spotify-analysis/StreamingHistory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## Analysing Spotify Streaming History Data\n",
    "\n",
    "- Using my data streaming data.\n",
    "- Using [`polars`](https://pola.rs) library for analysis.\n",
    "- Columns:\n",
    "    - `endTime`: Repersents the time at which the track ends streaming _(in datatime format)_.\n",
    "    - `artistName`: Name of the artist.\n",
    "    - `trackName`: Name of the song/music.\n",
    "    - `msPlayed`: The time that the track played for _(in milisecond)_.\n",
    "- **WARNING**: Maybe you are not able to see the graphs and plots on **GitHub** that's why you have to download and see it in local."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import typing\n",
    "\n",
    "import polars as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "| ShortCode | Description     |\n",
    "| :-------: | --------------- |\n",
    "|  **T/A**  | Track/Artist    |\n",
    "| **T/As**  | Track/Artist(s) |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some awesome insights\n",
    "\n",
    "- [x] Top T/As in whole dataset\n",
    "- [x] Top T/As in each month\n",
    "- [x] Monthly most listend Tracks and Artists\n",
    "- [x] First day when T/A was played\n",
    "- [x] No.of distinct T/As listened in each month/year\n",
    "- [x] A T/A streaming in barplot (which shows how you stream that during time-to-time)\n",
    "- [x] Which daytime user listen most and whom\n",
    "- [x] Tracks which have listened most times in a day\n",
    "- [x] Tracks streaming streak (by day/week)\n",
    "- [x] T/As which only played once\n",
    "- [ ] Dates when user does not any track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "polars.config.Config"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.Config.set_fmt_str_lengths(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29742, 4)\n"
     ]
    },
    {
     "data": {
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       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>endTime</th><th>artistName</th><th>trackName</th><th>msPlayed</th></tr><tr><td>str</td><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;2022-12-02 03:34&quot;</td><td>&quot;Pritam&quot;</td><td>&quot;Kahani (From &quot;Laal Singh Chaddha&quot;)&quot;</td><td>208539</td></tr><tr><td>&quot;2022-12-02 08:40&quot;</td><td>&quot;Zodiac Wave&quot;</td><td>&quot;Emptiness&quot;</td><td>253100</td></tr><tr><td>&quot;2022-12-02 09:14&quot;</td><td>&quot;Jordan Seigel&quot;</td><td>&quot;The Journey Begins&quot;</td><td>61048</td></tr><tr><td>&quot;2022-12-02 14:47&quot;</td><td>&quot;Anuv Jain&quot;</td><td>&quot;Riha&quot;</td><td>238915</td></tr><tr><td>&quot;2022-12-03 03:59&quot;</td><td>&quot;Piyush Bhisekar&quot;</td><td>&quot;Woh Raaz&quot;</td><td>277866</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "┌──────────────────┬─────────────────┬────────────────────────────────────┬──────────┐\n",
       "│ endTime          ┆ artistName      ┆ trackName                          ┆ msPlayed │\n",
       "│ ---              ┆ ---             ┆ ---                                ┆ ---      │\n",
       "│ str              ┆ str             ┆ str                                ┆ i64      │\n",
       "╞══════════════════╪═════════════════╪════════════════════════════════════╪══════════╡\n",
       "│ 2022-12-02 03:34 ┆ Pritam          ┆ Kahani (From \"Laal Singh Chaddha\") ┆ 208539   │\n",
       "│ 2022-12-02 08:40 ┆ Zodiac Wave     ┆ Emptiness                          ┆ 253100   │\n",
       "│ 2022-12-02 09:14 ┆ Jordan Seigel   ┆ The Journey Begins                 ┆ 61048    │\n",
       "│ 2022-12-02 14:47 ┆ Anuv Jain       ┆ Riha                               ┆ 238915   │\n",
       "│ 2022-12-03 03:59 ┆ Piyush Bhisekar ┆ Woh Raaz                           ┆ 277866   │\n",
       "└──────────────────┴─────────────────┴────────────────────────────────────┴──────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pl.concat(\n",
    "    [\n",
    "        pl.read_json(\"data.arv/Spotify Account Data/StreamingHistory0.json\"),\n",
    "        pl.read_json(\"data.arv/Spotify Account Data/StreamingHistory1.json\"),\n",
    "        pl.read_json(\"data.arv/Spotify-Data-5.Jan/StreamingHistory0.json\"),\n",
    "        pl.read_json(\"data.arv/Spotify-Data-5.Jan/StreamingHistory1.json\"),\n",
    "    ]\n",
    ").unique()\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>endTime</th><th>artistName</th><th>trackName</th><th>msPlayed</th></tr><tr><td>datetime[μs]</td><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>2022-12-02 03:34:00</td><td>&quot;Pritam&quot;</td><td>&quot;Kahani&quot;</td><td>208539</td></tr><tr><td>2022-12-02 08:40:00</td><td>&quot;Zodiac Wave&quot;</td><td>&quot;Emptiness&quot;</td><td>253100</td></tr><tr><td>2022-12-02 09:14:00</td><td>&quot;Jordan Seigel&quot;</td><td>&quot;The Journey Begins&quot;</td><td>61048</td></tr><tr><td>2022-12-02 14:47:00</td><td>&quot;Anuv Jain&quot;</td><td>&quot;Riha&quot;</td><td>238915</td></tr><tr><td>2022-12-03 03:59:00</td><td>&quot;Piyush Bhisekar&quot;</td><td>&quot;Woh Raaz&quot;</td><td>277866</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "┌─────────────────────┬─────────────────┬────────────────────┬──────────┐\n",
       "│ endTime             ┆ artistName      ┆ trackName          ┆ msPlayed │\n",
       "│ ---                 ┆ ---             ┆ ---                ┆ ---      │\n",
       "│ datetime[μs]        ┆ str             ┆ str                ┆ i64      │\n",
       "╞═════════════════════╪═════════════════╪════════════════════╪══════════╡\n",
       "│ 2022-12-02 03:34:00 ┆ Pritam          ┆ Kahani             ┆ 208539   │\n",
       "│ 2022-12-02 08:40:00 ┆ Zodiac Wave     ┆ Emptiness          ┆ 253100   │\n",
       "│ 2022-12-02 09:14:00 ┆ Jordan Seigel   ┆ The Journey Begins ┆ 61048    │\n",
       "│ 2022-12-02 14:47:00 ┆ Anuv Jain       ┆ Riha               ┆ 238915   │\n",
       "│ 2022-12-03 03:59:00 ┆ Piyush Bhisekar ┆ Woh Raaz           ┆ 277866   │\n",
       "└─────────────────────┴─────────────────┴────────────────────┴──────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.with_columns(\n",
    "    pl.col(\"endTime\").str.to_datetime(),\n",
    "    pl.col(\"trackName\")\n",
    "    .str.replace(r\"\\(.*\", \"\")\n",
    "    .str.replace(r\"-.*\", \"\")\n",
    "    .str.strip_chars_end(),\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess `msPlayed`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (8, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>value</th><th>value_min</th></tr><tr><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>166146.338948</td><td>2.769106</td></tr><tr><td>&quot;std&quot;</td><td>104243.160473</td><td>1.737386</td></tr><tr><td>&quot;min&quot;</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;25%&quot;</td><td>90606.0</td><td>1.5101</td></tr><tr><td>&quot;50%&quot;</td><td>184090.0</td><td>3.068167</td></tr><tr><td>&quot;75%&quot;</td><td>227784.0</td><td>3.7964</td></tr><tr><td>&quot;max&quot;</td><td>3.884711e6</td><td>64.745183</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (8, 3)\n",
       "┌────────────┬───────────────┬───────────┐\n",
       "│ statistic  ┆ value         ┆ value_min │\n",
       "│ ---        ┆ ---           ┆ ---       │\n",
       "│ str        ┆ f64           ┆ f64       │\n",
       "╞════════════╪═══════════════╪═══════════╡\n",
       "│ null_count ┆ 0.0           ┆ 0.0       │\n",
       "│ mean       ┆ 166146.338948 ┆ 2.769106  │\n",
       "│ std        ┆ 104243.160473 ┆ 1.737386  │\n",
       "│ min        ┆ 0.0           ┆ 0.0       │\n",
       "│ 25%        ┆ 90606.0       ┆ 1.5101    │\n",
       "│ 50%        ┆ 184090.0      ┆ 3.068167  │\n",
       "│ 75%        ┆ 227784.0      ┆ 3.7964    │\n",
       "│ max        ┆ 3.884711e6    ┆ 64.745183 │\n",
       "└────────────┴───────────────┴───────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_column(\"msPlayed\").describe().filter(\n",
    "    pl.col(\"statistic\").ne(\"count\"),\n",
    ").with_columns(\n",
    "    pl.col(\"value\").truediv(60_000).alias(\"value_min\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%opts magic unavailable (pyparsing cannot be imported)\n",
      "%compositor magic unavailable (pyparsing cannot be imported)\n"
     ]
    },
    {
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i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
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else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
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     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1002'>\n",
       "  <div id=\"c05edb7b-dcd1-4fb7-b273-17df68fd7f5c\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"7764b969-061c-4f13-9749-a6edb84455c7\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"751eeaec029f4f95b76f727c9c65ffd4\",\"client_comm_id\":\"5ed8a2d7660d4abaa3c2e20bda247782\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"7764b969-061c-4f13-9749-a6edb84455c7\",\"roots\":{\"p1002\":\"c05edb7b-dcd1-4fb7-b273-17df68fd7f5c\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
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      ],
      "text/plain": [
       ":Distribution   [msPlayed]   (Density)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1004"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_column(\"msPlayed\").__truediv__(60_000).plot.kde(\"msPlayed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Conclusion!**\n",
    "  - Some tracks have `0ms` playtime.\n",
    "  - Some tracks are podcast too. Because `max` playtime is **64 minutes** long.\n",
    "  - On the other hand, **25% is `1.5min`** and **50% is `3.0min`**.\n",
    "- **Why?**\n",
    "  - The `64min` long tracks can podcast. To tackle this, we have to decide a threshold of playtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<small>shape: (20, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>artistName</th><th>trackName</th><th>msPlayed</th><th>minutesPlayed</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Prakhar Ke Pravachan&quot;</td><td>&quot;Aman Dhattarwal shut me up for 3 hours …</td><td>3884711</td><td>64</td></tr><tr><td>&quot;Talks at Google&quot;</td><td>&quot;Ep231&quot;</td><td>2329387</td><td>38</td></tr><tr><td>&quot;Lex Fridman Podcast&quot;</td><td>&quot;#267 – Mark Zuckerberg: Meta, Facebook,…</td><td>1729189</td><td>28</td></tr><tr><td>&quot;Lex Fridman Podcast&quot;</td><td>&quot;#298 – Susan Cain: The Power of Introve…</td><td>1626436</td><td>27</td></tr><tr><td>&quot;The Open Podcast - Podcast By An Open L…</td><td>&quot;E40 — Third Wave in India: Omicron Saga…</td><td>1232825</td><td>20</td></tr><tr><td>&quot;Dostcast&quot;</td><td>&quot;English Teacher ROASTS Indian Schools |…</td><td>1149724</td><td>19</td></tr><tr><td>&quot;Commander Karan Saxena &quot;</td><td>&quot;Ek Vaigyanik Ki Talash : Chapter 2&quot;</td><td>1074589</td><td>17</td></tr><tr><td>&quot;Commander Karan Saxena &quot;</td><td>&quot;Ek Vaigyanik Ki Talash : Chapter 1&quot;</td><td>1045081</td><td>17</td></tr><tr><td>&quot;Lovemotives&quot;</td><td>&quot;Boost Happiness and Positivity&quot;</td><td>1024000</td><td>17</td></tr><tr><td>&quot;Lovemotives&quot;</td><td>&quot;Boost Happiness and Positivity&quot;</td><td>1024000</td><td>17</td></tr><tr><td>&quot;Talks at Google&quot;</td><td>&quot;Ep231&quot;</td><td>944703</td><td>15</td></tr><tr><td>&quot;A.R. Rahman&quot;</td><td>&quot;Challa&quot;</td><td>922713</td><td>15</td></tr><tr><td>&quot;The Vishal Yadav Show&quot;</td><td>&quot;Rich Dad Poor Dad In Hindi&quot;</td><td>914783</td><td>15</td></tr><tr><td>&quot;Yatharth Geeta (Hindi)&quot;</td><td>&quot;प्रकथन&quot;</td><td>863936</td><td>14</td></tr><tr><td>&quot;Mithoon&quot;</td><td>&quot;Soch Liya&quot;</td><td>848810</td><td>14</td></tr><tr><td>&quot;The Open Podcast - Podcast By An Open L…</td><td>&quot;E39 — The Omicron Saga: Part 1&quot;</td><td>826321</td><td>13</td></tr><tr><td>&quot;Book Reviewer In Urdu/Hindi&quot;</td><td>&quot;The Psychology of Money by Morgan House…</td><td>808806</td><td>13</td></tr><tr><td>&quot;528Hz Whole Body Regeneration&quot;</td><td>&quot;Emotional Cleansing&quot;</td><td>793148</td><td>13</td></tr><tr><td>&quot;528Hz Whole Body Regeneration&quot;</td><td>&quot;Emotional Cleansing&quot;</td><td>793148</td><td>13</td></tr><tr><td>&quot;Abhijeet Srivastava&quot;</td><td>&quot;Chashni&quot;</td><td>792699</td><td>13</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (20, 4)\n",
       "┌───────────────────────────────────┬───────────────────────────────────┬──────────┬───────────────┐\n",
       "│ artistName                        ┆ trackName                         ┆ msPlayed ┆ minutesPlayed │\n",
       "│ ---                               ┆ ---                               ┆ ---      ┆ ---           │\n",
       "│ str                               ┆ str                               ┆ i64      ┆ i64           │\n",
       "╞═══════════════════════════════════╪═══════════════════════════════════╪══════════╪═══════════════╡\n",
       "│ Prakhar Ke Pravachan              ┆ Aman Dhattarwal shut me up for 3  ┆ 3884711  ┆ 64            │\n",
       "│                                   ┆ hours s…                          ┆          ┆               │\n",
       "│ Talks at Google                   ┆ Ep231                             ┆ 2329387  ┆ 38            │\n",
       "│ Lex Fridman Podcast               ┆ #267 – Mark Zuckerberg: Meta,     ┆ 1729189  ┆ 28            │\n",
       "│                                   ┆ Facebook, …                       ┆          ┆               │\n",
       "│ Lex Fridman Podcast               ┆ #298 – Susan Cain: The Power of   ┆ 1626436  ┆ 27            │\n",
       "│                                   ┆ Introver…                         ┆          ┆               │\n",
       "│ The Open Podcast - Podcast By An  ┆ E40 — Third Wave in India:        ┆ 1232825  ┆ 20            │\n",
       "│ Open Le…                          ┆ Omicron Saga …                    ┆          ┆               │\n",
       "│ …                                 ┆ …                                 ┆ …        ┆ …             │\n",
       "│ The Open Podcast - Podcast By An  ┆ E39 — The Omicron Saga: Part 1    ┆ 826321   ┆ 13            │\n",
       "│ Open Le…                          ┆                                   ┆          ┆               │\n",
       "│ Book Reviewer In Urdu/Hindi       ┆ The Psychology of Money by Morgan ┆ 808806   ┆ 13            │\n",
       "│                                   ┆ Housel…                           ┆          ┆               │\n",
       "│ 528Hz Whole Body Regeneration     ┆ Emotional Cleansing               ┆ 793148   ┆ 13            │\n",
       "│ 528Hz Whole Body Regeneration     ┆ Emotional Cleansing               ┆ 793148   ┆ 13            │\n",
       "│ Abhijeet Srivastava               ┆ Chashni                           ┆ 792699   ┆ 13            │\n",
       "└───────────────────────────────────┴───────────────────────────────────┴──────────┴───────────────┘"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top_k streams by=\"msPlayed\"\n",
    "df.top_k(20, by=\"msPlayed\").with_columns(\n",
    "    pl.col(\"msPlayed\").truediv(60_000).cast(int).alias(\"minutesPlayed\"),\n",
    ").drop(\"endTime\").sort(\"msPlayed\", descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>artistName</th><th>trackName</th><th>msPlayed</th></tr><tr><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Prakhar Ke Pravachan&quot;</td><td>&quot;Aman Dhattarwal shut me up for 3 hours …</td><td>3884711</td></tr><tr><td>&quot;Talks at Google&quot;</td><td>&quot;Ep231&quot;</td><td>2329387</td></tr><tr><td>&quot;Lex Fridman Podcast&quot;</td><td>&quot;#267 – Mark Zuckerberg: Meta, Facebook,…</td><td>1729189</td></tr><tr><td>&quot;Lex Fridman Podcast&quot;</td><td>&quot;#298 – Susan Cain: The Power of Introve…</td><td>1626436</td></tr><tr><td>&quot;The Open Podcast - Podcast By An Open L…</td><td>&quot;E40 — Third Wave in India: Omicron Saga…</td><td>1232825</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌───────────────────────────────────────────┬───────────────────────────────────────────┬──────────┐\n",
       "│ artistName                                ┆ trackName                                 ┆ msPlayed │\n",
       "│ ---                                       ┆ ---                                       ┆ ---      │\n",
       "│ str                                       ┆ str                                       ┆ i64      │\n",
       "╞═══════════════════════════════════════════╪═══════════════════════════════════════════╪══════════╡\n",
       "│ Prakhar Ke Pravachan                      ┆ Aman Dhattarwal shut me up for 3 hours s… ┆ 3884711  │\n",
       "│ Talks at Google                           ┆ Ep231                                     ┆ 2329387  │\n",
       "│ Lex Fridman Podcast                       ┆ #267 – Mark Zuckerberg: Meta, Facebook, … ┆ 1729189  │\n",
       "│ Lex Fridman Podcast                       ┆ #298 – Susan Cain: The Power of Introver… ┆ 1626436  │\n",
       "│ The Open Podcast - Podcast By An Open Le… ┆ E40 — Third Wave in India: Omicron Saga … ┆ 1232825  │\n",
       "└───────────────────────────────────────────┴───────────────────────────────────────────┴──────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Streams greater than & equal to 20min\n",
    "df.filter(\n",
    "    pl.col(\"msPlayed\").ge(60_000 * 20),\n",
    ").drop(\"endTime\").sort(\"msPlayed\", descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Conclusion!**\n",
    "  - These long playtime streams are podcasts.\n",
    "  - We can eliminate streams which are greater than `20min` playtime.\n",
    "  - Above you can see that some tracks (songs) have `13-14min` long playtime. Also, I listen some songs which are really 13-14min long.\n",
    "- **Why?**\n",
    "  - Generally podcasts have longer playtime like `1hr+`.\n",
    "  - Podcasts are not tracks or songs, these are a separate category and I am not focusing on podcasts in this notebook.\n",
    "  - Tracks having longer playtime but they seems to be a normal track (song), maybe this happening because the user is streaming with a **(single track repeat mode)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3_170, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>artistName</th><th>trackName</th><th>msPlayed</th></tr><tr><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;The Weeknd&quot;</td><td>&quot;I Heard You’re Married&quot;</td><td>9991</td></tr><tr><td>&quot;Kendrick Lamar&quot;</td><td>&quot;LOVE. FEAT. ZACARI.&quot;</td><td>9986</td></tr><tr><td>&quot;Sachet Tandon&quot;</td><td>&quot;Mehram&quot;</td><td>9984</td></tr><tr><td>&quot;James Krivchenia&quot;</td><td>&quot;The Eternal Spectator&quot;</td><td>9973</td></tr><tr><td>&quot;Bharat Chauhan&quot;</td><td>&quot;Usne Kaha Tha&quot;</td><td>9972</td></tr><tr><td>&quot;Mahendra Kapoor&quot;</td><td>&quot;Tum Agar Saath Dene Ka Vada Karo&quot;</td><td>9961</td></tr><tr><td>&quot;Sachin-Jigar&quot;</td><td>&quot;Apna Bana Le&quot;</td><td>9960</td></tr><tr><td>&quot;Ram Sampath&quot;</td><td>&quot;Yeh Beetey Din&quot;</td><td>9945</td></tr><tr><td>&quot;Anuv Jain&quot;</td><td>&quot;Alag Aasmaan&quot;</td><td>9942</td></tr><tr><td>&quot;Mickey Singh&quot;</td><td>&quot;Feels Like&quot;</td><td>9941</td></tr><tr><td>&quot;Sachet Tandon&quot;</td><td>&quot;Ram Siya Ram&quot;</td><td>9940</td></tr><tr><td>&quot;Aditya Rikhari&quot;</td><td>&quot;Samjho Na&quot;</td><td>9938</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Dream Note&quot;</td><td>&quot;Waqt Ki Baatein&quot;</td><td>0</td></tr><tr><td>&quot;Ayushmann Khurrana&quot;</td><td>&quot;Mere Liye Tum Kaafi Ho&quot;</td><td>0</td></tr><tr><td>&quot;Selena Gomez &amp; The Scene&quot;</td><td>&quot;My Dilemma&quot;</td><td>0</td></tr><tr><td>&quot;Selena Gomez &amp; The Scene&quot;</td><td>&quot;A Year Without Rain&quot;</td><td>0</td></tr><tr><td>&quot;Pritam&quot;</td><td>&quot;Mere Dholna Revisited&quot;</td><td>0</td></tr><tr><td>&quot;Azaan Sami Khan&quot;</td><td>&quot;Jaadugari&quot;</td><td>0</td></tr><tr><td>&quot;Jess Loco&quot;</td><td>&quot;Missing Me&quot;</td><td>0</td></tr><tr><td>&quot;Virginia To Vegas&quot;</td><td>&quot;Malibu&quot;</td><td>0</td></tr><tr><td>&quot;Harnoor&quot;</td><td>&quot;Mamacita&quot;</td><td>0</td></tr><tr><td>&quot;Justin Hurwitz&quot;</td><td>&quot;Mia &amp; Sebastian’s Theme&quot;</td><td>0</td></tr><tr><td>&quot;Anirudh Ravichander&quot;</td><td>&quot;Bae&quot;</td><td>0</td></tr><tr><td>&quot;Piyush Bhisekar&quot;</td><td>&quot;Tu Hai Wajah&quot;</td><td>0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3_170, 3)\n",
       "┌─────────────────────┬─────────────────────────┬──────────┐\n",
       "│ artistName          ┆ trackName               ┆ msPlayed │\n",
       "│ ---                 ┆ ---                     ┆ ---      │\n",
       "│ str                 ┆ str                     ┆ i64      │\n",
       "╞═════════════════════╪═════════════════════════╪══════════╡\n",
       "│ The Weeknd          ┆ I Heard You’re Married  ┆ 9991     │\n",
       "│ Kendrick Lamar      ┆ LOVE. FEAT. ZACARI.     ┆ 9986     │\n",
       "│ Sachet Tandon       ┆ Mehram                  ┆ 9984     │\n",
       "│ James Krivchenia    ┆ The Eternal Spectator   ┆ 9973     │\n",
       "│ Bharat Chauhan      ┆ Usne Kaha Tha           ┆ 9972     │\n",
       "│ …                   ┆ …                       ┆ …        │\n",
       "│ Virginia To Vegas   ┆ Malibu                  ┆ 0        │\n",
       "│ Harnoor             ┆ Mamacita                ┆ 0        │\n",
       "│ Justin Hurwitz      ┆ Mia & Sebastian’s Theme ┆ 0        │\n",
       "│ Anirudh Ravichander ┆ Bae                     ┆ 0        │\n",
       "│ Piyush Bhisekar     ┆ Tu Hai Wajah            ┆ 0        │\n",
       "└─────────────────────┴─────────────────────────┴──────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Streams lesser than & equal to 10sec\n",
    "df.filter(\n",
    "    pl.col(\"msPlayed\").lt(10_000),\n",
    ").drop(\"endTime\").sort(\"msPlayed\", descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Conclusion!**\n",
    "  - These streams lesser than `10sec`.\n",
    "  - We can **eliminate them** from analysis because these will work as outliers.\n",
    "  - These are in very large number (`3.2k`).\n",
    "- **Why?**\n",
    "  - Due to a bad click on tracks.\n",
    "  - Bad track played while shuffle playing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the concluded!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We've dropped 3176\n"
     ]
    }
   ],
   "source": [
    "prev_height = df.height\n",
    "df = df.filter(\n",
    "    # Drop tracks whose playtime is lesser than 10sec\n",
    "    pl.col(\"msPlayed\").gt(10_000),\n",
    "    # Drop tracks whose playtime is greater than 20min\n",
    "    pl.col(\"msPlayed\").lt(60_000 * 20),\n",
    ")\n",
    "\n",
    "new_height = df.height\n",
    "print(f\"We've dropped {prev_height - new_height}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (9, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>value</th><th>value_minutes</th></tr><tr><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>26566.0</td><td>0.442767</td></tr><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>185222.450877</td><td>3.087041</td></tr><tr><td>&quot;std&quot;</td><td>87858.807315</td><td>1.464313</td></tr><tr><td>&quot;min&quot;</td><td>10004.0</td><td>0.166733</td></tr><tr><td>&quot;25%&quot;</td><td>138754.0</td><td>2.312567</td></tr><tr><td>&quot;50%&quot;</td><td>193606.0</td><td>3.226767</td></tr><tr><td>&quot;75%&quot;</td><td>233386.0</td><td>3.889767</td></tr><tr><td>&quot;max&quot;</td><td>1.149724e6</td><td>19.162067</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (9, 3)\n",
       "┌────────────┬───────────────┬───────────────┐\n",
       "│ statistic  ┆ value         ┆ value_minutes │\n",
       "│ ---        ┆ ---           ┆ ---           │\n",
       "│ str        ┆ f64           ┆ f64           │\n",
       "╞════════════╪═══════════════╪═══════════════╡\n",
       "│ count      ┆ 26566.0       ┆ 0.442767      │\n",
       "│ null_count ┆ 0.0           ┆ 0.0           │\n",
       "│ mean       ┆ 185222.450877 ┆ 3.087041      │\n",
       "│ std        ┆ 87858.807315  ┆ 1.464313      │\n",
       "│ min        ┆ 10004.0       ┆ 0.166733      │\n",
       "│ 25%        ┆ 138754.0      ┆ 2.312567      │\n",
       "│ 50%        ┆ 193606.0      ┆ 3.226767      │\n",
       "│ 75%        ┆ 233386.0      ┆ 3.889767      │\n",
       "│ max        ┆ 1.149724e6    ┆ 19.162067     │\n",
       "└────────────┴───────────────┴───────────────┘"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_column(\"msPlayed\").describe().with_columns(\n",
    "    pl.col(\"value\").truediv(60_000).name.suffix(\"_minutes\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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.pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1070\"},{\"id\":\"p1068\"},{\"id\":\"p1069\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"42b7fd6f-023b-4a63-bfb5-9cb1a4068bcb\",\"roots\":{\"p1067\":\"a3f91b08-f3d3-4508-a627-6e9239102044\"},\"root_ids\":[\"p1067\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Distribution   [msPlayed]   (Density)"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1067"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_column(\"msPlayed\").__truediv__(60_000).plot.kde(\"msPlayed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datetime range of dataset: (02 January, 2022) — (02 December, 2023)\n"
     ]
    }
   ],
   "source": [
    "_start = df[\"endTime\"].dt.date().min()\n",
    "_end = df[\"endTime\"].dt.date().max()\n",
    "\n",
    "print(f\"Datetime range of dataset: ({_start:%d %B, %Y}) — ({_end:%d %B, %Y})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distinct T/As present in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>artistName</th><th>trackName</th><th>uniqueTrackName</th></tr><tr><td>u32</td><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>1541</td><td>3911</td><td>4180</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 3)\n",
       "┌────────────┬───────────┬─────────────────┐\n",
       "│ artistName ┆ trackName ┆ uniqueTrackName │\n",
       "│ ---        ┆ ---       ┆ ---             │\n",
       "│ u32        ┆ u32       ┆ u32             │\n",
       "╞════════════╪═══════════╪═════════════════╡\n",
       "│ 1541       ┆ 3911      ┆ 4180            │\n",
       "└────────────┴───────────┴─────────────────┘"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eg: 1234 tracks are played by the user\n",
    "df.select(\n",
    "    pl.col(\"artistName\").n_unique(),\n",
    "    pl.col(\"trackName\").n_unique(),\n",
    "    # Some track's (song's) name are same\n",
    "    # To create a distinction use both (artistName and trackName) for n_unique count\n",
    "    pl.col(\"artistName\").add(pl.col(\"trackName\")).n_unique().alias(\"uniqueTrackName\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No. of unique T/As (monthly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (24, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>year</th><th>month</th><th>artistName</th><th>trackName</th><th>uniqueTrackName</th></tr><tr><td>i32</td><td>i8</td><td>u32</td><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>2022</td><td>1</td><td>213</td><td>370</td><td>374</td></tr><tr><td>2022</td><td>2</td><td>155</td><td>267</td><td>269</td></tr><tr><td>2022</td><td>3</td><td>115</td><td>168</td><td>171</td></tr><tr><td>2022</td><td>4</td><td>245</td><td>364</td><td>383</td></tr><tr><td>2022</td><td>5</td><td>178</td><td>300</td><td>304</td></tr><tr><td>2022</td><td>6</td><td>179</td><td>374</td><td>378</td></tr><tr><td>2022</td><td>7</td><td>272</td><td>527</td><td>542</td></tr><tr><td>2022</td><td>8</td><td>210</td><td>433</td><td>443</td></tr><tr><td>2022</td><td>9</td><td>192</td><td>374</td><td>381</td></tr><tr><td>2022</td><td>10</td><td>209</td><td>367</td><td>376</td></tr><tr><td>2022</td><td>11</td><td>229</td><td>397</td><td>403</td></tr><tr><td>2022</td><td>12</td><td>202</td><td>405</td><td>424</td></tr><tr><td>2023</td><td>1</td><td>396</td><td>733</td><td>748</td></tr><tr><td>2023</td><td>2</td><td>243</td><td>473</td><td>481</td></tr><tr><td>2023</td><td>3</td><td>282</td><td>597</td><td>618</td></tr><tr><td>2023</td><td>4</td><td>218</td><td>441</td><td>449</td></tr><tr><td>2023</td><td>5</td><td>228</td><td>405</td><td>409</td></tr><tr><td>2023</td><td>6</td><td>164</td><td>382</td><td>387</td></tr><tr><td>2023</td><td>7</td><td>264</td><td>594</td><td>604</td></tr><tr><td>2023</td><td>8</td><td>238</td><td>625</td><td>634</td></tr><tr><td>2023</td><td>9</td><td>308</td><td>824</td><td>837</td></tr><tr><td>2023</td><td>10</td><td>259</td><td>646</td><td>654</td></tr><tr><td>2023</td><td>11</td><td>182</td><td>457</td><td>461</td></tr><tr><td>2023</td><td>12</td><td>38</td><td>72</td><td>72</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (24, 5)\n",
       "┌──────┬───────┬────────────┬───────────┬─────────────────┐\n",
       "│ year ┆ month ┆ artistName ┆ trackName ┆ uniqueTrackName │\n",
       "│ ---  ┆ ---   ┆ ---        ┆ ---       ┆ ---             │\n",
       "│ i32  ┆ i8    ┆ u32        ┆ u32       ┆ u32             │\n",
       "╞══════╪═══════╪════════════╪═══════════╪═════════════════╡\n",
       "│ 2022 ┆ 1     ┆ 213        ┆ 370       ┆ 374             │\n",
       "│ 2022 ┆ 2     ┆ 155        ┆ 267       ┆ 269             │\n",
       "│ 2022 ┆ 3     ┆ 115        ┆ 168       ┆ 171             │\n",
       "│ 2022 ┆ 4     ┆ 245        ┆ 364       ┆ 383             │\n",
       "│ 2022 ┆ 5     ┆ 178        ┆ 300       ┆ 304             │\n",
       "│ …    ┆ …     ┆ …          ┆ …         ┆ …               │\n",
       "│ 2023 ┆ 8     ┆ 238        ┆ 625       ┆ 634             │\n",
       "│ 2023 ┆ 9     ┆ 308        ┆ 824       ┆ 837             │\n",
       "│ 2023 ┆ 10    ┆ 259        ┆ 646       ┆ 654             │\n",
       "│ 2023 ┆ 11    ┆ 182        ┆ 457       ┆ 461             │\n",
       "│ 2023 ┆ 12    ┆ 38         ┆ 72        ┆ 72              │\n",
       "└──────┴───────┴────────────┴───────────┴─────────────────┘"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.group_by(\n",
    "        pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "        pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "    )\n",
    "    .agg(\n",
    "        pl.col(\"artistName\", \"trackName\").n_unique(),\n",
    "        pl.col(\"artistName\")\n",
    "        .add(pl.col(\"trackName\"))\n",
    "        .n_unique()\n",
    "        .alias(\"uniqueTrackName\"),\n",
    "    )\n",
    "    .sort(\"year\", \"month\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top T/As in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1130'>\n",
       "  <div id=\"fceeb006-aeb7-40f6-94b2-692442e45ab7\" data-root-id=\"p1130\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"c60e71be-c6e1-4a94-9cff-6050689afb64\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"p1130\",\"attributes\":{\"name\":\"Row01265\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 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Kumar\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1136\",\"attributes\":{\"tags\":[[[\"msPlayed\",\"msPlayed\",null]],{\"type\":\"map\",\"entries\":[[\"invert_yaxis\",false],[\"autorange\",false]]}],\"end\":2423.2,\"reset_start\":0.0,\"reset_end\":2423.2}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1152\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1153\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1145\",\"attributes\":{\"text\":\"Top 10 artists\",\"text_color\":\"black\",\"text_font_size\":\"12pt\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1182\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1173\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1174\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1175\"},\"data\":{\"type\":\"map\",\"entries\":[[\"artistName\",[\"Prateek Kuhad\",\"Bharat Chauhan\",\"Piyush Bhisekar\",\"Pritam\",\"Bayaan\",\"Taylor Swift\",\"Big Thief\",\"The Weeknd\",\"Suzonn\",\"Kishore Kumar\"]],[\"msPlayed\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAAAGokAAAAAAAPqgQAAAAAAA5qBAAAAAAAAsmUAAAAAAAKiXQAAAAAAAMJdAAAAAAACwlkAAAAAAAPiVQAAAAAAAZJJAAAAAAADkkUA=\"},\"shape\":[10],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1183\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1184\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1179\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"artistName\"},\"width\":{\"type\":\"value\",\"value\":0.8},\"top\":{\"type\":\"field\",\"field\":\"msPlayed\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"hatch_color\":{\"type\":\"value\",\"value\":\"#30a2da\"}}},\"selection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1185\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"artistName\"},\"width\":{\"type\":\"value\",\"value\":0.8},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"msPlayed\"},\"line_color\":{\"type\":\"value\",\"value\":\"black\"},\"line_alpha\":{\"type\":\"value\",\"value\":1.0},\"line_width\":{\"type\":\"value\",\"value\":1},\"line_join\":{\"type\":\"value\",\"value\":\"bevel\"},\"line_cap\":{\"type\":\"value\",\"value\":\"butt\"},\"line_dash\":{\"type\":\"value\",\"value\":[]},\"line_dash_offset\":{\"type\":\"value\",\"value\":0},\"fill_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"fill_alpha\":{\"type\":\"value\",\"value\":1.0},\"hatch_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":1.0},\"hatch_scale\":{\"type\":\"value\",\"value\":12.0},\"hatch_pattern\":{\"type\":\"value\",\"value\":null},\"hatch_weight\":{\"type\":\"value\",\"value\":1.0}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1180\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"artistName\"},\"width\":{\"type\":\"value\",\"value\":0.8},\"top\":{\"type\":\"field\",\"field\":\"msPlayed\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1181\",\"attributes\":{\"tags\":[\"apply_ranges\"],\"x\":{\"type\":\"field\",\"field\":\"artistName\"},\"width\":{\"type\":\"value\",\"value\":0.8},\"top\":{\"type\":\"field\",\"field\":\"msPlayed\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"value\",\"value\":\"#30a2da\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1151\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1140\",\"attributes\":{\"tags\":[\"hv_created\"],\"renderers\":\"auto\",\"zoom_together\":\"none\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1141\",\"attributes\":{\"tags\":[\"hv_created\"],\"renderers\":[{\"id\":\"p1182\"}],\"tooltips\":[[\"artistName\",\"@{artistName}\"],[\"msPlayed\",\"@{msPlayed}\"]]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1164\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1165\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1166\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1167\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1172\"}],\"active_drag\":{\"id\":\"p1165\"},\"active_scroll\":{\"id\":\"p1140\"}}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1159\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1160\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1161\"},\"axis_label\":\"msPlayed\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1162\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1154\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1155\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1156\"},\"axis_label\":\"artistName\",\"major_label_orientation\":0.7853981633974483,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1157\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1158\",\"attributes\":{\"axis\":{\"id\":\"p1154\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1163\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1159\"},\"grid_line_color\":null}}],\"min_border_top\":10,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"output_backend\":\"webgl\"}},{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1187\",\"attributes\":{\"name\":\"HSpacer01273\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1133\"},{\"id\":\"p1131\"},{\"id\":\"p1132\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"c60e71be-c6e1-4a94-9cff-6050689afb64\",\"roots\":{\"p1130\":\"fceeb006-aeb7-40f6-94b2-692442e45ab7\"},\"root_ids\":[\"p1130\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Bars   [artistName]   (msPlayed)"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1130"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top artists in dataset\n",
    "(\n",
    "    df.group_by(\"artistName\")\n",
    "    .agg(\n",
    "        pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "    )\n",
    "    .top_k(10, by=\"msPlayed\")\n",
    "    .plot.bar(\"artistName\", \"msPlayed\", title=\"Top 10 artists\", rot=45)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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       "</div>\n",
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\"axis_label\":\"trackName\",\"major_label_orientation\":0.7853981633974483,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1219\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1220\",\"attributes\":{\"axis\":{\"id\":\"p1216\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1225\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1221\"},\"grid_line_color\":null}}],\"min_border_top\":10,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"output_backend\":\"webgl\"}},{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1249\",\"attributes\":{\"name\":\"HSpacer01383\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1195\"},{\"id\":\"p1193\"},{\"id\":\"p1194\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}}]}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"42b89799-4cfe-4f01-b17b-a312c8ebe873\",\"roots\":{\"p1192\":\"ec41b14d-2715-49f7-a431-e213484325e4\"},\"root_ids\":[\"p1192\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Bars   [trackName]   (msPlayed)"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1192"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top tracks in dataset\n",
    "(\n",
    "    df.group_by(\"artistName\", \"trackName\")\n",
    "    .agg(\n",
    "        pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "    )\n",
    "    .top_k(10, by=\"msPlayed\")\n",
    "    .plot.bar(\n",
    "        \"trackName\",\n",
    "        \"msPlayed\",\n",
    "        title=\"Top 10 tracks\",\n",
    "        rot=45,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Monthly top T/As"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_monthly_top(\n",
    "    df: pl.DataFrame,\n",
    "    name: typing.Literal[\"artistName\", \"trackName\"],\n",
    "    year: int,\n",
    "    month: int,\n",
    "):\n",
    "    \"\"\"Plot monthly top track or artist. Just choose year and month.\"\"\"\n",
    "    return (\n",
    "        df.group_by(\n",
    "            pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "            pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "            name,\n",
    "        )\n",
    "        .agg(\n",
    "            pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "        )\n",
    "        .sort(\"year\", \"month\", \"msPlayed\", descending=True)\n",
    "        .group_by(\"year\", \"month\")\n",
    "        .map_groups(lambda x: x.limit(10))\n",
    "        .filter(\n",
    "            pl.col(\"year\").eq(year),\n",
    "            pl.col(\"month\").eq(month),\n",
    "        )\n",
    "        .plot.bar(\n",
    "            name,\n",
    "            \"msPlayed\",\n",
    "            title=f\"Top 10 tracks in (Month {month})\",\n",
    "            rot=45,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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       "  var render_items = [{\"docid\":\"d22247ec-e9be-40cb-a4bc-352d99479da9\",\"roots\":{\"p1254\":\"f77d7023-380e-4f58-b236-4147ec324414\"},\"root_ids\":[\"p1254\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
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       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
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       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
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       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
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       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Bars   [artistName]   (msPlayed)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1254"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_monthly_top(df, \"artistName\", 2023, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First day when T/A was played"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def streamed_first_time(\n",
    "    df: pl.DataFrame,\n",
    "    *,\n",
    "    track: str | None = None,\n",
    "    artist: str | None = None,\n",
    "):\n",
    "    \"\"\"Check when you stream that track/artist first time.\"\"\"\n",
    "    if track is None and artist is None:\n",
    "        raise ValueError(\"Must pass either track or artist, or both.\")\n",
    "    if track:\n",
    "        df = df.filter(pl.col(\"trackName\").eq(track))\n",
    "    if artist:\n",
    "        df = df.filter(pl.col(\"artistName\").eq(artist))\n",
    "    return df[\"endTime\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 7, 28, 5, 8)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streamed_first_time(df, track=\"Thank You\", artist=\"Dido\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A T/A streaming graph which shows how you stream them\n",
    "\n",
    "- Day-by-Day\n",
    "- Week-by-Week\n",
    "- Month-by-Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas 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       "  var render_items = [{\"docid\":\"cca205f3-be16-4f30-9d30-0306fdd1bd83\",\"roots\":{\"p1316\":\"e4eafd7b-b751-429b-aab0-c6c9b0a6edd8\"},\"root_ids\":[\"p1316\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Bars   [month]   (msPlayed)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1316"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"trackName\").eq(\"Bloom\"),\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "    )\n",
    "    .agg(\n",
    "        pl.sum(\"msPlayed\").truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"month\")\n",
    "    .plot.bar(\"month\", \"msPlayed\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Daytime insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract \"daytime\" feature from \"endTime\"\n",
    "df = df.with_columns(\n",
    "    pl.col(\"endTime\")\n",
    "    .dt.hour()\n",
    "    .cut(\n",
    "        # These breaks & labels are from my POV\n",
    "        breaks=[0, 4, 10, 15, 19, 23],\n",
    "        labels=[\n",
    "            \"Night\",\n",
    "            \"Mid-Night\",\n",
    "            \"Morning\",\n",
    "            \"Afternoon\",\n",
    "            \"Evening\",\n",
    "            \"Study-Hour\",\n",
    "            \"Night\",\n",
    "        ],\n",
    "        left_closed=True,\n",
    "    )\n",
    "    # .cast(pl.Categorical)  # TODO: Learn how to do ordering on Catergorical dtype\n",
    "    .alias(\"daytime\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming pattern of T/A during `\"daytime\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.holoviews_exec.v0+json": "",
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       "</div>\n",
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       "  var render_items = [{\"docid\":\"f150dd44-d89a-4e7e-9cae-e439ce5e23d7\",\"roots\":{\"p1378\":\"cc6a1ad4-b94e-4e80-bc4f-aae544bf23e9\"},\"root_ids\":[\"p1378\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":Curve   [daytime]   (msPlayed)"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1378"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"artistName\").eq(\"Mukesh\"),\n",
    "    )\n",
    "    .group_by(\"daytime\")\n",
    "    .agg(\n",
    "        pl.sum(\"msPlayed\").truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"daytime\")\n",
    "    .plot.line(\"daytime\", \"msPlayed\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming pattern of T/As during `\"daytime\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "artists = [\n",
    "    \"Mukesh\",\n",
    "    \"Kishore Kumar\",\n",
    "    \"Lata Mangeshkar\",\n",
    "    \"Salma Agha\",\n",
    "    \"Amit Trivedi\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"3733986a-e522-4c36-bf74-2f263324005b\",\"roots\":{\"p1440\":\"bb5bd1d9-bb7c-435e-b211-8487e0c03b2b\"},\"root_ids\":[\"p1440\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [artistName]\n",
       "   :Curve   [daytime]   (msPlayed)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1440"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"artistName\").is_in(artists),\n",
    "    )\n",
    "    .group_by(\"artistName\", \"daytime\")\n",
    "    .agg(\n",
    "        pl.sum(\"msPlayed\").truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"daytime\")\n",
    "    .plot.line(\"daytime\", \"msPlayed\", by=\"artistName\", legend=\"top\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracks = [\n",
    "    \"Bloom\",\n",
    "    \"Awaraa Ho\",\n",
    "    \"Shauq\",\n",
    "    \"Dil Mere\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1576'>\n",
       "  <div id=\"e88fbbad-fbad-465c-a52b-bf0c5c69aba6\" data-root-id=\"p1576\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"84df6df6-cb2f-4dbf-b1ec-132425d23628\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"p1576\",\"attributes\":{\"name\":\"Row02130\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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sicTickFormatter\",\"id\":\"p1613\"},\"axis_label\":\"msPlayed\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1614\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1637\",\"attributes\":{\"location\":[0,0],\"orientation\":\"horizontal\",\"title\":\"trackName\",\"click_policy\":\"mute\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1638\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Bloom\"},\"renderers\":[{\"id\":\"p1634\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1652\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Dil Mere\"},\"renderers\":[{\"id\":\"p1649\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1666\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Shauq\"},\"renderers\":[{\"id\":\"p1663\"}]}}]}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1606\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1607\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1608\"},\"axis_label\":\"daytime\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1609\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1610\",\"attributes\":{\"axis\":{\"id\":\"p1606\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1615\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1611\"},\"grid_line_color\":null}}],\"min_border_top\":10,\"min_border_bottom\":10,\"min_border_left\":10,\"min_border_right\":10,\"output_backend\":\"webgl\"}},{\"type\":\"object\",\"name\":\"Spacer\",\"id\":\"p1669\",\"attributes\":{\"name\":\"HSpacer02138\",\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n  background-size: auto calc(min(50%, 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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"84df6df6-cb2f-4dbf-b1ec-132425d23628\",\"roots\":{\"p1576\":\"e88fbbad-fbad-465c-a52b-bf0c5c69aba6\"},\"root_ids\":[\"p1576\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [trackName]\n",
       "   :Curve   [daytime]   (msPlayed)"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1576"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"trackName\").is_in(tracks),\n",
    "    )\n",
    "    .group_by(\"trackName\", \"daytime\")\n",
    "    .agg(\n",
    "        pl.sum(\"msPlayed\").truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"daytime\")\n",
    "    .plot.line(\"daytime\", \"msPlayed\", by=\"trackName\", legend=\"top\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming time series\n",
    "\n",
    "- By Day\n",
    "- By Week\n",
    "- By Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p1676'>\n",
       "  <div id=\"a5c2786d-50d3-489e-9313-93598e6a66fe\" data-root-id=\"p1676\" style=\"display: contents;\"></div>\n",
       "</div>\n",
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       "  var render_items = [{\"docid\":\"0c9ca08a-b121-4863-9729-6a175e325d67\",\"roots\":{\"p1676\":\"a5c2786d-50d3-489e-9313-93598e6a66fe\"},\"root_ids\":[\"p1676\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [month]\n",
       "   :Curve   [date]   (msPlayed)"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1676"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.date().alias(\"date\"),\n",
    "        pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "    )\n",
    "    .agg(\n",
    "        pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "    )\n",
    "    .plot.line(\"date\", \"msPlayed\", by=\"month\", width=1000)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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       "  <div id=\"c2acef8e-bb52-4a75-91a6-ab4c0f4db796\" data-root-id=\"p1962\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
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       "  var render_items = [{\"docid\":\"c15c9d4e-71d3-4afe-9a40-8bc0d20d4d38\",\"roots\":{\"p1962\":\"c2acef8e-bb52-4a75-91a6-ab4c0f4db796\"},\"root_ids\":[\"p1962\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [year]\n",
       "   :Curve   [ordinal_day]   (msPlayed)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1962"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.group_by(\n",
    "        pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "        pl.col(\"endTime\").dt.ordinal_day().alias(\"ordinal_day\"),\n",
    "    )\n",
    "    .agg(\n",
    "        pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"ordinal_day\")\n",
    "    .plot.line(\n",
    "        \"ordinal_day\",\n",
    "        \"msPlayed\",\n",
    "        by=\"year\",\n",
    "        xlabel=\"Day Of Year\",\n",
    "        ylabel=\"Tracks Streams (in Min.)\",\n",
    "        width=1000,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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       "  <div id=\"fb4b952a-2140-444d-9d16-971df18599b5\" data-root-id=\"p2046\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"64c9f051-310f-4a45-b048-a347f791aaa0\",\"roots\":{\"p2046\":\"fb4b952a-2140-444d-9d16-971df18599b5\"},\"root_ids\":[\"p2046\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [year]\n",
       "   :Curve   [month]   (msPlayed)"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p2046"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.group_by(\n",
    "        pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "        pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "    )\n",
    "    .agg(\n",
    "        pl.col(\"msPlayed\").sum().truediv(60_000).ceil(),\n",
    "    )\n",
    "    .sort(\"month\")\n",
    "    .plot.line(\n",
    "        \"month\",\n",
    "        \"msPlayed\",\n",
    "        by=\"year\",\n",
    "        ylabel=\"Tracks Streams (in Min.)\",\n",
    "        width=1000,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p2130'>\n",
       "  <div id=\"cd4cc24c-3a14-4100-b42e-309af245f89d\" data-root-id=\"p2130\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"5edfb8d8-39fb-4661-8dd3-762665d44e71\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Row\",\"id\":\"p2130\",\"attributes\":{\"name\":\"Row03086\",\"tags\":[\"embedded\"],\"stylesheets\":[\"\\n:host(.pn-loading.pn-arc):before, .pn-loading.pn-arc:before {\\n  background-image: 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       "  var render_items = [{\"docid\":\"5edfb8d8-39fb-4661-8dd3-762665d44e71\",\"roots\":{\"p2130\":\"cd4cc24c-3a14-4100-b42e-309af245f89d\"},\"root_ids\":[\"p2130\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [year]\n",
       "   :Curve   [month]   (len)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p2130"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.group_by(\n",
    "        pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "        pl.col(\"endTime\").dt.month().alias(\"month\"),\n",
    "    )\n",
    "    .len()\n",
    "    .sort(\"month\")\n",
    "    .plot.line(\"month\", \"len\", by=\"year\", ylabel=\"No. of Tracks\", width=1000)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='p2214'>\n",
       "  <div id=\"d6631b97-7c77-4f22-95cd-2f9cec299011\" data-root-id=\"p2214\" style=\"display: contents;\"></div>\n",
       "</div>\n",
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       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [year]\n",
       "   :Curve   [week]   (len)"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p2214"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.group_by(\n",
    "        pl.col(\"endTime\").dt.year().alias(\"year\"),\n",
    "        pl.col(\"endTime\").dt.week().alias(\"week\"),\n",
    "    )\n",
    "    .len()\n",
    "    .sort(\"week\")\n",
    "    .plot.line(\"week\", \"len\", by=\"year\", ylabel=\"No. of Tracks\", width=1000)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracks/Artists Streaming Count\n",
    "\n",
    "- By Day\n",
    "- By Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>trackName</th><th>len</th></tr><tr><td>date</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>2023-05-11</td><td>&quot;Tu Hi Bataa&quot;</td><td>9</td></tr><tr><td>2023-08-28</td><td>&quot;Deathcab&quot;</td><td>7</td></tr><tr><td>2023-08-26</td><td>&quot;A Love of Some Kind&quot;</td><td>6</td></tr><tr><td>2023-06-25</td><td>&quot;Kya Dekhu&quot;</td><td>6</td></tr><tr><td>2023-08-24</td><td>&quot;Madhubala&quot;</td><td>6</td></tr><tr><td>2023-06-28</td><td>&quot;Aasaan Nahin Hota&quot;</td><td>6</td></tr><tr><td>2023-10-27</td><td>&quot;Ik Dooje Ke Liye&quot;</td><td>6</td></tr><tr><td>2023-05-22</td><td>&quot;Humara Ho Gaya&quot;</td><td>5</td></tr><tr><td>2023-09-09</td><td>&quot;Sapna&quot;</td><td>5</td></tr><tr><td>2023-09-15</td><td>&quot;Faasle&quot;</td><td>5</td></tr><tr><td>2023-08-30</td><td>&quot;Roshni Ka Sira&quot;</td><td>5</td></tr><tr><td>2023-03-28</td><td>&quot;Mera Musafir&quot;</td><td>5</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 3)\n",
       "┌────────────┬─────────────────────┬─────┐\n",
       "│ date       ┆ trackName           ┆ len │\n",
       "│ ---        ┆ ---                 ┆ --- │\n",
       "│ date       ┆ str                 ┆ u32 │\n",
       "╞════════════╪═════════════════════╪═════╡\n",
       "│ 2023-05-11 ┆ Tu Hi Bataa         ┆ 9   │\n",
       "│ 2023-08-28 ┆ Deathcab            ┆ 7   │\n",
       "│ 2023-08-26 ┆ A Love of Some Kind ┆ 6   │\n",
       "│ 2023-06-25 ┆ Kya Dekhu           ┆ 6   │\n",
       "│ 2023-08-24 ┆ Madhubala           ┆ 6   │\n",
       "│ …          ┆ …                   ┆ …   │\n",
       "│ 2023-05-22 ┆ Humara Ho Gaya      ┆ 5   │\n",
       "│ 2023-09-09 ┆ Sapna               ┆ 5   │\n",
       "│ 2023-09-15 ┆ Faasle              ┆ 5   │\n",
       "│ 2023-08-30 ┆ Roshni Ka Sira      ┆ 5   │\n",
       "│ 2023-03-28 ┆ Mera Musafir        ┆ 5   │\n",
       "└────────────┴─────────────────────┴─────┘"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tracks streaming (by day)\n",
    "# TODO: which type of plot/graph\n",
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.date().alias(\"date\"),\n",
    "        \"artistName\",\n",
    "        \"trackName\",\n",
    "    )\n",
    "    .len()\n",
    "    .filter(pl.col(\"len\").ge(5))\n",
    "    .group_by(\"len\")\n",
    "    .map_groups(lambda x: x.head())\n",
    "    .sort(\"len\", descending=True)\n",
    "    .select(\"date\", \"trackName\", \"len\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (17, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>artistName</th><th>len</th></tr><tr><td>date</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>2023-06-25</td><td>&quot;Osho Jain&quot;</td><td>27</td></tr><tr><td>2023-03-28</td><td>&quot;Bayaan&quot;</td><td>27</td></tr><tr><td>2023-08-25</td><td>&quot;Big Thief&quot;</td><td>26</td></tr><tr><td>2023-08-10</td><td>&quot;Piyush Bhisekar&quot;</td><td>25</td></tr><tr><td>2023-10-27</td><td>&quot;Ankur Tewari&quot;</td><td>23</td></tr><tr><td>2023-09-19</td><td>&quot;Jagjit Singh&quot;</td><td>22</td></tr><tr><td>2023-08-05</td><td>&quot;Prateek Kuhad&quot;</td><td>22</td></tr><tr><td>2023-06-23</td><td>&quot;Big Thief&quot;</td><td>22</td></tr><tr><td>2023-07-06</td><td>&quot;Tajdar Junaid&quot;</td><td>21</td></tr><tr><td>2023-07-11</td><td>&quot;Tanmaya Bhatnagar&quot;</td><td>21</td></tr><tr><td>2023-09-01</td><td>&quot;Adrianne Lenker&quot;</td><td>21</td></tr><tr><td>2023-06-19</td><td>&quot;Big Thief&quot;</td><td>21</td></tr><tr><td>2023-09-09</td><td>&quot;Bayaan&quot;</td><td>21</td></tr><tr><td>2023-08-22</td><td>&quot;AMAN&quot;</td><td>20</td></tr><tr><td>2023-05-05</td><td>&quot;Shashwat Sachdev&quot;</td><td>20</td></tr><tr><td>2023-11-11</td><td>&quot;Bharat Chauhan&quot;</td><td>20</td></tr><tr><td>2023-03-02</td><td>&quot;Suzonn&quot;</td><td>20</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (17, 3)\n",
       "┌────────────┬──────────────────┬─────┐\n",
       "│ date       ┆ artistName       ┆ len │\n",
       "│ ---        ┆ ---              ┆ --- │\n",
       "│ date       ┆ str              ┆ u32 │\n",
       "╞════════════╪══════════════════╪═════╡\n",
       "│ 2023-06-25 ┆ Osho Jain        ┆ 27  │\n",
       "│ 2023-03-28 ┆ Bayaan           ┆ 27  │\n",
       "│ 2023-08-25 ┆ Big Thief        ┆ 26  │\n",
       "│ 2023-08-10 ┆ Piyush Bhisekar  ┆ 25  │\n",
       "│ 2023-10-27 ┆ Ankur Tewari     ┆ 23  │\n",
       "│ …          ┆ …                ┆ …   │\n",
       "│ 2023-09-09 ┆ Bayaan           ┆ 21  │\n",
       "│ 2023-08-22 ┆ AMAN             ┆ 20  │\n",
       "│ 2023-05-05 ┆ Shashwat Sachdev ┆ 20  │\n",
       "│ 2023-11-11 ┆ Bharat Chauhan   ┆ 20  │\n",
       "│ 2023-03-02 ┆ Suzonn           ┆ 20  │\n",
       "└────────────┴──────────────────┴─────┘"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Artists streaming (by day)\n",
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.date().alias(\"date\"),\n",
    "        \"artistName\",\n",
    "    )\n",
    "    .len()\n",
    "    .filter(pl.col(\"len\").ge(20))\n",
    "    .group_by(\"len\")\n",
    "    .map_groups(lambda x: x.head())\n",
    "    .sort(\"len\", descending=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (23, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>week</th><th>trackName</th><th>len</th></tr><tr><td>str</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;(35) 2023&quot;</td><td>&quot;Deathcab&quot;</td><td>18</td></tr><tr><td>&quot;(26) 2023&quot;</td><td>&quot;Aasaan Nahin Hota&quot;</td><td>17</td></tr><tr><td>&quot;(19) 2023&quot;</td><td>&quot;Tu Hi Bataa&quot;</td><td>16</td></tr><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Paul&quot;</td><td>15</td></tr><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Certainty&quot;</td><td>14</td></tr><tr><td>&quot;(18) 2023&quot;</td><td>&quot;Mera Musafir&quot;</td><td>14</td></tr><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Change&quot;</td><td>14</td></tr><tr><td>&quot;(35) 2023&quot;</td><td>&quot;A Love of Some Kind&quot;</td><td>14</td></tr><tr><td>&quot;(30) 2023&quot;</td><td>&quot;Tu Jo Paas&quot;</td><td>13</td></tr><tr><td>&quot;(24) 2023&quot;</td><td>&quot;Khoya Khoya&quot;</td><td>13</td></tr><tr><td>&quot;(35) 2023&quot;</td><td>&quot;Roshni Ka Sira&quot;</td><td>13</td></tr><tr><td>&quot;(04) 2023&quot;</td><td>&quot;The Wonder Of You&quot;</td><td>12</td></tr><tr><td>&quot;(12) 2023&quot;</td><td>&quot;Baarishein&quot;</td><td>12</td></tr><tr><td>&quot;(35) 2023&quot;</td><td>&quot;Snow Song&quot;</td><td>11</td></tr><tr><td>&quot;(42) 2023&quot;</td><td>&quot;Faasle&quot;</td><td>11</td></tr><tr><td>&quot;(37) 2023&quot;</td><td>&quot;Faasle&quot;</td><td>11</td></tr><tr><td>&quot;(43) 2023&quot;</td><td>&quot;Ik Dooje Ke Liye&quot;</td><td>11</td></tr><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Simulation Swarm&quot;</td><td>11</td></tr><tr><td>&quot;(12) 2023&quot;</td><td>&quot;Awaaz Do&quot;</td><td>10</td></tr><tr><td>&quot;(34) 2023&quot;</td><td>&quot;Madhubala&quot;</td><td>10</td></tr><tr><td>&quot;(35) 2023&quot;</td><td>&quot;Disappear&quot;</td><td>10</td></tr><tr><td>&quot;(13) 2023&quot;</td><td>&quot;Mera Musafir&quot;</td><td>10</td></tr><tr><td>&quot;(46) 2023&quot;</td><td>&quot;Khwaab Boney Do&quot;</td><td>10</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (23, 3)\n",
       "┌───────────┬───────────────────┬─────┐\n",
       "│ week      ┆ trackName         ┆ len │\n",
       "│ ---       ┆ ---               ┆ --- │\n",
       "│ str       ┆ str               ┆ u32 │\n",
       "╞═══════════╪═══════════════════╪═════╡\n",
       "│ (35) 2023 ┆ Deathcab          ┆ 18  │\n",
       "│ (26) 2023 ┆ Aasaan Nahin Hota ┆ 17  │\n",
       "│ (19) 2023 ┆ Tu Hi Bataa       ┆ 16  │\n",
       "│ (25) 2023 ┆ Paul              ┆ 15  │\n",
       "│ (25) 2023 ┆ Certainty         ┆ 14  │\n",
       "│ …         ┆ …                 ┆ …   │\n",
       "│ (12) 2023 ┆ Awaaz Do          ┆ 10  │\n",
       "│ (34) 2023 ┆ Madhubala         ┆ 10  │\n",
       "│ (35) 2023 ┆ Disappear         ┆ 10  │\n",
       "│ (13) 2023 ┆ Mera Musafir      ┆ 10  │\n",
       "│ (46) 2023 ┆ Khwaab Boney Do   ┆ 10  │\n",
       "└───────────┴───────────────────┴─────┘"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tracks streaming (by week)\n",
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.strftime(\"(%W) %Y\").alias(\"week\"),\n",
    "        \"artistName\",\n",
    "        \"trackName\",\n",
    "    )\n",
    "    .len()\n",
    "    .filter(pl.col(\"len\").ge(10))\n",
    "    .group_by(\"len\")\n",
    "    .map_groups(lambda x: x.head())\n",
    "    .sort(\"len\", descending=True)\n",
    "    .select(\"week\", \"trackName\", \"len\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (10, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>week</th><th>artistName</th><th>len</th></tr><tr><td>str</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Big Thief&quot;</td><td>99</td></tr><tr><td>&quot;(35) 2023&quot;</td><td>&quot;Adrianne Lenker&quot;</td><td>90</td></tr><tr><td>&quot;(12) 2023&quot;</td><td>&quot;Piyush Bhisekar&quot;</td><td>62</td></tr><tr><td>&quot;(26) 2023&quot;</td><td>&quot;Bharat Chauhan&quot;</td><td>59</td></tr><tr><td>&quot;(38) 2023&quot;</td><td>&quot;Jagjit Singh&quot;</td><td>58</td></tr><tr><td>&quot;(25) 2023&quot;</td><td>&quot;Osho Jain&quot;</td><td>56</td></tr><tr><td>&quot;(45) 2023&quot;</td><td>&quot;Bharat Chauhan&quot;</td><td>53</td></tr><tr><td>&quot;(32) 2023&quot;</td><td>&quot;Piyush Bhisekar&quot;</td><td>53</td></tr><tr><td>&quot;(45) 2023&quot;</td><td>&quot;Bayaan&quot;</td><td>52</td></tr><tr><td>&quot;(43) 2023&quot;</td><td>&quot;Ankur Tewari&quot;</td><td>51</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (10, 3)\n",
       "┌───────────┬─────────────────┬─────┐\n",
       "│ week      ┆ artistName      ┆ len │\n",
       "│ ---       ┆ ---             ┆ --- │\n",
       "│ str       ┆ str             ┆ u32 │\n",
       "╞═══════════╪═════════════════╪═════╡\n",
       "│ (25) 2023 ┆ Big Thief       ┆ 99  │\n",
       "│ (35) 2023 ┆ Adrianne Lenker ┆ 90  │\n",
       "│ (12) 2023 ┆ Piyush Bhisekar ┆ 62  │\n",
       "│ (26) 2023 ┆ Bharat Chauhan  ┆ 59  │\n",
       "│ (38) 2023 ┆ Jagjit Singh    ┆ 58  │\n",
       "│ (25) 2023 ┆ Osho Jain       ┆ 56  │\n",
       "│ (45) 2023 ┆ Bharat Chauhan  ┆ 53  │\n",
       "│ (32) 2023 ┆ Piyush Bhisekar ┆ 53  │\n",
       "│ (45) 2023 ┆ Bayaan          ┆ 52  │\n",
       "│ (43) 2023 ┆ Ankur Tewari    ┆ 51  │\n",
       "└───────────┴─────────────────┴─────┘"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Artists streaming (by week)\n",
    "(\n",
    "    df.filter(\n",
    "        pl.col(\"endTime\").dt.year().eq(2023),\n",
    "    )\n",
    "    .group_by(\n",
    "        pl.col(\"endTime\").dt.strftime(\"(%W) %Y\").alias(\"week\"),\n",
    "        \"artistName\",\n",
    "    )\n",
    "    .len()\n",
    "    .filter(pl.col(\"len\").ge(50))\n",
    "    .group_by(\"len\")\n",
    "    .map_groups(lambda x: x.head())\n",
    "    .sort(\"len\", descending=True)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T/As which only played once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1_792, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>artistName</th><th>trackName</th><th>len</th></tr><tr><td>str</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;Sonu Nigam&quot;</td><td>&quot;Main Agar Kahoon&quot;</td><td>1</td></tr><tr><td>&quot;Anirudh Ravichander&quot;</td><td>&quot;Climax Fight&quot;</td><td>1</td></tr><tr><td>&quot;Yatharth Geeta (Hindi)&quot;</td><td>&quot;1. प्रथम अध्याय&quot;</td><td>1</td></tr><tr><td>&quot;RADWIMPS&quot;</td><td>&quot;First Aid&quot;</td><td>1</td></tr><tr><td>&quot;Aman Pant&quot;</td><td>&quot;Bharam&quot;</td><td>1</td></tr><tr><td>&quot;Sanjeeta Bhattacharya&quot;</td><td>&quot;Red&quot;</td><td>1</td></tr><tr><td>&quot;The Japanese House&quot;</td><td>&quot;i saw you in a dream&quot;</td><td>1</td></tr><tr><td>&quot;Maple Leaf Learning&quot;</td><td>&quot;Pumpkin, Pumpkin&quot;</td><td>1</td></tr><tr><td>&quot;JVKE&quot;</td><td>&quot;golden hour&quot;</td><td>1</td></tr><tr><td>&quot;Vishal Mishra&quot;</td><td>&quot;Woh Chaand Kahan Se Laogi&quot;</td><td>1</td></tr><tr><td>&quot;Raprocksrini&quot;</td><td>&quot;Vaathi Is Back&quot;</td><td>1</td></tr><tr><td>&quot;Jessie J&quot;</td><td>&quot;Bang Bang&quot;</td><td>1</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Amit Trivedi&quot;</td><td>&quot;Aafaton Ke Parinde&quot;</td><td>1</td></tr><tr><td>&quot;DIVINE&quot;</td><td>&quot;Mirchi&quot;</td><td>1</td></tr><tr><td>&quot;Billie Eilish&quot;</td><td>&quot;Bored&quot;</td><td>1</td></tr><tr><td>&quot;Hans Zimmer&quot;</td><td>&quot;Herald of the Change&quot;</td><td>1</td></tr><tr><td>&quot;SickDude&quot;</td><td>&quot;Tere Sang Yaara&quot;</td><td>1</td></tr><tr><td>&quot;j brij.&quot;</td><td>&quot;Remembering&quot;</td><td>1</td></tr><tr><td>&quot;One Direction&quot;</td><td>&quot;18&quot;</td><td>1</td></tr><tr><td>&quot;Ramin Djawadi&quot;</td><td>&quot;Video Games&quot;</td><td>1</td></tr><tr><td>&quot;Wajid&quot;</td><td>&quot;Andha Ghoda Race Mein Dauda&quot;</td><td>1</td></tr><tr><td>&quot;Rik Rok&quot;</td><td>&quot;Should I Tell Her&quot;</td><td>1</td></tr><tr><td>&quot;Shroomi&quot;</td><td>&quot;Sunflower&quot;</td><td>1</td></tr><tr><td>&quot;Chalchitra Talks&quot;</td><td>&quot;#123&quot;</td><td>1</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1_792, 3)\n",
       "┌────────────────────────┬─────────────────────────────┬─────┐\n",
       "│ artistName             ┆ trackName                   ┆ len │\n",
       "│ ---                    ┆ ---                         ┆ --- │\n",
       "│ str                    ┆ str                         ┆ u32 │\n",
       "╞════════════════════════╪═════════════════════════════╪═════╡\n",
       "│ Sonu Nigam             ┆ Main Agar Kahoon            ┆ 1   │\n",
       "│ Anirudh Ravichander    ┆ Climax Fight                ┆ 1   │\n",
       "│ Yatharth Geeta (Hindi) ┆ 1. प्रथम अध्याय               ┆ 1   │\n",
       "│ RADWIMPS               ┆ First Aid                   ┆ 1   │\n",
       "│ Aman Pant              ┆ Bharam                      ┆ 1   │\n",
       "│ …                      ┆ …                           ┆ …   │\n",
       "│ Ramin Djawadi          ┆ Video Games                 ┆ 1   │\n",
       "│ Wajid                  ┆ Andha Ghoda Race Mein Dauda ┆ 1   │\n",
       "│ Rik Rok                ┆ Should I Tell Her           ┆ 1   │\n",
       "│ Shroomi                ┆ Sunflower                   ┆ 1   │\n",
       "│ Chalchitra Talks       ┆ #123                        ┆ 1   │\n",
       "└────────────────────────┴─────────────────────────────┴─────┘"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.group_by(\"artistName\", \"trackName\").len().filter(\n",
    "    pl.col(\"len\").eq(1),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dates when user does not played any track"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(700,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
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       "}\n",
       "</style>\n",
       "<small>shape: (10,)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date_range</th></tr><tr><td>date</td></tr></thead><tbody><tr><td>2022-01-02</td></tr><tr><td>2022-01-03</td></tr><tr><td>2022-01-04</td></tr><tr><td>2022-01-05</td></tr><tr><td>2022-01-06</td></tr><tr><td>2022-01-07</td></tr><tr><td>2022-01-08</td></tr><tr><td>2022-01-09</td></tr><tr><td>2022-01-10</td></tr><tr><td>2022-01-11</td></tr></tbody></table></div>"
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      "text/plain": [
       "shape: (10,)\n",
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       "\t2022-01-05\n",
       "\t2022-01-06\n",
       "\t2022-01-07\n",
       "\t2022-01-08\n",
       "\t2022-01-09\n",
       "\t2022-01-10\n",
       "\t2022-01-11\n",
       "]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = df.get_column(\"endTime\").dt.date().min()\n",
    "end_date = df.get_column(\"endTime\").dt.date().max()\n",
    "date_range: pl.Series = (\n",
    "    pl.datetime_range(\n",
    "        start_date,  # type: ignore\n",
    "        end_date,  # type: ignore\n",
    "        interval=\"1d\",\n",
    "        eager=True,\n",
    "    )\n",
    "    .dt.date()\n",
    "    .alias(\"date_range\")\n",
    ")\n",
    "date_range.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.date(2022, 1, 2), datetime.date(2023, 12, 2))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date, end_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
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       "}\n",
       "</style>\n",
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      ],
      "text/plain": [
       "shape: (8,)\n",
       "Series: 'date_range' [date]\n",
       "[\n",
       "\t2022-03-24\n",
       "\t2022-06-20\n",
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       "\t2022-10-05\n",
       "\t2022-10-27\n",
       "\t2023-06-10\n",
       "]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_range.filter(\n",
    "    date_range.is_in(\n",
    "        df.get_column(\"endTime\").dt.date(),\n",
    "    ).not_(),\n",
    ")"
   ]
  }
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