{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hot100.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dK3V9DgI3FFb"
      },
      "source": [
        "For this assignment you will perform an exploratory data analysis (EDA) for historic data from\n",
        "Billboard Hot 100 Chart. The Billboard Hot 100 is the music industry standard record chart in the\n",
        "United States for songs, published weekly by Billboard magazine. Chart rankings are based on\n",
        "sales, radio play, and online streaming in the United States.\n",
        "Every week, Billboard releases The Hot 100 chart of songs that were trending on sales and airplay\n",
        "for that week. The dataset you will analyse is a collection of all The Hot 100 charts released since\n",
        "its inception in 1958. The song with rank 1 is the most popular song that week.\n",
        "Marks will be awarded for complete and correct answers to all 10 questions. For full marks, you\n",
        "must show clearly all steps and computations in your answers. You must submit your completed\n",
        "Jupyter notebook in .ipynb and PDF format. To create a PDF of your notebook select File →\n",
        "Download as → PDF via LaTeX (.pdf)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTiPTez5fNKa"
      },
      "source": [
        "import pandas as pd\n",
        "import datetime\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import StrMethodFormatter\n",
        "%matplotlib inline"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIj7tE1i3KAe"
      },
      "source": [
        "1. Load the charts.csv dataset into Python as a pandas DataFrame. Include the arguments\n",
        "parse_dates=True in the read_csv function to convert the date strings into datetime objects.\n",
        "Describe the data. Comment on the size of the dataset and the type of data in each column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsQdEQ1UffcD",
        "outputId": "19df2256-6894-46e9-a240-b5af23ea3b50"
      },
      "source": [
        "df = pd.read_csv('charts.csv', parse_dates=True)\n",
        "print(df.info())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 328487 entries, 0 to 328486\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   rank            328487 non-null  int64  \n",
            " 1   song            328487 non-null  object \n",
            " 2   artist          328487 non-null  object \n",
            " 3   last-week       296422 non-null  float64\n",
            " 4   peak-rank       328487 non-null  int64  \n",
            " 5   weeks-on-board  328487 non-null  int64  \n",
            " 6   date            328487 non-null  object \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 17.5+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QWJ8eIT3PIB"
      },
      "source": [
        "2. Write code to answer the following questions:\n",
        "  * How many different dates are included in the data set?\n",
        "How many different artists featured in the charts since its inception in 1958?\n",
        "  * Determine the top 10 artists that feature in the charts the most amount of times?\n",
        "  * What is the maximum length of time a song featured in the charts? What was this song\n",
        "and who was the artist?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1a7nowfzrq5q",
        "outputId": "c3a09f99-3934-4bbb-9217-f35c0ef83628"
      },
      "source": [
        "print(\"Unique Dates in the Dataset: \", df['date'].nunique())\n",
        "print(\"Different artists to have appeared in charts: \", df['artist'].nunique())\n",
        "n = 10\n",
        "top_10 = df['artist'].value_counts()[:n].index.tolist()\n",
        "print('Top 10 Artists to have appeared in the Charts: ')\n",
        "for i in range(n):\n",
        "  print(i+1, top_10[i])\n",
        "\n",
        "most_appeared_count = df['song'].value_counts(dropna=False)[0]\n",
        "most_appeared_song = df['song'].value_counts().index.tolist()[0]\n",
        "most_appeared_song_artist = df.loc[df['song'] == most_appeared_song, 'artist'].iloc[0]\n",
        "print(f'The most appearances by a song is {most_appeared_count} by {most_appeared_song} sang by {most_appeared_song_artist}')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Dates in the Dataset:  3285\n",
            "Different artists to have appeared in charts:  10100\n",
            "Top 10 Artists to have appeared in the Charts: \n",
            "1 Taylor Swift\n",
            "2 Elton John\n",
            "3 Madonna\n",
            "4 Kenny Chesney\n",
            "5 Drake\n",
            "6 Tim McGraw\n",
            "7 Keith Urban\n",
            "8 Stevie Wonder\n",
            "9 Rod Stewart\n",
            "10 Mariah Carey\n",
            "The most appearances by a song is 208 by Stay sang by BTS\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-loX4hu4Gah"
      },
      "source": [
        "3. Determing how many missing values there are in each column of the dataset. Can you think\n",
        "of a reason why why these values are missing? Replace any missing values in the last-week\n",
        "column with the integer 101. Is this a reasonable value to replace the missing values with?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y2sU_niW6MKB",
        "outputId": "8f3b9ae8-eec1-49df-8d3d-969944d01c3c"
      },
      "source": [
        "print(\"Columns and the number of missing values: \")\n",
        "print(df.isna().sum())\n",
        "print(\"....................................................\")\n",
        "df['last-week'] = df['last-week'].fillna(101)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns and the number of missing values: \n",
            "rank                  0\n",
            "song                  0\n",
            "artist                0\n",
            "last-week         32065\n",
            "peak-rank             0\n",
            "weeks-on-board        0\n",
            "date                  0\n",
            "dtype: int64\n",
            "....................................................\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bVcKwaD7Gdf"
      },
      "source": [
        "Replacing missing values with 101 is not a reasonable way to handle the missing values since 101 is not a true representation of lack of a value. 0 would be a better option"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ch3MnK-U4OQL"
      },
      "source": [
        "4. For each entry compute the difference between its rank last week and its current rank. Add\n",
        "the change in ranking as an additional column to the DataFrame. Create a numerical sum\u0002mary of change in ranking (mean, standard deviation, minimum, maximum, etc.) and inter\u0002pret your results.\n",
        "\n",
        "Note: If the song is higher up the charts this week, the change in ranking should be positive.\n",
        "While if the song falls down the charts, the change in ranking should be negative."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yrc9_MVk7C80",
        "outputId": "a0c12bd6-c5b7-497a-d4e1-38ba33e16248"
      },
      "source": [
        "df['change-in-ranking'] = df['last-week'] - df['rank']\n",
        "print(df.head())\n",
        "print(\"Mean change in Rank: \", df['change-in-ranking'].mean())\n",
        "print(\"Standard Deviation Change in Rank: \", df['change-in-ranking'].std())\n",
        "print(\"Minimum change in Rank: \", df['change-in-ranking'].min())\n",
        "print(\"Maximum change in Rank: \", df['change-in-ranking'].max())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   rank                            song  ...        date  change-in-ranking\n",
            "0     1                          Butter  ...  2021-07-17                0.0\n",
            "1     2                        Good 4 U  ...  2021-07-17                0.0\n",
            "2     3                      Levitating  ...  2021-07-17                1.0\n",
            "3     4                    Kiss Me More  ...  2021-07-17               -1.0\n",
            "4     5  Montero (Call Me By Your Name)  ...  2021-07-17                3.0\n",
            "\n",
            "[5 rows x 8 columns]\n",
            "Mean change in Rank:  2.312746014301936\n",
            "Standard Deviation Change in Rank:  12.803090919085838\n",
            "Minimum change in Rank:  -80.0\n",
            "Maximum change in Rank:  100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAbCrYWH4U5l"
      },
      "source": [
        "5. Create a scatter plot of the current rank versus the change in ranking from the previous week.\n",
        "Describe and interpret the plot. Compute the correlation coefficient between the current rank\n",
        "and the change in ranking. Does is agree with your what you found in your plot?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "F-d6Na_P-yHu",
        "outputId": "fd71820f-7783-4877-b876-98a21d2566e8"
      },
      "source": [
        "df.plot.scatter(x = 'rank', y = 'change-in-ranking')\n",
        "print('Correlation Coefficient: ', df['rank'].corr(df['change-in-ranking']))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation Coefficient:  -0.048228471168613465\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebRMuXpAAPMb"
      },
      "source": [
        "From the scatter plot, there is a general trend that an increase in the change in ranking leads to an better rank of the song. The correlation coefficient of -0.04 shows that ther is no meaningful relationship between the two columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NfypA8qv4ZEn"
      },
      "source": [
        "6. Songs will typically appear in the charts for a number of weeks, as such, there are a number\n",
        "of duplicate entries. Delete all duplicate entries, keeping only the most recent entries of a\n",
        "particular song by a particular artist in the charts. How many unique songs (with unique\n",
        "artists) have appeared in the charts since its inception in 1958? Repeat Q2C for the new\n",
        "DataFrame, with unique entries only. Are the top 10 entries the same? If not, discuss why\n",
        "they may have changed.\n",
        "You may find the pandas method drop_duplicates useful. See the pandas documentation\n",
        "for details on how to apply this method to a DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMwTgBv7ebjN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd178e29-c150-404c-8040-73a45e9c46e9"
      },
      "source": [
        "uniquedf = df.drop_duplicates(subset=['song', 'artist'], inplace=False, keep='first')\n",
        "print(len(uniquedf), \"unique songs with unique artists\")\n",
        "top_10 = uniquedf['artist'].value_counts()[:n].index.tolist()\n",
        "print('Top 10 Artists to have appeared in the Charts: ')\n",
        "for i in range(n):\n",
        "  print(i+1, top_10[i])\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29467 unique songs with unique artists\n",
            "Top 10 Artists to have appeared in the Charts: \n",
            "1 Glee Cast\n",
            "2 Taylor Swift\n",
            "3 Drake\n",
            "4 The Beatles\n",
            "5 Aretha Franklin\n",
            "6 Elton John\n",
            "7 The Rolling Stones\n",
            "8 The Beach Boys\n",
            "9 Stevie Wonder\n",
            "10 Madonna\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gy_GMeXOtoJX"
      },
      "source": [
        "The top 10 artists with most appearances has changed with the new dataset. This new list consist of artists with the highest number of unique songs in the billboard charts as compared to the previous one where most appearances included repeat appearances for the same song"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EUj3aPJ4f0R"
      },
      "source": [
        "7. Recompute the summary statistics for the change in ranking from the previous week and\n",
        "recreate the scatter plot of the current ratings versus the change in ranking for the new\n",
        "DataFrame. Discuss and interpret any changes in these statistics and the plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "pudpY7LUt_yY",
        "outputId": "e54b0e6c-3ee0-4ffe-b0a2-45141298ad41"
      },
      "source": [
        "uniquedf['change-in-ranking'] = uniquedf['last-week'] - uniquedf['rank']\n",
        "print(\"Mean change in Rank: \", uniquedf['change-in-ranking'].mean())\n",
        "print(\"Standard Deviation Change in Rank: \", uniquedf['change-in-ranking'].std())\n",
        "print(\"Minimum change in Rank: \", uniquedf['change-in-ranking'].min())\n",
        "print(\"Maximum change in Rank: \", uniquedf['change-in-ranking'].max())\n",
        "\n",
        "uniquedf.plot.scatter(x = 'rank', y = 'change-in-ranking')\n",
        "print('Correlation Coefficient: ', uniquedf['rank'].corr(uniquedf['change-in-ranking']))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean change in Rank:  -7.5868938134184\n",
            "Standard Deviation Change in Rank:  15.88071740116989\n",
            "Minimum change in Rank:  -80.0\n",
            "Maximum change in Rank:  92.0\n",
            "Correlation Coefficient:  -0.15500198550902927\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Xzs5eGOuyDS"
      },
      "source": [
        "Mean change in rank has changed from ~2.3 to ~7.58\n",
        "Standard deviation has changed from ~12.8 to ~15.88\n",
        "Maximum change in rankhas dropped to 92 from 100\n",
        "The correlation coefficient has changed from 0.04 to 1.15, indicating a weak relationship between current week's rank and change in rank"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gmgJ8sM4uf-"
      },
      "source": [
        "8. Using the new DataFrame, with unique entries only, create a histogram for the length of\n",
        "time each entry spent in the charts. Describe and interpret your plot. Compute the summary\n",
        "statistics for the length of time a song spends in the charts. Discuss these statistics in relation\n",
        "to your histogram.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "hdPjG6v4vnTJ",
        "outputId": "69786a84-ce14-4b50-a315-815e79a287c2"
      },
      "source": [
        "ax = uniquedf.hist(column='weeks-on-board', bins=100, grid=False, figsize=(12,8), color='#86bf91', zorder=2, rwidth=0.9)\n",
        "\n",
        "ax = ax[0]\n",
        "for x in ax:\n",
        "\n",
        "    # Despine\n",
        "    x.spines['right'].set_visible(False)\n",
        "    x.spines['top'].set_visible(False)\n",
        "    x.spines['left'].set_visible(False)\n",
        "\n",
        "    # Switch off ticks\n",
        "    x.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
        "\n",
        "    # Draw horizontal axis lines\n",
        "    vals = x.get_yticks()\n",
        "    for tick in vals:\n",
        "        x.axhline(y=tick, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
        "    \n",
        "    # Remove title\n",
        "    x.set_title(\"\")\n",
        "\n",
        "    # Set x-axis label\n",
        "    x.set_xlabel(\"Weeks On Board\", labelpad=20, weight='bold', size=12)\n",
        "\n",
        "    # Set y-axis label\n",
        "    x.set_ylabel(\"Cumulative weeks on board\", labelpad=20, weight='bold', size=12)\n",
        "\n",
        "    # Format y-axis label\n",
        "    x.yaxis.set_major_formatter(StrMethodFormatter('{x:,g}'))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLp5ogmIyC4n"
      },
      "source": [
        "Majority of songs spent less than 40 weeks on chart, with very few songs spending more than 60 weeks on chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8WoGvmnzDpH",
        "outputId": "9f51d204-8c3e-4ff9-f6e7-69f300831572"
      },
      "source": [
        "print(\"Mean time on the charts: \", uniquedf['weeks-on-board'].mean())\n",
        "print(\"Standard Deviation Weeks on board: \", uniquedf['weeks-on-board'].std())\n",
        "print(\"Minimum weeks on Board: \", uniquedf['weeks-on-board'].min())\n",
        "print(\"Maximum weeks on Board: \", uniquedf['weeks-on-board'].max())\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean time on the charts:  11.149082023959005\n",
            "Standard Deviation Weeks on board:  8.287710754543093\n",
            "Minimum weeks on Board:  1\n",
            "Maximum weeks on Board:  87\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qG6-bRHizoqA"
      },
      "source": [
        "Mean time on charts is 11 weeks, with a deviation of 8, coinciding with the histogram visualization that majority of the songs spend between 1 and 20 weeks on the board"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXCu1-nj4y2u"
      },
      "source": [
        "9. Using the new DataFrame, with unique entries only, create a histogram for the peak rank.\n",
        "Describe and interpret your plot. Compute the summary statistics for peak rank. Discuss\n",
        "these statistics in relation to your histogram."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "jOgUUuSd0A2w",
        "outputId": "b37b27d2-9aec-4a53-fc01-347a14d67347"
      },
      "source": [
        "ax = uniquedf.hist(column='peak-rank', bins=100, grid=False, figsize=(12,8), color='#86bf91', zorder=2, rwidth=0.9)\n",
        "\n",
        "ax = ax[0]\n",
        "for x in ax:\n",
        "\n",
        "    # Despine\n",
        "    x.spines['right'].set_visible(False)\n",
        "    x.spines['top'].set_visible(False)\n",
        "    x.spines['left'].set_visible(False)\n",
        "\n",
        "    # Switch off ticks\n",
        "    x.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
        "\n",
        "    # Draw horizontal axis lines\n",
        "    vals = x.get_yticks()\n",
        "    for tick in vals:\n",
        "        x.axhline(y=tick, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
        "    \n",
        "    # Remove title\n",
        "    x.set_title(\"\")\n",
        "\n",
        "    # Set x-axis label\n",
        "    x.set_xlabel(\"Peak Rank\", labelpad=20, weight='bold', size=12)\n",
        "\n",
        "    # Set y-axis label\n",
        "    x.set_ylabel(\"Cumulative Peak Rank\", labelpad=20, weight='bold', size=12)\n",
        "\n",
        "    # Format y-axis label\n",
        "    x.yaxis.set_major_formatter(StrMethodFormatter('{x:,g}'))\n",
        "\n",
        "print(\"Mean peak rank: \", uniquedf['peak-rank'].mean())\n",
        "print(\"Standard Deviation Peak Rank: \", uniquedf['peak-rank'].std())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean peak rank:  46.67930227033631\n",
            "Standard Deviation Peak Rank:  30.541818343055507\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Pg88hzD0nBp"
      },
      "source": [
        "The histogram portrays a generally uniform graph. The graph shows us that majority of the songs have a peak rank of 1 and the lowestpeak rank is 100. The charts have a peak rank of 46 with a standard deviation of 30.54. This mean and the large deviation lead to the generally uniform shape of the histogram, showing that majority of the songs are within 30 peak ranks of the mean peak rank of 46."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWrktPc340v0"
      },
      "source": [
        "10. Write a short summary on what you have learnt about the Billboard Hot 100 Chart from this\n",
        "analysis. (5-10 sentences)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGXd_SXH1r2S"
      },
      "source": [
        "There are 29,467 unique songs in the billboard charts, which are far fewer than he total of 328,487. Glee cast has the highest number of unique songs on the billboard charts. The overall artist with the most appearances is Taylor Swift, indicating that she may be top due to the same songs staying on the chars for long periods of time. This ultimately means that the Billboard Hot 100 Chart often contains duplicate entries for different weeks."
      ]
    }
  ]
}