Sprint2Day4_Exercise.ipynb

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   "source": [
    "# Day 4- Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Prep"
   ]
  },
  {
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   "source": [
    "# read and process the charts dataset\n",
    "charts_df = pd.read_csv('data/spotify_daily_charts.csv')\n",
    "#transform date column into a datetime column\n",
    "charts_df['date'] = pd.to_datetime(charts_df['date'])\n",
    "charts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read and process the tracks dataset\n",
    "tracks_df = pd.read_csv('data/spotify_daily_charts_tracks.csv')\n",
    "tracks_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the charts and tracks dataset\n",
    "df = charts_df.merge(tracks_df, on='track_id', how='left')\n",
    "#\n",
    "df = df.drop(columns='track_name_y')\n",
    "df = df.rename(columns={'track_name_x':'track_name'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. (*Easy- Individual Work*) Among those included in the Spotify charts, pick 1 artist you like to analyze. \n",
    "\n",
    "   a. Plot the streams and positions of their top 5 streamed songs.\n",
    "\n",
    "   b. Compare these charts with streams and positions of what you feel to be a possible collaborator/competitor/related artist. \n",
    "\n",
    "   What insights can you draw from the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code for Part 1 here"
   ]
  },
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   "metadata": {},
   "source": [
    "2. (*Intermediate - Group Work*) A song may be classified as follows:\n",
    "\n",
    "   - **Mainstay** - Song with high streams ($>X_1$ streams) and position ($>P_1$ position) all throughout the year\n",
    "   - **Viral** - Song that reach the peak position fast with high increase in streams ($>X_2$ streams/day),\n",
    "     followed by a rapid decline in position ($P_2$ places/day) and streams ($>X_3$ streams/day)\n",
    "   - **Seasonal** - Song that consistently appear ($>C$ autocorrelation score, or some other metric) OR stay and garner considerable streams ($>X_4$ streams) within a certain season and go into low ranks/ disappear from the chart after the season\n",
    "    \n",
    "   a. In Google Jamboard, discuss among your group how you would define and set values to the thresholds that you will use to classify the songs according to the categories as described above. (You may add more thresholds to refine the definitions, as you see fit)\n",
    "   \n",
    "   b. Using your group's definition in (a) name as many songs as you can per classification and plot their streams and position as a time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paste link to Google Jamboard here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code for Part 2 here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. (*Advanced - Individual, Optional*) What percentage of Spotify charts streams from 2018-2020 are from mainstay songs? viral songs? seasonal songs? songs that do not belong in any of these categories? What does this reveal about the streaming market?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code for Part 3 here"
   ]
  }
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