Lastfm-Python-pt2.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "source": [
                "<h1>Using the Last Fm API to extract data and analyzing it with Python. (Part 2)</h1>"
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "As you already know, Last Fm is a service (kind of a social network) to store your music listening activity, that is called *scrobbling*, and I have been using that service since late 2008. The goal of this project is to create new visualization, Last Fm itself shows some nice charts and numbers but I wanted a little more.\n",
                "\n",
                "\n",
                "In the first part of the project I extracted and cleaned the data using Python, If you wanna read it, please go to this link: https://github.com/chmedinap/my-data-science-practices/blob/master/Lastfm-Python-pt1.ipynb\n",
                "\n",
                "In this second part I'm going to show you how to manipulate the data with Pandas 🐼  and some basic charts using Seaborn 🐍, but please keep in mind that **I'm new in the data analytics/science world and English is not my native language. I'm still learning. If you could give  me your feedback about these two topics, I would really appreciate it.** 🤗\n",
                "\n",
                "For this post I'm going to use the extracted data from my friend Maria and I. We both have used Last Fm since 2009 in a regular basis and we have different tastes in music. \n",
                "\n",
                "What I'm going to do: \n",
                "* 👾 Search missing fields in the data (Nan)\n",
                "* 👾 Plot the timeline for both users.\n",
                "* 👾 Graph the scrobbling trend month by month.\n",
                "* 👾 Show the most listened artist per year. \n",
                "* 👾 Find the artists we have in common. \n",
                "\n",
                "<h2> 🤓 Changing the name of the columns:</h2>\n",
                "\n",
                "For a better understanding I changed some names and dropped some columns I didn't need.\n",
                "\n",
                "Read the files"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "source": [
                "import pandas as pd\n",
                "import numpy as np\n",
                "import seaborn as sns\n",
                "import matplotlib as mpl\n",
                "import matplotlib.pyplot as plt\n",
                "from matplotlib import pyplot"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "source": [
                "df_ch = pd.read_csv('../Archivos/LastFm/Carlos_scrob.csv')\n",
                "df_maria = pd.read_csv('../Archivos/LastFm/Maria_scrob.csv')\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Changed names and drop"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "source": [
                "df_ch=df_ch.drop(['uts','artist_mbid','album_mbid','track_mbid'],axis = 1)\n",
                "df_ch.columns=['Date','Artist','Album','Song']\n",
                "df_maria=df_maria.drop(['uts','artist_mbid','album_mbid','track_mbid'],axis = 1)\n",
                "df_maria.columns=['Date','Artist','Album','Song']"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Added columns"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "source": [
                "df_ch['User']='Ch'\n",
                "df_maria['User']= 'Maria'"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "At this point the data-frame looked like this:"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "source": [
                "df_maria.head(5)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
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                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Date</th>\n",
                            "      <th>Artist</th>\n",
                            "      <th>Album</th>\n",
                            "      <th>Song</th>\n",
                            "      <th>User</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>23 Aug 2021, 16:17</td>\n",
                            "      <td>Grouplove</td>\n",
                            "      <td>Never Trust a Happy Song</td>\n",
                            "      <td>Tongue Tied</td>\n",
                            "      <td>Maria</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>23 Aug 2021, 16:13</td>\n",
                            "      <td>Phoenix</td>\n",
                            "      <td>Wolfgang Amadeus Phoenix</td>\n",
                            "      <td>Lisztomania</td>\n",
                            "      <td>Maria</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>23 Aug 2021, 16:08</td>\n",
                            "      <td>White Lies</td>\n",
                            "      <td>To Lose My Life ...</td>\n",
                            "      <td>Fifty on Our Foreheads</td>\n",
                            "      <td>Maria</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>23 Aug 2021, 16:03</td>\n",
                            "      <td>Justice</td>\n",
                            "      <td>We Are Your Friends</td>\n",
                            "      <td>We Are Your Friends - Justice Vs Simian</td>\n",
                            "      <td>Maria</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>23 Aug 2021, 15:58</td>\n",
                            "      <td>MGMT</td>\n",
                            "      <td>Little Dark Age</td>\n",
                            "      <td>Me and Michael</td>\n",
                            "      <td>Maria</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                 Date      Artist                     Album  \\\n",
                            "0  23 Aug 2021, 16:17   Grouplove  Never Trust a Happy Song   \n",
                            "1  23 Aug 2021, 16:13     Phoenix  Wolfgang Amadeus Phoenix   \n",
                            "2  23 Aug 2021, 16:08  White Lies       To Lose My Life ...   \n",
                            "3  23 Aug 2021, 16:03     Justice       We Are Your Friends   \n",
                            "4  23 Aug 2021, 15:58        MGMT           Little Dark Age   \n",
                            "\n",
                            "                                      Song   User  \n",
                            "0                              Tongue Tied  Maria  \n",
                            "1                              Lisztomania  Maria  \n",
                            "2                   Fifty on Our Foreheads  Maria  \n",
                            "3  We Are Your Friends - Justice Vs Simian  Maria  \n",
                            "4                           Me and Michael  Maria  "
                        ]
                    },
                    "metadata": {},
                    "execution_count": 9
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "<h2>🧯Missing fields in the data</h2>\n",
                "When you are working with new data-frames, I recomend to always search for missing values because it gives you a better understanding of the data and it helps you to avoid mistakes."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "source": [
                "percent_missing_ch=df_ch.isnull().sum()*100/len(df_ch)\n",
                "percent_missing_maria=df_maria.isnull().sum()*100/len(df_maria)\n",
                "df_missing_value=pd.DataFrame(\n",
                "    {\n",
                "    'Ch':percent_missing_ch,\n",
                "    'Maria':percent_missing_maria,\n",
                "    })\n",
                "df_missing_value"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
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                            "\n",
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                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Ch</th>\n",
                            "      <th>Maria</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>Date</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Artist</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Album</th>\n",
                            "      <td>7.886792</td>\n",
                            "      <td>1.818633</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Song</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>User</th>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.000000</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "              Ch     Maria\n",
                            "Date    0.000000  0.000000\n",
                            "Artist  0.000000  0.000000\n",
                            "Album   7.886792  1.818633\n",
                            "Song    0.000000  0.000000\n",
                            "User    0.000000  0.000000"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 10
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "As you can see, there is some missing data in the \"Album\" column. That happens because sometimes Last Fm does not match the correct names. For example: \n",
                "For us the song: \n",
                "\n",
                "| Master of Puppets  | Metallica | S&M   |\n",
                "\n",
                "is the same that\n",
                "\n",
                "| Master of Puppets | Metallica | Symphony and Metallica |\n",
                "\n",
                "\n",
                "But for Last Fm is not because the official album name is \"S&M\", so the other name does not match and it will be left empty and that is our nan in the data. \n",
                "\n",
                "<h2> 🔌 Plot the timeline for both users.</h2>\n",
                "\n",
                "I wanted to do a simple timeline showing how many songs each user listened over the years. The first thing was to extract the year and the month from the \"Date\" column.\n",
                "\n"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "source": [
                "df_ch['Date']=pd.to_datetime(df_ch['Date'])\n",
                "df_ch['Year']=pd.DatetimeIndex(df_ch['Date']).year\n",
                "df_ch['Month']=pd.DatetimeIndex(df_ch['Date']).month\n",
                "df_maria['Date']=pd.to_datetime(df_maria['Date'])\n",
                "df_maria['Year']=pd.DatetimeIndex(df_maria['Date']).year\n",
                "df_maria['Month']=pd.DatetimeIndex(df_maria['Date']).month"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "The result was this"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "source": [
                "df_ch.head(5)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
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                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>Date</th>\n",
                            "      <th>Artist</th>\n",
                            "      <th>Album</th>\n",
                            "      <th>Song</th>\n",
                            "      <th>User</th>\n",
                            "      <th>Year</th>\n",
                            "      <th>Month</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>2021-08-26 00:29:00</td>\n",
                            "      <td>King Crimson</td>\n",
                            "      <td>Larks' Tongues In Aspic (Expanded &amp; Remastered...</td>\n",
                            "      <td>Larks' Tongues in Aspic (Part I)</td>\n",
                            "      <td>Ch</td>\n",
                            "      <td>2021</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>2021-08-26 00:23:00</td>\n",
                            "      <td>Coheed and Cambria</td>\n",
                            "      <td>Good Apollo I'm Burning Star IV Volume One: Fr...</td>\n",
                            "      <td>Welcome Home</td>\n",
                            "      <td>Ch</td>\n",
                            "      <td>2021</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>2021-08-26 00:15:00</td>\n",
                            "      <td>Marillion</td>\n",
                            "      <td>Script For A Jester's Tear</td>\n",
                            "      <td>Forgotten Sons - 1997 Remaster</td>\n",
                            "      <td>Ch</td>\n",
                            "      <td>2021</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>2021-08-26 00:09:00</td>\n",
                            "      <td>Steve Hackett</td>\n",
                            "      <td>Voyage Of The Acolyte</td>\n",
                            "      <td>Ace Of Wands - Remastered 2005</td>\n",
                            "      <td>Ch</td>\n",
                            "      <td>2021</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>2021-08-25 23:47:00</td>\n",
                            "      <td>Yes</td>\n",
                            "      <td>Relayer (Deluxe Edition)</td>\n",
                            "      <td>The Gates of Delirium - 2003 Remaster</td>\n",
                            "      <td>Ch</td>\n",
                            "      <td>2021</td>\n",
                            "      <td>8</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                 Date              Artist  \\\n",
                            "0 2021-08-26 00:29:00        King Crimson   \n",
                            "1 2021-08-26 00:23:00  Coheed and Cambria   \n",
                            "2 2021-08-26 00:15:00           Marillion   \n",
                            "3 2021-08-26 00:09:00       Steve Hackett   \n",
                            "4 2021-08-25 23:47:00                 Yes   \n",
                            "\n",
                            "                                               Album  \\\n",
                            "0  Larks' Tongues In Aspic (Expanded & Remastered...   \n",
                            "1  Good Apollo I'm Burning Star IV Volume One: Fr...   \n",
                            "2                         Script For A Jester's Tear   \n",
                            "3                              Voyage Of The Acolyte   \n",
                            "4                           Relayer (Deluxe Edition)   \n",
                            "\n",
                            "                                    Song User  Year  Month  \n",
                            "0       Larks' Tongues in Aspic (Part I)   Ch  2021      8  \n",
                            "1                           Welcome Home   Ch  2021      8  \n",
                            "2         Forgotten Sons - 1997 Remaster   Ch  2021      8  \n",
                            "3         Ace Of Wands - Remastered 2005   Ch  2021      8  \n",
                            "4  The Gates of Delirium - 2003 Remaster   Ch  2021      8  "
                        ]
                    },
                    "metadata": {},
                    "execution_count": 14
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Now, the next stept was to group by and transform to dataframe 📂. \n",
                "\n",
                "For my data:"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "source": [
                "ChbyYear=df_ch.groupby(df_ch.Year).Song.count()\n",
                "df_ChbyYear=ChbyYear.to_frame()\n",
                "df_ChbyYear['User']='Ch'\n",
                "df_ChbyYear['Year']= ChbyYear.index\n",
                "df_ChbyYear.reset_index(drop=True, inplace=True)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "For Maria's data"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "source": [
                "MariabyYear=df_maria.groupby(df_maria.Year).Song.count()\n",
                "df_MariabyYear=MariabyYear.to_frame()\n",
                "df_MariabyYear['User']='Maria'\n",
                "df_MariabyYear['Year']= MariabyYear.index\n",
                "df_MariabyYear.reset_index(drop=True, inplace=True)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "Then, plot"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "source": [
                "df_concatenated = pd.concat([df_ChbyYear, df_MariabyYear])\n",
                "fig, ax1 =pyplot.subplots(figsize=(20,7))\n",
                "plt.title('Scrobbling trend throughout the years', fontsize = 20) \n",
                "plt.xlabel('Year', fontsize=16)\n",
                "plt.ylabel('Scrobbling', fontsize=16)\n",
                "sns.set_style(\"darkgrid\")\n",
                "sns.despine()\n",
                "sns.lineplot(data=df_concatenated,\n",
                "            x=\"Year\", \n",
                "            y=\"Song\",\n",
                "            hue=\"User\",\n",
                "            style=\"User\",\n",
                "            markers=True, dashes=False)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "<AxesSubplot:title={'center':'Scrobbling trend throughout the years'}, xlabel='Year', ylabel='Scrobbling'>"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 36
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1440x504 with 1 Axes>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "As you can see, we both started in the same year but I used to save way more scrobbling songs than Maria. An important thing in this is that Maria reduced the listening in 2020, but mine almost duplicated.\n",
                "\n",
                "<h2> 🏮 Graph the scrobbling trend month by month.</h2>\n",
                "\n",
                "In this I did heatmaps, I think is the best way. For this I grouped the data by month selecting a year and then count the songs: \n",
                "\n",
                "For my data:"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "source": [
                "x=2008\n",
                "dfs=[]\n",
                "while x<2022:\n",
                "    year_tmp=df_ch[df_ch['Year']==x]\n",
                "    year_tmp=year_tmp.groupby('Month').Song.count()\n",
                "    df_temp1=year_tmp.to_frame()\n",
                "    df_temp1.columns=['Count']\n",
                "    df_temp1['Month']= year_tmp.index\n",
                "    df_temp1.reset_index(drop=True, inplace=True)\n",
                "    df_temp2=df_temp1.sort_values('Count', ascending=False)\n",
                "    df_temp2['Year']=x\n",
                "    df_temp2.reset_index(drop=True, inplace=True)\n",
                "    dfs.append(df_temp2)\n",
                "    x=x+1\n",
                "df_ChbyMonthYear=pd.concat(dfs, ignore_index=True)\n",
                "df_ChHeat=df_ChbyMonthYear.pivot('Month','Year','Count')"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "For Maria's data"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "source": [
                "y=2008\n",
                "dfs=[]\n",
                "while y<2022:\n",
                "    year_tmp=df_maria[df_maria['Year']==y]\n",
                "    year_tmp=year_tmp.groupby('Month').Song.count()\n",
                "    df_temp1=year_tmp.to_frame()\n",
                "    df_temp1.columns=['Count']\n",
                "    df_temp1['Month']= year_tmp.index\n",
                "    df_temp1.reset_index(drop=True, inplace=True)\n",
                "    df_temp2=df_temp1.sort_values('Count', ascending=False)\n",
                "    df_temp2['Year']=y\n",
                "    df_temp2.reset_index(drop=True, inplace=True)\n",
                "    dfs.append(df_temp2)\n",
                "    y=y+1\n",
                "df_MariabyMonthYear=pd.concat(dfs, ignore_index=True)\n",
                "df_MariaHeat=df_MariabyMonthYear.pivot('Month','Year','Count')"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "The most importang thing in here was to pivot data. The final result was this:"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "source": [
                "df_MariaHeat"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th>Year</th>\n",
                            "      <th>2009</th>\n",
                            "      <th>2010</th>\n",
                            "      <th>2011</th>\n",
                            "      <th>2012</th>\n",
                            "      <th>2013</th>\n",
                            "      <th>2014</th>\n",
                            "      <th>2015</th>\n",
                            "      <th>2016</th>\n",
                            "      <th>2017</th>\n",
                            "      <th>2018</th>\n",
                            "      <th>2019</th>\n",
                            "      <th>2020</th>\n",
                            "      <th>2021</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Month</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>140.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>654.0</td>\n",
                            "      <td>1114.0</td>\n",
                            "      <td>1795.0</td>\n",
                            "      <td>1240.0</td>\n",
                            "      <td>70.0</td>\n",
                            "      <td>609.0</td>\n",
                            "      <td>1739.0</td>\n",
                            "      <td>14.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>246.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>329.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>146.0</td>\n",
                            "      <td>1566.0</td>\n",
                            "      <td>780.0</td>\n",
                            "      <td>344.0</td>\n",
                            "      <td>224.0</td>\n",
                            "      <td>306.0</td>\n",
                            "      <td>646.0</td>\n",
                            "      <td>55.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>602.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>124.0</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>149.0</td>\n",
                            "      <td>916.0</td>\n",
                            "      <td>350.0</td>\n",
                            "      <td>441.0</td>\n",
                            "      <td>424.0</td>\n",
                            "      <td>164.0</td>\n",
                            "      <td>573.0</td>\n",
                            "      <td>44.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>680.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>78.0</td>\n",
                            "      <td>430.0</td>\n",
                            "      <td>444.0</td>\n",
                            "      <td>1357.0</td>\n",
                            "      <td>1135.0</td>\n",
                            "      <td>532.0</td>\n",
                            "      <td>530.0</td>\n",
                            "      <td>175.0</td>\n",
                            "      <td>800.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>509.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>5</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>89.0</td>\n",
                            "      <td>505.0</td>\n",
                            "      <td>235.0</td>\n",
                            "      <td>1284.0</td>\n",
                            "      <td>1425.0</td>\n",
                            "      <td>358.0</td>\n",
                            "      <td>1378.0</td>\n",
                            "      <td>792.0</td>\n",
                            "      <td>827.0</td>\n",
                            "      <td>50.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>306.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>41.0</td>\n",
                            "      <td>333.0</td>\n",
                            "      <td>615.0</td>\n",
                            "      <td>1704.0</td>\n",
                            "      <td>1988.0</td>\n",
                            "      <td>683.0</td>\n",
                            "      <td>727.0</td>\n",
                            "      <td>468.0</td>\n",
                            "      <td>364.0</td>\n",
                            "      <td>133.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>714.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>176.0</td>\n",
                            "      <td>487.0</td>\n",
                            "      <td>186.0</td>\n",
                            "      <td>946.0</td>\n",
                            "      <td>1054.0</td>\n",
                            "      <td>647.0</td>\n",
                            "      <td>309.0</td>\n",
                            "      <td>759.0</td>\n",
                            "      <td>292.0</td>\n",
                            "      <td>130.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>551.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>8</th>\n",
                            "      <td>227.0</td>\n",
                            "      <td>300.0</td>\n",
                            "      <td>339.0</td>\n",
                            "      <td>617.0</td>\n",
                            "      <td>629.0</td>\n",
                            "      <td>1435.0</td>\n",
                            "      <td>1064.0</td>\n",
                            "      <td>299.0</td>\n",
                            "      <td>108.0</td>\n",
                            "      <td>548.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>45.0</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>9</th>\n",
                            "      <td>116.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>651.0</td>\n",
                            "      <td>1337.0</td>\n",
                            "      <td>747.0</td>\n",
                            "      <td>744.0</td>\n",
                            "      <td>318.0</td>\n",
                            "      <td>140.0</td>\n",
                            "      <td>696.0</td>\n",
                            "      <td>399.0</td>\n",
                            "      <td>65.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>10</th>\n",
                            "      <td>312.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>323.0</td>\n",
                            "      <td>1548.0</td>\n",
                            "      <td>961.0</td>\n",
                            "      <td>1304.0</td>\n",
                            "      <td>695.0</td>\n",
                            "      <td>38.0</td>\n",
                            "      <td>759.0</td>\n",
                            "      <td>282.0</td>\n",
                            "      <td>8.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>11</th>\n",
                            "      <td>530.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>356.0</td>\n",
                            "      <td>1962.0</td>\n",
                            "      <td>1092.0</td>\n",
                            "      <td>1540.0</td>\n",
                            "      <td>1463.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>767.0</td>\n",
                            "      <td>143.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>784.0</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>12</th>\n",
                            "      <td>354.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>337.0</td>\n",
                            "      <td>1022.0</td>\n",
                            "      <td>1669.0</td>\n",
                            "      <td>1546.0</td>\n",
                            "      <td>509.0</td>\n",
                            "      <td>396.0</td>\n",
                            "      <td>1475.0</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>438.0</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "Year    2009   2010   2011    2012    2013    2014    2015    2016    2017  \\\n",
                            "Month                                                                        \n",
                            "1        NaN  140.0    NaN   654.0  1114.0  1795.0  1240.0    70.0   609.0   \n",
                            "2        NaN  329.0    NaN   146.0  1566.0   780.0   344.0   224.0   306.0   \n",
                            "3        NaN  124.0   19.0   149.0   916.0   350.0   441.0   424.0   164.0   \n",
                            "4        NaN   78.0  430.0   444.0  1357.0  1135.0   532.0   530.0   175.0   \n",
                            "5        NaN   89.0  505.0   235.0  1284.0  1425.0   358.0  1378.0   792.0   \n",
                            "6        NaN   41.0  333.0   615.0  1704.0  1988.0   683.0   727.0   468.0   \n",
                            "7        NaN  176.0  487.0   186.0   946.0  1054.0   647.0   309.0   759.0   \n",
                            "8      227.0  300.0  339.0   617.0   629.0  1435.0  1064.0   299.0   108.0   \n",
                            "9      116.0    NaN  651.0  1337.0   747.0   744.0   318.0   140.0   696.0   \n",
                            "10     312.0    NaN  323.0  1548.0   961.0  1304.0   695.0    38.0   759.0   \n",
                            "11     530.0    NaN  356.0  1962.0  1092.0  1540.0  1463.0     NaN   767.0   \n",
                            "12     354.0    NaN  337.0  1022.0  1669.0  1546.0   509.0   396.0  1475.0   \n",
                            "\n",
                            "Year     2018   2019   2020   2021  \n",
                            "Month                               \n",
                            "1      1739.0   14.0    NaN  246.0  \n",
                            "2       646.0   55.0    NaN  602.0  \n",
                            "3       573.0   44.0    NaN  680.0  \n",
                            "4       800.0    NaN    NaN  509.0  \n",
                            "5       827.0   50.0    NaN  306.0  \n",
                            "6       364.0  133.0    NaN  714.0  \n",
                            "7       292.0  130.0    NaN  551.0  \n",
                            "8       548.0    NaN    NaN   45.0  \n",
                            "9       399.0   65.0    NaN    NaN  \n",
                            "10      282.0    8.0    NaN    NaN  \n",
                            "11      143.0    NaN  784.0    NaN  \n",
                            "12        NaN    NaN  438.0    NaN  "
                        ]
                    },
                    "metadata": {},
                    "execution_count": 22
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "The heatmaps"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "source": [
                "fig, ax2 = plt.subplots(ncols=3,figsize=(20,6) ,gridspec_kw=dict(width_ratios=[20,20,0.5]))\n",
                "plt.suptitle('Scrobbling trend month by month', fontsize = 20) \n",
                "plt.xlabel('Scrobbling', fontsize = 15) \n",
                "ax2[0].set_title('Carlos',fontsize = 15)\n",
                "ax2[1].set_title('Maria',fontsize = 15)\n",
                "sns.heatmap(df_ChHeat, annot=False, cbar=False, ax=ax2[0], vmin=1,cmap=\"YlGnBu\")\n",
                "sns.heatmap(df_MariaHeat, annot=False, yticklabels=True, cbar=False, ax=ax2[1], vmax=7000,cmap=\"YlGnBu\")\n",
                "fig.colorbar(ax2[1].collections[0], cax=ax2[2])\n",
                "plt.show()"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 1440x432 with 3 Axes>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "<h2> 📌 Show the most listened artist per year.</h2>\n",
                "Similar to the last, some grouping and then plot"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "source": [
                "x=2008\n",
                "dfs=[]\n",
                "while x<2022:\n",
                "    year_tmp=df_ch[df_ch['Year']==x]\n",
                "    year_tmp=year_tmp.groupby('Artist').Artist.count()\n",
                "    df_temp1=year_tmp.to_frame()\n",
                "    df_temp1.columns=['Count']\n",
                "    df_temp1['Artist']= year_tmp.index\n",
                "    df_temp1.reset_index(drop=True, inplace=True)\n",
                "    df_temp2=df_temp1.sort_values('Count', ascending=False).head(1)\n",
                "    df_temp2['Year']=x\n",
                "    df_temp2.reset_index(drop=True, inplace=True)\n",
                "    dfs.append(df_temp2)\n",
                "    x=x+1\n",
                "df_ChbyArtistYear=pd.concat(dfs, ignore_index=True)\n"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "And plot"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 47,
            "source": [
                "g=sns.relplot(x=\"Year\", y=\"Count\", hue=\"Artist\", size=\"Artist\",\n",
                "            sizes=(50, 200), alpha=.5, palette=\"muted\",\n",
                "            height=6, data=df_ChbyArtistYear)\n",
                "g.fig.set_size_inches(15,3)\n",
                "sns.set_style(\"darkgrid\")\n",
                "plt.title(\"Carlos' most listened artist per year\", fontsize = 20) \n",
                "plt.xlabel('Year', fontsize=16)\n",
                "plt.ylabel('Scrobbling', fontsize=16)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Text(17.70017361111112, 0.5, 'Scrobbling')"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 47
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "For Maria:"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "source": [
                "x=2008\n",
                "dfs=[]\n",
                "while x<2022:\n",
                "    year_tmp=df_maria[df_maria['Year']==x]\n",
                "    year_tmp=year_tmp.groupby('Artist').Artist.count()\n",
                "    df_temp1=year_tmp.to_frame()\n",
                "    df_temp1.columns=['Count']\n",
                "    df_temp1['Artist']= year_tmp.index\n",
                "    df_temp1.reset_index(drop=True, inplace=True)\n",
                "    df_temp2=df_temp1.sort_values('Count', ascending=False).head(1)\n",
                "    df_temp2['Year']=x\n",
                "    df_temp2.reset_index(drop=True, inplace=True)\n",
                "    dfs.append(df_temp2)\n",
                "    x=x+1\n",
                "df_MariabyArtistYear=pd.concat(dfs, ignore_index=True)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "And plot"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 49,
            "source": [
                "g=sns.relplot(x=\"Year\", y=\"Count\", hue=\"Artist\", size=\"Artist\",\n",
                "            sizes=(30, 200), alpha=.5, palette=\"muted\",\n",
                "            height=6, data=df_MariabyArtistYear)\n",
                "g.fig.set_size_inches(15,3)\n",
                "sns.set_style(\"darkgrid\")\n",
                "plt.title(\"Maria's most listened artist per year\", fontsize = 20) \n",
                "plt.xlabel('Year', fontsize=16)\n",
                "plt.ylabel('Scrobbling', fontsize=16)"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/plain": [
                            "Text(20.63194444444445, 0.5, 'Scrobbling')"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 49
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "<h2> 📜 Find the artists we have in common.</h2>\n",
                "Before that, I needed to know the percent of artists we have in common, but this was a sad 6.5 %"
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "source": [
                "year_tmp3=df_maria.groupby('Artist').Song.count()\n",
                "df_temp3=year_tmp3.to_frame()\n",
                "df_temp3.columns=['Count']\n",
                "df_temp3['Artist']= df_temp3.index\n",
                "df_temp3['User']= 'Maria'\n",
                "df_temp3.reset_index(drop=True, inplace=True)\n",
                "df_MariaArtist=df_temp3.sort_values('Count', ascending=False)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "source": [
                "year_tmp3=df_ch.groupby('Artist').Song.count()\n",
                "df_temp3=year_tmp3.to_frame()\n",
                "df_temp3.columns=['Count']\n",
                "df_temp3['Artist']= df_temp3.index\n",
                "df_temp3['User']= 'Carlos'\n",
                "df_temp3.reset_index(drop=True, inplace=True)\n",
                "df_CarlosArtist=df_temp3.sort_values('Count', ascending=False)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "source": [
                "df_artist=pd.concat([df_CarlosArtist, df_MariaArtist],ignore_index=True)"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "source": [
                "match=df_artist.Artist.duplicated().sum()\n",
                "rest=df_artist['Artist'].nunique()\n",
                "percent=match*100/rest\n",
                "print(percent)"
            ],
            "outputs": [
                {
                    "output_type": "stream",
                    "name": "stdout",
                    "text": [
                        "6.486486486486487\n"
                    ]
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "markdown",
            "source": [
                "I created a new data-frame for organization."
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "source": [
                "df_artist2=df_artist.loc[df_artist.Artist.duplicated(keep=False)]\n",
                "df_artist2.reset_index(drop=True, inplace=True)\n",
                "df_artist3=df_artist2.pivot('Artist','User','Count')\n",
                "df_artist3['Band']=df_artist3.index\n",
                "df_artist3.reset_index(drop=True, inplace=True)\n",
                "df_artist3=df_artist3.sort_values(['Maria', 'Band'], ascending=[False, False])"
            ],
            "outputs": [],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "source": [
                "df_artist3"
            ],
            "outputs": [
                {
                    "output_type": "execute_result",
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th>User</th>\n",
                            "      <th>Carlos</th>\n",
                            "      <th>Maria</th>\n",
                            "      <th>Band</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>200</th>\n",
                            "      <td>9</td>\n",
                            "      <td>6584</td>\n",
                            "      <td>New Order</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>72</th>\n",
                            "      <td>3</td>\n",
                            "      <td>4544</td>\n",
                            "      <td>Death Cab for Cutie</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>195</th>\n",
                            "      <td>152</td>\n",
                            "      <td>4483</td>\n",
                            "      <td>Nada Surf</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>249</th>\n",
                            "      <td>74</td>\n",
                            "      <td>3118</td>\n",
                            "      <td>Sigur Rós</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>106</th>\n",
                            "      <td>31</td>\n",
                            "      <td>2788</td>\n",
                            "      <td>Franz Ferdinand</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>...</th>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "      <td>...</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>7</th>\n",
                            "      <td>32</td>\n",
                            "      <td>1</td>\n",
                            "      <td>Airbag</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>1869</td>\n",
                            "      <td>1</td>\n",
                            "      <td>AC/DC</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>3</td>\n",
                            "      <td>1</td>\n",
                            "      <td>A Flock of Seagulls</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>1</td>\n",
                            "      <td>1</td>\n",
                            "      <td>::M∆DE::IN::HEIGHTS::</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>11</td>\n",
                            "      <td>1</td>\n",
                            "      <td>3 Doors Down</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>348 rows × 3 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "User  Carlos  Maria                   Band\n",
                            "200        9   6584              New Order\n",
                            "72         3   4544    Death Cab for Cutie\n",
                            "195      152   4483              Nada Surf\n",
                            "249       74   3118              Sigur Rós\n",
                            "106       31   2788        Franz Ferdinand\n",
                            "..       ...    ...                    ...\n",
                            "7         32      1                 Airbag\n",
                            "4       1869      1                  AC/DC\n",
                            "3          3      1    A Flock of Seagulls\n",
                            "2          1      1  ::M∆DE::IN::HEIGHTS::\n",
                            "0         11      1           3 Doors Down\n",
                            "\n",
                            "[348 rows x 3 columns]"
                        ]
                    },
                    "metadata": {},
                    "execution_count": 35
                }
            ],
            "metadata": {}
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "source": [
                "f, ax = plt.subplots(figsize=(6, 15))\n",
                "sns.set_color_codes(\"pastel\")\n",
                "sns.barplot(x=\"Maria\", y=\"Band\", data=df_artist3.head(50),\n",
                "            label=\"Maria\", color=\"b\")\n",
                "sns.set_color_codes(\"muted\")\n",
                "sns.barplot(x=\"Carlos\", y=\"Band\", data=df_artist3.head(50),\n",
                "            label=\"Carlos\", color=\"b\")\n",
                "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
                "ax.set(xlim=(0, 7000), ylabel=\"Band\",\n",
                "       xlabel=\"Scrobbling\")\n",
                "sns.despine(left=True, bottom=True)"
            ],
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 432x1080 with 1 Axes>"
                        ]
                    },
                    "metadata": {}
                }
            ],
            "metadata": {}
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        {
            "cell_type": "markdown",
            "source": [
                "<h2> 📍 To conclude, some important facts: </h2>\n",
                "\n",
                "* 👾  Maria and I only have 6% of similar artists in the total of the artists we listened to. This percentage represents 348 artists, but most have only one song. The most representative artists are:New Order, Interpol, The Killers, Within Temptation and Two Door Cinema Club. \n",
                "\n",
                "* 👾  My data is the one with the highest percentage of errors. This must be because my main music source is MP3 files and it may have bad tags.\n",
                "\n",
                "* 👾  The most popular years of using Last Fm for the two of us were 2013 and 2014.\n",
                "\n",
                "* 👾  In 2020, due to the Covid-19 pandemic, we changed many patterns of behavior. This can be seen very clearly in our graphics; Maria didn't use Last Fm in 10 months, and I duplicated my numbers. \n",
                "\n",
                "* 👾 I am a more of a conservative person when it comes to my artists since it shows that through the years, they vary little. Maria, on the other hand, has a larger variety of artists.\n",
                "\n",
                "To write this post I read a loooot of the Towards Datascience blog. https://towardsdatascience.com/ 👩‍💻\n",
                "\n",
                "Thanks for reading, and remember I'm new in the data analytics/science world and English is not my native language. I'm still learning🧑‍🎓. If you could give me your feedback about these two topics, I would really appreciate it.🙇‍♂️\n",
                "\n",
                "**Carlos Medina** 🎩"
            ],
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        }
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