18 | {'label': 'Instagram Likes', 'value': 'InstagramLikes'}, 19 | ], 20 | 'key': 'network', 21 | 'action_id': 'top_artists' 22 | 23 | }, { 24 | 'label': 'limit', 25 | 'type': 'slider', 26 | 'max': 100, 27 | 'value': 30, 28 | 'key': 'n', 29 | 'action_id': 'top_artists' 30 | } 31 | 32 | ] 33 | 34 | outputs = [{ 35 | 'type': 'table', 36 | 'id': 'top_artists' 37 | 38 | }] 39 | 40 | def get_social_data(self): 41 | con = lite.connect('nbs.db') 42 | artists_query = "select id, name from artists" 43 | artists_df = pd.read_sql(artists_query, con) 44 | 45 | socail_query = "select * from social_data" 46 | social_df = pd.read_sql(socail_query, con) 47 | df_merged = pd.merge(artists_df, social_df, left_on='id', right_on='artist_id') 48 | return df_merged.drop(['id', 'artist_id'], axis=1) 49 | 50 | def top_artists(self, params): 51 | n = int(params['n']) 52 | network = params['network'] 53 | """return the top n artists from network""" 54 | df_social = self.get_social_data() # get the data from sql 55 | 56 | # sort the result by the given network in descending order 57 | df_sorted = df_social.sort_values(by=network, ascending=False) 58 | 59 | # limit the results to the top n artists 60 | df_limited = df_sorted.head(n) 61 | 62 | # convert the values to integers so they look nicer 63 | df_limited.loc[:, network] = df_limited.loc[:, network].astype('int') 64 | 65 | # return the results but just include the values for the network of interest 66 | return df_limited[['name', network]] 67 | 68 | 69 | app = TopArtistsApp() 70 | app.launch() 71 | --------------------------------------------------------------------------------