tiktok dreams workbook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from cm_api import *\n",
    "from cm_config import token\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from decimal import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get refresh token\n",
    "rt = token['refresh_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get api token\n",
    "api_token = get_api_token(rt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find Chartmetric ID for Dreams by Fleetwood Mac\n",
    "Since we know that the 2004 Remastered version was the one used in the tiktok video, we need to\n",
    "get the ID associated with that specific track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isrc #:  USWB11301111\n",
      "track name :  Dreams - 2004 Remastered Edition\n",
      "chartmetric ID :  16026997\n",
      "artist name :  Fleetwood Mac\n",
      "album name :  Rumours (Super Deluxe)\n",
      "release date :  1977-02-04T00:00:00.000Z\n",
      "\n",
      "\n",
      "isrc #:  USWB19900178\n",
      "track name :  Dreams\n",
      "chartmetric ID :  16032762\n",
      "artist name :  Fleetwood Mac\n",
      "album name :  Greatest Hits\n",
      "release date :  1988-11-21T00:00:00.000Z\n",
      "\n",
      "\n",
      "isrc #:  USWB10400046\n",
      "track name :  Dreams\n",
      "chartmetric ID :  16016833\n",
      "artist name :  Fleetwood Mac\n",
      "album name :  Rumours\n",
      "release date :  1977-02-04T00:00:00.000Z\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#use search function form cm_api to find chartmetric id for Dreams\n",
    "dreams = search(api_token, 'Dreams', 'Fleetwood Mac', limit=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save dataframe as csv file\n",
    "dreams.to_csv('data/dreams_info.csv', index_label='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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",
       "      <th>isrc</th>\n",
       "      <th>track</th>\n",
       "      <th>chartmetric id</th>\n",
       "      <th>artist</th>\n",
       "      <th>album</th>\n",
       "      <th>release date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USWB11301111</td>\n",
       "      <td>Dreams - 2004 Remastered Edition</td>\n",
       "      <td>16026997</td>\n",
       "      <td>Fleetwood Mac</td>\n",
       "      <td>Rumours (Super Deluxe)</td>\n",
       "      <td>1977-02-04T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USWB19900178</td>\n",
       "      <td>Dreams</td>\n",
       "      <td>16032762</td>\n",
       "      <td>Fleetwood Mac</td>\n",
       "      <td>Greatest Hits</td>\n",
       "      <td>1988-11-21T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USWB10400046</td>\n",
       "      <td>Dreams</td>\n",
       "      <td>16016833</td>\n",
       "      <td>Fleetwood Mac</td>\n",
       "      <td>Rumours</td>\n",
       "      <td>1977-02-04T00:00:00.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           isrc                             track  chartmetric id  \\\n",
       "0  USWB11301111  Dreams - 2004 Remastered Edition        16026997   \n",
       "1  USWB19900178                            Dreams        16032762   \n",
       "2  USWB10400046                            Dreams        16016833   \n",
       "\n",
       "          artist                   album              release date  \n",
       "0  Fleetwood Mac  Rumours (Super Deluxe)  1977-02-04T00:00:00.000Z  \n",
       "1  Fleetwood Mac           Greatest Hits  1988-11-21T00:00:00.000Z  \n",
       "2  Fleetwood Mac                 Rumours  1977-02-04T00:00:00.000Z  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dreams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's grab the chartmetric id associated with the 2004 remastered version and save as a variable\n",
    "dreams2004rem_cmid = dreams.loc[dreams['track'] == 'Dreams - 2004 Remastered Edition']['chartmetric id'][0]\n",
    "dreamsgreatesthits_cmid = dreams.loc[dreams['track'] == 'Dreams']['chartmetric id'][1]\n",
    "dreamsrumours_cmid = dreams.loc[dreams['track'] == 'Dreams']['chartmetric id'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect Historical Data On Spotify Playlist Positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Playlist activity is a good indicator of how popular a particular track is. Features such as number of followers, official or not, and track position within a particular playlist can tell you alot about how large its reach is. The description of a playlist can also suggest the mood/vibe the author had in mind when deciding whether or not they should at the track"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dreams (2004 Remaster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_track_playlist(api_token, dreams2004rem_cmid, 'spotify', 'current', '2020-09-01', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['playlist_id', 'track_id', 'position', 'added_at', 'period', 'id', 'name', 'description', 'image_url', 'sys_last_updated', 'last_updated', 'personalized', 'code2', 'owner_name', 'owner_id', 'user_id', 'official', 'tags', 'followers', 'num_track', 'fdiff_week', 'fdiff_month', 'catalog', 'active_ratio'])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take a look at availavle data features\n",
    "data[1]['playlist'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'playlist_id': '3IBrsav3Sh8AImtaGoaP07',\n",
       " 'track_id': 66204,\n",
       " 'position': 20,\n",
       " 'added_at': '2020-10-06T00:00:00.000Z',\n",
       " 'period': 1,\n",
       " 'id': 1880171,\n",
       " 'name': 'Good Mornings - Happily Positive Music to Start The Day',\n",
       " 'description': 'Good morning music to wake up happy and start your day right! We’ve put together the perfect playlist of upbeat pop classics and positive songs to carry your stress away and start with a smile - <a href=\"https://rb.gy/hbsqjf\">Submit Your Music</a>',\n",
       " 'image_url': 'https://i.scdn.co/image/ab67706c0000bebb721f9cf1078e59cbf457989e',\n",
       " 'sys_last_updated': '2020-10-07T00:32:05.919Z',\n",
       " 'last_updated': '2020-10-07T00:32:05.848Z',\n",
       " 'personalized': False,\n",
       " 'code2': None,\n",
       " 'owner_name': 'Chilled Mumoro',\n",
       " 'owner_id': 269820,\n",
       " 'user_id': '2k995mbzd5f9ui49qh23xp2tr',\n",
       " 'official': False,\n",
       " 'tags': [{'id': 188, 'name': 'pop'}, {'id': 9544, 'name': 'folk-pop'}],\n",
       " 'followers': 25569,\n",
       " 'num_track': '688',\n",
       " 'fdiff_week': 920,\n",
       " 'fdiff_month': 4780,\n",
       " 'catalog': 'catalogue ',\n",
       " 'active_ratio': 0.988372093023256}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[5]['playlist']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parse metadata for Dreams 2004 Remaster playlist postions from 100 spotify playlists\n",
    "\n",
    "data_bucket = []\n",
    "for playlist in data:\n",
    "    playlist_tuple = (playlist['playlist']['playlist_id'],playlist['playlist']['name'], playlist['playlist']['position'], \n",
    "                      playlist['playlist']['added_at'], playlist['playlist']['description'], \n",
    "                      playlist['playlist']['official'],playlist['playlist']['followers'])\n",
    "    data_bucket.append(playlist_tuple)\n",
    "    \n",
    "playlists_dreamsrem = pd.DataFrame(data_bucket, columns=['playlist id', 'playlist name', 'positon', 'add date', 'description', 'official', 'followers'])\n",
    "\n",
    "playlists_dreamsrem.to_csv('data/dreamsremas_spotplayl.csv', index='playlist id')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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",
       "      <th>playlist id</th>\n",
       "      <th>playlist name</th>\n",
       "      <th>positon</th>\n",
       "      <th>add date</th>\n",
       "      <th>description</th>\n",
       "      <th>official</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37i9dQZEVXbMH2jvi6jvjk</td>\n",
       "      <td>South Africa Top 50</td>\n",
       "      <td>45</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>18626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37i9dQZEVXbK4NvPi6Sxit</td>\n",
       "      <td>India Viral 50</td>\n",
       "      <td>50</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>Your daily update of the most viral tracks in ...</td>\n",
       "      <td>True</td>\n",
       "      <td>12819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37i9dQZEVXbJiyhoAPEfMK</td>\n",
       "      <td>Switzerland Top 50</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>113238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0AF4jgCJ48VYmYPMwofk2s</td>\n",
       "      <td>Reels songs you can't get out of your head | T...</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>biggest viral hits from instagram reels, updat...</td>\n",
       "      <td>False</td>\n",
       "      <td>31471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37i9dQZEVXbO5MSE9RdfN2</td>\n",
       "      <td>Australia Viral 50</td>\n",
       "      <td>47</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>Your daily update of the most viral tracks in ...</td>\n",
       "      <td>True</td>\n",
       "      <td>68300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>70zigMLHC6xxayqgePLGyA</td>\n",
       "      <td>TIKTOK SONGS YOU LOVE ⏳⏳</td>\n",
       "      <td>322</td>\n",
       "      <td>2020-09-28T00:00:00.000Z</td>\n",
       "      <td>tiktok songs, tik tok songs, tiktok playlist, ...</td>\n",
       "      <td>False</td>\n",
       "      <td>155249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>37i9dQZEVXbM8SIrkERIYl</td>\n",
       "      <td>New Zealand Top 50</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-09-27T00:00:00.000Z</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>202142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6KYJ17PEQ1af7rvyCNnswy</td>\n",
       "      <td>Clásicos</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-09-23T00:00:00.000Z</td>\n",
       "      <td>Clásicos que han inspirado a más de una genera...</td>\n",
       "      <td>False</td>\n",
       "      <td>22814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7y15TCWmOoqxeYyiALg775</td>\n",
       "      <td>Old but gold 💛</td>\n",
       "      <td>837</td>\n",
       "      <td>2020-09-06T00:00:00.000Z</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0WvTLRAGBHjTMj70Xrrby3</td>\n",
       "      <td>All Out Legacy</td>\n",
       "      <td>296</td>\n",
       "      <td>2020-09-05T00:00:00.000Z</td>\n",
       "      <td>From the birth of Rock ’n’ Roll to hippies and...</td>\n",
       "      <td>False</td>\n",
       "      <td>29492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               playlist id                                      playlist name  \\\n",
       "0   37i9dQZEVXbMH2jvi6jvjk                                South Africa Top 50   \n",
       "1   37i9dQZEVXbK4NvPi6Sxit                                     India Viral 50   \n",
       "2   37i9dQZEVXbJiyhoAPEfMK                                 Switzerland Top 50   \n",
       "3   0AF4jgCJ48VYmYPMwofk2s  Reels songs you can't get out of your head | T...   \n",
       "4   37i9dQZEVXbO5MSE9RdfN2                                 Australia Viral 50   \n",
       "..                     ...                                                ...   \n",
       "95  70zigMLHC6xxayqgePLGyA                           TIKTOK SONGS YOU LOVE ⏳⏳   \n",
       "96  37i9dQZEVXbM8SIrkERIYl                                 New Zealand Top 50   \n",
       "97  6KYJ17PEQ1af7rvyCNnswy                                           Clásicos   \n",
       "98  7y15TCWmOoqxeYyiALg775                                     Old but gold 💛   \n",
       "99  0WvTLRAGBHjTMj70Xrrby3                                     All Out Legacy   \n",
       "\n",
       "    positon                  add date  \\\n",
       "0        45  2020-10-12T00:00:00.000Z   \n",
       "1        50  2020-10-12T00:00:00.000Z   \n",
       "2        46  2020-10-12T00:00:00.000Z   \n",
       "3        15  2020-10-12T00:00:00.000Z   \n",
       "4        47  2020-10-12T00:00:00.000Z   \n",
       "..      ...                       ...   \n",
       "95      322  2020-09-28T00:00:00.000Z   \n",
       "96        5  2020-09-27T00:00:00.000Z   \n",
       "97        3  2020-09-23T00:00:00.000Z   \n",
       "98      837  2020-09-06T00:00:00.000Z   \n",
       "99      296  2020-09-05T00:00:00.000Z   \n",
       "\n",
       "                                          description  official  followers  \n",
       "0   Your daily update of the most played tracks in...      True      18626  \n",
       "1   Your daily update of the most viral tracks in ...      True      12819  \n",
       "2   Your daily update of the most played tracks in...      True     113238  \n",
       "3   biggest viral hits from instagram reels, updat...     False      31471  \n",
       "4   Your daily update of the most viral tracks in ...      True      68300  \n",
       "..                                                ...       ...        ...  \n",
       "95  tiktok songs, tik tok songs, tiktok playlist, ...     False     155249  \n",
       "96  Your daily update of the most played tracks in...      True     202142  \n",
       "97  Clásicos que han inspirado a más de una genera...     False      22814  \n",
       "98                                                NaN     False      48136  \n",
       "99  From the birth of Rock ’n’ Roll to hippies and...     False      29492  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlists_dreamsrem = pd.read_csv('data/dreamsremas_spotplayl.csv', index_col=0)\n",
    "playlists_dreamsrem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert pandas series object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert datetime series to just year-month-day format\n",
    "playlists_dreamsrem['add date'] = pd.to_datetime(playlists_dreamsrem['add date'])\n",
    "playlists_dreamsrem['add date'] = playlists_dreamsrem['add date'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2020-10-12\n",
       "1     2020-10-12\n",
       "2     2020-10-12\n",
       "3     2020-10-12\n",
       "4     2020-10-12\n",
       "         ...    \n",
       "94    2020-09-28\n",
       "96    2020-09-27\n",
       "97    2020-09-23\n",
       "98    2020-09-06\n",
       "99    2020-09-05\n",
       "Name: add date, Length: 100, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlists_dreamsrem['add date'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Playlist Add Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize how many times the track was added to playlists\n",
    "plot=sns.countplot(x=playlists_dreamsrem['add date'])\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=45);\n",
    "plt.title(\"Spotify Playlist Adds - Dreams (2004 Remaster)\")\n",
    "plt.ylabel(\"# of Adds\")\n",
    "plot.invert_xaxis()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15, 7)\n",
    "plt.savefig('images/spotify_playlistadds_dreamsrmstr.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Dataframe with converted Datetime Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists_dreamsrem.to_csv('data/dreamsremas_spotplayl.csv', index='playlist id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#open dreams rmstr df\n",
    "\n",
    "# dreamsrmstr_spotpl = pd.read_csv('data/dreamsremas_spotplayl.csv', index_col=0)\n",
    "# dreamsrmstr_spotpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dreams (Greatest Hits Album)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "dreams_greatesthits = get_track_playlist(api_token, dreamsgreatesthits_cmid, 'spotify', 'current', '2020-09-01', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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",
       "      <th>playlist id</th>\n",
       "      <th>playlist name</th>\n",
       "      <th>positon</th>\n",
       "      <th>add date</th>\n",
       "      <th>description</th>\n",
       "      <th>official</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2oBCXRXgCxGWw9G7dyoTrF</td>\n",
       "      <td>Recently on Beats 1</td>\n",
       "      <td>56</td>\n",
       "      <td>2020-10-12T00:00:00.000Z</td>\n",
       "      <td>[Experimental, unofficial] Tracks recently pla...</td>\n",
       "      <td>False</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6ZpWruVslJK4S2WTZpTYiu</td>\n",
       "      <td>CARTE BLANCHE CLASSICS - credible en plat, van...</td>\n",
       "      <td>1740</td>\n",
       "      <td>2020-10-11T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1EeVUBBs9gnatbSdlHKIb4</td>\n",
       "      <td>Solitude Is Bliss (A Collection)</td>\n",
       "      <td>1325</td>\n",
       "      <td>2020-10-11T00:00:00.000Z</td>\n",
       "      <td>A story told through music, from the beginning...</td>\n",
       "      <td>False</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5WOBxamp6KHeDJekr6AYKn</td>\n",
       "      <td>Interior Crocodile Alligator</td>\n",
       "      <td>687</td>\n",
       "      <td>2020-10-10T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7fiKrIPqG5HyGN0FCLXDPp</td>\n",
       "      <td>Hits Favs</td>\n",
       "      <td>777</td>\n",
       "      <td>2020-10-10T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0WA0gmevwPkbGioseFxsnE</td>\n",
       "      <td>FluffedMojo</td>\n",
       "      <td>798</td>\n",
       "      <td>2020-09-15T00:00:00.000Z</td>\n",
       "      <td>All of it.</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>6V1Nfi4cLEYdOrs1R7AzrM</td>\n",
       "      <td>Bruneaux's Favorite Songs of All-Time</td>\n",
       "      <td>997</td>\n",
       "      <td>2020-09-10T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>74avQSetPDwWZptTsTj2eP</td>\n",
       "      <td>Get Motivated Playlist</td>\n",
       "      <td>1845</td>\n",
       "      <td>2020-09-09T00:00:00.000Z</td>\n",
       "      <td>&lt;a href=\"http://www.strayer.edu/?mcv=DG82\"&gt; Cl...</td>\n",
       "      <td>False</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0Xi527A8PH30qJzvc2XWcX</td>\n",
       "      <td>70's, 80's, 90's Super Hits</td>\n",
       "      <td>474</td>\n",
       "      <td>2020-09-07T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3BFlGH7ZYSJKukpG6jpC6l</td>\n",
       "      <td>Polar Bear Bar</td>\n",
       "      <td>200</td>\n",
       "      <td>2020-09-05T00:00:00.000Z</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               playlist id                                      playlist name  \\\n",
       "0   2oBCXRXgCxGWw9G7dyoTrF                                Recently on Beats 1   \n",
       "1   6ZpWruVslJK4S2WTZpTYiu  CARTE BLANCHE CLASSICS - credible en plat, van...   \n",
       "2   1EeVUBBs9gnatbSdlHKIb4                   Solitude Is Bliss (A Collection)   \n",
       "3   5WOBxamp6KHeDJekr6AYKn                       Interior Crocodile Alligator   \n",
       "4   7fiKrIPqG5HyGN0FCLXDPp                                          Hits Favs   \n",
       "..                     ...                                                ...   \n",
       "95  0WA0gmevwPkbGioseFxsnE                                        FluffedMojo   \n",
       "96  6V1Nfi4cLEYdOrs1R7AzrM              Bruneaux's Favorite Songs of All-Time   \n",
       "97  74avQSetPDwWZptTsTj2eP                             Get Motivated Playlist   \n",
       "98  0Xi527A8PH30qJzvc2XWcX                        70's, 80's, 90's Super Hits   \n",
       "99  3BFlGH7ZYSJKukpG6jpC6l                                     Polar Bear Bar   \n",
       "\n",
       "    positon                  add date  \\\n",
       "0        56  2020-10-12T00:00:00.000Z   \n",
       "1      1740  2020-10-11T00:00:00.000Z   \n",
       "2      1325  2020-10-11T00:00:00.000Z   \n",
       "3       687  2020-10-10T00:00:00.000Z   \n",
       "4       777  2020-10-10T00:00:00.000Z   \n",
       "..      ...                       ...   \n",
       "95      798  2020-09-15T00:00:00.000Z   \n",
       "96      997  2020-09-10T00:00:00.000Z   \n",
       "97     1845  2020-09-09T00:00:00.000Z   \n",
       "98      474  2020-09-07T00:00:00.000Z   \n",
       "99      200  2020-09-05T00:00:00.000Z   \n",
       "\n",
       "                                          description  official  followers  \n",
       "0   [Experimental, unofficial] Tracks recently pla...     False         71  \n",
       "1                                                         False         26  \n",
       "2   A story told through music, from the beginning...     False         25  \n",
       "3                                                         False         20  \n",
       "4                                                         False          7  \n",
       "..                                                ...       ...        ...  \n",
       "95                                         All of it.     False          4  \n",
       "96                                                        False         51  \n",
       "97  <a href=\"http://www.strayer.edu/?mcv=DG82\"> Cl...     False        353  \n",
       "98                                                        False        123  \n",
       "99                                                        False         23  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parse metadata for Dreams (Greatest Hits album version) playlist postions from 100 spotify playlists\n",
    "\n",
    "data_bucket = []\n",
    "for playlist in dreams_greatesthits:\n",
    "    playlist_tuple = (playlist['playlist']['playlist_id'],playlist['playlist']['name'], playlist['playlist']['position'], \n",
    "                      playlist['playlist']['added_at'], playlist['playlist']['description'], \n",
    "                      playlist['playlist']['official'],playlist['playlist']['followers'])\n",
    "    data_bucket.append(playlist_tuple)\n",
    "    \n",
    "playlists_dreamsgreatesthits = pd.DataFrame(data_bucket, columns=['playlist id', 'playlist name', 'positon', 'add date', 'description', 'official', 'followers'])\n",
    "\n",
    "playlists_dreamsgreatesthits.to_csv('data/dreamsgreatesthits_spotplayl.csv', index='playlist id')\n",
    "playlists_dreamsgreatesthits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists_dreamsgreatesthits = pd.read_csv('data/dreamsgreatesthits_spotplayl.csv', index_col=0)\n",
    "# playlists_dreamsgreatesthits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert pandas series object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert fatetime series to just year-month-day format\n",
    "playlists_dreamsgreatesthits['add date'] = pd.to_datetime(playlists_dreamsgreatesthits['add date'])\n",
    "playlists_dreamsgreatesthits['add date'] = playlists_dreamsgreatesthits['add date'].dt.date\n",
    "# playlists_dreamsgreatesthits['add date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Playlist Add Trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize how many times the track was added to playlists\n",
    "plot=sns.countplot(x=playlists_dreamsgreatesthits['add date'])\n",
    "plot.set_xticklabels(plot.get_xticklabels(), rotation=45);\n",
    "plt.title(\"Spotify Playlist Adds - Dreams - Greatest Hits version\")\n",
    "plt.ylabel(\"# of Adds\")\n",
    "plot.invert_xaxis()\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15, 7)\n",
    "plt.savefig('images/spotify_playlistadds_dreamsgrreatesthits.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists_dreamsgreatesthits.to_csv('data/dreamsgreatesthits_spotplayl.csv', index='playlist id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dreams (Rumours Album)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dreams_rumours = get_track_playlist(api_token, dreamsrumours_cmid, 'spotify', 'current', '2020-09-01', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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",
       "      <th>playlist id</th>\n",
       "      <th>playlist name</th>\n",
       "      <th>positon</th>\n",
       "      <th>add date</th>\n",
       "      <th>description</th>\n",
       "      <th>official</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [playlist id, playlist name, positon, add date, description, official, followers]\n",
       "Index: []"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parse metadata for Dreams (Rumours album version) playlist postions from 100 spotify playlists\n",
    "\n",
    "data_bucket = []\n",
    "for playlist in dreams_rumours:\n",
    "    playlist_tuple = (playlist['playlist']['playlist_id'],playlist['playlist']['name'], playlist['playlist']['position'], \n",
    "                      playlist['playlist']['added_at'], playlist['playlist']['description'], \n",
    "                      playlist['playlist']['official'],playlist['playlist']['followers'])\n",
    "    data_bucket.append(playlist_tuple)\n",
    "    \n",
    "playlists_rumours = pd.DataFrame(data_bucket, columns=['playlist id', 'playlist name', 'positon', 'add date', 'description', 'official', 'followers'])\n",
    "\n",
    "playlists_rumours.to_csv('data/rumours_spotplayl.csv', index='playlist id')\n",
    "playlists_rumours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there's no record of any playlists adding the Rumours version of Dreams, so let's not worry about that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Total Daily Playlist Adds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists_dreamsgreatesthits = pd.read_csv('data/dreamsgreatesthits_spotplayl.csv', index_col=0)\n",
    "dreamsrmstr_spotpl = pd.read_csv('data/dreamsremas_spotplayl.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create New DF that combines the daily playlist adds for each tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the lists to series and get the counts\n",
    "d1 = pd.Series(dreamsrmstr_spotpl['add date'].value_counts())\n",
    "\n",
    "d2 = pd.Series(playlists_dreamsgreatesthits['add date'].value_counts())\n",
    "\n",
    "# #get the counts as a dataframe\n",
    "df=pd.concat([d1,d2],axis=1)\n",
    "df.columns=['Dreams 2004 Remaster','Dreams Greatest Hits']\n",
    "\n",
    "# # # # melt the data frame so it has a \"tidy\" data format\n",
    "df = df.reset_index().melt(id_vars=['index'])\n",
    "df.columns=['date', 'track name', '# of Playlist Adds']\n",
    "\n",
    "# #Fill NaN values with 0\n",
    "df['# of Playlist Adds'] = df['# of Playlist Adds'].fillna(0)\n",
    "\n",
    "# #sort dateframe by date\n",
    "df.sort_values('date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Playlist Adds per Day before:  1.0\n",
      "Median Playlist Adds per Day after:  5.0\n"
     ]
    }
   ],
   "source": [
    "#calculate the median # of playlist adds per day before and after original post\n",
    "pre_viral_post = df.loc[df['date'] < '2020-09-25']\n",
    "post_viral_post = df.loc[df['date'] >= '2020-09-25']\n",
    "\n",
    "print(\"Median Playlist Adds per Day before: \", pre_viral_post['# of Playlist Adds'].median())\n",
    "print(\"Median Playlist Adds per Day after: \", post_viral_post['# of Playlist Adds'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot daily playlist adds using double bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a bar graph and assign track name variable to hue\n",
    "double_plot = sns.barplot(\n",
    "    x='date',\n",
    "    y='# of Playlist Adds',\n",
    "    hue='track name',\n",
    "    data=df,\n",
    "    palette=['blue','red'],\n",
    "    alpha=1,\n",
    "    dodge=True,\n",
    ")\n",
    "\n",
    "for item in double_plot.get_xticklabels():\n",
    "    item.set_rotation(45)\n",
    "    \n",
    "\n",
    "for p in double_plot.patches:\n",
    "    double_plot.annotate(format(p.get_height(), '.0f'), \n",
    "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                   ha = 'center', va = 'center', \n",
    "                   xytext = (0, 9), \n",
    "                   textcoords = 'offset points')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,7)\n",
    "plt.title(\"Spotify Playlist Adds by Day - Fleetwood Mac's Dreams\")\n",
    "plt.savefig('images/spotify_playlistadds_bothdreams.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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",
       "      <th>track name</th>\n",
       "      <th>Median PL Adds Before TikTok</th>\n",
       "      <th>Median PL Adds After TikTok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dreams 2004 Remaster</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dreams Greatest Hits</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             track name  Median PL Adds Before TikTok  \\\n",
       "0  Dreams 2004 Remaster                           0.0   \n",
       "1  Dreams Greatest Hits                           1.5   \n",
       "\n",
       "   Median PL Adds After TikTok  \n",
       "0                          5.0  \n",
       "1                          4.5  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before_tiktok = pd.pivot_table(df.loc[df['date'] < '2020-09-25'], index='track name', \n",
    "                values='# of Playlist Adds', aggfunc='median' ).reset_index()\n",
    "before_tiktok.columns=['track name', 'Median PL Adds Before TikTok']\n",
    "\n",
    "after_tiktok = pd.pivot_table(df.loc[df['date'] >= '2020-09-25'], index='track name', \n",
    "                              values='# of Playlist Adds', aggfunc='median' ).reset_index()\n",
    "after_tiktok.columns=['track name', 'Median PL Adds After TikTok']\n",
    "\n",
    "pivot_table = before_tiktok.merge(after_tiktok, how='outer', on='track name')\n",
    "\n",
    "pivot_table.rename(mapper={'track name':'Track Name', 'Median PL Adds Before TikTok_x': 'Median PL Adds Before TikTok',\n",
    "                          'Median PL Adds After TikTok_y': 'Median PL Adds After TikTok'}, inplace=True)\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Total Daily Playlist Reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "playlists_dreamsgreatesthits = pd.read_csv('data/dreamsgreatesthits_spotplayl.csv', index_col=0)\n",
    "dreamsrmstr_spotpl = pd.read_csv('data/dreamsremas_spotplayl.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create New DF that combines the daily playlist reach for each tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the lists to series and get the counts\n",
    "d1 = dreamsrmstr_spotpl.groupby('add date')['followers'].sum()\n",
    "\n",
    "d2 = playlists_dreamsgreatesthits.groupby('add date')['followers'].sum()\n",
    "\n",
    "# #get the counts as a dataframe\n",
    "df=pd.concat([d1,d2],axis=1)\n",
    "df.columns=['Dreams 2004 Remaster','Dreams Greatest Hits']\n",
    "\n",
    "# # # # melt the data frame so it has a \"tidy\" data format\n",
    "df = df.reset_index().melt(id_vars='index')\n",
    "df.columns=['date', 'track name', 'total reach']\n",
    "\n",
    "# #Fill NaN values with 0\n",
    "df['total reach'] = df['total reach'].fillna(0)\n",
    "\n",
    "# #sort dateframe by date\n",
    "df.sort_values('date', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot daily playlist reach using double bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Total Followers (Daily Reach) - Fleetwood Mac's Dreams\")"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a bar graph and assign track name variable to hue\n",
    "double_plot = sns.barplot(\n",
    "    x='date',\n",
    "    y='total reach',\n",
    "    hue='track name',\n",
    "    data=df,\n",
    "    palette=['blue','red'],\n",
    "    alpha=1,\n",
    "    dodge=True,\n",
    ")\n",
    "\n",
    "for item in double_plot.get_xticklabels():\n",
    "    item.set_rotation(45)\n",
    "    \n",
    "\n",
    "for p in double_plot.patches:\n",
    "    double_plot.annotate(format(p.get_height(), '.0f'), \n",
    "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                   ha = 'center', va = 'center', \n",
    "                   xytext = (0, 9), \n",
    "                   textcoords = 'offset points')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,7)\n",
    "plt.title(\"Total Followers (Daily Reach) - Fleetwood Mac's Dreams\")\n",
    "# plt.savefig('images/spotify_playlistadds_bothdreams.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly, the remastered track had a significantly larger reach than the greatest hits version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Natural Log of Daily Total Reach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace 0s with NaN values and drop those rows cause you can't take the log of zero\n",
    "df = df.replace(0.0, np.nan)\n",
    "df = df.dropna(how='all', axis=0)\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflog = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "dflog['total reach - log(x)'] = dflog['total reach'].apply(lambda x: np.log(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a bar graph and assign track name variable to hue\n",
    "double_plot = sns.barplot(\n",
    "    x='date',\n",
    "    y='total reach - log(x)',\n",
    "    hue='track name',\n",
    "    data=dflog,\n",
    "    palette=['red', 'blue'],\n",
    "    alpha=1,\n",
    "    dodge=True,\n",
    ")\n",
    "double_plot.set_ylim(0,dflog['total reach - log(x)'].max())\n",
    "\n",
    "for item in double_plot.get_xticklabels():\n",
    "    item.set_rotation(45)\n",
    "\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(15,8)\n",
    "plt.title(\"Daily Reach - log(x) - Fleetwood Mac's Dreams\")\n",
    "plt.savefig('images/spotify_dailyreachlog_bothdreams.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pivot Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "before_tiktok_reach = pd.pivot_table(df.loc[df['date'] < '2020-09-25'], index='track name', \n",
    "                values='total reach', aggfunc='median' ).reset_index()\n",
    "before_tiktok_reach.columns=['track name', 'Median PL Reach Before TikTok']\n",
    "\n",
    "after_tiktok_reach = pd.pivot_table(df.loc[df['date'] >= '2020-09-25'], index='track name', \n",
    "                              values='total reach', aggfunc='median' ).reset_index()\n",
    "after_tiktok_reach.columns=['track name', 'Median PL Reach After TikTok']\n",
    "\n",
    "pivot_table_reach = before_tiktok_reach.merge(after_tiktok_reach, how='outer', on='track name')\n",
    "\n",
    "pivot_table_reach.rename(mapper={'Track Name':'Track Name', 'Median PL Reach Before TikTok_x': 'Median PL Reach Before TikTok',\n",
    "                          'Median PL Reach After TikTok_y': 'Median PL Reach After TikTok'}, inplace=True)\n",
    "# pivot_table_reach.set_index('track name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table_reach.set_index('track name', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "median_plot = pivot_table_reach.plot(kind='bar', logy=True, ylabel='Total Reach (log)', table=True,\n",
    "                      figsize=(10,5), )\n",
    "plt.xticks([] )\n",
    "plt.title('Median Spotify Reach Before & After')\n",
    "plt.savefig('images/pt_before_after_median_reach.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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",
       "      <th>playlist id</th>\n",
       "      <th>playlist name</th>\n",
       "      <th>positon</th>\n",
       "      <th>add date</th>\n",
       "      <th>description</th>\n",
       "      <th>official</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37i9dQZEVXbMH2jvi6jvjk</td>\n",
       "      <td>South Africa Top 50</td>\n",
       "      <td>45</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>18626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37i9dQZEVXbK4NvPi6Sxit</td>\n",
       "      <td>India Viral 50</td>\n",
       "      <td>50</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>Your daily update of the most viral tracks in ...</td>\n",
       "      <td>True</td>\n",
       "      <td>12819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37i9dQZEVXbJiyhoAPEfMK</td>\n",
       "      <td>Switzerland Top 50</td>\n",
       "      <td>46</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>113238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0AF4jgCJ48VYmYPMwofk2s</td>\n",
       "      <td>Reels songs you can't get out of your head | T...</td>\n",
       "      <td>15</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>biggest viral hits from instagram reels, updat...</td>\n",
       "      <td>False</td>\n",
       "      <td>31471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37i9dQZEVXbO5MSE9RdfN2</td>\n",
       "      <td>Australia Viral 50</td>\n",
       "      <td>47</td>\n",
       "      <td>2020-10-12</td>\n",
       "      <td>Your daily update of the most viral tracks in ...</td>\n",
       "      <td>True</td>\n",
       "      <td>68300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>70zigMLHC6xxayqgePLGyA</td>\n",
       "      <td>TIKTOK SONGS YOU LOVE ⏳⏳</td>\n",
       "      <td>322</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>tiktok songs, tik tok songs, tiktok playlist, ...</td>\n",
       "      <td>False</td>\n",
       "      <td>155249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>37i9dQZEVXbM8SIrkERIYl</td>\n",
       "      <td>New Zealand Top 50</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-09-27</td>\n",
       "      <td>Your daily update of the most played tracks in...</td>\n",
       "      <td>True</td>\n",
       "      <td>202142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6KYJ17PEQ1af7rvyCNnswy</td>\n",
       "      <td>Clásicos</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-09-23</td>\n",
       "      <td>Clásicos que han inspirado a más de una genera...</td>\n",
       "      <td>False</td>\n",
       "      <td>22814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7y15TCWmOoqxeYyiALg775</td>\n",
       "      <td>Old but gold 💛</td>\n",
       "      <td>837</td>\n",
       "      <td>2020-09-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>48136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0WvTLRAGBHjTMj70Xrrby3</td>\n",
       "      <td>All Out Legacy</td>\n",
       "      <td>296</td>\n",
       "      <td>2020-09-05</td>\n",
       "      <td>From the birth of Rock ’n’ Roll to hippies and...</td>\n",
       "      <td>False</td>\n",
       "      <td>29492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               playlist id                                      playlist name  \\\n",
       "0   37i9dQZEVXbMH2jvi6jvjk                                South Africa Top 50   \n",
       "1   37i9dQZEVXbK4NvPi6Sxit                                     India Viral 50   \n",
       "2   37i9dQZEVXbJiyhoAPEfMK                                 Switzerland Top 50   \n",
       "3   0AF4jgCJ48VYmYPMwofk2s  Reels songs you can't get out of your head | T...   \n",
       "4   37i9dQZEVXbO5MSE9RdfN2                                 Australia Viral 50   \n",
       "..                     ...                                                ...   \n",
       "95  70zigMLHC6xxayqgePLGyA                           TIKTOK SONGS YOU LOVE ⏳⏳   \n",
       "96  37i9dQZEVXbM8SIrkERIYl                                 New Zealand Top 50   \n",
       "97  6KYJ17PEQ1af7rvyCNnswy                                           Clásicos   \n",
       "98  7y15TCWmOoqxeYyiALg775                                     Old but gold 💛   \n",
       "99  0WvTLRAGBHjTMj70Xrrby3                                     All Out Legacy   \n",
       "\n",
       "    positon    add date                                        description  \\\n",
       "0        45  2020-10-12  Your daily update of the most played tracks in...   \n",
       "1        50  2020-10-12  Your daily update of the most viral tracks in ...   \n",
       "2        46  2020-10-12  Your daily update of the most played tracks in...   \n",
       "3        15  2020-10-12  biggest viral hits from instagram reels, updat...   \n",
       "4        47  2020-10-12  Your daily update of the most viral tracks in ...   \n",
       "..      ...         ...                                                ...   \n",
       "95      322  2020-09-28  tiktok songs, tik tok songs, tiktok playlist, ...   \n",
       "96        5  2020-09-27  Your daily update of the most played tracks in...   \n",
       "97        3  2020-09-23  Clásicos que han inspirado a más de una genera...   \n",
       "98      837  2020-09-06                                                NaN   \n",
       "99      296  2020-09-05  From the birth of Rock ’n’ Roll to hippies and...   \n",
       "\n",
       "    official  followers  \n",
       "0       True      18626  \n",
       "1       True      12819  \n",
       "2       True     113238  \n",
       "3      False      31471  \n",
       "4       True      68300  \n",
       "..       ...        ...  \n",
       "95     False     155249  \n",
       "96      True     202142  \n",
       "97     False      22814  \n",
       "98     False      48136  \n",
       "99     False      29492  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dreamsrmstr_spotpl = pd.read_csv('data/dreamsremas_spotplayl.csv', index_col=0)\n",
    "dreamsrmstr_spotpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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",
       "      <th>official</th>\n",
       "      <th>followers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>4511928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>30978536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   official  followers\n",
       "0     False    4511928\n",
       "1      True   30978536"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivottable_offical = pd.pivot_table((dreamsrmstr_spotpl.loc[dreamsrmstr_spotpl['add date'] >= '2020-09-27']), index='official', \n",
    "                values='followers', aggfunc='sum' ).reset_index()\n",
    "pivottable_offical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Reach After TikTok Post')"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myplot = pivottable_offical.plot(kind='bar', logy=True, figsize=(10,8), table=True, fontsize=10)\n",
    "# myplot.table(colLabels=['Unofficial PLaylists', 'Official Playlists'])\n",
    "\n",
    "plt.xticks(ticks=[0,1], labels=['Unoffical Playlists', 'Official Playlists'], rotation='horizontal', )\n",
    "plt.ylabel('Total Reach (log)')\n",
    "plt.title('Total Reach After TikTok Post')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = 0\n",
    "running_total = []\n",
    "for item in date_sorted_dreams['followers']:\n",
    "    counter += item\n",
    "    running_total.append(counter)\n",
    "    \n",
    "date_sorted_dreams['running total'] = running_total\n",
    "\n",
    "date_sorted_dreams.plot(kind='line', x='add date', y='running total', rot=45, figsize=(15,8))\n",
    "plt.yscale('log')\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "362px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}