homework2.ipynb

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      "source": [
        "# CIS 5450 Homework 2: SQL\n",
        "## Due: Thursday, October 13rd 2022, 10pm EST \n",
        "### Worth 100 points in total\n",
        "\n",
        "Welcome to Homework 2! By now, you should be familiar with the world of data science and the Pandas library. This assignment focuses on helping you get to grips with a new tool: SQL.\n",
        "\n",
        "Through this homework, we will be working with SQL (specifically **pandasql**) by exploring a Spotify dataset containing song reviews and statistics. We will also conduct some text analysis of song reviews.\n",
        "\n",
        " <!-- We will finish off the homework with some text analysis. -->\n",
        "\n",
        "We are introducing a lot of new things in this homework, and this is often where students start to get lost. Thus, we **strongly** encourage you to review the slides/material as you work through this assignment. \n",
        "\n",
        "**Before you begin:**\n",
        "- Be sure to click \"Copy to Drive\" to make sure you're working on your own personal version of the homework\n",
        "- Check the pinned FAQ post on Ed for updates! If you have been stuck, chances are other students have also faced similar problems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0hcZWDcqCUL"
      },
      "source": [
        "## Part 0: Libraries and Set Up Jargon (The usual wall of imports)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "ylkUtozb2Oc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13b7f400-54f1-4f34-e8f9-8338f9e6f265"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://test.pypi.org/simple/, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: penn-grader==0.5.0 in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from penn-grader==0.5.0) (0.3.5.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from penn-grader==0.5.0) (6.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandasql in /usr/local/lib/python3.7/dist-packages (0.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.21.6)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.4.41)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandasql) (2022.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandasql) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->pandasql) (1.1.3.post0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->pandasql) (4.13.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->sqlalchemy->pandasql) (3.9.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->sqlalchemy->pandasql) (4.1.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install -i https://test.pypi.org/simple/ penn-grader==0.5.0\n",
        "!pip install pandasql"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "LAXVD0Lo454x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "408dcdfa-80d9-40b2-bc42-3df16e757821"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "from penngrader.grader import *\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import pandasql as ps #SQL on Pandas Dataframe\n",
        "import nltk\n",
        "import numpy as np\n",
        "nltk.download('punkt')\n",
        "\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "0TmMEXVcwONB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2e6c36e-e53c-4177-bcd0-3d6536b2a2f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File ‘spotify_features.csv’ already there; not retrieving.\n",
            "\n",
            "File ‘spotify_songs.csv’ already there; not retrieving.\n",
            "\n",
            "File ‘spotify_rankings.csv’ already there; not retrieving.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Three datasets we're using\n",
        "! wget -nc https://storage.googleapis.com/penn-cis5450/spotify_features.csv\n",
        "! wget -nc https://storage.googleapis.com/penn-cis5450/spotify_songs.csv\n",
        "! wget -nc https://storage.googleapis.com/penn-cis5450/spotify_rankings.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "b5NogkmdhhQ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31a99d45-f01f-4a36-dcf5-f92d647c18f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3.5\n"
          ]
        }
      ],
      "source": [
        "print(pd.__version__ )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeyWoMn6pxSC"
      },
      "source": [
        "### PennGrader Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "4ds2HHSkpvBO"
      },
      "outputs": [],
      "source": [
        "# PLEASE ENSURE YOUR PENN-ID IS ENTERED CORRECTLY. IF NOT, THE AUTOGRADER WON'T KNOW WHO \n",
        "# TO ASSIGN POINTS TO YOU IN OUR BACKEND\n",
        "STUDENT_ID = 20723731 # YOUR PENN-ID GOES HERE AS AN INTEGER #\n",
        "\n",
        "# You should also update this to a unique \"secret\" just for this homework, to\n",
        "# authenticate this is YOUR submission\n",
        "SECRET = STUDENT_ID"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile config.yaml\n",
        "\n",
        "grader_api_url: 'https://wyv616tp17.execute-api.us-east-1.amazonaws.com/default/Grader'\n",
        "grader_api_key: 'Kd32fl3g3p917iM0zwjiO23Bitj4PO9ga4LektOa'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgbImBgjSmhy",
        "outputId": "b87a00ed-4992-4399-8ff4-588cc4171b64"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting config.yaml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAsLYJGotKbO",
        "outputId": "5e42e860-3396-4afb-ca58-16841d7349bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PennGrader initialized with Student ID: 20723731\n",
            "\n",
            "Make sure this correct or we will not be able to store your grade\n"
          ]
        }
      ],
      "source": [
        "grader = PennGrader('config.yaml', 'CIS_5450_Fall22_HW2', STUDENT_ID, SECRET)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzZ6JNnKLplQ"
      },
      "source": [
        "\n",
        "\n",
        "# Music for everyone!\n",
        "\n",
        "<br>\n",
        "<center><img src = \"https://upload.wikimedia.org/wikipedia/commons/3/33/Spotify_logo13.png\" width= \"500\" align =\"center\"/></center>\n",
        "<br>\n",
        "\n",
        "We'll be working with a dataset containing Top Tracks on Spotify in 2017, along with their reviews and rankings.\n",
        "\n",
        "In this homework, we'll be exploring the following attributes of the song data:\n",
        "\n",
        "*   Song-specific metadata (eg. artist name) \n",
        "\n",
        "*   The songs' rankings based on date and listener location\n",
        "\n",
        "*   Acoustic / musical properties of the song, eg. danceability\n",
        "\n",
        "\n",
        "We'll be loading this data into various dataframes and querying them. We will primarily be using PandaSQL for these tasks. However, for some of the initial questions, we'll ask you to perform the same operations in Pandas. The purpose of conducting the same tasks in both Pandas and PandaSQL is to help you become more familiar with the similarites and differences of these two tools."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oENmSeFkFRCo"
      },
      "source": [
        "## Part 1: Load & Process our Datasets [9 points total]\n",
        "\n",
        "Before we get into the data, we first need to load and clean our datasets. \n",
        "\n",
        "# Metadata\n",
        "You'll be working with three CSV files:\n",
        "- `spotify_features.csv`\n",
        "- `songs_features.csv`\n",
        "- `spotify_rankings.csv`\n",
        "\n",
        "The file `spotify_features.csv` contains musical / acoustic data for each song, obtained via the Spotify API.\n",
        "\n",
        "The file `songs_features.csv` includes the names, artists, Spotify URLs of the Top Songs of 2017. \n",
        "\n",
        "Each row in the file `spotify_rankings.csv` contains a song's ranking on a specific day in a particular region, where regions are sorted alphabetically.\n",
        "\n",
        "For example, the first 200 rows of `spotify_rankings.csv` represent the ranks of various songs on 1st of January in Argentina. The next 200 rows contain the  rankings of songs during the 2nd of January in Argentina. \n",
        "\n",
        "Note that the Spotify dataset may containing missing data, so don't rely on the interval between successive dates as 200 rows. \n",
        "\n",
        "Instead, it would be preferable to read all the data and then filter by region & date. This way, you can be sure that you have the required data.\n",
        "\n",
        "\n",
        "**TODO**:\n",
        "* Load `spotify_songs.csv` and save the data to a dataframe called `songs_df`.\n",
        "* Load `spotify_rankings.csv` and save the data to a dataframe called `rankings_df`.\n",
        "* Load `spotify_features.csv` and save the data to a dataframe called `features_df` without the index column being included.\n",
        "\n",
        "**Note**: If you see a column called `Unnamed: 0` when loading the data,be sure to drop this column while loading the dataset. This is necessary for one to receive full credit in all the following parts of this homework."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "zfMn38A25Vt8"
      },
      "outputs": [],
      "source": [
        "# TODO: Import the datasets to pandas dataframes -- make sure the dataframes are named correctly! \n",
        "songs_df=pd.read_csv(\"spotify_songs.csv\")\n",
        "rankings_df=pd.read_csv(\"spotify_rankings.csv\")\n",
        "features_df=pd.read_csv(\"spotify_features.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view songs_df to make sure the import was successful\n",
        "songs_df.head()"
      ],
      "metadata": {
        "id": "mysH4yw7T0Cw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "eb149cb5-3575-4016-f22a-c2009b8d833e"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                      id                                name  \\\n",
              "0  7qiZfU4dY1lWllzX7mPBI                        Shape of You   \n",
              "1  5CtI0qwDJkDQGwXD1H1cL                   Despacito - Remix   \n",
              "2  4aWmUDTfIPGksMNLV2rQP  Despacito (Featuring Daddy Yankee)   \n",
              "3  6RUKPb4LETWmmr3iAEQkt            Something Just Like This   \n",
              "4  3DXncPQOG4VBw3QHh3S81                         I'm the One   \n",
              "\n",
              "            artists  duration_ms  time_signature  \\\n",
              "0        Ed Sheeran       233713               4   \n",
              "1        Luis Fonsi       228827               4   \n",
              "2        Luis Fonsi       228200               4   \n",
              "3  The Chainsmokers       247160               4   \n",
              "4         DJ Khaled       288600               4   \n",
              "\n",
              "                                             reviews  \n",
              "0                     vocal has a nice warm quality.  \n",
              "1  Very European feeling. I like that the singer ...  \n",
              "2  Unique and quirky. Arrangement was good and ni...  \n",
              "3  Tastefully put together song. Good instrumenta...  \n",
              "4                              Your voice is awesome  "
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              "      <td>Something Just Like This</td>\n",
              "      <td>The Chainsmokers</td>\n",
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              "      <td>4</td>\n",
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              "      <td>I'm the One</td>\n",
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              "      <td>4</td>\n",
              "      <td>Your voice is awesome</td>\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "Py_XvfZiNzXx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7d1832de-576f-470c-facf-5e9b5cf2163a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Position                  Track Name         Artist  Streams  \\\n",
              "0           0         1  Reggaetón Lento (Bailemos)           CNCO    19272   \n",
              "1           1         2                    Chantaje        Shakira    19270   \n",
              "2           2         3   Otra Vez (feat. J Balvin)  Zion & Lennox    15761   \n",
              "3           3         4                Vente Pa' Ca   Ricky Martin    14954   \n",
              "4           4         5                      Safari       J Balvin    14269   \n",
              "\n",
              "                                                 URL        Date Region  \n",
              "0  https://open.spotify.com/track/3AEZUABDXNtecAO...  2017-01-01     ec  \n",
              "1  https://open.spotify.com/track/6mICuAdrwEjh6Y6...  2017-01-01     ec  \n",
              "2  https://open.spotify.com/track/3QwBODjSEzelZyV...  2017-01-01     ec  \n",
              "3  https://open.spotify.com/track/7DM4BPaS7uofFul...  2017-01-01     ec  \n",
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          "metadata": {},
          "execution_count": 172
        }
      ],
      "source": [
        "# view rankings_df to make sure the import was successful\n",
        "rankings_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "hZJwhz1lx4ZV",
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          "height": 206
        },
        "outputId": "9100787f-553d-496e-8ffa-a9cef28ad427"
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              "   Unnamed: 0                     id  danceability  energy   key  loudness  \\\n",
              "0           0  7qiZfU4dY1lWllzX7mPBI         0.825   0.652   1.0    -3.183   \n",
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              "\n",
              "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
              "0   0.0       0.0802        0.5810          0.000000    0.0931    0.931   \n",
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              "\n",
              "     tempo  \n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 173
        }
      ],
      "source": [
        "# view features_df to make sure the import was successful\n",
        "features_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPitr7eRJfO0"
      },
      "source": [
        "### 1.1 Data Preprocessing\n",
        "We are now going to clean our dataframes `songs_df` and `rankings_df` by performing the following tasks:       \n",
        "1) fixing columns        \n",
        "2) changing datatypes             \n",
        "3) handling nulls.\n",
        "\n",
        "First, let us view the first few rows of `songs_df`. You may also call `.info()` and check the cardinality of each column to view the specifics of the dataframe. This is a good first step to take during Exploratory Data Analysis (EDA)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "id": "2nCrp9Z2SCRD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ea12f79-0a7f-461e-80e5-e9df9a59a6f8"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 6 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   id              100 non-null    object\n",
            " 1   name            100 non-null    object\n",
            " 2   artists         100 non-null    object\n",
            " 3   duration_ms     100 non-null    int64 \n",
            " 4   time_signature  100 non-null    int64 \n",
            " 5   reviews         100 non-null    object\n",
            "dtypes: int64(2), object(4)\n",
            "memory usage: 4.8+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3441197 entries, 0 to 3441196\n",
            "Data columns (total 8 columns):\n",
            " #   Column      Dtype \n",
            "---  ------      ----- \n",
            " 0   Unnamed: 0  int64 \n",
            " 1   Position    int64 \n",
            " 2   Track Name  object\n",
            " 3   Artist      object\n",
            " 4   Streams     int64 \n",
            " 5   URL         object\n",
            " 6   Date        object\n",
            " 7   Region      object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 210.0+ MB\n"
          ]
        }
      ],
      "source": [
        "# view info information regarding movies_df\n",
        "songs_df.info()\n",
        "rankings_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-ukI21sPkYV"
      },
      "source": [
        "#### 1.1.1 Cleaning `songs_df`\n",
        "\n",
        "`.info()` gives us meaningful information regarding columns, their types, and the amount of nulls. With this information, we can now clean our dataframe. \n",
        "\n",
        "**TODO**:\n",
        "* Drop the column `time_signature`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "id": "cMmQTXV7P4YK"
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      "outputs": [],
      "source": [
        "# TODO: clean songs_df\n",
        "songs_df=songs_df.drop(columns=[\"time_signature\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "id": "9SYxGtgOQKLY",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "d6cd287f-404b-442e-e7b6-4c00dcc075fd"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 3/3 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 3 points\n",
        "grader.grade(test_case_id = 'test_cleaning_songs', answer = songs_df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aI_zsI3hNJCf"
      },
      "source": [
        "#### 1.1.2 Processing Rankings\n",
        "\n",
        "`rankings_df` contains a `URL` column that represents the URL of the Spotify track. This URL contains the track ID. We would like to extract the ID corresponding to each song. \n",
        "\n",
        "To maintain consistency, begin by sorting this dataframe according to `Date` in ascending order. \n",
        "\n",
        "**TODO**:\n",
        "- Sort the `Date` column in ascending order\n",
        "- Split the strings in the `URL` column to extract the ID for each song, and save this as a new column called `ID` in `rankings_df`\n",
        "- Drop the `URL` column\n",
        "\n",
        "**Example**:\n",
        ">URL | ID\n",
        ">--- | ---\n",
        "> https://open.spotify.com/track/3AEZUABDXNtecAOSC1qTf | 3AEZUABDXNtecAOSC1qTf\n",
        "\n",
        "After performing these steps, `rankings_df` should have the following schema:\n",
        "**Final Schema**:\n",
        ">Position | Track Name | Artist | Streams | Date | Region | ID\n",
        ">--- | --- | --- |--- |--- |--- |--- |\n",
        "\n",
        "**Hint**: Note the delimiter within the URL and consult the documentation for the `.split()` function"
      ]
    },
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          "metadata": {},
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        }
      ],
      "source": [
        "# TODO: extract ID and drop URL\n",
        "df=rankings_df\n",
        "df=df.sort_values(by=[\"Date\"],ascending=True)\n",
        "df[\"ID\"]=df[\"URL\"].apply(lambda x:str(x).split(\"/\")[-1])\n",
        "df=df.drop(columns=[\"URL\"])\n",
        "df.head()\n",
        "rankings_df=df\n",
        "rankings_df.head()\n",
        "#rankings_df.drop(columns=[\"URL\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "id": "sKA2_ScM6nWv",
        "colab": {
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        "outputId": "2fa4fd8b-bb7d-4150-e098-237bdff604e2"
      },
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Correct! You earned 4/4 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 4 points\n",
        "grader.grade(test_case_id = 'test_rankings_processing', answer = rankings_df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38JSvyEGCGYN"
      },
      "source": [
        "#### 1.1.3 Cleaning `rankings_df`\n",
        "\n",
        "Now let's clean `rankings_df` and make it usable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "id": "vKv7Gdi_CGYQ"
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      "source": [
        "# Examine rankings_df using the .info() function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cz3Xx84WCGYU"
      },
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        "\n",
        "**TODO**:\n",
        "* Drop all rows that have a null value in `rankings_df`\n",
        "* Convert column `Date` into type `datetime64[ns]`.\n"
      ]
    },
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-468e854b-0203-4247-a599-99415370fc57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-468e854b-0203-4247-a599-99415370fc57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ],
      "source": [
        "#TODO: \n",
        "rankings_df=rankings_df.dropna()\n",
        "rankings_df=rankings_df.astype({\"Date\":\"datetime64[ns]\"})\n",
        "rankings_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {
        "id": "PacFnS9JCGYW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fb5b076-9899-4852-befa-96f97a90c615"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_cleaning_rankings', answer = [len(rankings_df),rankings_df.head()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYp9fW_SvG3g"
      },
      "source": [
        "### 1.2 Your Sandbox \n",
        "\n",
        ".info() is just one of many basic tools that you can use for Exploratory Data Analysis (EDA). Instead of throwing you straight into the deep end, we wanted to give you a chance to take some time and explore the data on your own. **This section is not graded**, so for the speedrunners out there feel free to just skip this section (1.2), but we wanted to at least give you a small space to utilize your EDA toolkit to familiarize yourself with all the data you just downloaded.\n",
        "\n",
        "Some suggestions to get you started:\n",
        "- `df.head()`\n",
        "- `df.describe()`\n",
        "- `Series.unique()`"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rankings_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QtE493Nk415I",
        "outputId": "14fc13e3-2735-4da5-b1f3-80c2cdadf288"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0      Position       Streams\n",
              "count  3.440540e+06  3.440540e+06  3.440540e+06\n",
              "mean   1.720591e+06  9.465220e+01  5.188452e+04\n",
              "std    9.933887e+05  5.739412e+01  2.017733e+05\n",
              "min    0.000000e+00  1.000000e+00  1.001000e+03\n",
              "25%    8.602838e+05  4.500000e+01  3.321000e+03\n",
              "50%    1.720594e+06  9.200000e+01  9.226000e+03\n",
              "75%    2.580891e+06  1.430000e+02  2.965600e+04\n",
              "max    3.441196e+06  2.000000e+02  1.138152e+07"
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Position</th>\n",
              "      <th>Streams</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.440540e+06</td>\n",
              "      <td>3.440540e+06</td>\n",
              "      <td>3.440540e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.720591e+06</td>\n",
              "      <td>9.465220e+01</td>\n",
              "      <td>5.188452e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.933887e+05</td>\n",
              "      <td>5.739412e+01</td>\n",
              "      <td>2.017733e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.001000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.602838e+05</td>\n",
              "      <td>4.500000e+01</td>\n",
              "      <td>3.321000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.720594e+06</td>\n",
              "      <td>9.200000e+01</td>\n",
              "      <td>9.226000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.580891e+06</td>\n",
              "      <td>1.430000e+02</td>\n",
              "      <td>2.965600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.441196e+06</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>1.138152e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0514ea61-2386-4c37-98e3-cd19c3c3ec74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0514ea61-2386-4c37-98e3-cd19c3c3ec74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0514ea61-2386-4c37-98e3-cd19c3c3ec74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "wArpz4LZ40Mr",
        "outputId": "4d22f892-303f-4df1-8f35-d38a9d6572dc"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0  danceability      energy         key    loudness  \\\n",
              "count  100.000000     100.00000  100.000000  100.000000  100.000000   \n",
              "mean    49.500000       0.69682    0.660690    5.570000   -5.652650   \n",
              "std     29.011492       0.12508    0.139207    3.731534    1.802067   \n",
              "min      0.000000       0.25800    0.346000    0.000000  -11.462000   \n",
              "25%     24.750000       0.63500    0.556500    2.000000   -6.594500   \n",
              "50%     49.500000       0.71400    0.667500    6.000000   -5.437000   \n",
              "75%     74.250000       0.77025    0.787500    9.000000   -4.326750   \n",
              "max     99.000000       0.92700    0.932000   11.000000   -2.396000   \n",
              "\n",
              "             mode  speechiness  acousticness  instrumentalness    liveness  \\\n",
              "count  100.000000   100.000000    100.000000        100.000000  100.000000   \n",
              "mean     0.580000     0.103969      0.166306          0.004796    0.150607   \n",
              "std      0.496045     0.095115      0.166730          0.026038    0.079011   \n",
              "min      0.000000     0.023200      0.000259          0.000000    0.042400   \n",
              "25%      0.000000     0.043125      0.039100          0.000000    0.098275   \n",
              "50%      1.000000     0.062650      0.106500          0.000000    0.125000   \n",
              "75%      1.000000     0.123000      0.231250          0.000013    0.179250   \n",
              "max      1.000000     0.431000      0.695000          0.210000    0.440000   \n",
              "\n",
              "          valence       tempo  \n",
              "count  100.000000  100.000000  \n",
              "mean     0.517049  119.202460  \n",
              "std      0.216436   27.952928  \n",
              "min      0.086200   75.016000  \n",
              "25%      0.375500   99.911750  \n",
              "50%      0.502500  112.468000  \n",
              "75%      0.679000  137.166000  \n",
              "max      0.966000  199.864000  "
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.00000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49.500000</td>\n",
              "      <td>0.69682</td>\n",
              "      <td>0.660690</td>\n",
              "      <td>5.570000</td>\n",
              "      <td>-5.652650</td>\n",
              "      <td>0.580000</td>\n",
              "      <td>0.103969</td>\n",
              "      <td>0.166306</td>\n",
              "      <td>0.004796</td>\n",
              "      <td>0.150607</td>\n",
              "      <td>0.517049</td>\n",
              "      <td>119.202460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>29.011492</td>\n",
              "      <td>0.12508</td>\n",
              "      <td>0.139207</td>\n",
              "      <td>3.731534</td>\n",
              "      <td>1.802067</td>\n",
              "      <td>0.496045</td>\n",
              "      <td>0.095115</td>\n",
              "      <td>0.166730</td>\n",
              "      <td>0.026038</td>\n",
              "      <td>0.079011</td>\n",
              "      <td>0.216436</td>\n",
              "      <td>27.952928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25800</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-11.462000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023200</td>\n",
              "      <td>0.000259</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.042400</td>\n",
              "      <td>0.086200</td>\n",
              "      <td>75.016000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.750000</td>\n",
              "      <td>0.63500</td>\n",
              "      <td>0.556500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-6.594500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.043125</td>\n",
              "      <td>0.039100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.098275</td>\n",
              "      <td>0.375500</td>\n",
              "      <td>99.911750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.500000</td>\n",
              "      <td>0.71400</td>\n",
              "      <td>0.667500</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>-5.437000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062650</td>\n",
              "      <td>0.106500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.502500</td>\n",
              "      <td>112.468000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>74.250000</td>\n",
              "      <td>0.77025</td>\n",
              "      <td>0.787500</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>-4.326750</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123000</td>\n",
              "      <td>0.231250</td>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.179250</td>\n",
              "      <td>0.679000</td>\n",
              "      <td>137.166000</td>\n",
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              "      <th>max</th>\n",
              "      <td>99.000000</td>\n",
              "      <td>0.92700</td>\n",
              "      <td>0.932000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>-2.396000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.431000</td>\n",
              "      <td>0.695000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.966000</td>\n",
              "      <td>199.864000</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-602c83d1-4cc8-4501-a424-02833ba5aa82')\"\n",
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              "        \n",
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              "                                                     [key], {});\n",
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              "\n",
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      "execution_count": 186,
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            "text/plain": [
              "   Unnamed: 0                     id  danceability  energy   key  loudness  \\\n",
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              "                      id                                name  \\\n",
              "0  7qiZfU4dY1lWllzX7mPBI                        Shape of You   \n",
              "1  5CtI0qwDJkDQGwXD1H1cL                   Despacito - Remix   \n",
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              "\n",
              "            artists  duration_ms  \\\n",
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              "1        Luis Fonsi       228827   \n",
              "2        Luis Fonsi       228200   \n",
              "3  The Chainsmokers       247160   \n",
              "4         DJ Khaled       288600   \n",
              "\n",
              "                                             reviews  \n",
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              "1  Very European feeling. I like that the singer ...  \n",
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              "                                                     [key], {});\n",
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              "\n",
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      ],
      "source": [
        "# Your EDA here! Feel free to add more cells\n",
        "songs_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHFdRtQKLbti"
      },
      "source": [
        "## Part 2: Exploring the Data with PandasSQL (and Pandas) [62 points total]\n",
        "\n",
        "Now that you're more familiar with the dataset, we'll now introduce you to SQL language. Specifically, we'll be using **pandasql**: a library that allows users to query Pandas DataFrames using SQL statements.\n",
        "\n",
        "The typical flow of using pandasql (shortened to **ps**) is as follows:\n",
        "1. Write a SQL query in the form of a string (Tip: use triple quotes \"\"\"x\"\"\" to write multi-line strings)\n",
        "2. Run the query using **ps.sqldf(your_query, locals())**\n",
        "\n",
        "PandaSQL is convenient as it allows you to reference the dataframes that are currently defined in your notebook, so you will be able to fully utilize the dataframes `songs_df`, `rankings_df` and `features_df` that you have created above!\n",
        "\n",
        "Given that SQL is a brand new language, we wanted to give you a chance to directly compare the similarities &differences of Pandas and SQL. \n",
        "Thus, for each of the simpler queries, we ask that you **perform the tasks in each question twice: once with Pandas and once with PandaSQL**. \n",
        "\n",
        "Each answer (unless specified otherwise) will thus require two dataframes, one dataframe whose name is prefixed with `pd_`, and another dataframe whose name is prefixed with `sql_`. You will submit these two dataframes seperately to the autograder. **We will be reviewing your code to make sure your code for each dataframe is written in the correct language.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gIjaNk9BnJ-"
      },
      "source": [
        "###2.1 Bruno Mars songs\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geYyH57csade"
      },
      "source": [
        "#### 2.1.1 How many Bruno Mars' songs were popular in 2017?\n",
        "\n",
        "Note: Only Pandas is required for this question.\n",
        "The dataframe `songs_df` contains all top songs in 2017. We want to know whether Bruno Mars was a part of it (he obviously was) - but which of his songs made it to the top?\n",
        "\n",
        "**TODO:** Using **pandas**, filter out the songs and reviews from `songs_df` that were by `Bruno Mars`. Then, save this data to a DataFrame called `bruno_df` that has the following schema:\n",
        "\n",
        ">name | reviews\n",
        ">--- | ---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Use pandas to obtain songs by `Bruno Mars`\n",
        "bruno_df=songs_df[songs_df[\"artists\"]==\"Bruno Mars\"]\n",
        "bruno_df=bruno_df[[\"name\",\"reviews\"]]\n",
        "bruno_df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 112
        },
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      },
      "execution_count": 188,
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        {
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            "text/plain": [
              "                  name                                            reviews\n",
              "8   That's What I Like  You have a really cool voice! I like the way y...\n",
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      "cell_type": "code",
      "execution_count": 189,
      "metadata": {
        "id": "gmRuF4phW_CT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "200ab027-3362-4378-f932-53a108f2cdc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_bruno', answer = bruno_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hj58V6drh88p"
      },
      "source": [
        "#### 2.1.2 How many of Bruno Mars' songs were deemed \"good\"?\n",
        "We now want to see which of these songs contained the word \"good\" in the `reviews` column.\n",
        "\n",
        "**TODO:** Using **pandasql**, update `bruno_df` so that it only contains songs that have the word 'good' in the `reviews` column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name                                     reviews\n",
              "0  24K Magic  Lyrics are very good. Backing sounds nice."
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "# TODO: Use pandasql to obtain only \"good\" songs of bruno mars\n",
        "good_song_query = \"\"\"SELECT * \n",
        "FROM bruno_df \n",
        "WHERE reviews LIKE \"%good%\" \"\"\"\n",
        "\n",
        "bruno_df = ps.sqldf(good_song_query, locals())\n",
        "bruno_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {
        "id": "XKddWLnIIF7v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5a598ea-4935-4a72-e956-44a77f682a45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_good_songs_df', answer = bruno_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2.2 Finding the hit songs"
      ],
      "metadata": {
        "id": "2UVthIxONbN9"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHzBYzlDYoLx"
      },
      "source": [
        "\n",
        "\n",
        "#### 2.2.1 Extract the total no. of streams\n",
        "We now want to see what songs formed the top 75% of the year 2017 from `rankings_df`. We can measure the popularity of the songs using the total number of streams the song received.\n",
        "\n",
        "**TODO**: \n",
        "* Find the total number of streams per song and save it into a dataframe called `streams_df`\n",
        "* Understand the quartile ranges in `streams_df`\n",
        "\n",
        "**Hint**: you may find it helpful to look up `.describe()` to understand quartiles. It would be helpful to save the necessary quartile value to use in the querying section that follows.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "metadata": {
        "id": "m2FvwI2CaA0X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbaf898c-4327-47cf-dce7-de7511e6f1df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Track Name\n",
              "\"All That Is or Ever Was or Ever Will Be\"       7311\n",
              "\"Read All About It, Pt. III\"                   57025\n",
              "#99                                            31826\n",
              "#Askip                                        296862\n",
              "#Biziz - feat. Lil Bege                       403591\n",
              "                                              ...   \n",
              "전야 前夜 The Eve                                 976392\n",
              "지나갈 테니 Been Through                            87456\n",
              "쩔어 DOPE                                       209493\n",
              "피 땀 눈물                                         68673\n",
              "피카부 Peek-A-Boo                               2078217\n",
              "Name: Streams, Length: 18597, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ],
      "source": [
        "# TODO: Using pandas extract the total number of streams per song from rankings_df\n",
        "df=rankings_df\n",
        "streams_df=df.groupby(\"Track Name\")[\"Streams\"].sum()\n",
        "streams_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_pd_describe', answer = len(streams_df))"
      ],
      "metadata": {
        "id": "FCoUgzTZMMZb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2e60f2-923b-474f-aacf-21ad1025943a"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.2.2 Top 75% of streams\n",
        "\n",
        "Now that we've seen the distribution of the streams, we'd like to extract songs with streams within the top 75%, i.e 75% or more streams. For this part, use the quartile values you got via the `.describe()` function.\n",
        "\n",
        "**TODO**: Using **pandas**, \n",
        "Filter out songs from `streams_df` whose stream count is in the top 75%, then save this data as the Pandas dataframe `pd_top_streams`."
      ],
      "metadata": {
        "id": "YKzlT4lWLvUo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Using pandas extract the top 75% based on number of streams\n",
        "streams_df=streams_df.reset_index()\n",
        "streams_df.quantile(0.25)\n",
        "pd_top_streams=streams_df[streams_df[\"Streams\"]>21334]\n",
        "pd_top_streams"
      ],
      "metadata": {
        "id": "F3efkzK9yrNw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "56db6d98-995f-4ec8-b6f0-03d67914ec0c"
      },
      "execution_count": 194,
      "outputs": [
        {
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              "                         Track Name  Streams\n",
              "1      \"Read All About It, Pt. III\"    57025\n",
              "2                               #99    31826\n",
              "3                            #Askip   296862\n",
              "4           #Biziz - feat. Lil Bege   403591\n",
              "5                             #CTZK   669563\n",
              "...                             ...      ...\n",
              "18592                 전야 前夜 The Eve   976392\n",
              "18593           지나갈 테니 Been Through    87456\n",
              "18594                       쩔어 DOPE   209493\n",
              "18595                        피 땀 눈물    68673\n",
              "18596                피카부 Peek-A-Boo  2078217\n",
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    {
      "cell_type": "code",
      "execution_count": 195,
      "metadata": {
        "id": "_ZAJ1otoIktJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1c1cca2-cd31-493a-a12a-2496d1191f5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_pd_songs', answer = pd_top_streams)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repeat this process using **pandasql**, and save the data in the variable `sql_top_streams`"
      ],
      "metadata": {
        "id": "bcdI8haEOVIo"
      }
    },
    {
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              "                         Track Name  Streams\n",
              "0      \"Read All About It, Pt. III\"    57025\n",
              "1                               #99    31826\n",
              "2                            #Askip   296862\n",
              "3           #Biziz - feat. Lil Bege   403591\n",
              "4                             #CTZK   669563\n",
              "...                             ...      ...\n",
              "13941                 전야 前夜 The Eve   976392\n",
              "13942           지나갈 테니 Been Through    87456\n",
              "13943                       쩔어 DOPE   209493\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "# TODO: Using pandasql extract the top 75% based on number of streams\n",
        "top_query = \"\"\"\n",
        "SELECT *\n",
        "FROM streams_df\n",
        "WHERE \"Streams\">21334\n",
        "\"\"\"\n",
        "\n",
        "sql_top_streams = ps.sqldf(top_query, locals())\n",
        "sql_top_streams"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {
        "id": "7CDAyh7F1XiJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aec9441-befc-4a4e-86bf-5208b3bb0908"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_sql_songs', answer = sql_top_streams)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.3 Duration of songs\n"
      ],
      "metadata": {
        "id": "HtyFP5fTTofv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we know which songs are hits, we'd like to listen to songs that are not too short nor too long.\n",
        "\n",
        "**TODO**: Using **pandas**, \n",
        "Filter out songs from `songs_df` whose duration is between 3 and 5 minutes.\n",
        "\n",
        "- Create a new column in `songs_df` called \"duration_min\" that converts the duration in \"duration_ms\" from milliseconds to minutes\n",
        "- Extract only songs whose duration is at least 3 minutes and at most 5 minutes. Then, save the output to `ideal_songs_df`.\n"
      ],
      "metadata": {
        "id": "2L4-w56v1kJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "songs_df[\"duration_min\"]=songs_df[\"duration_ms\"]/60000\n",
        "ideal_songs_df=songs_df[(songs_df[\"duration_min\"]>=3)&(songs_df[\"duration_min\"]<=5)]\n",
        "ideal_songs_df"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "wkEb8A56Htdx",
        "outputId": "53a5d110-0656-4d30-8a04-cae6991e7c14"
      },
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       id                                name  \\\n",
              "0   7qiZfU4dY1lWllzX7mPBI                        Shape of You   \n",
              "1   5CtI0qwDJkDQGwXD1H1cL                   Despacito - Remix   \n",
              "2   4aWmUDTfIPGksMNLV2rQP  Despacito (Featuring Daddy Yankee)   \n",
              "3   6RUKPb4LETWmmr3iAEQkt            Something Just Like This   \n",
              "4   3DXncPQOG4VBw3QHh3S81                         I'm the One   \n",
              "..                    ...                                 ...   \n",
              "94  2fQrGHiQOvpL9UgPvtYy6                        Bank Account   \n",
              "95  1PSBzsahR2AKwLJgx8ehB    Bad Things (with Camila Cabello)   \n",
              "96  0QsvXIfqM0zZoerQfsI9l                   Don't Let Me Down   \n",
              "98  7i2DJ88J7jQ8K7zqFX2fW                        Now Or Never   \n",
              "99  1j4kHkkpqZRBwE0A4CN4Y         Dusk Till Dawn - Radio Edit   \n",
              "\n",
              "              artists  duration_ms  \\\n",
              "0          Ed Sheeran       233713   \n",
              "1          Luis Fonsi       228827   \n",
              "2          Luis Fonsi       228200   \n",
              "3    The Chainsmokers       247160   \n",
              "4           DJ Khaled       288600   \n",
              "..                ...          ...   \n",
              "94          21 Savage       220307   \n",
              "95  Machine Gun Kelly       239293   \n",
              "96   The Chainsmokers       208053   \n",
              "98             Halsey       214802   \n",
              "99               ZAYN       239000   \n",
              "\n",
              "                                              reviews  duration_min  \n",
              "0                      vocal has a nice warm quality.      3.895217  \n",
              "1   Very European feeling. I like that the singer ...      3.813783  \n",
              "2   Unique and quirky. Arrangement was good and ni...      3.803333  \n",
              "3   Tastefully put together song. Good instrumenta...      4.119333  \n",
              "4                               Your voice is awesome      4.810000  \n",
              "..                                                ...           ...  \n",
              "94  Good vocal, Good arrangement, Good subject mat...      3.671783  \n",
              "95                                  Good strong voice      3.988217  \n",
              "96                             good movie sound track      3.467550  \n",
              "98                                     Good lyrics...      3.580033  \n",
              "99  Good hook - unique vocal presentation - I like...      3.983333  \n",
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              "      <td>288600</td>\n",
              "      <td>Your voice is awesome</td>\n",
              "      <td>4.810000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6</td>\n",
              "      <td>Bank Account</td>\n",
              "      <td>21 Savage</td>\n",
              "      <td>220307</td>\n",
              "      <td>Good vocal, Good arrangement, Good subject mat...</td>\n",
              "      <td>3.671783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1PSBzsahR2AKwLJgx8ehB</td>\n",
              "      <td>Bad Things (with Camila Cabello)</td>\n",
              "      <td>Machine Gun Kelly</td>\n",
              "      <td>239293</td>\n",
              "      <td>Good strong voice</td>\n",
              "      <td>3.988217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0QsvXIfqM0zZoerQfsI9l</td>\n",
              "      <td>Don't Let Me Down</td>\n",
              "      <td>The Chainsmokers</td>\n",
              "      <td>208053</td>\n",
              "      <td>good movie sound track</td>\n",
              "      <td>3.467550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>7i2DJ88J7jQ8K7zqFX2fW</td>\n",
              "      <td>Now Or Never</td>\n",
              "      <td>Halsey</td>\n",
              "      <td>214802</td>\n",
              "      <td>Good lyrics...</td>\n",
              "      <td>3.580033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1j4kHkkpqZRBwE0A4CN4Y</td>\n",
              "      <td>Dusk Till Dawn - Radio Edit</td>\n",
              "      <td>ZAYN</td>\n",
              "      <td>239000</td>\n",
              "      <td>Good hook - unique vocal presentation - I like...</td>\n",
              "      <td>3.983333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b80104e6-40a6-441b-ac72-dacf1455f712')\"\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_song_duration', answer = ideal_songs_df)"
      ],
      "metadata": {
        "id": "MsffWUCu95hV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d293757-7fd5-417d-b0d8-bfc24427e59d"
      },
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4 Who are the highest ranked artists?"
      ],
      "metadata": {
        "id": "TPfX04pDAAWf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ooVqjTGm9eCA"
      },
      "source": [
        "Which artists have been ranked #1 the most times in 2017?\n",
        "\n",
        "**TODO**: Using the dataframe `rankings_df`, perform the following tasks twice, once using **pandas** and once using **pandasql**:\n",
        "- Extract the names of artist that have `position` as 1, and store this data in `pd_pos_df`\n",
        "- Using `pd_pos_df`, find the number of times each artist was ranked #1\n",
        "- Get the Top 10 artists, i.e. the 10 artists which have been ranked #1 the most times. \n",
        "\n",
        "The dataframe `pd_pos_df` should have the following schema:\n",
        "\n",
        ">Artist | Position\n",
        ">--- | ---"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rankings_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oLDo4myjM8Ln",
        "outputId": "94696b7c-7279-4683-f362-7ce1773a0846"
      },
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  </div>\n",
              "  "
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          "metadata": {},
          "execution_count": 200
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        {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
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              "             Artist  Position\n",
              "107      Luis Fonsi      4085\n",
              "54       Ed Sheeran      3780\n",
              "144     Post Malone      1737\n",
              "79         J Balvin      1195\n",
              "114          Maluma       900\n",
              "130   Natti Natasha       416\n",
              "16        Bad Bunny       311\n",
              "167    Taylor Swift       304\n",
              "47      Danny Ocean       291\n",
              "29   Camila Cabello       272"
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "# TODO: pandas version\n",
        "pd_pos_df=rankings_df[rankings_df[\"Position\"]==1]\n",
        "pd_pos_df[\"Count\"]=1\n",
        "pd_pos_df=pd_pos_df.groupby(\"Artist\")[\"Count\"].sum()\n",
        "pd_pos_df=pd_pos_df.reset_index()\n",
        "pd_pos_df=pd_pos_df.sort_values(by=[\"Count\"],ascending=False)\n",
        "pd_pos_df=pd_pos_df.rename(columns={\"Count\":\"Position\"})\n",
        "pd_pos_df=pd_pos_df.iloc[0:10]\n",
        "pd_pos_df"
      ]
    },
    {
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          "height": 363
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              "           Artist  Position\n",
              "0      Luis Fonsi      4085\n",
              "1      Ed Sheeran      3780\n",
              "2     Post Malone      1737\n",
              "3        J Balvin      1195\n",
              "4          Maluma       900\n",
              "5   Natti Natasha       416\n",
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              "7    Taylor Swift       304\n",
              "8     Danny Ocean       291\n",
              "9  Camila Cabello       272"
            ],
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              "          const element = document.querySelector('#df-08c44b26-b208-4a50-83ad-cc8446ba06da');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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        }
      ],
      "source": [
        "# TODO: pandasql version\n",
        "pos_query = \"\"\" \n",
        "SELECT Artist, COUNT(*) AS Position\n",
        "FROM rankings_df \n",
        "WHERE Position=1 \n",
        "GROUP BY Artist \n",
        "ORDER BY Position DESC LIMIT 10\n",
        "\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "sql_pos_df = ps.sqldf(pos_query, locals())\n",
        "sql_pos_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "metadata": {
        "id": "EdqFmCL6xBPv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6592e178-cc2f-49e9-f478-77f8fda9bb26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 6/6 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 6 points\n",
        "grader.grade(test_case_id = 'test_artist_rank', answer = (pd_pos_df, sql_pos_df, pos_query))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.5 Popular Artists!"
      ],
      "metadata": {
        "id": "z0pHH3APITSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Are there artists whose songs are streamed more often than others? Let's find out!"
      ],
      "metadata": {
        "id": "e454FhRkJJvY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TODO:**\n",
        "\n",
        "Perform the following task involving the dataframe `rankings_df` twice, once using **pandas** and once using **pandasql**. Call the output dataframe `pd_summer_df` or `sql_summer_df` (based on whether you used pandas or pandasql).\n",
        "\n",
        "- Consider rows in `rankings_df` that are during Summer 2017 \n",
        "  - Note: Consider the duration of summer to be from 15th June 2017 to 16th September 2017 (both dates inclusive)\n",
        "- Find the total number of streams corresponding to each artist, then store this data in a new column called `Number`. \n",
        "- Sort this dataframe on the `Number` column so that the most popular artists appear first (i.e. sort according to the `Number` column in descending order).\n",
        "- Remember to call the output dataframe `pd_summer_df` or `sql_summer_df` (based on whether you used pandas or pandasql).\n",
        "\n",
        "The dataframes `pd_summer_df` and `sql_summer_df` should both have the following schema:\n",
        "\n",
        ">Artist | Number\n",
        ">--- | ---\n",
        "\n"
      ],
      "metadata": {
        "id": "EtTpU657JK1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rankings_df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2bnRlwQpFzqX",
        "outputId": "3bd39c83-6eae-43f7-bac3-b00ed21d1767"
      },
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0  Position  \\\n",
              "0                 0         1   \n",
              "2658317     2658317       118   \n",
              "2658316     2658316       117   \n",
              "2658315     2658315       116   \n",
              "2658314     2658314       115   \n",
              "...             ...       ...   \n",
              "1498830     1498830       109   \n",
              "1498829     1498829       108   \n",
              "1498828     1498828       107   \n",
              "1498841     1498841       120   \n",
              "3441196     3441196       200   \n",
              "\n",
              "                                                Track Name           Artist  \\\n",
              "0                               Reggaetón Lento (Bailemos)             CNCO   \n",
              "2658317  Steady 1234 (feat. Jasmine Thompson & Skizzy M...             Vice   \n",
              "2658316                     Dynamite (feat. Pretty Sister)            Nause   \n",
              "2658315                                              Hello            Adele   \n",
              "2658314                                    One Night Stand          B-Brave   \n",
              "...                                                    ...              ...   \n",
              "1498830                                         Sky Walker           Miguel   \n",
              "1498829  Back to You (feat. Bebe Rexha & Digital Farm A...  Louis Tomlinson   \n",
              "1498828                                           Betrayed          Lil Xan   \n",
              "1498841            Rockabye (feat. Sean Paul & Anne-Marie)     Clean Bandit   \n",
              "3441196                                         Let Her Go        Passenger   \n",
              "\n",
              "         Streams       Date Region                     ID  \n",
              "0          19272 2017-01-01     ec  3AEZUABDXNtecAOSC1qTf  \n",
              "2658317    15142 2017-01-01     nl  40UroIGvsMPLPBYwH8rMN  \n",
              "2658316    15152 2017-01-01     nl  2Ae5awwKvQpTBKQHr1TYC  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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            ]
          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "dateString=[]\n",
        "df=rankings_df\n",
        "for date in df[\"Date\"]:\n",
        "  dateString.append(dt.datetime.strptime(str(date),\"%Y-%m-%d %H:%M:%S\").strftime(\"%Y-%m-%d\"))\n",
        "df[\"Date\"]=dateString\n",
        "#Put separately because it is annoying to debug"
      ],
      "metadata": {
        "id": "KTCSBvWOipW_"
      },
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: pandas\n",
        "\n",
        "\n",
        "pd_summer_df=df[(rankings_df[\"Date\"]>=\"2017-06-15\")&(rankings_df[\"Date\"]<=\"2017-09-16\")] \n",
        "pd_summer_df=pd_summer_df.groupby(\"Artist\")[\"Streams\"].sum()\n",
        "pd_summer_df=pd_summer_df.reset_index()\n",
        "pd_summer_df=pd_summer_df.rename(columns={\"Streams\":\"Number\"})\n",
        "pd_summer_df=pd_summer_df.sort_values(by=[\"Number\"],ascending=False)\n",
        "pd_summer_df"
      ],
      "metadata": {
        "id": "e6ZDtxd6JP3p",
        "colab": {
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          "height": 424
        },
        "outputId": "d8a82e8a-51aa-481e-87bc-92502b20d780"
      },
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Artist      Number\n",
              "766          Ed Sheeran  1331033447\n",
              "584           DJ Khaled  1298364289\n",
              "1657         Luis Fonsi  1186212514\n",
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              "1183           J Balvin  1021849673\n",
              "...                 ...         ...\n",
              "3005         Á Móti Sól        1052\n",
              "1129             Huntar        1049\n",
              "2666          The Panas        1032\n",
              "2535  Stefán Hilmarsson        1022\n",
              "673              Delano        1010\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#TODO: pandasql\n",
        "# summer_query = \"\"\"\n",
        "# SELECT Artist,SUM(Streams) AS Number\n",
        "# FROM df\n",
        "# WHERE Date BETWEEN \"2017-06-15\" AND \"2017-09-16\"\n",
        "# GROUP BY Artist ORDER BY Number DESC\n",
        "# \"\"\"\n",
        "summer_query = \"\"\"\n",
        "SELECT Artist, SUM(Streams) AS Number \n",
        "FROM df \n",
        "WHERE Date BETWEEN \"2017-06-15\" AND \"2017-09-16\" \n",
        "GROUP BY Artist ORDER BY Number DESC\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "sql_summer_df = ps.sqldf(summer_query, locals())\n",
        "sql_summer_df"
      ],
      "metadata": {
        "id": "ZpfohK_wJQly",
        "colab": {
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          "height": 424
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        "outputId": "bb94f477-e2b8-402d-e82c-b467a019e976"
      },
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                 Artist      Number\n",
              "0            Ed Sheeran  1331033447\n",
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              "3096         Á Móti Sól        1052\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7 points\n",
        "grader.grade(test_case_id = 'test_summer', answer = (summer_query,pd_summer_df,sql_summer_df))"
      ],
      "metadata": {
        "id": "RbmJFcVzJTCa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ed25a41-c70a-480f-c30b-b9affbc98f38"
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 7/7 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.6 Which songs are danceable but also mellow?"
      ],
      "metadata": {
        "id": "z_-YPBndAwO2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let us switch gears and examine `songs_df` and `features_df`. In particular, we want to find the songs with high danceability and low tempo.\n",
        "\n",
        "**TODO**: Perform the following tasks, once using **pandas** and once using **pandasql**:\n",
        "- Before performing any operations,  round the `danceability` column to one decimal place, and call the resultant column `r_danceability`. This will allow us to conduct a more general (coarser) analysis of the data.\n",
        "- Merge `songs_df` and `features_df`, then sort the songs with danceability in *descending* order and tempo in *ascending* order. (When sorting, make sure to use the `r_danceability` column.)\n",
        "- Call the output dataframe `pd_songs_features_df` or `sql_songs_features_df` based on whether you used pandas or pandasql.\n"
      ],
      "metadata": {
        "id": "KXfQna4lAqHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "songs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YiRWjI8O_d1e",
        "outputId": "3f8e7db7-8bf7-4e1a-9ae5-22411829f4bb"
      },
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                       id                                name  \\\n",
              "0   7qiZfU4dY1lWllzX7mPBI                        Shape of You   \n",
              "1   5CtI0qwDJkDQGwXD1H1cL                   Despacito - Remix   \n",
              "2   4aWmUDTfIPGksMNLV2rQP  Despacito (Featuring Daddy Yankee)   \n",
              "3   6RUKPb4LETWmmr3iAEQkt            Something Just Like This   \n",
              "4   3DXncPQOG4VBw3QHh3S81                         I'm the One   \n",
              "..                    ...                                 ...   \n",
              "95  1PSBzsahR2AKwLJgx8ehB    Bad Things (with Camila Cabello)   \n",
              "96  0QsvXIfqM0zZoerQfsI9l                   Don't Let Me Down   \n",
              "97  7mldq42yDuxiUNn08nvzH               Body Like A Back Road   \n",
              "98  7i2DJ88J7jQ8K7zqFX2fW                        Now Or Never   \n",
              "99  1j4kHkkpqZRBwE0A4CN4Y         Dusk Till Dawn - Radio Edit   \n",
              "\n",
              "              artists  duration_ms  \\\n",
              "0          Ed Sheeran       233713   \n",
              "1          Luis Fonsi       228827   \n",
              "2          Luis Fonsi       228200   \n",
              "3    The Chainsmokers       247160   \n",
              "4           DJ Khaled       288600   \n",
              "..                ...          ...   \n",
              "95  Machine Gun Kelly       239293   \n",
              "96   The Chainsmokers       208053   \n",
              "97           Sam Hunt       165387   \n",
              "98             Halsey       214802   \n",
              "99               ZAYN       239000   \n",
              "\n",
              "                                              reviews  duration_min  \n",
              "0                      vocal has a nice warm quality.      3.895217  \n",
              "1   Very European feeling. I like that the singer ...      3.813783  \n",
              "2   Unique and quirky. Arrangement was good and ni...      3.803333  \n",
              "3   Tastefully put together song. Good instrumenta...      4.119333  \n",
              "4                               Your voice is awesome      4.810000  \n",
              "..                                                ...           ...  \n",
              "95                                  Good strong voice      3.988217  \n",
              "96                             good movie sound track      3.467550  \n",
              "97                    Good melody. A little different      2.756450  \n",
              "98                                     Good lyrics...      3.580033  \n",
              "99  Good hook - unique vocal presentation - I like...      3.983333  \n",
              "\n",
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              "      <td>165387</td>\n",
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              "      <td>2.756450</td>\n",
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              "      <td>7i2DJ88J7jQ8K7zqFX2fW</td>\n",
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              "<p>100 rows × 6 columns</p>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 209
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "features_df"
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        "id": "C8PVyF-f_dPu",
        "outputId": "2c0644f1-2018-4948-f12e-b1f22bc3cd0e"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0                     id  danceability  energy   key  loudness  \\\n",
              "0            0  7qiZfU4dY1lWllzX7mPBI         0.825   0.652   1.0    -3.183   \n",
              "1            1  5CtI0qwDJkDQGwXD1H1cL         0.694   0.815   2.0    -4.328   \n",
              "2            2  4aWmUDTfIPGksMNLV2rQP         0.660   0.786   2.0    -4.757   \n",
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              "4            4  3DXncPQOG4VBw3QHh3S81         0.609   0.668   7.0    -4.284   \n",
              "..         ...                    ...           ...     ...   ...       ...   \n",
              "95          95  1PSBzsahR2AKwLJgx8ehB         0.675   0.690   2.0    -4.761   \n",
              "96          96  0QsvXIfqM0zZoerQfsI9l         0.542   0.859  11.0    -5.651   \n",
              "97          97  7mldq42yDuxiUNn08nvzH         0.731   0.469   5.0    -7.226   \n",
              "98          98  7i2DJ88J7jQ8K7zqFX2fW         0.658   0.588   6.0    -4.902   \n",
              "99          99  1j4kHkkpqZRBwE0A4CN4Y         0.258   0.437  11.0    -6.593   \n",
              "\n",
              "    mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
              "0    0.0       0.0802        0.5810          0.000000    0.0931   0.9310   \n",
              "1    1.0       0.1200        0.2290          0.000000    0.0924   0.8130   \n",
              "2    1.0       0.1700        0.2090          0.000000    0.1120   0.8460   \n",
              "3    0.0       0.0317        0.0498          0.000014    0.1640   0.4460   \n",
              "4    1.0       0.0367        0.0552          0.000000    0.1670   0.8110   \n",
              "..   ...          ...           ...               ...       ...      ...   \n",
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              "96   1.0       0.1970        0.1600          0.004660    0.1370   0.4030   \n",
              "97   1.0       0.0326        0.4630          0.000001    0.1030   0.6310   \n",
              "98   0.0       0.0367        0.1050          0.000001    0.1250   0.4340   \n",
              "99   0.0       0.0390        0.1010          0.000001    0.1060   0.0967   \n",
              "\n",
              "      tempo  \n",
              "0    95.977  \n",
              "1    88.931  \n",
              "2   177.833  \n",
              "3   103.019  \n",
              "4    80.924  \n",
              "..      ...  \n",
              "95  137.817  \n",
              "96  159.797  \n",
              "97   98.963  \n",
              "98  110.075  \n",
              "99  180.043  \n",
              "\n",
              "[100 rows x 13 columns]"
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              "<p>100 rows × 13 columns</p>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 210
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      ]
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    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "id": "JULj_WXFPIuQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "cdece99d-a391-46ef-a7b2-0d837aee1283"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0                     id  danceability  energy   key  loudness  \\\n",
              "94          94  2fQrGHiQOvpL9UgPvtYy6         0.884   0.346   8.0    -8.228   \n",
              "42          42  5bcTCxgc7xVfSaMV3RuVk         0.893   0.745  11.0    -3.105   \n",
              "47          47  6mICuAdrwEjh6Y6lroV2K         0.852   0.773   8.0    -2.921   \n",
              "38          38  6EpRaXYhGOB3fj4V2uDkM         0.869   0.485   6.0    -5.595   \n",
              "91          91  4c2W3VKsOFoIg2SFaO6DY         0.855   0.624   1.0    -4.093   \n",
              "..         ...                    ...           ...     ...   ...       ...   \n",
              "52          52  4pdPtRcBmOSQDlJ3Fk945         0.476   0.718   8.0    -5.309   \n",
              "22          22  5uCax9HTNlzGybIStD3vD         0.358   0.557  10.0    -7.398   \n",
              "63          63  6520aj0B4FSKGVuKNsOCO         0.448   0.801   0.0    -5.363   \n",
              "66          66  5hYTyyh2odQKphUbMqc5g         0.314   0.555   9.0    -9.601   \n",
              "99          99  1j4kHkkpqZRBwE0A4CN4Y         0.258   0.437  11.0    -6.593   \n",
              "\n",
              "    mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
              "94   0.0       0.3510        0.0151          0.000007    0.0871   0.3760   \n",
              "42   0.0       0.0571        0.0642          0.000000    0.0943   0.8720   \n",
              "47   0.0       0.0776        0.1870          0.000030    0.1590   0.9070   \n",
              "38   1.0       0.0545        0.2460          0.000000    0.0765   0.5270   \n",
              "91   1.0       0.0488        0.1580          0.000000    0.0513   0.9620   \n",
              "..   ...          ...           ...               ...       ...      ...   \n",
              "52   1.0       0.0576        0.0784          0.000010    0.1220   0.1420   \n",
              "22   1.0       0.0590        0.6950          0.000000    0.0902   0.4940   \n",
              "63   1.0       0.1650        0.0733          0.000000    0.1460   0.4620   \n",
              "66   1.0       0.3700        0.1570          0.000108    0.0670   0.1590   \n",
              "99   0.0       0.0390        0.1010          0.000001    0.1060   0.0967   \n",
              "\n",
              "      tempo  r_danceability                            name        artists  \\\n",
              "94   75.016             0.9                    Bank Account      21 Savage   \n",
              "42  101.018             0.9                           Feels  Calvin Harris   \n",
              "47  102.034             0.9                        Chantaje        Shakira   \n",
              "38  106.028             0.9                 Strip That Down     Liam Payne   \n",
              "91  117.959             0.9                       Your Song       Rita Ora   \n",
              "..      ...             ...                             ...            ...   \n",
              "52  199.864             0.5                 Let Me Love You       DJ Snake   \n",
              "22   85.043             0.4            Say You Won't Let Go   James Arthur   \n",
              "63  189.798             0.4           Chained To The Rhythm     Katy Perry   \n",
              "66  179.666             0.3  How Far I'll Go - From \"Moana\"   Alessia Cara   \n",
              "99  180.043             0.3     Dusk Till Dawn - Radio Edit           ZAYN   \n",
              "\n",
              "    duration_ms                                            reviews  \\\n",
              "94       220307  Good vocal, Good arrangement, Good subject mat...   \n",
              "42       223413                        Overall quite a nice sound.   \n",
              "47       195840  NICE TUNE WITH SOME NEAT CHORD CHANGES....VERY...   \n",
              "38       204502  Rich clean vocals. Nice incidental instrumenta...   \n",
              "91       180757        Great music ,Great voice ,arrangement, etc.   \n",
              "..          ...                                                ...   \n",
              "52       205947  Nice melodies, especially in the beginning. Ha...   \n",
              "22       211467                           truely unique, i like it   \n",
              "63       237734          Like the recording .nice and clear,smooth   \n",
              "66       175517                   Kind of reminds me of new music.   \n",
              "99       239000  Good hook - unique vocal presentation - I like...   \n",
              "\n",
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              "..           ...  \n",
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              "      <th>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>danceability</th>\n",
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              "      <td>Overall quite a nice sound.</td>\n",
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              "      <td>0.9070</td>\n",
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              "      <td>0.9</td>\n",
              "      <td>Chantaje</td>\n",
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              "      <td>195840</td>\n",
              "      <td>NICE TUNE WITH SOME NEAT CHORD CHANGES....VERY...</td>\n",
              "      <td>3.264000</td>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>38</td>\n",
              "      <td>6EpRaXYhGOB3fj4V2uDkM</td>\n",
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              "      <td>0.485</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0765</td>\n",
              "      <td>0.5270</td>\n",
              "      <td>106.028</td>\n",
              "      <td>0.9</td>\n",
              "      <td>Strip That Down</td>\n",
              "      <td>Liam Payne</td>\n",
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              "      <td>Rich clean vocals. Nice incidental instrumenta...</td>\n",
              "      <td>3.408367</td>\n",
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              "      <th>91</th>\n",
              "      <td>91</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>4pdPtRcBmOSQDlJ3Fk945</td>\n",
              "      <td>0.476</td>\n",
              "      <td>0.718</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>0.1220</td>\n",
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              "      <td>0.358</td>\n",
              "      <td>0.557</td>\n",
              "      <td>10.0</td>\n",
              "      <td>-7.398</td>\n",
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              "      <td>0.0902</td>\n",
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              "      <td>211467</td>\n",
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              "      <td>3.524450</td>\n",
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              "      <th>63</th>\n",
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              "      <td>3.962233</td>\n",
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              "      <td>9.0</td>\n",
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              "      <td>0.000108</td>\n",
              "      <td>0.0670</td>\n",
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              "      <td>179.666</td>\n",
              "      <td>0.3</td>\n",
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              "      <td>Alessia Cara</td>\n",
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              "      <td>Kind of reminds me of new music.</td>\n",
              "      <td>2.925283</td>\n",
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              "    <tr>\n",
              "      <th>99</th>\n",
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              "      <td>ZAYN</td>\n",
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              "      <td>3.983333</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "# TODO: pandas\n",
        "features_df[\"r_danceability\"]=features_df[\"danceability\"].round(1)\n",
        "pd_songs_features_df=features_df.join(songs_df.set_index(\"id\"),on=\"id\")\n",
        "pd_songs_features_df=pd_songs_features_df.sort_values(by=[\"r_danceability\",\"tempo\"],ascending=[False,True])\n",
        "pd_songs_features_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4 points\n",
        "grader.grade(test_case_id = 'test_danceability_tempo_pd', answer = pd_songs_features_df[['id', 'r_danceability', 'tempo']])"
      ],
      "metadata": {
        "id": "Giz4shRf3eAZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f081bf8e-ba76-4d74-c432-b2567c677e46"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 4/4 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "metadata": {
        "id": "_EMJdmivPIuR",
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          "height": 887
        },
        "outputId": "fd48edaa-d6e8-481d-edac-c3eb3d7729e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       id                            name        artists  \\\n",
              "0   2fQrGHiQOvpL9UgPvtYy6                    Bank Account      21 Savage   \n",
              "1   5bcTCxgc7xVfSaMV3RuVk                           Feels  Calvin Harris   \n",
              "2   6mICuAdrwEjh6Y6lroV2K                        Chantaje        Shakira   \n",
              "3   6EpRaXYhGOB3fj4V2uDkM                 Strip That Down     Liam Payne   \n",
              "4   4c2W3VKsOFoIg2SFaO6DY                       Your Song       Rita Ora   \n",
              "..                    ...                             ...            ...   \n",
              "95  4pdPtRcBmOSQDlJ3Fk945                 Let Me Love You       DJ Snake   \n",
              "96  5uCax9HTNlzGybIStD3vD            Say You Won't Let Go   James Arthur   \n",
              "97  6520aj0B4FSKGVuKNsOCO           Chained To The Rhythm     Katy Perry   \n",
              "98  5hYTyyh2odQKphUbMqc5g  How Far I'll Go - From \"Moana\"   Alessia Cara   \n",
              "99  1j4kHkkpqZRBwE0A4CN4Y     Dusk Till Dawn - Radio Edit           ZAYN   \n",
              "\n",
              "    duration_ms                                            reviews  \\\n",
              "0        220307  Good vocal, Good arrangement, Good subject mat...   \n",
              "1        223413                        Overall quite a nice sound.   \n",
              "2        195840  NICE TUNE WITH SOME NEAT CHORD CHANGES....VERY...   \n",
              "3        204502  Rich clean vocals. Nice incidental instrumenta...   \n",
              "4        180757        Great music ,Great voice ,arrangement, etc.   \n",
              "..          ...                                                ...   \n",
              "95       205947  Nice melodies, especially in the beginning. Ha...   \n",
              "96       211467                           truely unique, i like it   \n",
              "97       237734          Like the recording .nice and clear,smooth   \n",
              "98       175517                   Kind of reminds me of new music.   \n",
              "99       239000  Good hook - unique vocal presentation - I like...   \n",
              "\n",
              "    duration_min  Unnamed: 0                     id  danceability  energy  \\\n",
              "0       3.671783          94  2fQrGHiQOvpL9UgPvtYy6         0.884   0.346   \n",
              "1       3.723550          42  5bcTCxgc7xVfSaMV3RuVk         0.893   0.745   \n",
              "2       3.264000          47  6mICuAdrwEjh6Y6lroV2K         0.852   0.773   \n",
              "3       3.408367          38  6EpRaXYhGOB3fj4V2uDkM         0.869   0.485   \n",
              "4       3.012617          91  4c2W3VKsOFoIg2SFaO6DY         0.855   0.624   \n",
              "..           ...         ...                    ...           ...     ...   \n",
              "95      3.432450          52  4pdPtRcBmOSQDlJ3Fk945         0.476   0.718   \n",
              "96      3.524450          22  5uCax9HTNlzGybIStD3vD         0.358   0.557   \n",
              "97      3.962233          63  6520aj0B4FSKGVuKNsOCO         0.448   0.801   \n",
              "98      2.925283          66  5hYTyyh2odQKphUbMqc5g         0.314   0.555   \n",
              "99      3.983333          99  1j4kHkkpqZRBwE0A4CN4Y         0.258   0.437   \n",
              "\n",
              "     key  loudness  mode  speechiness  acousticness  instrumentalness  \\\n",
              "0    8.0    -8.228   0.0       0.3510        0.0151          0.000007   \n",
              "1   11.0    -3.105   0.0       0.0571        0.0642          0.000000   \n",
              "2    8.0    -2.921   0.0       0.0776        0.1870          0.000030   \n",
              "3    6.0    -5.595   1.0       0.0545        0.2460          0.000000   \n",
              "4    1.0    -4.093   1.0       0.0488        0.1580          0.000000   \n",
              "..   ...       ...   ...          ...           ...               ...   \n",
              "95   8.0    -5.309   1.0       0.0576        0.0784          0.000010   \n",
              "96  10.0    -7.398   1.0       0.0590        0.6950          0.000000   \n",
              "97   0.0    -5.363   1.0       0.1650        0.0733          0.000000   \n",
              "98   9.0    -9.601   1.0       0.3700        0.1570          0.000108   \n",
              "99  11.0    -6.593   0.0       0.0390        0.1010          0.000001   \n",
              "\n",
              "    liveness  valence    tempo  r_danceability  \n",
              "0     0.0871   0.3760   75.016             0.9  \n",
              "1     0.0943   0.8720  101.018             0.9  \n",
              "2     0.1590   0.9070  102.034             0.9  \n",
              "3     0.0765   0.5270  106.028             0.9  \n",
              "4     0.0513   0.9620  117.959             0.9  \n",
              "..       ...      ...      ...             ...  \n",
              "95    0.1220   0.1420  199.864             0.5  \n",
              "96    0.0902   0.4940   85.043             0.4  \n",
              "97    0.1460   0.4620  189.798             0.4  \n",
              "98    0.0670   0.1590  179.666             0.3  \n",
              "99    0.1060   0.0967  180.043             0.3  \n",
              "\n",
              "[100 rows x 20 columns]"
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              "      <th>id</th>\n",
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              "      <th>0</th>\n",
              "      <td>2fQrGHiQOvpL9UgPvtYy6</td>\n",
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              "      <th>1</th>\n",
              "      <td>5bcTCxgc7xVfSaMV3RuVk</td>\n",
              "      <td>Feels</td>\n",
              "      <td>Calvin Harris</td>\n",
              "      <td>223413</td>\n",
              "      <td>Overall quite a nice sound.</td>\n",
              "      <td>3.723550</td>\n",
              "      <td>42</td>\n",
              "      <td>5bcTCxgc7xVfSaMV3RuVk</td>\n",
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              "      <th>2</th>\n",
              "      <td>6mICuAdrwEjh6Y6lroV2K</td>\n",
              "      <td>Chantaje</td>\n",
              "      <td>Shakira</td>\n",
              "      <td>195840</td>\n",
              "      <td>NICE TUNE WITH SOME NEAT CHORD CHANGES....VERY...</td>\n",
              "      <td>3.264000</td>\n",
              "      <td>47</td>\n",
              "      <td>6mICuAdrwEjh6Y6lroV2K</td>\n",
              "      <td>0.852</td>\n",
              "      <td>0.773</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-2.921</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0776</td>\n",
              "      <td>0.1870</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.1590</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6EpRaXYhGOB3fj4V2uDkM</td>\n",
              "      <td>Strip That Down</td>\n",
              "      <td>Liam Payne</td>\n",
              "      <td>204502</td>\n",
              "      <td>Rich clean vocals. Nice incidental instrumenta...</td>\n",
              "      <td>3.408367</td>\n",
              "      <td>38</td>\n",
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              "      <td>0.869</td>\n",
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              "      <td>6.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4c2W3VKsOFoIg2SFaO6DY</td>\n",
              "      <td>Your Song</td>\n",
              "      <td>Rita Ora</td>\n",
              "      <td>180757</td>\n",
              "      <td>Great music ,Great voice ,arrangement, etc.</td>\n",
              "      <td>3.012617</td>\n",
              "      <td>91</td>\n",
              "      <td>4c2W3VKsOFoIg2SFaO6DY</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>4pdPtRcBmOSQDlJ3Fk945</td>\n",
              "      <td>Let Me Love You</td>\n",
              "      <td>DJ Snake</td>\n",
              "      <td>205947</td>\n",
              "      <td>Nice melodies, especially in the beginning. Ha...</td>\n",
              "      <td>3.432450</td>\n",
              "      <td>52</td>\n",
              "      <td>4pdPtRcBmOSQDlJ3Fk945</td>\n",
              "      <td>0.476</td>\n",
              "      <td>0.718</td>\n",
              "      <td>8.0</td>\n",
              "      <td>-5.309</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0576</td>\n",
              "      <td>0.0784</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.1220</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>199.864</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>5uCax9HTNlzGybIStD3vD</td>\n",
              "      <td>Say You Won't Let Go</td>\n",
              "      <td>James Arthur</td>\n",
              "      <td>211467</td>\n",
              "      <td>truely unique, i like it</td>\n",
              "      <td>3.524450</td>\n",
              "      <td>22</td>\n",
              "      <td>5uCax9HTNlzGybIStD3vD</td>\n",
              "      <td>0.358</td>\n",
              "      <td>0.557</td>\n",
              "      <td>10.0</td>\n",
              "      <td>-7.398</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0590</td>\n",
              "      <td>0.6950</td>\n",
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              "      <td>0.0902</td>\n",
              "      <td>0.4940</td>\n",
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              "      <td>0.4</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6520aj0B4FSKGVuKNsOCO</td>\n",
              "      <td>Chained To The Rhythm</td>\n",
              "      <td>Katy Perry</td>\n",
              "      <td>237734</td>\n",
              "      <td>Like the recording .nice and clear,smooth</td>\n",
              "      <td>3.962233</td>\n",
              "      <td>63</td>\n",
              "      <td>6520aj0B4FSKGVuKNsOCO</td>\n",
              "      <td>0.448</td>\n",
              "      <td>0.801</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.1460</td>\n",
              "      <td>0.4620</td>\n",
              "      <td>189.798</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5hYTyyh2odQKphUbMqc5g</td>\n",
              "      <td>How Far I'll Go - From \"Moana\"</td>\n",
              "      <td>Alessia Cara</td>\n",
              "      <td>175517</td>\n",
              "      <td>Kind of reminds me of new music.</td>\n",
              "      <td>2.925283</td>\n",
              "      <td>66</td>\n",
              "      <td>5hYTyyh2odQKphUbMqc5g</td>\n",
              "      <td>0.314</td>\n",
              "      <td>0.555</td>\n",
              "      <td>9.0</td>\n",
              "      <td>-9.601</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>0.1570</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>0.0670</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>179.666</td>\n",
              "      <td>0.3</td>\n",
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              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1j4kHkkpqZRBwE0A4CN4Y</td>\n",
              "      <td>Dusk Till Dawn - Radio Edit</td>\n",
              "      <td>ZAYN</td>\n",
              "      <td>239000</td>\n",
              "      <td>Good hook - unique vocal presentation - I like...</td>\n",
              "      <td>3.983333</td>\n",
              "      <td>99</td>\n",
              "      <td>1j4kHkkpqZRBwE0A4CN4Y</td>\n",
              "      <td>0.258</td>\n",
              "      <td>0.437</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-6.593</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0390</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.0967</td>\n",
              "      <td>180.043</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 20 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ],
      "source": [
        "# TODO: pandasql version\n",
        "song_feature_query = \"\"\"\n",
        "SELECT *\n",
        "FROM songs_df\n",
        "INNER JOIN features_df\n",
        "ON features_df.id=songs_df.id\n",
        "ORDER BY features_df.r_danceability DESC, features_df.tempo ASC\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "sql_song_features_df = ps.sqldf(song_feature_query, locals())\n",
        "sql_song_features_df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4 points\n",
        "grader.grade(test_case_id = 'test_danceability_tempo_sql', answer = sql_song_features_df)"
      ],
      "metadata": {
        "id": "T-gAq3Qatstp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2937a4f3-6e4d-400d-a175-a479d486e4e0"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 4/4 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.7 Do we like the same songs?"
      ],
      "metadata": {
        "id": "5DnnjgEAJ5oS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.7.1 Which regions have the most streams?"
      ],
      "metadata": {
        "id": "7ZY94UNaJ9dg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TODO**: \n",
        "Perform the following tasks, once using **pandas** and once using **pandasql**:\n",
        "- Extract rows belonging to the top 2 regions that have the most streams. \n",
        "- Store your output in a new dataframe called `pd_top_regions_df` or `sql_top_regions_df` based on whether you used pandas or pandasql. Both of these dataframes should have same schema as `rankings_df`. Sort both these dataframes in descending order by `Streams`.\n",
        "\n",
        "Note: Since we want to focus on specific regions, we should disregard rows where the `Region` column has the value `\"global\"`."
      ],
      "metadata": {
        "id": "1SVGhv8VKB0U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rankings_df"
      ],
      "metadata": {
        "colab": {
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          },
          "metadata": {},
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: pandas\n",
        "pd_top_two_regions_df=rankings_df[rankings_df[\"Region\"]!=\"global\"].groupby(\"Region\")[\"Streams\"].sum()\n",
        "pd_top_two_regions_df=pd_top_two_regions_df.reset_index()\n",
        "pd_top_two_regions_df=pd_top_two_regions_df.sort_values(by=[\"Streams\"],ascending=False)\n",
        "pd_top_two_regions_df= pd_top_two_regions_df.reset_index()\n",
        "pd_top_two_regions_df=pd_top_two_regions_df.drop(columns=[\"index\"])\n",
        "a,b=pd_top_two_regions_df[\"Region\"][0],pd_top_two_regions_df[\"Region\"][1]\n",
        "pd_top_two_regions_df\n",
        "pd_top_regions_df=rankings_df[(rankings_df[\"Region\"]==a)|(rankings_df[\"Region\"]==b)]\n",
        "pd_top_regions_df=pd_top_regions_df.sort_values(by=[\"Streams\"],ascending=False)\n",
        "pd_top_regions_df"
      ],
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          "height": 424
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      "execution_count": 216,
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              "      <td>3042640</td>\n",
              "      <td>198</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#TODO: pandasql\n",
        "top_regions_query = \"\"\"\n",
        "SELECT * \n",
        "FROM rankings_df \n",
        "WHERE Region IN (SELECT Region FROM rankings_df WHERE Region!=\"global\" GROUP BY Region ORDER BY SUM(Streams) DESC LIMIT 2)\n",
        "\"\"\"\n",
        "\n",
        "sql_top_regions_df = ps.sqldf(top_regions_query, locals())\n",
        "sql_top_regions_df"
      ],
      "metadata": {
        "id": "7zOe_J6QKM5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "83102ee6-0046-4542-d97c-96836aabec36"
      },
      "execution_count": 217,
      "outputs": [
        {
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              "        Unnamed: 0  Position                          Track Name  \\\n",
              "0          3042585       143                           The Sound   \n",
              "1          3042584       142                Is This Love - Remix   \n",
              "2          3042583       141                           Sidewalks   \n",
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              "148371      845634        13                         Candy Paint   \n",
              "148372      845632        11  Codeine Dreaming (feat. Lil Wayne)   \n",
              "148373      845662        41                         Bad At Love   \n",
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              "                          Artist  Streams        Date Region  \\\n",
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            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7 points\n",
        "grader.grade(test_case_id = 'test_top_regions', answer = (top_regions_query,  pd_top_regions_df.head(1000),  sql_top_regions_df.head(1000), \n",
        "                                                          len(pd_top_regions_df), len(sql_top_regions_df)))"
      ],
      "metadata": {
        "id": "2WTQl8skKPKq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05914df0-c5e9-4a99-8f4a-3c34348de450"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 7/7 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.7.2 Do the regions with the most streams like different songs?"
      ],
      "metadata": {
        "id": "DVctGoa6KTY1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TODO**: Perform the following task, once using **pandas** and once using **pandasql**:\n",
        "- Find the songs that the two regions (found in 2.7.1) **DO NOT** have in common\n",
        "- Store the result in a new dataframe called `pd_diff_tracks_df` or `sql_diff_tracks_df` (based on whether you used pandas or pandasql). These two dataframes should both have the following schema:\n",
        "\n",
        ">ID | Track Name | Artist\n",
        ">--- | --- | ---\n",
        "\n",
        "**Hint**: Do we need to join any tables here? If yes, which type of join should we use?"
      ],
      "metadata": {
        "id": "z_LbdLyYKUPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: pandas\n",
        "a_df=rankings_df[rankings_df[\"Region\"]==a]\n",
        "b_df=rankings_df[rankings_df[\"Region\"]==b]\n",
        "#pd_diff_tracks_df=\n",
        "union=pd.Series(np.union1d(a_df[\"ID\"],b_df[\"ID\"]))\n",
        "intersection=pd.Series(np.intersect1d(a_df[\"ID\"],b_df[\"ID\"]))\n",
        "not_common_series = union[~union.isin(intersection)]\n",
        "not_common_series\n",
        "pd_diff_tracks_df = rankings_df[(rankings_df[\"ID\"].isin(not_common_series))&(rankings_df[\"Region\"].isin([a,b]))]\n",
        "pd_diff_tracks_df = pd_diff_tracks_df[['ID','Track Name','Artist']]\n",
        "pd_diff_tracks_df.drop_duplicates(subset='ID',inplace=True)\n",
        "\n",
        "pd_diff_tracks_df\n"
      ],
      "metadata": {
        "id": "tZj5inZjKbYj",
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          "base_uri": "https://localhost:8080/",
          "height": 424
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        "outputId": "99b2d594-1107-4735-d6a7-616af8fb627c"
      },
      "execution_count": 219,
      "outputs": [
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              "                            ID            Track Name                    Artist\n",
              "3042585  316r1KLN0bcmpr7TZcMCX             The Sound                  The 1975\n",
              "3042584  1w5sLDYzYAGI0AkLc6FPl  Is This Love - Remix  Bob Marley & The Wailers\n",
              "3042582  7gZQfdEQpmwAoPHSbEHzm           False Alarm                    Matoma\n",
              "3042580  1jNOi6m3Hn8nLEeHCp5Ms     Seven Nation Army         The White Stripes\n",
              "3042579  3gbBpTdY8lnQwqxNCcf79               Pompeii                  Bastille\n",
              "...                        ...                   ...                       ...\n",
              "3115739  4PzsYXrHh4r2BxtpNjtvo               Jumanji                   B Young\n",
              "3116237  3qIxnOusGoTpHvNVMLHsc              Tropical                        SL\n",
              "845595   2mfahQ0EaaZWq4cFNg6A1             All On Me              Devin Dawson\n",
              "3116394  761QvVHTibYjEi2r6A4g4              Get Free              Lana Del Rey\n",
              "845681   1LXbv9jiTtdBdHadZ317G              Designer                  Lil Pump\n",
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    {
      "cell_type": "code",
      "source": [
        "#TODO: pandasql\n",
        "diff_tracks_query = \"\"\"\n",
        "SELECT DISTINCT ID, \"Track Name\", Artist \n",
        "FROM rankings_df \n",
        "WHERE Region IN (\"us\",\"gb\") \n",
        "AND ID NOT IN (SELECT ID FROM rankings_df WHERE Region IN (\"us\") INTERSECT SELECT ID FROM rankings_df WHERE Region IN (\"gb\"))\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "sql_diff_tracks_df = ps.sqldf(diff_tracks_query, locals())\n",
        "sql_diff_tracks_df"
      ],
      "metadata": {
        "id": "y0rjmOsWKg2X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "2012f89f-bcf5-496d-c1fa-3ef019d0e66e"
      },
      "execution_count": 220,
      "outputs": [
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            "text/plain": [
              "                         ID            Track Name                    Artist\n",
              "0     316r1KLN0bcmpr7TZcMCX             The Sound                  The 1975\n",
              "1     1w5sLDYzYAGI0AkLc6FPl  Is This Love - Remix  Bob Marley & The Wailers\n",
              "2     7gZQfdEQpmwAoPHSbEHzm           False Alarm                    Matoma\n",
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              "4     3gbBpTdY8lnQwqxNCcf79               Pompeii                  Bastille\n",
              "...                     ...                   ...                       ...\n",
              "2447  4PzsYXrHh4r2BxtpNjtvo               Jumanji                   B Young\n",
              "2448  3qIxnOusGoTpHvNVMLHsc              Tropical                        SL\n",
              "2449  2mfahQ0EaaZWq4cFNg6A1             All On Me              Devin Dawson\n",
              "2450  761QvVHTibYjEi2r6A4g4              Get Free              Lana Del Rey\n",
              "2451  1LXbv9jiTtdBdHadZ317G              Designer                  Lil Pump\n",
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              "      <th>2448</th>\n",
              "      <td>3qIxnOusGoTpHvNVMLHsc</td>\n",
              "      <td>Tropical</td>\n",
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              "      <th>2449</th>\n",
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              "\n",
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              "          const element = document.querySelector('#df-5b276568-576d-4ffb-bfcb-01e2494f5921');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 15 points\n",
        "grader.grade(test_case_id = 'test_diff_tracks', answer = (diff_tracks_query, pd_diff_tracks_df, sql_diff_tracks_df))"
      ],
      "metadata": {
        "id": "gOqIiZB2KjHt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54ef49c2-5759-4d14-d4b0-16c74173860a"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 15/15 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.8 New Years Eve Party!"
      ],
      "metadata": {
        "id": "DLBPgtdYL9wR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Who doesn't love to dance? Let's find some songs to groove to!"
      ],
      "metadata": {
        "id": "KTLCzLSEL-0b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TODO**: Perform the following tasks using **pandasql** only:\n",
        "\n",
        "- Find the songs that made it to the charts in December 2017 (2017-12-01 to 2017-12-31) and whose duration is longer than 3 minutes\n",
        "- Find the artist and the danceability of these songs. Be sure to only include songs with danceability > 0.5.\n",
        "- Store the result in a new dataframe called `sql_dance_df` that has the following schema:\n",
        "\n",
        ">Artist | Track Name | danceability\n",
        ">--- | --- | ---\n",
        "\n",
        "\n",
        "**Hint**: Think about which data resides in which table!"
      ],
      "metadata": {
        "id": "n13Tlz8YMBbT"
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    },
    {
      "cell_type": "code",
      "source": [
        "#TODO: pandasql\n",
        "dance_query = \"\"\"\n",
        "SELECT DISTINCT Artist,\"Track Name\",danceability FROM songs_df JOIN features_df ON songs_df.id=features_df.id JOIN rankings_df ON songs_df.id=rankings_df.id WHERE rankings_df.Date>=\"2017-12-01\" AND rankings_df.DATE<=\"2017-12-31\" AND duration_ms>180000 AND danceability>0.5\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "sql_dance_df = ps.sqldf(dance_query, locals())\n",
        "sql_dance_df"
      ],
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        "id": "4O2qzjmFKlVy",
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        },
        "outputId": "9d488159-0e1a-4fb5-89ba-e1a5a062c965"
      },
      "execution_count": 222,
      "outputs": [
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              "               Artist                        Track Name  danceability\n",
              "0          Ed Sheeran                      Shape of You         0.825\n",
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              "4            Dua Lipa                         New Rules         0.771\n",
              "..                ...                               ...           ...\n",
              "80           Maroon 5               Cold (feat. Future)         0.697\n",
              "81         Steve Aoki                      Just Hold On         0.647\n",
              "82  Machine Gun Kelly  Bad Things (with Camila Cabello)         0.675\n",
              "83                AJR                              Weak         0.673\n",
              "84          The Vamps                         All Night         0.544\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# 7 points\n",
        "grader.grade(test_case_id = 'test_dance', answer = (dance_query, sql_dance_df))"
      ],
      "metadata": {
        "id": "ClsNu7kwMPmm",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1840db3-19f2-4dae-8429-f601e20cdf60"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 7/7 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3: Data Visualization [15 points total -- MANUALLY GRADED]"
      ],
      "metadata": {
        "id": "i9vvT8NkMTwR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The popularity of songs fluctuates as time progresses. We want to create a graph that illustrates the no. of streams for the most and least popular songs during each month in 2017.\n",
        "\n",
        "Perform the following tasks:\n",
        "- Find the song that had the *most* streams on `2017-01-01`\n",
        "- Find the song that had the *least* streams on `2017-01-01`\n",
        "- Find the no. of streams that these two songs received on the first day of each month in 2017 (eg. 2017-01-01, 2017-02-01, 2017-03-01 ... 2017-12-01) \n",
        "\n",
        "Plot a line graph which shows the trend you found! Make sure you use the ID of the two songs when creating this graph.\n",
        "\n",
        "**Hint**: This link is a useful resource: https://seaborn.pydata.org/generated/seaborn.lineplot.html"
      ],
      "metadata": {
        "id": "AnovSmOVMU4Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make sure your line graph has the following features:\n",
        "1. The X-axis should be labelled \"Date\", and the Y-axis should be labelled \"Streams\".\n",
        "2. There should be markers on the plot to specify the no. of streams each song received ia particular month.\n",
        "3. The lines corresponding to the two songs should have different colors.\n",
        "\n",
        "This section will be **manually graded**.\n"
      ],
      "metadata": {
        "id": "4cOKJDFZMZwd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rankings_df"
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      "execution_count": 224,
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba1951c0-cfa6-4052-8a2b-febac0e9855d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ba1951c0-cfa6-4052-8a2b-febac0e9855d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Plot a line chart\n",
        "\n",
        "\n",
        "max_streams=rankings_df[rankings_df[\"Date\"]==\"2017-01-01\"]\n",
        "max_streams=max_streams.sort_values(by=\"Streams\",ascending=False)\n",
        "max_streams=max_streams.reset_index()\n",
        "max_streams_id=max_streams[\"ID\"][0]\n",
        "max_streams_s=max_streams[\"Track Name\"][0]\n",
        "\n",
        "min_streams=rankings_df[rankings_df[\"Date\"]==\"2017-01-01\"]\n",
        "min_streams=min_streams.sort_values(by=[\"Streams\"],ascending=True)\n",
        "min_streams=min_streams.reset_index()\n",
        "min_streams_id=min_streams[\"ID\"][0]\n",
        "min_streams_s=min_streams[\"Track Name\"][0]\n",
        "\n",
        "\n",
        "max_streams_song=rankings_df[rankings_df[\"ID\"]==max_streams_id]\n",
        "\n",
        "max_streams_song=max_streams_song[max_streams_song[\"Date\"].astype(str).str.contains(\"2017-01-01|2017-02-01|2017-03-01|2017-04-01|2017-05-01|2017-06-01|2017-07-01|2017-08-01|2017-09-01|2017-10-01|2017-11-01|2017-12-01\")]\n",
        "max_streams_song=max_streams_song.groupby(\"Date\")[\"Streams\"].sum()\n",
        "max_streams_song=max_streams_song.reset_index()\n",
        "\n",
        "min_streams_song=rankings_df[rankings_df[\"ID\"]==min_streams_id]\n",
        "\n",
        "min_streams_song=min_streams_song[min_streams_song[\"Date\"].astype(str).str.contains(\"2017-01-01|2017-02-01|2017-03-01|2017-04-01|2017-05-01|2017-06-01|2017-07-01|2017-08-01|2017-09-01|2017-10-01|2017-11-01|2017-12-01\")]\n",
        "min_streams_song=min_streams_song.groupby(\"Date\")[\"Streams\"].sum()\n",
        "min_streams_song=min_streams_song.reset_index()\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(min_streams_song[\"Date\"],min_streams_song[\"Streams\"],marker=\"x\",label=min_streams_s)\n",
        "plt.plot(max_streams_song[\"Date\"],max_streams_song[\"Streams\"],marker=\"o\",label=max_streams_s)\n",
        "plt.xlim(\"2017-01-01\",\"2017-12-01\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Streams\")\n",
        "plt.title(\"Trend of Streams of \"+max_streams_s+\" and\"+min_streams_s)\n",
        "plt.legend([min_streams_s,max_streams_s])\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "GDFb24_HMclQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "92e5abce-b8eb-43d2-c4a3-20ced97da381"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbNUhlz9ftHI"
      },
      "source": [
        "## Part 4: Working with Text Data [14 points]\n",
        "\n",
        "Now, let's switch gears and try to text-based analysis. Textual data is complex, but can also be used to generate extremely interpretable results, making it both valuable and interesting. \n",
        "\n",
        "Throughout this section, we will attempt to answer the following question:\n",
        "\n",
        "**According to the `songs_df` dataframe, what do the reviews for the Top Tracks of 2017 look like?**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNwLCBeTOLy3"
      },
      "source": [
        "###4.1 Tokenizing the text\n",
        "\n",
        "We are going to split the contents of in the Reviews column into a list of words. We will use the **nltk** library, which contains an extensive set of tools for text processing. Now, this homework would be interminably long if we went into all the details of nltk. Thus, we are only going to use the following components of the library:\n",
        "- `nltk.word_tokenize()`: a function used to tokenize text\n",
        "- `nltk.corpus.stopwords`: a list of commonly used words such as \"a\", \"an\",\"in\" that are often ignored in text analysis\n",
        "\n",
        "Note that for this question, we didn't have to clean the text data first as our original dataset was well-formatted. However, in practice, we would typically clean the text first using regular expressions (regex). Keep this in mind as you work on the project later on in the semester.\n",
        "\n",
        "**TODO:** Perform the following tasks:\n",
        "- Use **nltk.corpus.stopwords** to create a set containing the most common English stopwords.\n",
        "- Implement the function **tokenized_content(content)**, which takes in a string and does the following:\n",
        "1. Tokenize the text\n",
        "2. Keep tokens that only contain alphabetic characters (i.e. tokens with no punctuation)\n",
        "3. Convert each token to lowercase\n",
        "4. Remove stopwords (commonly used words such as \"a\", \"an\", \"in\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 226,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AnFn2xJY5YK_",
        "outputId": "7d049a8f-681b-4bb9-9837-f29e13508356"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'3.7'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 226
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 227,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUDt0d68oCfu",
        "outputId": "a2a85bad-2304-4864-d938-f6943ec4abc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "stopwords = set(stopwords.words('english'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 228,
      "metadata": {
        "id": "3bLwPIx2nPy-"
      },
      "outputs": [],
      "source": [
        "# TODO: tokenize and flatten\n",
        "def tokenize_content(content):\n",
        "  t=nltk.tokenize.word_tokenize(content)\n",
        "  t=[token.lower() for token in t if token.isalpha()]\n",
        "  t=[token for token in t if token not in stopwords]\n",
        "  return t\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es5zPleHSROw"
      },
      "source": [
        "**TODO**: Also perform the following tasks: \n",
        "- Extract the `reviews` column of `songs_df` as a list called `reviews`. \n",
        "- Apply your `tokenize_content()` function to each item in the list `reviews`. Call the resultant list `top_tokens_list`. \n",
        "- Flatten the list `top_tokens_list`, and call the resultant list **top_tokens**. The autograder will be examining the contents of this list."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "songs_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "4hiXPizGxRoB",
        "outputId": "b01a4ef3-b4a1-49fe-af05-10cf28b74df2"
      },
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       id                                name  \\\n",
              "0   7qiZfU4dY1lWllzX7mPBI                        Shape of You   \n",
              "1   5CtI0qwDJkDQGwXD1H1cL                   Despacito - Remix   \n",
              "2   4aWmUDTfIPGksMNLV2rQP  Despacito (Featuring Daddy Yankee)   \n",
              "3   6RUKPb4LETWmmr3iAEQkt            Something Just Like This   \n",
              "4   3DXncPQOG4VBw3QHh3S81                         I'm the One   \n",
              "..                    ...                                 ...   \n",
              "95  1PSBzsahR2AKwLJgx8ehB    Bad Things (with Camila Cabello)   \n",
              "96  0QsvXIfqM0zZoerQfsI9l                   Don't Let Me Down   \n",
              "97  7mldq42yDuxiUNn08nvzH               Body Like A Back Road   \n",
              "98  7i2DJ88J7jQ8K7zqFX2fW                        Now Or Never   \n",
              "99  1j4kHkkpqZRBwE0A4CN4Y         Dusk Till Dawn - Radio Edit   \n",
              "\n",
              "              artists  duration_ms  \\\n",
              "0          Ed Sheeran       233713   \n",
              "1          Luis Fonsi       228827   \n",
              "2          Luis Fonsi       228200   \n",
              "3    The Chainsmokers       247160   \n",
              "4           DJ Khaled       288600   \n",
              "..                ...          ...   \n",
              "95  Machine Gun Kelly       239293   \n",
              "96   The Chainsmokers       208053   \n",
              "97           Sam Hunt       165387   \n",
              "98             Halsey       214802   \n",
              "99               ZAYN       239000   \n",
              "\n",
              "                                              reviews  duration_min  \n",
              "0                      vocal has a nice warm quality.      3.895217  \n",
              "1   Very European feeling. I like that the singer ...      3.813783  \n",
              "2   Unique and quirky. Arrangement was good and ni...      3.803333  \n",
              "3   Tastefully put together song. Good instrumenta...      4.119333  \n",
              "4                               Your voice is awesome      4.810000  \n",
              "..                                                ...           ...  \n",
              "95                                  Good strong voice      3.988217  \n",
              "96                             good movie sound track      3.467550  \n",
              "97                    Good melody. A little different      2.756450  \n",
              "98                                     Good lyrics...      3.580033  \n",
              "99  Good hook - unique vocal presentation - I like...      3.983333  \n",
              "\n",
              "[100 rows x 6 columns]"
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>artists</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>reviews</th>\n",
              "      <th>duration_min</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7qiZfU4dY1lWllzX7mPBI</td>\n",
              "      <td>Shape of You</td>\n",
              "      <td>Ed Sheeran</td>\n",
              "      <td>233713</td>\n",
              "      <td>vocal has a nice warm quality.</td>\n",
              "      <td>3.895217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5CtI0qwDJkDQGwXD1H1cL</td>\n",
              "      <td>Despacito - Remix</td>\n",
              "      <td>Luis Fonsi</td>\n",
              "      <td>228827</td>\n",
              "      <td>Very European feeling. I like that the singer ...</td>\n",
              "      <td>3.813783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4aWmUDTfIPGksMNLV2rQP</td>\n",
              "      <td>Despacito (Featuring Daddy Yankee)</td>\n",
              "      <td>Luis Fonsi</td>\n",
              "      <td>228200</td>\n",
              "      <td>Unique and quirky. Arrangement was good and ni...</td>\n",
              "      <td>3.803333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6RUKPb4LETWmmr3iAEQkt</td>\n",
              "      <td>Something Just Like This</td>\n",
              "      <td>The Chainsmokers</td>\n",
              "      <td>247160</td>\n",
              "      <td>Tastefully put together song. Good instrumenta...</td>\n",
              "      <td>4.119333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3DXncPQOG4VBw3QHh3S81</td>\n",
              "      <td>I'm the One</td>\n",
              "      <td>DJ Khaled</td>\n",
              "      <td>288600</td>\n",
              "      <td>Your voice is awesome</td>\n",
              "      <td>4.810000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1PSBzsahR2AKwLJgx8ehB</td>\n",
              "      <td>Bad Things (with Camila Cabello)</td>\n",
              "      <td>Machine Gun Kelly</td>\n",
              "      <td>239293</td>\n",
              "      <td>Good strong voice</td>\n",
              "      <td>3.988217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0QsvXIfqM0zZoerQfsI9l</td>\n",
              "      <td>Don't Let Me Down</td>\n",
              "      <td>The Chainsmokers</td>\n",
              "      <td>208053</td>\n",
              "      <td>good movie sound track</td>\n",
              "      <td>3.467550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>7mldq42yDuxiUNn08nvzH</td>\n",
              "      <td>Body Like A Back Road</td>\n",
              "      <td>Sam Hunt</td>\n",
              "      <td>165387</td>\n",
              "      <td>Good melody. A little different</td>\n",
              "      <td>2.756450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>7i2DJ88J7jQ8K7zqFX2fW</td>\n",
              "      <td>Now Or Never</td>\n",
              "      <td>Halsey</td>\n",
              "      <td>214802</td>\n",
              "      <td>Good lyrics...</td>\n",
              "      <td>3.580033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1j4kHkkpqZRBwE0A4CN4Y</td>\n",
              "      <td>Dusk Till Dawn - Radio Edit</td>\n",
              "      <td>ZAYN</td>\n",
              "      <td>239000</td>\n",
              "      <td>Good hook - unique vocal presentation - I like...</td>\n",
              "      <td>3.983333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 6 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "metadata": {
        "id": "3F5mW4qoq2n8"
      },
      "outputs": [],
      "source": [
        "# TODO: tokenize and flatten\n",
        "top_tokens=[]\n",
        "top_tokens_list=[]\n",
        "reviews=np.array(songs_df[\"reviews\"].values)\n",
        "for word in reviews:\n",
        "  top_tokens.append(tokenize_content(word))\n",
        "def token_flattening_function(T):\n",
        "  flat=[]\n",
        "  for w in T:\n",
        "    if isinstance(w,list):\n",
        "      flat.extend(token_flattening_function(w))\n",
        "    else:\n",
        "      flat.append(w)\n",
        "  return flat\n",
        "\n",
        "top_tokens=token_flattening_function(top_tokens)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "metadata": {
        "id": "A7PcX0mtG40R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd367a0e-bcab-4397-ccf7-fdcf2404f2ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 point\n",
        "grader.grade(test_case_id = 'test_top_tokens', answer = len(top_tokens))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JT0sTQHvp5E6"
      },
      "source": [
        "### 4.2 Most Frequent Words\n",
        "**TODO**: Now, find the 20 most common words in the list `top_tokens`. Save the result as a list of `(word, count)` tuples, in descending order of `count`.\n",
        "\n",
        "**Hint**: For this question, you can use `Counter` from the Python `collections` library: https://docs.python.org/2/library/collections.html#counter-objects"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "freq=Counter(top_tokens)\n",
        "top_most_common=freq.most_common(20)\n",
        "top_most_common\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdhxZh04ye0A",
        "outputId": "6ca9b557-7b3c-41f5-d20d-d68b61c124b2"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('good', 35),\n",
              " ('nice', 31),\n",
              " ('like', 26),\n",
              " ('song', 21),\n",
              " ('voice', 18),\n",
              " ('great', 14),\n",
              " ('really', 13),\n",
              " ('unique', 12),\n",
              " ('lyrics', 12),\n",
              " ('sound', 11),\n",
              " ('love', 9),\n",
              " ('interesting', 9),\n",
              " ('vocal', 8),\n",
              " ('tune', 8),\n",
              " ('vocals', 8),\n",
              " ('instrumentation', 7),\n",
              " ('melody', 7),\n",
              " ('music', 7),\n",
              " ('feel', 6),\n",
              " ('overall', 6)]"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "metadata": {
        "id": "4VPDgzbuIyKO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd19258a-34a7-4ad7-e4b8-bb237f96336a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct! You earned 2/2 points. You are a star!\n",
            "\n",
            "Your submission has been successfully recorded in the gradebook.\n"
          ]
        }
      ],
      "source": [
        "# 2 points\n",
        "grader.grade(test_case_id = 'test_top_most_common', answer = top_most_common)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEAi4a6OcjE4"
      },
      "source": [
        "### 4.3 Word Clouds [10 points]\n",
        "\n",
        "Before we move on from this dataset, let's visualize our results using a word cloud.\n",
        "\n",
        "**TODO**: Create a word cloud containing all the words in the list `top_tokens` (created in question 4.1). [The WordCloud documentation](https://amueller.github.io/word_cloud/generated/wordcloud.WordCloud.html) contains instructions on how to do this. \n",
        "\n",
        "*Please make sure your wordcloud has a **white** background.*\n",
        "\n",
        "We will be going through your notebooks and **manually grading** your word cloud. This is worth 10 points. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "metadata": {
        "id": "1Yt7PRqUrb8Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "d296f560-f479-4518-abd4-b2750557d154"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fcb7329d6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 234
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# TODO: make a word cloud for top tokens (MANUALLY GRADED)\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "wordCloud=WordCloud(background_color=\"white\").generate(\" \".join(top_tokens))\n",
        "plt.imshow(wordCloud)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcrcDEJNlrN0"
      },
      "source": [
        "# HW Submission\n",
        "\n",
        "<br>\n",
        "<center><img src = \"https://memegenerator.net/img/instances/73124265/good-job.jpg\" width= \"500\" align =\"center\"/></center>\n",
        "<br>\n",
        "\n",
        "Congratulations on finishing this homework! The good news is that similar to HW1, you basically know your score when you submit to Gradescope. \n",
        "However, this time, we will be manually grading your lineplots and wordclouds, so the autograder score is not final! Remember that we will also be checking for plagiarism, so please make sure to cite your sources (if any) by commenting the urls / links you looked at.\n",
        "\n",
        "Before you submit on Gradescope (you must submit your notebook to receive credit):\n",
        "\n",
        "1.   Please rerun your notebook on Colab by clicking \"Restart and Run-All\", and make sure there is nothing wrong with your notebook.\n",
        "2.   **Double check that you have the correct PennID (all numbers) saved in the autograder**. \n",
        "3. Make sure you've run all the PennGrader cells and have received a score.\n",
        "4. Go to the \"File\" tab at the top left, and click \"Download .ipynb\" + \"Download .py\". Please name the `.ipynb` and `.py` files **\"homework2.ipynb\"** and **\"homework2.py\"** respectively. Then, upload both the `.py` and `.ipynb` files to Gradescope. \n",
        "\n",
        "###Be sure to name your files correctly!!!\n",
        "\n",
        "**Please let the course staff know ASAP if you have any issues submitting.**"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "UcrcDEJNlrN0"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}