DS_Project1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **PREDICTING SPOTIFY SONG POPULARITY:**    **MODELLING THE \"WHOOP\"**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PURPOSE:**\n",
    "    \n",
    "    Let us model song elements that are hypothesized to affect song popularity. \n",
    "    This project will cover an investigation of the hypothetically popular \"Millenial Whoop\" [MW]\n",
    "    \n",
    "    A) Can we train a model to identify the \"Millenial Whoop\" in a spotify song? \n",
    "    B) Will the occurance of the MW coorelate with a song's popularity metric as determined by Spotify? \n",
    "    \n",
    "    In this project we investigate Question A) by modelling MW pitch patterns. \n",
    "    Question B) will later be investigated.\n",
    "\n",
    "**BACKGROUND:**\n",
    "\n",
    "    [Insert dude's name] identified a common 3-5 chord progression used commonly in modern pop music. \n",
    "    The claim is that this 3-5 chord progression known as the \"Millenial Whoop\" is a satisfying song element \n",
    "    that helps ensure song likability. Song likability reduces the financial risk incured by a record company \n",
    "    upon release of a new song. For more on [insert dude's name]'s work, see here [insert link].\n",
    "    \n",
    "**APPROACH:**\n",
    "\n",
    "    For the sake of of this project we will try to a) train a model to identify the \"Millenial Whoop\" in a song, \n",
    "    and b) see if there exists a relationship between the rate of occurance of a song \"Whoop\" and the popularity \n",
    "    of that song, as measured using Spotify's algorithm. \n",
    "    \n",
    "**TOOLS:**\n",
    "\n",
    "     The majority of data collection and has been done outside the scope of this notebook using web scraping \n",
    "     techniques. Data wrangling tools include a functional F# program which uses REST calls to generate tokens \n",
    "     and download JSON song information from the Spotify Web API. Various track JSON files were downloaded \n",
    "     for exploration including: \"Track\", \"Audio Features\" and \"Audio Analysis\" \n",
    "\n",
    "**DATA:**\n",
    "     \n",
    "     Original Spotify Audio Analysis JSON files contain much more information that what has been brought \n",
    "     into this notebook, and such information is a useful source of embelishment for later versions of \n",
    "     this project. \n",
    "     \n",
    "     The format of the collected audio information that you will see here is of the form 39X216 - where 216 \n",
    "     is a concatenated array of pitch occurances.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA COLLECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Python Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "#graphical imports\n",
    "from IPython.display import Image\n",
    "import matplotlib as mlp\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import mpl_toolkits.mplot3d as m3d\n",
    "\n",
    "#numerical, os and dataframe imports\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "#data modelling imports \n",
    "#from sklearn import cross_validation \n",
    "import sklearn\n",
    "from sklearn import datasets\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn import ensemble\n",
    "from sklearn import neighbors\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing \n",
    "from sklearn import model_selection\n",
    "from sklearn import feature_selection\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#stats imports\n",
    "from scipy import stats\n",
    "from scipy import stats, special\n",
    "\n",
    "#file imports\n",
    "import json\n",
    "import ijson\n",
    "from pandas.io.json import json_normalize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore format of raw Spotify JSON Audio Analysis file, for key 'Segments'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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0.542, 'Duration': 0.24381, 'LoudnessEnd': 0, 'LoudnessMax': -7.163, 'LoudnessMaxTime': 0.03685, 'LoudnessStart': -13.582, 'Pitches': [0.151, 0.622, 0.715, 0.493, 0.39, 0.505, 0.542, 0.357, 0.398, 1, 0.382, 0.17], 'Start': 47.10186, 'Timbre': [49.714, 97.635, 6.487, 42.363, 0.132, -28.037, -19.415, 17.324, -1.021, 0.145, -30.096, 18.258]}, {'Confidence': 0.965, 'Duration': 0.12703, 'LoudnessEnd': 0, 'LoudnessMax': -5.723, 'LoudnessMaxTime': 0.0292, 'LoudnessStart': -20.049, 'Pitches': [0.758, 1, 0.11, 0.095, 0.34, 0.244, 0.481, 0.483, 0.692, 0.681, 0.679, 0.053], 'Start': 47.34567, 'Timbre': [49.046, 81.441, 4.923, 60.077, -26.525, 2.406, -3.679, -5.953, 6.123, 9.144, -23.77, -1.435]}, {'Confidence': 1, 'Duration': 0.40122, 'LoudnessEnd': 0, 'LoudnessMax': -0.455, 'LoudnessMaxTime': 0.26279, 'LoudnessStart': -21.055, 'Pitches': [0.266, 0.497, 0.522, 0.535, 1, 0.531, 0.392, 0.376, 0.521, 0.85, 0.458, 0.292], 'Start': 47.4727, 'Timbre': [54.682, 74.435, 88.074, -20.922, 47.845, 53.227, 16.492, -3.423, 16.837, 10.497, 3.736, -0.312]}]}]\n"
     ]
    }
   ],
   "source": [
    "file = 'C:\\\\Users\\\\Jnfr\\\\Projects\\\\SpotifyAnalyticsApp\\\\whoopsegments.json'\n",
    "\n",
    "with open(file) as train_file:\n",
    "    dict_train = json.load(train_file)\n",
    "\n",
    "print(dict_train)\n",
    "df = pd.DataFrame.from_dict(dict_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>id@</th>\n",
       "      <th>segmentData@</th>\n",
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       "      <td>6i0YwBLhYs3kkb6qmty0uC</td>\n",
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       "      <td>5Hroj5K7vLpIG4FNCRIjbP</td>\n",
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       "      <td>2WoAXWr5SWHUxaTXlTkncJ</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0UlHiqi7seV02XNHLMtfGK</td>\n",
       "      <td>[{'Confidence': 0.961, 'Duration': 0.22209, 'L...</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0zljdkHj9xLHtXsc844Vep</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0tr0D36L1RwPdUTFfyNq37</td>\n",
       "      <td>[{'Confidence': 0.425, 'Duration': 0.2322, 'Lo...</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5Gtn8HgCAo0TUiaKKgP6us</td>\n",
       "      <td>[{'Confidence': 0.921, 'Duration': 0.24281, 'L...</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5CLGzJsGqhCEECcpnFQA8x</td>\n",
       "      <td>[{'Confidence': 0.849, 'Duration': 0.178, 'Lou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2Ro4drWDoSFIMNuArYJDk5</td>\n",
       "      <td>[{'Confidence': 1, 'Duration': 0.22667, 'Loudn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1dadMFSGIJATXdcNqIUTOA</td>\n",
       "      <td>[{'Confidence': 0.623, 'Duration': 0.18091, 'L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5VGlqQANWDKJFl0MBG3sg2</td>\n",
       "      <td>[{'Confidence': 0.128, 'Duration': 0.39397, 'L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1I0k04eIuAYfd4ObZ1POli</td>\n",
       "      <td>[{'Confidence': 0.49, 'Duration': 0.1107, 'Lou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3m7fbVDdVKzTfF8umdsfFp</td>\n",
       "      <td>[{'Confidence': 1, 'Duration': 0.4644, 'Loudne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>42d6bxB7OQ6f8ct2ihYNBw</td>\n",
       "      <td>[{'Confidence': 0.303, 'Duration': 0.29596, 'L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3EnD9mAbEHTFKDGjhZixBL</td>\n",
       "      <td>[{'Confidence': 1, 'Duration': 0.24544, 'Loudn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1fr92Vupmcs2vgLMFVQ7rd</td>\n",
       "      <td>[{'Confidence': 0.265, 'Duration': 0.19215, 'L...</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0hDQV9X1Da5JrwhK8gu86p</td>\n",
       "      <td>[{'Confidence': 0.727, 'Duration': 0.1741, 'Lo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3QvtXiPD5vlfWrb9FF42PB</td>\n",
       "      <td>[{'Confidence': 0.465, 'Duration': 0.13955, 'L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>273uCXd7NPrInaiNqtkOrA</td>\n",
       "      <td>[{'Confidence': 0.658, 'Duration': 0.26703, 'L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3VfnegwNori4wN0qtAH0MV</td>\n",
       "      <td>[{'Confidence': 0.112, 'Duration': 0.12172, 'L...</td>\n",
       "    </tr>\n",
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       "28  1dadMFSGIJATXdcNqIUTOA  [{'Confidence': 0.623, 'Duration': 0.18091, 'L...\n",
       "29  5VGlqQANWDKJFl0MBG3sg2  [{'Confidence': 0.128, 'Duration': 0.39397, 'L...\n",
       "30  1I0k04eIuAYfd4ObZ1POli  [{'Confidence': 0.49, 'Duration': 0.1107, 'Lou...\n",
       "31  3m7fbVDdVKzTfF8umdsfFp  [{'Confidence': 1, 'Duration': 0.4644, 'Loudne...\n",
       "32  42d6bxB7OQ6f8ct2ihYNBw  [{'Confidence': 0.303, 'Duration': 0.29596, 'L...\n",
       "33  3EnD9mAbEHTFKDGjhZixBL  [{'Confidence': 1, 'Duration': 0.24544, 'Loudn...\n",
       "34  1fr92Vupmcs2vgLMFVQ7rd  [{'Confidence': 0.265, 'Duration': 0.19215, 'L...\n",
       "35  0hDQV9X1Da5JrwhK8gu86p  [{'Confidence': 0.727, 'Duration': 0.1741, 'Lo...\n",
       "36  3QvtXiPD5vlfWrb9FF42PB  [{'Confidence': 0.465, 'Duration': 0.13955, 'L...\n",
       "37  273uCXd7NPrInaiNqtkOrA  [{'Confidence': 0.658, 'Duration': 0.26703, 'L...\n",
       "38  3VfnegwNori4wN0qtAH0MV  [{'Confidence': 0.112, 'Duration': 0.12172, 'L..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "#del df['id@']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WRANGLE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.564</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.502</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.783</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.084</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.452</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.489</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.165</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.991</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.710</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.387</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.407</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.287</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.072</td>\n",
       "      <td>0.074</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.044</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.231</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.360</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.227</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.182</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.397</td>\n",
       "      <td>0.642</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.580</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.214</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.265</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.753</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.886</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.857</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.117</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.224</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.228</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.120</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.247</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.055</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.258</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.192</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.933</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.228</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.573</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.601</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.335</td>\n",
       "      <td>0.540</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.170</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.315</td>\n",
       "      <td>0.506</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.569</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374</td>\n",
       "      <td>0.448</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.438</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.442</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.720</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.188</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.884</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.931</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.082</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.866</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.389</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.469</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.639</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.461</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.229</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.487</td>\n",
       "      <td>0.367</td>\n",
       "      <td>0.371</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.450</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.687</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.289</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.497</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.170</td>\n",
       "      <td>0.202</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.234</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.827</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.014</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.182</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.318</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.725</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.298</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.604</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.501</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.382</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.445</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.112</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.212</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.248</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.264</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.569</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.653</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.384</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.414</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.771</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.244</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.620</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.473</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.272</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.343</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.013</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.090</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.213</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.510</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.765</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.309</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.423</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.558</td>\n",
       "      <td>0.314</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.383</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.473</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.779</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.396</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.431</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.335</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.306</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.474</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.768</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.326</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.396</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.240</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.355</td>\n",
       "      <td>0.382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.854</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.811</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.618</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.525</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.321</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.491</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.535</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39 rows × 216 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0      1      2      3      4      5      6      7      8      9    \\\n",
       "0   0.564  0.946  0.615  0.502  0.659  0.685  0.814  0.840  0.771  0.783   \n",
       "1   0.183  1.000  0.983  0.377  0.173  0.139  0.276  0.107  0.089  0.084   \n",
       "2   0.489  0.329  0.142  0.774  0.173  0.106  0.228  0.709  0.181  0.165   \n",
       "3   0.710  1.000  0.387  0.215  0.464  0.350  0.121  0.282  0.142  0.051   \n",
       "4   0.072  0.074  1.000  0.110  0.038  0.218  0.029  0.027  0.028  0.044   \n",
       "5   0.231  0.275  0.639  0.127  0.065  0.079  0.360  1.000  0.103  0.227   \n",
       "6   0.397  0.642  1.000  0.749  0.580  0.871  0.358  0.214  0.147  0.209   \n",
       "7   0.753  0.771  0.675  0.588  0.971  0.909  0.886  1.000  0.428  0.424   \n",
       "8   0.117  0.160  0.084  0.087  0.191  0.096  0.142  0.125  0.224  1.000   \n",
       "9   0.120  0.021  0.118  0.018  0.041  0.079  0.101  0.021  0.040  0.247   \n",
       "10  0.055  0.196  0.045  0.066  0.128  0.258  1.000  0.068  0.063  0.047   \n",
       "11  0.933  0.953  0.285  0.262  0.434  0.454  0.432  0.412  0.536  0.578   \n",
       "12  0.335  0.540  1.000  0.123  0.039  0.122  0.079  0.200  0.096  0.139   \n",
       "13  0.315  0.506  0.347  0.212  0.251  0.569  1.000  0.963  0.714  0.602   \n",
       "14  0.438  1.000  0.776  0.670  0.425  0.421  0.305  0.352  0.239  0.322   \n",
       "15  0.720  1.000  0.379  0.342  0.127  0.087  0.314  0.188  0.075  0.035   \n",
       "16  1.000  0.455  0.191  0.064  0.090  0.094  0.101  0.104  0.035  0.063   \n",
       "17  0.082  0.058  0.099  0.352  0.073  0.071  0.034  0.138  0.080  0.119   \n",
       "18  0.866  1.000  0.096  0.054  0.061  0.051  0.074  0.076  0.162  0.174   \n",
       "19  0.469  0.742  0.860  0.639  0.421  0.434  0.587  0.258  0.461  1.000   \n",
       "20  0.450  0.744  0.652  0.687  1.000  0.874  0.292  0.330  0.092  0.026   \n",
       "21  0.497  0.187  0.225  0.170  0.202  1.000  0.185  0.296  0.306  0.375   \n",
       "22  0.827  1.000  0.602  0.026  0.027  0.177  0.022  0.020  0.057  0.014   \n",
       "23  0.725  1.000  0.537  0.671  0.712  0.430  0.495  0.298  0.566  0.604   \n",
       "24  0.150  0.382  1.000  0.309  0.079  0.186  0.734  0.972  0.314  0.143   \n",
       "25  0.112  0.048  0.042  0.066  0.212  1.000  0.342  0.017  0.020  0.049   \n",
       "26  0.264  1.000  0.227  0.261  0.569  0.569  0.204  0.199  0.754  0.728   \n",
       "27  0.384  1.000  0.253  0.414  0.362  0.524  0.335  0.351  0.226  0.326   \n",
       "28  0.620  0.314  0.342  0.222  0.556  0.716  0.473  1.000  0.319  0.450   \n",
       "29  0.013  0.016  0.274  0.023  0.015  0.013  0.068  1.000  0.041  0.090   \n",
       "30  1.000  0.805  0.350  0.345  0.373  0.312  0.399  0.364  0.751  0.510   \n",
       "31  0.309  1.000  0.207  0.205  0.159  0.384  0.163  0.324  0.694  0.194   \n",
       "32  0.558  0.314  1.000  0.514  0.293  0.132  0.132  0.166  0.095  0.145   \n",
       "33  0.383  0.068  0.061  0.037  0.042  0.114  0.473  1.000  0.211  0.038   \n",
       "34  0.779  0.885  0.705  0.961  0.577  0.396  0.383  0.431  1.000  0.335   \n",
       "35  0.474  0.346  0.155  0.156  0.258  0.256  0.591  0.768  1.000  0.951   \n",
       "36  0.396  1.000  0.240  0.128  0.084  0.127  0.359  0.246  0.355  0.382   \n",
       "37  0.811  0.247  0.100  0.404  0.618  1.000  0.552  0.265  0.332  0.525   \n",
       "38  0.321  1.000  0.951  0.259  0.313  0.271  0.491  0.422  0.430  0.454   \n",
       "\n",
       "    ...      206    207    208    209    210    211    212    213    214  \\\n",
       "0   ...    1.000  0.800  0.286  0.141  0.248  0.139  0.183  0.639  0.235   \n",
       "1   ...    1.000  0.452  0.227  0.230  0.480  0.241  0.152  0.132  0.256   \n",
       "2   ...    0.156  0.193  0.386  0.328  0.675  0.674  0.980  0.991  1.000   \n",
       "3   ...    0.494  0.292  0.518  0.215  0.407  1.000  0.287  0.093  0.078   \n",
       "4   ...    0.150  0.109  0.264  1.000  0.237  0.082  0.083  0.108  0.185   \n",
       "5   ...    0.182  0.061  0.225  0.071  0.182  1.000  0.119  0.023  0.052   \n",
       "6   ...    0.034  0.057  0.265  1.000  0.304  0.060  0.038  0.114  0.053   \n",
       "7   ...    0.319  0.276  0.687  0.599  0.857  1.000  0.504  0.498  0.076   \n",
       "8   ...    0.129  0.228  1.000  0.259  0.240  0.069  0.048  0.065  0.057   \n",
       "9   ...    1.000  0.099  0.051  0.088  0.124  0.340  0.072  0.126  0.026   \n",
       "10  ...    0.172  0.256  0.127  0.149  0.129  0.192  1.000  0.232  0.211   \n",
       "11  ...    0.222  0.228  0.535  0.573  1.000  0.601  0.510  0.510  0.783   \n",
       "12  ...    0.129  0.099  0.170  1.000  0.247  0.276  0.025  0.131  0.292   \n",
       "13  ...    0.374  0.448  1.000  0.244  0.196  0.199  0.245  0.227  0.209   \n",
       "14  ...    0.768  0.774  0.751  0.695  0.611  0.485  0.440  0.442  0.418   \n",
       "15  ...    0.688  0.477  0.611  0.662  0.884  1.000  0.931  0.619  0.370   \n",
       "16  ...    0.217  0.151  0.183  0.142  0.129  0.176  0.122  0.162  0.418   \n",
       "17  ...    0.605  1.000  0.632  0.276  0.515  0.485  0.165  0.489  0.629   \n",
       "18  ...    0.372  0.539  0.389  0.472  0.240  0.127  0.248  0.194  0.158   \n",
       "19  ...    0.375  0.229  0.251  0.215  0.487  0.367  0.371  0.508  0.380   \n",
       "20  ...    0.289  0.276  0.120  0.031  0.014  0.037  0.014  0.020  0.233   \n",
       "21  ...    0.159  0.089  0.134  0.408  0.137  0.162  0.176  0.234  1.000   \n",
       "22  ...    0.381  0.283  0.210  0.309  0.313  0.092  0.182  0.145  0.318   \n",
       "23  ...    0.232  0.427  0.501  0.384  0.141  0.224  0.233  0.381  0.283   \n",
       "24  ...    0.808  0.770  0.604  0.254  0.445  0.259  0.158  0.203  0.132   \n",
       "25  ...    0.022  0.022  0.248  1.000  0.145  0.292  0.016  0.024  0.004   \n",
       "26  ...    0.685  0.653  1.000  0.884  0.932  0.928  0.453  0.541  0.133   \n",
       "27  ...    0.596  0.771  0.735  0.643  0.533  0.461  0.456  0.231  0.244   \n",
       "28  ...    0.094  0.104  0.272  1.000  0.343  0.259  0.178  0.435  0.149   \n",
       "29  ...    0.086  0.055  0.213  1.000  0.379  0.111  0.125  0.124  0.064   \n",
       "30  ...    0.224  0.765  1.000  0.400  0.496  0.220  0.322  0.858  0.308   \n",
       "31  ...    0.241  0.148  0.423  0.508  0.921  0.557  0.651  0.215  0.256   \n",
       "32  ...    1.000  0.052  0.038  0.029  0.090  0.054  0.362  0.366  0.144   \n",
       "33  ...    0.060  0.090  0.033  0.015  0.021  0.052  0.041  0.020  0.024   \n",
       "34  ...    0.361  0.230  0.239  0.306  1.000  0.620  0.333  0.213  0.226   \n",
       "35  ...    0.194  0.126  0.384  0.166  0.157  0.628  0.326  1.000  0.249   \n",
       "36  ...    0.385  0.337  0.702  0.854  1.000  0.810  0.455  0.365  0.200   \n",
       "37  ...    0.160  0.236  0.231  0.794  0.217  0.072  0.029  0.099  0.037   \n",
       "38  ...    0.522  0.535  1.000  0.531  0.392  0.376  0.521  0.850  0.458   \n",
       "\n",
       "      215  \n",
       "0   0.364  \n",
       "1   0.273  \n",
       "2   0.048  \n",
       "3   0.521  \n",
       "4   0.557  \n",
       "5   0.229  \n",
       "6   0.086  \n",
       "7   0.034  \n",
       "8   0.152  \n",
       "9   0.195  \n",
       "10  0.163  \n",
       "11  0.214  \n",
       "12  0.585  \n",
       "13  0.393  \n",
       "14  0.916  \n",
       "15  0.402  \n",
       "16  0.345  \n",
       "17  0.322  \n",
       "18  0.691  \n",
       "19  0.387  \n",
       "20  0.030  \n",
       "21  0.339  \n",
       "22  1.000  \n",
       "23  0.235  \n",
       "24  0.420  \n",
       "25  0.033  \n",
       "26  0.026  \n",
       "27  1.000  \n",
       "28  0.139  \n",
       "29  0.106  \n",
       "30  0.424  \n",
       "31  0.072  \n",
       "32  0.088  \n",
       "33  0.235  \n",
       "34  0.285  \n",
       "35  0.277  \n",
       "36  0.233  \n",
       "37  0.643  \n",
       "38  0.292  \n",
       "\n",
       "[39 rows x 216 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#stores 216 song pitches for each segment, concatenated in list of 18 segments/song\n",
    "###list = []\n",
    "\n",
    "df_pitches = pd.DataFrame(columns=[x for x in range(216)])\n",
    "\n",
    "#for index 0...38, row id@, segmentData@\n",
    "for index, row in df.iterrows():\n",
    "    \n",
    "    for n in range(18):\n",
    "        if n == 0:\n",
    "            templist = []\n",
    "            templist = row['segmentData@'][n]['Pitches']\n",
    "        elif n <=18:\n",
    "            templist += row['segmentData@'][n]['Pitches']\n",
    "    \n",
    "    ###list+=templist\n",
    "    \n",
    "    df_pitches.loc[index] = templist\n",
    "# list is 12 * 18 * 39 (12 pitches/segment * 18 segments/song * 39 songs)\n",
    "df_pitches\n",
    "\n",
    "###print(len(list))\n",
    "###print(list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATA EXPLORATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing whoop pitch patterns between two songs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26cd0b83fd0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cd0b26400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pitches.loc[0].plot()\n",
    "df_pitches.loc[1].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vizualizing overlay of pitch range across 39 \"whoop\" songs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26cd0c95cf8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cd0c282b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pitches.plot(kind='hist', legend=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HYPOTHESIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL DATA: SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 216)\n",
      "(39,)\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1]\n",
      "[1]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The number of classes has to be greater than one; got 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-c915231589a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;31m#xtrain = clf.fit(X,y)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;31m#xpredict =\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'C'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         sample_weight = np.asarray([]\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_validate_targets\u001b[1;34m(self, y)\u001b[0m\n\u001b[0;32m    504\u001b[0m             raise ValueError(\n\u001b[0;32m    505\u001b[0m                 \u001b[1;34m\"The number of classes has to be greater than one; got %d\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 506\u001b[1;33m                 % len(cls))\n\u001b[0m\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The number of classes has to be greater than one; got 1"
     ]
    }
   ],
   "source": [
    "#df_pitches.as_matrix()\n",
    "\n",
    "feature_cols = [x for x in range(216)]\n",
    "\n",
    "#grab all rows, and all cols\n",
    "X = df_pitches.loc[:, feature_cols]\n",
    "\n",
    "#X = pd.get_dummies(df_pitches)\n",
    "\n",
    "# y response value in training set is always true, for 39 songs\n",
    "y = np.array([(0 or 1) for x in range(39)])\n",
    "\n",
    "print(X.shape) # (5,4)\n",
    "print(y.shape) # (5,)\n",
    "print(y)\n",
    "print(np.unique(y))\n",
    "\n",
    "clf = SVC(kernel='linear', C=1.0)\n",
    "#xtrain = clf.fit(X,y)\n",
    "test_size = 20\n",
    "clf.fit(X[:test_size],y[:test_size])\n",
    "\n",
    "#xpredict = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS AND MODEL ACCURACY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DISCUSSION AND LIMITATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUMMARY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**______x__________x__________x__________x__________x_____ ARCHIVE TO FOLLOW _____x__________x__________x__________x__________x_____**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UNUSED CODE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.plot(df);\n",
    "#data_dict = dict(zip((str(df['start'])), (df['pitches'])))\n",
    "#data_dict = dict((str(pitch),pitch) for pitch in df['pitches'])\n",
    "#py.plot(data_dict, filename='basic-scatter')\n",
    "#scatter(df['pitches'], df['start']);\n",
    "\n",
    "#df.plot(x='start', y='pitches')\n",
    "\n",
    "#plt.scatter(df['start'], df['pitches'])\n",
    "#plt.show()\n",
    "#df.columns\n",
    "#dataframe = pd.DataFrame(df['pitches'])\n",
    "#dataframe\n",
    "\n",
    "#df.T.plot( kind='bar') # or df.T.plot.bar()\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'C:\\\\Users\\\\Jnfr\\\\Projects\\\\SpotifyAnalyticsApp\\\\amy_winehouse_addicted.json'\n",
    "\n",
    "with open(file, 'r') as json_data2:\n",
    "    track = ijson.items(json_data2, 'track')\n",
    "    trackCols = list(track)\n",
    "    \n",
    "df2 = pd.DataFrame.from_dict(json_normalize(trackCols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(file, 'r') as json_data3:\n",
    "    sections = ijson.items(json_data3, 'sections')\n",
    "    secCols = list(sections)\n",
    "    \n",
    "df3 = pd.DataFrame.from_dict(json_normalize(secCols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(file, 'r') as json_data3:\n",
    "    beats = ijson.items(json_data3, 'beats')\n",
    "    beatCols = list(beats)\n",
    "    \n",
    "df4 = pd.DataFrame.from_dict(json_normalize(beatCols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CREATE FREQUENCY DATAFRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'octave_num': ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '1', '1','1','1','1','1','1','1','1','1','1',\n",
    "                       '2', '2','2','2','2','2','2','2','2','2','2','2','3','3','3','3','3','3','3','3','3','3','3','3','4','4','4','4','4','4','4','4','4','4','4','4',\n",
    "                       '5','5','5','5','5','5','5','5','5','5','5','5','6','6','6','6','6','6','6','6','6','6','6','6','7','7','7','7','7','7','7','7','7','7','7','7',\n",
    "                       '8','8','8','8','8','8','8','8','8','8','8','8'], 'note': [ 'C', 'C#','D', 'Eb', 'E','F', 'F#', 'G', 'G#', 'A', 'Bb', 'B', 'C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B',\n",
    "                       'C', 'C#','D','Eb','E','F','F#', 'G', 'G#', 'A', 'Bb', 'B','C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B',\n",
    "                       'C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B','C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B',\n",
    "                       'C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B', 'C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B',\n",
    "                       'C', 'C#','D', 'Eb', 'E', 'F', 'F#', 'G', 'G#', 'A', 'Bb', 'B'],'frequency': [16.35,17.32,18.35,19.45,20.60,21.83,23.12,24.50,25.96,27.50,29.14,30.87,32.70,34.65,36.71,38.89,41.20,43.65,46.25,49.00,51.91,55.00,58.27,61.74,65.41,69.30,73.42,77.78,82.41,87.31,92.50,98.00,103.8,110.0,116.5,123.5,130.8,138.6,146.8,155.6,164.8,174.6,185.0,196.0,207.7,220.0,233.1,246.9,261.6,277.2,293.7,311.1,329.6,349.2,370.0,392.0,415.3,440.0,466.2,493.9,523.3,554.4,587.3,622.3,659.3,698.5,740.0,784.0,830.6,880.0,932.3,987.8,1047,1109,1175,1245,1319,1397,1480,1568,1661,1760,1865,1976,2093,2217,2349,2489,2637,2794,2960,3136,3322,3520,3729,3951,4186,4435,4699,4978,5274,5588,5920,6272,6645,7040,7459,7902]}\n",
    "\n",
    "\n",
    "df_frequency = pd.DataFrame(data)\n",
    "df_frequency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load whoop data\n",
    "import csv\n",
    "from io import StringIO\n",
    "import re\n",
    "import pandas as pd\n",
    "\n",
    "file_whoop = \"c:\\\\Users\\\\Jnfr\\\\Projects\\\\SpotifyAnalyticsApp\\\\whoops.csv\"\n",
    "\n",
    "# get the data as a python string\n",
    "with open (file_whoop, \"r\") as myfile:\n",
    "    data=myfile.read()\n",
    "\n",
    "# munge in python - get rid of the garbage in the input (lots of xff bytes)\n",
    "data = re.sub(r'[^,a-zA-Z0-9_\\.;:\\n]', '', data) # get rid of the rubbish\n",
    "#data = data + '\\n' # the very last one is missing?\n",
    "#data = re.sub(r';\\n', r'\\n', data) # last ; separator on line is problematic\n",
    "\n",
    "# now let's suck into a pandas DataFrame\n",
    "columns = ['artist', 'song_name', 'year', 'whoop_time', 'youtube_url']\n",
    "df = pd.read_csv(StringIO(data), index_col=None, header=0, sep=',')\n",
    "df.columns = columns\n",
    "'''with open(file_whoop, 'r') as f:\n",
    "    readCSV = csv.reader(f, delimiter=',')\n",
    "    print(readCSV)\n",
    "    #df = pd.DataFrame(object) '''\n",
    "\n",
    "\n",
    "#df = pd.read_csv(file_whoop, header=0,encoding='iso-8859-1', error_bad_lines=False)\n",
    "#df = pd.read_csv(StringIO(file_whoop), encoding = \"cp1252\")\n",
    "df\n",
    "#list = open(file_whoop, 'r')\n",
    "#df_whoops = pd.DataFrame([list.read()])\n",
    "#print(df_whoops)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CREATE WHOOP DATAFRAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CONCLUSION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DISCUSSION & LIMITATIONS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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