Billboard_Hot_100_Spotify_API_ETL.ipynb

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        "The Goal of this project is to take data from every week of the billboard Top 100 from 1957-2021 and all of those songs coresponding song information as given by the Spotify Web API, and combine the data. From there I plan to clean the data up, getting rid of duplicates and NAs to have workable data. I will then split the data up into 3 tables, the first containing the Hot 100 data with a song being represented by its song_week_ID and songID, the second being a table containing every songs IDs information such as artist and genre, and a third table containing the spotify API data for every song. I then plan to use those 3 tables to explore trends over decades and discover other historical data such as the top artist or the top songs during a certain time period using SQL and other python libraries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1MVE7tC9DP7"
      },
      "source": [
        "#Downloading Data and Checking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "yKtOCndKoToC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8hPJ3ZOmpEAI"
      },
      "outputs": [],
      "source": [
        "#hotAudio contains all song information for every song in the hot database, obtained using the spotify WebAPI\n",
        "hotAudio = pd.read_excel('https://query.data.world/s/lwuble5ivkp2qrvuzay56ssa7qjptf?dws=00000')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "arEgidiUqCi1"
      },
      "outputs": [],
      "source": [
        "#hot contains the top 100 songs from every week on the billboard Hot 100 since 1957\n",
        "hot = pd.read_csv('https://query.data.world/s/brp7wpuq25dqg5yjfp4ohwtsjgxose?dws=00000')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "GCz054i3L0Vg",
        "outputId": "32367cb4-05f5-4cf7-a81b-54f74d9a980c"
      },
      "outputs": [],
      "source": [
        "#Shows me that there is 29,000 rows and 22 columns, meaning 29,000 songs represented\n",
        "hotAudio.shape"
      ]
    },
    {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SongID</th>\n",
              "      <th>Performer</th>\n",
              "      <th>Song</th>\n",
              "      <th>spotify_genre</th>\n",
              "      <th>spotify_track_id</th>\n",
              "      <th>spotify_track_preview_url</th>\n",
              "      <th>spotify_track_duration_ms</th>\n",
              "      <th>spotify_track_explicit</th>\n",
              "      <th>spotify_track_album</th>\n",
              "      <th>danceability</th>\n",
              "      <th>...</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",
              "      <th>time_signature</th>\n",
              "      <th>spotify_track_popularity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-twistin'-White Silver SandsBill Black's Combo</td>\n",
              "      <td>Bill Black's Combo</td>\n",
              "      <td>-twistin'-White Silver Sands</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>¿Dònde Està Santa Claus? (Where Is Santa Claus...</td>\n",
              "      <td>Augie Rios</td>\n",
              "      <td>¿Dònde Està Santa Claus? (Where Is Santa Claus?)</td>\n",
              "      <td>['novelty']</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>......And Roses And RosesAndy Williams</td>\n",
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              "      <td>......And Roses And Roses</td>\n",
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              "      <td>3tvqPPpXyIgKrm4PR9HCf0</td>\n",
              "      <td>https://p.scdn.co/mp3-preview/cef4883cfd1e0e53...</td>\n",
              "      <td>166106.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>The Essential Andy Williams</td>\n",
              "      <td>0.154</td>\n",
              "      <td>...</td>\n",
              "      <td>-14.063</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0315</td>\n",
              "      <td>0.91100</td>\n",
              "      <td>0.000267</td>\n",
              "      <td>0.112</td>\n",
              "      <td>0.150</td>\n",
              "      <td>83.969</td>\n",
              "      <td>4.0</td>\n",
              "      <td>38.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>...And Then There Were DrumsSandy Nelson</td>\n",
              "      <td>Sandy Nelson</td>\n",
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              "      <td>...</td>\n",
              "      <td>-17.278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0361</td>\n",
              "      <td>0.00256</td>\n",
              "      <td>0.745000</td>\n",
              "      <td>0.145</td>\n",
              "      <td>0.801</td>\n",
              "      <td>121.962</td>\n",
              "      <td>4.0</td>\n",
              "      <td>11.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>...Baby One More TimeBritney Spears</td>\n",
              "      <td>Britney Spears</td>\n",
              "      <td>...Baby One More Time</td>\n",
              "      <td>['dance pop', 'pop', 'post-teen pop']</td>\n",
              "      <td>3MjUtNVVq3C8Fn0MP3zhXa</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.759</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000131</td>\n",
              "      <td>0.443</td>\n",
              "      <td>0.907</td>\n",
              "      <td>92.960</td>\n",
              "      <td>4.0</td>\n",
              "      <td>77.0</td>\n",
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              "<p>5 rows × 22 columns</p>\n",
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            "text/plain": [
              "                                              SongID           Performer  \\\n",
              "0     -twistin'-White Silver SandsBill Black's Combo  Bill Black's Combo   \n",
              "1  ¿Dònde Està Santa Claus? (Where Is Santa Claus...          Augie Rios   \n",
              "2             ......And Roses And RosesAndy Williams       Andy Williams   \n",
              "3           ...And Then There Were DrumsSandy Nelson        Sandy Nelson   \n",
              "4                ...Baby One More TimeBritney Spears      Britney Spears   \n",
              "\n",
              "                                               Song  \\\n",
              "0                      -twistin'-White Silver Sands   \n",
              "1  ¿Dònde Està Santa Claus? (Where Is Santa Claus?)   \n",
              "2                         ......And Roses And Roses   \n",
              "3                      ...And Then There Were Drums   \n",
              "4                             ...Baby One More Time   \n",
              "\n",
              "                                       spotify_genre        spotify_track_id  \\\n",
              "0                                                 []                     NaN   \n",
              "1                                        ['novelty']                     NaN   \n",
              "2  ['adult standards', 'brill building pop', 'eas...  3tvqPPpXyIgKrm4PR9HCf0   \n",
              "3   ['rock-and-roll', 'space age pop', 'surf music']  1fHHq3qHU8wpRKHzhojZ4a   \n",
              "4              ['dance pop', 'pop', 'post-teen pop']  3MjUtNVVq3C8Fn0MP3zhXa   \n",
              "\n",
              "                           spotify_track_preview_url  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2  https://p.scdn.co/mp3-preview/cef4883cfd1e0e53...   \n",
              "3                                                NaN   \n",
              "4  https://p.scdn.co/mp3-preview/da2134a161f1cb34...   \n",
              "\n",
              "   spotify_track_duration_ms  spotify_track_explicit  \\\n",
              "0                        NaN                     NaN   \n",
              "1                        NaN                     NaN   \n",
              "2                   166106.0                     0.0   \n",
              "3                   172066.0                     0.0   \n",
              "4                   211066.0                     0.0   \n",
              "\n",
              "                              spotify_track_album  danceability  ...  \\\n",
              "0                                             NaN           NaN  ...   \n",
              "1                                             NaN           NaN  ...   \n",
              "2                     The Essential Andy Williams         0.154  ...   \n",
              "3                           Compelling Percussion         0.588  ...   \n",
              "4  ...Baby One More Time (Digital Deluxe Version)         0.759  ...   \n",
              "\n",
              "   loudness  mode  speechiness  acousticness  instrumentalness  liveness  \\\n",
              "0       NaN   NaN          NaN           NaN               NaN       NaN   \n",
              "1       NaN   NaN          NaN           NaN               NaN       NaN   \n",
              "2   -14.063   1.0       0.0315       0.91100          0.000267     0.112   \n",
              "3   -17.278   0.0       0.0361       0.00256          0.745000     0.145   \n",
              "4    -5.745   0.0       0.0307       0.20200          0.000131     0.443   \n",
              "\n",
              "   valence    tempo  time_signature  spotify_track_popularity  \n",
              "0      NaN      NaN             NaN                       NaN  \n",
              "1      NaN      NaN             NaN                       NaN  \n",
              "2    0.150   83.969             4.0                      38.0  \n",
              "3    0.801  121.962             4.0                      11.0  \n",
              "4    0.907   92.960             4.0                      77.0  \n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#This shows me that some songs do not have spotify track data associated with them which means I will need to drop those in order to find trends\n",
        "#Also shows me the format is which the songID is created, a combination of the song and artist in a string\n",
        "hotAudio.head()"
      ]
    },
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          "data": {
            "text/plain": [
              "SongID                           0\n",
              "Performer                        0\n",
              "Song                             0\n",
              "spotify_genre                 1600\n",
              "spotify_track_id              5106\n",
              "spotify_track_preview_url    15012\n",
              "spotify_track_duration_ms     5106\n",
              "spotify_track_explicit        5106\n",
              "spotify_track_album           5112\n",
              "danceability                  5169\n",
              "energy                        5169\n",
              "key                           5169\n",
              "loudness                      5169\n",
              "mode                          5169\n",
              "speechiness                   5169\n",
              "acousticness                  5169\n",
              "instrumentalness              5169\n",
              "liveness                      5169\n",
              "valence                       5169\n",
              "tempo                         5169\n",
              "time_signature                5169\n",
              "spotify_track_popularity      5106\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#I'm missing 15,000 preview url data points, but I dont really care about the url so I will get rid of\n",
        "#that column before dropping clolumns in order to keep some that might still be useful to me but are missing the preview URL\n",
        "#A lot of columns are missing 5169 entries meaning if one song is missing energy its also missing mode, loudness, key etc.\n",
        "hotAudio.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIOHJgMzEf6m",
        "outputId": "ad49c016-a08d-48fd-eadd-ff9b78c323db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(327895, 10)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Seeing how many totl entires are in my data. 327,000 rows which\n",
        "#is a lot of entries but makes sense considering the time it spans\n",
        "hot.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "NSGSqYpCqr_a",
        "outputId": "b3f05c04-d5d5-4471-a63f-7b1d37530968"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>url</th>\n",
              "      <th>WeekID</th>\n",
              "      <th>Week Position</th>\n",
              "      <th>Song</th>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                 url     WeekID  \\\n",
              "0  http://www.billboard.com/charts/hot-100/1965-0...  7/17/1965   \n",
              "1  http://www.billboard.com/charts/hot-100/1965-0...  7/24/1965   \n",
              "2  http://www.billboard.com/charts/hot-100/1965-0...  7/31/1965   \n",
              "3  http://www.billboard.com/charts/hot-100/1965-0...   8/7/1965   \n",
              "4  http://www.billboard.com/charts/hot-100/1965-0...  8/14/1965   \n",
              "\n",
              "   Week Position                    Song   Performer  \\\n",
              "0             34  Don't Just Stand There  Patty Duke   \n",
              "1             22  Don't Just Stand There  Patty Duke   \n",
              "2             14  Don't Just Stand There  Patty Duke   \n",
              "3             10  Don't Just Stand There  Patty Duke   \n",
              "4              8  Don't Just Stand There  Patty Duke   \n",
              "\n",
              "                             SongID  Instance  Previous Week Position  \\\n",
              "0  Don't Just Stand TherePatty Duke         1                    45.0   \n",
              "1  Don't Just Stand TherePatty Duke         1                    34.0   \n",
              "2  Don't Just Stand TherePatty Duke         1                    22.0   \n",
              "3  Don't Just Stand TherePatty Duke         1                    14.0   \n",
              "4  Don't Just Stand TherePatty Duke         1                    10.0   \n",
              "\n",
              "   Peak Position  Weeks on Chart  \n",
              "0             34               4  \n",
              "1             22               5  \n",
              "2             14               6  \n",
              "3             10               7  \n",
              "4              8               8  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#This search shows me that weekID associated with each entry which I can use to classify by week and group by year later on. Will most likely need to convert to datetime to use\n",
        "#This dataset has the songID as well which I can use to link the two columns\n",
        "#Also contains other data that might be interesting to mess with such as weeks on chart\n",
        "hot.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEjMCCvT8VS1",
        "outputId": "67521920-093f-40b0-ec7a-05eaf070725e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "url                           0\n",
              "WeekID                        0\n",
              "Week Position                 0\n",
              "Song                          0\n",
              "Performer                     0\n",
              "SongID                        0\n",
              "Instance                      0\n",
              "Previous Week Position    31954\n",
              "Peak Position                 0\n",
              "Weeks on Chart                0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Cheking how many NA values. Just in the previous week position which makes sense for new entries\n",
        "#to the Hot 100. I dont need to Previous Week Position infomation for my aggregation so I can remove\n",
        "#the column later before NAs in order to not get rid of 32,000 songs data that I could use otherwise\n",
        "hot.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjfBIZTzveV7"
      },
      "source": [
        "#Using Spotify API to gather Spotify Data on every Song"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aTEDJBgfNc_y"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "AUTH_URL = \"https://accounts.spotify.com/api/token\"\n",
        "auth_response = requests.post(AUTH_URL, {\n",
        "    'grant_type': 'client_credentials',\n",
        "    'client_id': '',\n",
        "    'client_secret': '',\n",
        "})\n",
        "\n",
        "#Convert response to JSON\n",
        "auth_response_data = auth_response.json()\n",
        "\n",
        "#Save the access token\n",
        "access_token = auth_response_data['access_token']\n",
        "\n",
        "#Need to pass access token into header to send properly formed GET request to API server\n",
        "headers = {\n",
        "    'Authorization': 'Bearer {token}'.format(token=access_token)\n",
        "}\n",
        "\n",
        "#This is a search query that returns all instances of this song\n",
        "BASE_URL = 'https://api.spotify.com/v1/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pawKNqtJvmHi",
        "outputId": "f774f9f3-3d31-4850-a014-c7bd33a17503"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9a439328-55fa-49cb-b2ce-47620f3d3a15\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Song</th>\n",
              "      <th>Performer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Don't Just Stand There</td>\n",
              "      <td>Patty Duke</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Don't Keep Wasting My Time</td>\n",
              "      <td>Teddy Pendergrass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don't Knock My Love - Pt. 1</td>\n",
              "      <td>Wilson Pickett</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Don't Knock My Love</td>\n",
              "      <td>Diana Ross &amp; Marvin Gaye</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Don't Know Much</td>\n",
              "      <td>Bill Medley</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24355</th>\n",
              "      <td>You Let Your Heart Go Too Fast</td>\n",
              "      <td>Spin Doctors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24356</th>\n",
              "      <td>You Lied To Me</td>\n",
              "      <td>Cathy Dennis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24357</th>\n",
              "      <td>You Lied To Your Daddy</td>\n",
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              "      <th>24358</th>\n",
              "      <td>You Lie</td>\n",
              "      <td>The Band Perry</td>\n",
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              "    <tr>\n",
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              "      <td>You Light Up My Life/Believe In Humanity</td>\n",
              "      <td>Carole King</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                           Song                 Performer\n",
              "0                        Don't Just Stand There                Patty Duke\n",
              "1                    Don't Keep Wasting My Time         Teddy Pendergrass\n",
              "2                   Don't Knock My Love - Pt. 1            Wilson Pickett\n",
              "3                           Don't Knock My Love  Diana Ross & Marvin Gaye\n",
              "4                               Don't Know Much               Bill Medley\n",
              "...                                         ...                       ...\n",
              "24355            You Let Your Heart Go Too Fast              Spin Doctors\n",
              "24356                            You Lied To Me              Cathy Dennis\n",
              "24357                    You Lied To Your Daddy                  The Tams\n",
              "24358                                   You Lie            The Band Perry\n",
              "24359  You Light Up My Life/Believe In Humanity               Carole King\n",
              "\n",
              "[24360 rows x 2 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "songInfo = hot.drop_duplicates(\"Song\")\n",
        "songInfo = songInfo[['Song', 'Performer']]\n",
        "songInfo.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mWCcmN1F49rQ"
      },
      "outputs": [],
      "source": [
        "SpotifyAudioFeatures = pd.DataFrame()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "zeATp5U7vnTW",
        "outputId": "222159ea-adb9-45be-977c-9562852ff6c5"
      },
      "outputs": [],
      "source": [
        "for songIndex in range(len(songInfo)):\n",
        "  artist = songInfo.iloc[songIndex]['Performer']\n",
        "  artistWords = artist.split(\" \")\n",
        "  artistQuery = ''\n",
        "  for word in artistWords:\n",
        "    artistQuery += word + \"%20\"\n",
        "\n",
        "  song = songInfo.iloc[songIndex]['Song']\n",
        "  songWords = song.split(\" \")\n",
        "  songQuery = songWords[0]\n",
        "  for word in songWords[1:]:\n",
        "    songQuery += '%20' + word\n",
        "\n",
        "  r = requests.get(BASE_URL + 'search?q=' + artistQuery + songQuery + '&type=track', headers=headers)\n",
        "  r = r.json()\n",
        "\n",
        "  r2 = requests.get(BASE_URL + 'audio-features?ids=' + r[\"tracks\"][\"items\"][0][\"id\"], headers=headers)\n",
        "  r2 = r2.json()\n",
        "\n",
        "  hotAudio = pd.json_normalize(r2['audio_features'])\n",
        "  songID =  r['tracks']['items'][0][\"name\"] + r['tracks']['items'][0][\"artists\"][0]['name']\n",
        "  #hotAudio = hotAudio.assign(Artist = r['tracks']['items'][0][\"artists\"][0]['name'])\n",
        "  #hotAudio = hotAudio.assign(Song = r['tracks']['items'][0][\"name\"])\n",
        "  hotAudio = hotAudio.assign(SongID = songID)\n",
        "  SpotifyAudioFeatures = pd.concat([SpotifyAudioFeatures, hotAudio])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "rQRxjfTSlonq",
        "outputId": "d8ff436f-f95d-40dc-801d-f262fec47dfb"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>danceability</th>\n",
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              "  <tbody>\n",
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              "      <td>5takJXnMRsp38BWcPr5a2z</td>\n",
              "      <td>spotify:track:5takJXnMRsp38BWcPr5a2z</td>\n",
              "      <td>https://api.spotify.com/v1/tracks/5takJXnMRsp3...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/5tak...</td>\n",
              "      <td>158760</td>\n",
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              "      <td>Don't Just Stand TherePatty Duke</td>\n",
              "    </tr>\n",
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              "      <td>audio_features</td>\n",
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              "      <td>https://api.spotify.com/v1/tracks/47nJdvSobIGB...</td>\n",
              "      <td>https://api.spotify.com/v1/audio-analysis/47nJ...</td>\n",
              "      <td>354627</td>\n",
              "      <td>4</td>\n",
              "      <td>Turn off the LightsTeddy Pendergrass</td>\n",
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              "      <td>audio_features</td>\n",
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              "      <td>136293</td>\n",
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              "      <td>Don't Knock My Love, Pt. 1 - 2006 Remaster; Si...</td>\n",
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              "      <td>5</td>\n",
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              "      <td>https://api.spotify.com/v1/audio-analysis/3SnG...</td>\n",
              "      <td>323347</td>\n",
              "      <td>4</td>\n",
              "      <td>I'm Coming OutDiana Ross</td>\n",
              "    </tr>\n",
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              "      <td>https://api.spotify.com/v1/audio-analysis/2JCT...</td>\n",
              "      <td>289067</td>\n",
              "      <td>4</td>\n",
              "      <td>(I've Had) The Time Of My LifeBill Medley</td>\n",
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            ],
            "text/plain": [
              "   danceability  energy  key  loudness  mode  speechiness  acousticness  \\\n",
              "0         0.554   0.375    7    -6.979     1       0.0266         0.849   \n",
              "1         0.614   0.405    4   -12.386     0       0.0376         0.490   \n",
              "2         0.731   0.518    7   -14.753     0       0.0293         0.445   \n",
              "3         0.720   0.828    5    -9.211     1       0.0462         0.185   \n",
              "4         0.663   0.784    6   -11.245     0       0.0438         0.125   \n",
              "\n",
              "   instrumentalness  liveness  valence    tempo            type  \\\n",
              "0          0.000003    0.3860    0.513   83.293  audio_features   \n",
              "1          0.000132    0.3480    0.645  118.529  audio_features   \n",
              "2          0.000000    0.0884    0.954  107.669  audio_features   \n",
              "3          0.429000    0.0839    0.768  110.071  audio_features   \n",
              "4          0.003480    0.2140    0.505  108.492  audio_features   \n",
              "\n",
              "                       id                                   uri  \\\n",
              "0  5takJXnMRsp38BWcPr5a2z  spotify:track:5takJXnMRsp38BWcPr5a2z   \n",
              "1  47nJdvSobIGBYdN7y2caLM  spotify:track:47nJdvSobIGBYdN7y2caLM   \n",
              "2  1JkLbghZLWT1rRVsJANORc  spotify:track:1JkLbghZLWT1rRVsJANORc   \n",
              "3  3SnGymj6ijE2iuUfWxLo1q  spotify:track:3SnGymj6ijE2iuUfWxLo1q   \n",
              "4  2JCTiiJQFWQNC0Zp8rsJRB  spotify:track:2JCTiiJQFWQNC0Zp8rsJRB   \n",
              "\n",
              "                                          track_href  \\\n",
              "0  https://api.spotify.com/v1/tracks/5takJXnMRsp3...   \n",
              "1  https://api.spotify.com/v1/tracks/47nJdvSobIGB...   \n",
              "2  https://api.spotify.com/v1/tracks/1JkLbghZLWT1...   \n",
              "3  https://api.spotify.com/v1/tracks/3SnGymj6ijE2...   \n",
              "4  https://api.spotify.com/v1/tracks/2JCTiiJQFWQN...   \n",
              "\n",
              "                                        analysis_url  duration_ms  \\\n",
              "0  https://api.spotify.com/v1/audio-analysis/5tak...       158760   \n",
              "1  https://api.spotify.com/v1/audio-analysis/47nJ...       354627   \n",
              "2  https://api.spotify.com/v1/audio-analysis/1JkL...       136293   \n",
              "3  https://api.spotify.com/v1/audio-analysis/3SnG...       323347   \n",
              "4  https://api.spotify.com/v1/audio-analysis/2JCT...       289067   \n",
              "\n",
              "   time_signature                                             SongID  \n",
              "0               3                   Don't Just Stand TherePatty Duke  \n",
              "1               4               Turn off the LightsTeddy Pendergrass  \n",
              "2               4  Don't Knock My Love, Pt. 1 - 2006 Remaster; Si...  \n",
              "3               4                           I'm Coming OutDiana Ross  \n",
              "4               4          (I've Had) The Time Of My LifeBill Medley  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "SpotifyAudioFeatures.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fO03nEer9L8p"
      },
      "source": [
        "#Merging Data and Cleaning NAs and repeated columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h3weVLbHuJii"
      },
      "outputs": [],
      "source": [
        "#Creates a new table called hot100 with every hot100 song from all weeks, with its coresponding spotify data\n",
        "#Joined hotAudio to the hot table because some songs are repeated in the hot table so the Audio features need to be added to each of those entires\n",
        "hot100 = hot.merge(SpotifyAudioFeatures, left_on = 'SongID', right_on = 'SongID', how = 'left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0iT1rYDTuxSX",
        "outputId": "33e71dbc-2e0c-4587-96a3-05f4aa8ceb12"
      },
      "outputs": [
        {
          "data": {
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              "      <td>0.0928</td>\n",
              "      <td>0.675</td>\n",
              "      <td>130.864</td>\n",
              "      <td>4.0</td>\n",
              "      <td>51.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330458</th>\n",
              "      <td>http://www.billboard.com/charts/hot-100/1981-0...</td>\n",
              "      <td>5/23/1981</td>\n",
              "      <td>22</td>\n",
              "      <td>I Can't Stand It</td>\n",
              "      <td>Eric Clapton And His Band</td>\n",
              "      <td>I Can't Stand ItEric Clapton And His Band</td>\n",
              "      <td>1</td>\n",
              "      <td>12.0</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330459</th>\n",
              "      <td>http://www.billboard.com/charts/hot-100/1973-0...</td>\n",
              "      <td>9/29/1973</td>\n",
              "      <td>22</td>\n",
              "      <td>Here I Am Come &amp; Take Me</td>\n",
              "      <td>Al Green</td>\n",
              "      <td>Here I Am Come &amp; Take MeAl Green</td>\n",
              "      <td>1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
              "      <td>-12.238</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0364</td>\n",
              "      <td>0.3180</td>\n",
              "      <td>0.000084</td>\n",
              "      <td>0.0673</td>\n",
              "      <td>0.846</td>\n",
              "      <td>94.558</td>\n",
              "      <td>4.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>330460</th>\n",
              "      <td>https://www.billboard.com/charts/hot-100/2020-...</td>\n",
              "      <td>11/28/2020</td>\n",
              "      <td>22</td>\n",
              "      <td>Hawai</td>\n",
              "      <td>Maluma &amp; The Weeknd</td>\n",
              "      <td>HawaiMaluma &amp; The Weeknd</td>\n",
              "      <td>1</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>330461 rows × 31 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "          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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                      url      WeekID  \\\n",
              "0       http://www.billboard.com/charts/hot-100/1965-0...   7/17/1965   \n",
              "1       http://www.billboard.com/charts/hot-100/1965-0...   7/24/1965   \n",
              "2       http://www.billboard.com/charts/hot-100/1965-0...   7/31/1965   \n",
              "3       http://www.billboard.com/charts/hot-100/1965-0...    8/7/1965   \n",
              "4       http://www.billboard.com/charts/hot-100/1965-0...   8/14/1965   \n",
              "...                                                   ...         ...   \n",
              "330456  https://www.billboard.com/charts/hot-100/2018-...  10/20/2018   \n",
              "330457  http://www.billboard.com/charts/hot-100/1977-0...   5/21/1977   \n",
              "330458  http://www.billboard.com/charts/hot-100/1981-0...   5/23/1981   \n",
              "330459  http://www.billboard.com/charts/hot-100/1973-0...   9/29/1973   \n",
              "330460  https://www.billboard.com/charts/hot-100/2020-...  11/28/2020   \n",
              "\n",
              "        Week Position                    Song_x                Performer_x  \\\n",
              "0                  34    Don't Just Stand There                 Patty Duke   \n",
              "1                  22    Don't Just Stand There                 Patty Duke   \n",
              "2                  14    Don't Just Stand There                 Patty Duke   \n",
              "3                  10    Don't Just Stand There                 Patty Duke   \n",
              "4                   8    Don't Just Stand There                 Patty Duke   \n",
              "...               ...                       ...                        ...   \n",
              "330456             22            God Is A Woman              Ariana Grande   \n",
              "330457             22   I Wanna Get Next To You                 Rose Royce   \n",
              "330458             22          I Can't Stand It  Eric Clapton And His Band   \n",
              "330459             22  Here I Am Come & Take Me                   Al Green   \n",
              "330460             22                     Hawai        Maluma & The Weeknd   \n",
              "\n",
              "                                           SongID  Instance  \\\n",
              "0                Don't Just Stand TherePatty Duke         1   \n",
              "1                Don't Just Stand TherePatty Duke         1   \n",
              "2                Don't Just Stand TherePatty Duke         1   \n",
              "3                Don't Just Stand TherePatty Duke         1   \n",
              "4                Don't Just Stand TherePatty Duke         1   \n",
              "...                                           ...       ...   \n",
              "330456                God Is A WomanAriana Grande         1   \n",
              "330457          I Wanna Get Next To YouRose Royce         1   \n",
              "330458  I Can't Stand ItEric Clapton And His Band         1   \n",
              "330459           Here I Am Come & Take MeAl Green         1   \n",
              "330460                   HawaiMaluma & The Weeknd         1   \n",
              "\n",
              "        Previous Week Position  Peak Position  Weeks on Chart  ... loudness  \\\n",
              "0                         45.0             34               4  ...  -15.044   \n",
              "1                         34.0             22               5  ...  -15.044   \n",
              "2                         22.0             14               6  ...  -15.044   \n",
              "3                         14.0             10               7  ...  -15.044   \n",
              "4                         10.0              8               8  ...  -15.044   \n",
              "...                        ...            ...             ...  ...      ...   \n",
              "330456                    21.0              8              13  ...   -5.934   \n",
              "330457                    10.0             10              13  ...  -19.981   \n",
              "330458                    12.0             10              13  ...      NaN   \n",
              "330459                    16.0             10              13  ...  -12.238   \n",
              "330460                    12.0             12              13  ...      NaN   \n",
              "\n",
              "       mode speechiness acousticness instrumentalness  liveness  valence  \\\n",
              "0       1.0      0.0298       0.6100         0.000077    0.1000    0.568   \n",
              "1       1.0      0.0298       0.6100         0.000077    0.1000    0.568   \n",
              "2       1.0      0.0298       0.6100         0.000077    0.1000    0.568   \n",
              "3       1.0      0.0298       0.6100         0.000077    0.1000    0.568   \n",
              "4       1.0      0.0298       0.6100         0.000077    0.1000    0.568   \n",
              "...     ...         ...          ...              ...       ...      ...   \n",
              "330456  1.0      0.0558       0.0233         0.000060    0.2370    0.268   \n",
              "330457  1.0      0.0314       0.8350         0.030000    0.0928    0.675   \n",
              "330458  NaN         NaN          NaN              NaN       NaN      NaN   \n",
              "330459  0.0      0.0364       0.3180         0.000084    0.0673    0.846   \n",
              "330460  NaN         NaN          NaN              NaN       NaN      NaN   \n",
              "\n",
              "          tempo  time_signature  spotify_track_popularity  \n",
              "0        82.331             3.0                      21.0  \n",
              "1        82.331             3.0                      21.0  \n",
              "2        82.331             3.0                      21.0  \n",
              "3        82.331             3.0                      21.0  \n",
              "4        82.331             3.0                      21.0  \n",
              "...         ...             ...                       ...  \n",
              "330456  145.031             4.0                      62.0  \n",
              "330457  130.864             4.0                      51.0  \n",
              "330458      NaN             NaN                       NaN  \n",
              "330459   94.558             4.0                      52.0  \n",
              "330460      NaN             NaN                       NaN  \n",
              "\n",
              "[330461 rows x 31 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#checking it merged correctly\n",
        "#I can tell it merged correctly because Dont Just stand there\n",
        "#by Patty Duke has the same instrumentalness every week,\n",
        "#meaning that the Audio features correctly applied itself to repeated entries\n",
        "hot100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2imBDOd62Uk1",
        "outputId": "067c18e5-ac00-42ef-d8b8-bb7f2fff6b4c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(330461, 31)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Checking total number of rows, now 330,000 rows so somehow added 3,000 rows.\n",
        "#These will most likely be shown as NA values or duplicated entries which we will deal with later\n",
        "#Columns make sense tho because there was 22 and 10 columns in the two tables\n",
        "#and the tables shared SongID meaning they combined SongID leaving 31 columns\n",
        "hot100.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pViprbto2Xae",
        "outputId": "558ac9b1-7874-48d1-9a00-b4834c5294ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "url                               0\n",
              "WeekID                            0\n",
              "Week Position                     0\n",
              "Song_x                            0\n",
              "Performer_x                       0\n",
              "SongID                            0\n",
              "Instance                          0\n",
              "Previous Week Position        32181\n",
              "Peak Position                     0\n",
              "Weeks on Chart                    0\n",
              "Performer_y                      78\n",
              "Song_y                           78\n",
              "spotify_genre                 14586\n",
              "spotify_track_id              43337\n",
              "spotify_track_preview_url    160508\n",
              "spotify_track_duration_ms     43337\n",
              "spotify_track_explicit        43337\n",
              "spotify_track_album           43399\n",
              "danceability                  43895\n",
              "energy                        43895\n",
              "key                           43895\n",
              "loudness                      43895\n",
              "mode                          43895\n",
              "speechiness                   43895\n",
              "acousticness                  43895\n",
              "instrumentalness              43895\n",
              "liveness                      43895\n",
              "valence                       43895\n",
              "tempo                         43895\n",
              "time_signature                43895\n",
              "spotify_track_popularity      43337\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Preview URL and Previous week both have a ton on NAs\n",
        "#Since I dont care about those columns I will drop them to attempt to keep more of my data\n",
        "hot100.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1VhG0N6eMexF"
      },
      "outputs": [],
      "source": [
        "#dropping 5 columns that I dont care about before dropping NA's in an attempt to not drop as many rows of data because of columns that I dont need\n",
        "#We already have performer and song from the hot table so I dont need those. I also cant use the album to aggregate later so not needed\n",
        "hot100_dropped = hot100.drop(columns=['spotify_track_preview_url', 'spotify_track_album', 'Previous Week Position', 'Performer_y', 'Song_y'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5F3EjyM3ObBQ",
        "outputId": "66da311d-e42b-4297-ccdf-ed06e1b5ef97"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "url                              0\n",
              "WeekID                           0\n",
              "Week Position                    0\n",
              "Song_x                           0\n",
              "Performer_x                      0\n",
              "SongID                           0\n",
              "Instance                         0\n",
              "Peak Position                    0\n",
              "Weeks on Chart                   0\n",
              "spotify_genre                14586\n",
              "spotify_track_id             43337\n",
              "spotify_track_duration_ms    43337\n",
              "spotify_track_explicit       43337\n",
              "danceability                 43895\n",
              "energy                       43895\n",
              "key                          43895\n",
              "loudness                     43895\n",
              "mode                         43895\n",
              "speechiness                  43895\n",
              "acousticness                 43895\n",
              "instrumentalness             43895\n",
              "liveness                     43895\n",
              "valence                      43895\n",
              "tempo                        43895\n",
              "time_signature               43895\n",
              "spotify_track_popularity     43337\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Most of our data that is NA has the same number of NA values so its most likely from the same rows\n",
        "hot100_dropped.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BXX7ZHvXK1e3"
      },
      "outputs": [],
      "source": [
        "#Dropping NA values\n",
        "hot100_clean = hot100_dropped.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lh-lZzroLHLx",
        "outputId": "ed8e2113-0b55-4d0a-ee3c-ce435b8b8b03"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "url                          0\n",
              "WeekID                       0\n",
              "Week Position                0\n",
              "Song_x                       0\n",
              "Performer_x                  0\n",
              "SongID                       0\n",
              "Instance                     0\n",
              "Peak Position                0\n",
              "Weeks on Chart               0\n",
              "spotify_genre                0\n",
              "spotify_track_id             0\n",
              "spotify_track_duration_ms    0\n",
              "spotify_track_explicit       0\n",
              "danceability                 0\n",
              "energy                       0\n",
              "key                          0\n",
              "loudness                     0\n",
              "mode                         0\n",
              "speechiness                  0\n",
              "acousticness                 0\n",
              "instrumentalness             0\n",
              "liveness                     0\n",
              "valence                      0\n",
              "tempo                        0\n",
              "time_signature               0\n",
              "spotify_track_popularity     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hot100_clean.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vBLKIj-gKDy9"
      },
      "outputs": [],
      "source": [
        "#Reanming columns, specifically those with spaces to make transformations and SQL easier\n",
        "hot100_clean = hot100_clean.rename(columns = {'Song_x' : 'Song', 'Performer_x' : 'Performer', 'Week Position' : 'Week_Position', 'Peak Position' : 'Peak_Position', 'Weeks on Chart' : 'Weeks_on_Chart'})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWyB9pKjPsZn"
      },
      "outputs": [],
      "source": [
        "#When creating the hot100 database in AWS I need a primary key. A lot of songs repeated on the charts week to week,\n",
        "#but no song is on the charts more than once in a given week so I created a unique ID based on everysongs ID and week it was on the charts\n",
        "hot100_clean['weekstr'] = hot100_clean['WeekID'].str.replace('/', '')\n",
        "\n",
        "#Needed to factorize because the ID's were words and not numbers\n",
        "hot100_clean['SongKey'] = pd.factorize(hot100_clean['SongID'])[0].astype(str)\n",
        "\n",
        "#Creating a key based on the week and songkey\n",
        "hot100_clean['song_week_key'] = hot100_clean['weekstr'] + hot100_clean['SongKey']\n",
        "\n",
        "#Dropping unnecessary\n",
        "hot100_clean = hot100_clean.drop(columns = ['weekstr', 'SongKey'])\n",
        "\n",
        "#Dropping any songs that may have been on the Hot 100 more than once in a week, which is an error\n",
        "hot100_clean = hot100_clean.drop_duplicates('song_week_key')\n",
        "\n",
        "#Converted to datetime after creating the key\n",
        "hot100_clean['WeekID'] = pd.to_datetime(hot100_clean['WeekID'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4PM8jDj9e1d"
      },
      "source": [
        "#Creating new tables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jzOOIhRC9X4i"
      },
      "outputs": [],
      "source": [
        "#Data is too large to put into AWS table in a reasonable amount of time I'm limiting to only the top 5 from every week\n",
        "\n",
        "hot5 = hot100_clean[hot100_clean['Week_Position'] <= 5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzPCmgAelx-g",
        "outputId": "c6f6aedd-0aef-4a65-b85d-6f8c10eaa9f3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(14520, 27)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Now only 14,000 rows so it will be easier to work with and upload to AWS\n",
        "#but still gives me a good representation of the Hot 100\n",
        "hot5.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4beUy3S64Nj",
        "outputId": "49a787f6-6748-45d2-fd1f-8ffd3cb891fd"
      },
      "outputs": [],
      "source": [
        "#Converting datatypes\n",
        "hot5['Song'] = hot5['Song'].astype(str)\n",
        "hot5['Performer'] = hot5['Performer'].astype(str)\n",
        "hot5['SongID'] = hot5['SongID'].astype(str)\n",
        "hot5['spotify_genre'] = hot5['spotify_genre'].astype(str)\n",
        "hot5['spotify_track_id'] = hot5['spotify_track_id'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QpKiodN39EAS"
      },
      "outputs": [],
      "source": [
        "#Creating 3 tables from the hot5 table. The first table is chart which contains the weekly data of every song\n",
        "#The second table, songs, contains all song information based on the songID\n",
        "#The third table contains all spotify song data for each song based on songID\n",
        "chart = hot5[['song_week_key', 'url', 'WeekID', 'SongID', 'Week_Position', 'Peak_Position', 'Weeks_on_Chart']]\n",
        "songs = hot5[['Song', 'Performer', 'SongID', 'spotify_genre']]\n",
        "songs = songs.drop_duplicates('SongID')\n",
        "spotifyInfo = hot5.drop(columns=['song_week_key', 'url', 'WeekID', 'Week_Position', 'Peak_Position', 'Weeks_on_Chart', 'Song', 'Performer', 'Instance', 'spotify_genre'])\n",
        "spotifyInfo = spotifyInfo.drop_duplicates('SongID')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK95AMNVwXd9"
      },
      "source": [
        "#AWS and SQL Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rt40rrXJnc2J"
      },
      "outputs": [],
      "source": [
        "import psycopg2\n",
        "def get_conn_cur():\n",
        "\n",
        "  conn = psycopg2.connect(  \n",
        "    host=\"\",\n",
        "    database=\"\",\n",
        "    user=\"\",\n",
        "    password=\"\",\n",
        "    port='')\n",
        "\n",
        "  cur = conn.cursor()\n",
        "  return(conn, cur)\n",
        "\n",
        " # run_query function\n",
        "def run_query(query_string):\n",
        "\n",
        " conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        " cur.execute(query_string) # executing string as before\n",
        "\n",
        " my_data = cur.fetchall() # fetch query data as before\n",
        "\n",
        " # here we're extracting the 0th element for each item in cur.description\n",
        " colnames = [desc[0] for desc in cur.description]\n",
        "\n",
        " cur.close() # close\n",
        " conn.close() # close\n",
        "\n",
        " return(colnames, my_data) # return column names AND data\n",
        "\n",
        "# Column name function for checking out what's in a table\n",
        "def get_column_names(table_name): # arguement of table_name\n",
        " conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        " # Now select column names while inserting the table name into the WERE\n",
        " column_name_query = \"\"\"SELECT column_name FROM information_schema.columns\n",
        "    WHERE table_name = '%s' \"\"\" %table_name\n",
        "\n",
        " cur.execute(column_name_query) # exectue\n",
        " my_data = cur.fetchall() # store\n",
        "\n",
        " cur.close() # close\n",
        " conn.close() # close\n",
        "\n",
        " return(my_data) # return\n",
        "\n",
        "# Check table_names\n",
        "def get_table_names():\n",
        " conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        " # query to get table names\n",
        " table_name_query = \"\"\"SELECT table_name FROM information_schema.tables \"\"\"\n",
        "\n",
        " cur.execute(table_name_query) # execute\n",
        " my_data = cur.fetchall() # fetch results\n",
        "\n",
        " cur.close() #close cursor\n",
        " conn.close() # close connection\n",
        "\n",
        " return(my_data) # return your fetched results\n",
        "\n",
        "# make sql_head function\n",
        "def sql_head(table_name):\n",
        " conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        " # Now select column names while inserting the table name into the WERE\n",
        " head_query = \"\"\"SELECT * FROM %s LIMIT 5; \"\"\" %table_name\n",
        "\n",
        " cur.execute(head_query) # exectue\n",
        " colnames = [desc[0] for desc in cur.description] # get column names\n",
        " my_data = cur.fetchall() # store first five rows\n",
        "\n",
        " cur.close() # close\n",
        " conn.close() # close\n",
        "\n",
        " df = pd.DataFrame(data = my_data, columns = colnames) # make into df\n",
        "\n",
        " return(df) # return\n",
        "\n",
        "# put functions here and run\n",
        "\n",
        "# Same run_query function\n",
        "def run_query(query_string):\n",
        "\n",
        "  conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        "  cur.execute(query_string) # executing string as before\n",
        "\n",
        "  my_data = cur.fetchall() # fetch query data as before\n",
        "\n",
        "  # here we're extracting the 0th element for each item in cur.description\n",
        "  colnames = [desc[0] for desc in cur.description]\n",
        "\n",
        "  cur.close() # close\n",
        "  conn.close() # close\n",
        "\n",
        "  return(colnames, my_data) # return column names AND data\n",
        "\n",
        "# Column name function for checking out what's in a table\n",
        "def get_column_names(table_name): # arguement of table_name\n",
        "  conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        "  # Now select column names while inserting the table name into the WERE\n",
        "  column_name_query =  \"\"\"SELECT column_name FROM information_schema.columns\n",
        "       WHERE table_name = '%s' \"\"\" %table_name\n",
        "\n",
        "  cur.execute(column_name_query) # exectue\n",
        "  my_data = cur.fetchall() # store\n",
        "\n",
        "  cur.close() # close\n",
        "  conn.close() # close\n",
        "\n",
        "  return(my_data) # return\n",
        "\n",
        "# Check table_names\n",
        "def get_table_names():\n",
        "  conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        "  # query to get table names\n",
        "  table_name_query = \"\"\"SELECT table_name FROM information_schema.tables\n",
        "       WHERE table_schema = 'public' \"\"\"\n",
        "\n",
        "  cur.execute(table_name_query) # execute\n",
        "  my_data = cur.fetchall() # fetch results\n",
        "\n",
        "  cur.close() #close cursor\n",
        "  conn.close() # close connection\n",
        "\n",
        "  return(my_data) # return your fetched results\n",
        "\n",
        "def sql_head (table_name):\n",
        "  conn, cur = get_conn_cur() # get connection and cursor\n",
        "\n",
        "  cur.execute(\"\"\"SELECT * FROM %s LIMIT 5; \"\"\" %table_name)\n",
        "\n",
        "  my_data = cur.fetchall() # fetch query data as before\n",
        "\n",
        "  # here we're extracting the 0th element for each item in cur.description\n",
        "  colnames = [desc[0] for desc in cur.description]\n",
        "\n",
        "  cur.close() # close\n",
        "  conn.close() # close\n",
        "\n",
        "  head_df = pd.DataFrame(my_data, columns=colnames) #turn list into panda dataframe\n",
        "\n",
        "  return(head_df) # return column names AND data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "NAkq6zjrn8pi",
        "outputId": "1f46faa5-ff30-4c2b-f31b-95e33a1dacfa"
      },
      "outputs": [],
      "source": [
        "# Drop table\n",
        "# *** Only run this code if you've already created your table and want to delete it to start from scratch again.\n",
        "#\n",
        "conn, cur = get_conn_cur()\n",
        "table_name = ' ' # what table to drop\n",
        "drop_table_statement = \"DROP TABLE %s;\"%table_name # make your statement\n",
        "cur.execute(drop_table_statement) # execute it\n",
        "conn.commit()\n",
        "cur.close() # close your cursor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fe9LeI7Uwf5r"
      },
      "source": [
        "#Uploading 3 Dataframes into AWS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "LWkMwm5h1SdO",
        "outputId": "6fdab804-afaf-492b-b647-5aca2395e4bc"
      },
      "outputs": [],
      "source": [
        "#Create the hot 5 table\n",
        "tq = \"\"\"CREATE TABLE hot5 (\n",
        "     song_week_key VARCHAR(255) PRIMARY KEY,\n",
        "     SongID VARCHAR(255) NOT NULL,\n",
        "     WeekID timestamp NOT NULL,\n",
        "     url VARCHAR(255) NOT NULL,\n",
        "     Week_Position BIGINT NOT NULL,\n",
        "     Peak_Position BIGINT NOT NULL,\n",
        "     Weeks_on_Chart BIGINT NOT NULL\n",
        "     );\"\"\"\n",
        "\n",
        "# Get our connection and cursor and assign to conn and cur\n",
        "conn, cur = get_conn_cur()\n",
        "\n",
        "# execute our query we wrote above\n",
        "cur.execute(tq)\n",
        "\n",
        "# commit\n",
        "conn.commit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y_TIuuCx1fz3"
      },
      "outputs": [],
      "source": [
        "# make column order match AWS table\n",
        "chart = chart[['song_week_key', 'SongID', 'WeekID', 'url', 'Week_Position', 'Peak_Position', 'Weeks_on_Chart']]\n",
        "\n",
        "import numpy as np\n",
        "#Insert the data into a touple so it can be inserted into the table\n",
        "chart_np = chart.to_numpy();\n",
        "chart_np[:,1] = np.vectorize(lambda x: str(x))(chart_np[:,1])\n",
        "chart_tups = [tuple(x) for x in chart_np]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x3Llau1u1nfe"
      },
      "outputs": [],
      "source": [
        "#Insert data string\n",
        "iq = \"\"\"INSERT INTO hot5(song_week_key,SongID,WeekID,url,Week_Position,Peak_Position,Weeks_on_Chart) VALUES(%s, %s, %s, %s, %s, %s, %s);\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "haSI_qT91k9_"
      },
      "outputs": [],
      "source": [
        "# Upload data\n",
        "conn, cur = get_conn_cur()\n",
        "cur.executemany(iq, chart_tups)\n",
        "conn.commit()\n",
        "conn.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WD_v2WiBrGr9"
      },
      "outputs": [],
      "source": [
        "tq = \"\"\"CREATE TABLE songs (\n",
        "     SongID VARCHAR(255) PRIMARY KEY,\n",
        "     Song VARCHAR(255) NOT NULL,\n",
        "     Performer VARCHAR(255) NOT NULL,\n",
        "     spotify_genre VARCHAR(400) NOT NULL\n",
        "     );\"\"\"\n",
        "\n",
        "# Get our connection and cursor and assign to conn and cur\n",
        "conn, cur = get_conn_cur()\n",
        "\n",
        "# execute our query we wrote above\n",
        "cur.execute(tq)\n",
        "\n",
        "# commit\n",
        "conn.commit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vWfywFp01lY4"
      },
      "outputs": [],
      "source": [
        "# make column order match AWS table\n",
        "songs = songs[['SongID', 'Song', 'Performer', 'spotify_genre']]\n",
        "\n",
        "songs_np = songs.to_numpy();\n",
        "songs_tups = [tuple(x) for x in songs_np]\n",
        "\n",
        "iq = \"\"\"INSERT INTO songs(SongID,Song,Performer,spotify_genre) VALUES(%s, %s, %s, %s);\"\"\"\n",
        "\n",
        "# Upload data\n",
        "conn, cur = get_conn_cur()\n",
        "cur.executemany(iq, songs_tups)\n",
        "conn.commit()\n",
        "conn.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGKR8GQ8skD8",
        "outputId": "6939c3bc-8147-4a33-b6f7-16981279b5d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SongID                        object\n",
              "spotify_track_id              object\n",
              "spotify_track_duration_ms    float64\n",
              "spotify_track_explicit       float64\n",
              "danceability                 float64\n",
              "energy                       float64\n",
              "key                          float64\n",
              "loudness                     float64\n",
              "mode                         float64\n",
              "speechiness                  float64\n",
              "acousticness                 float64\n",
              "instrumentalness             float64\n",
              "liveness                     float64\n",
              "valence                      float64\n",
              "tempo                        float64\n",
              "time_signature               float64\n",
              "spotify_track_popularity     float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "spotifyInfo.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QyIWoY2WrS9c"
      },
      "outputs": [],
      "source": [
        "tq = \"\"\"CREATE TABLE spotifyInfo (\n",
        "     SongID VARCHAR(255) PRIMARY KEY,\n",
        "     spotify_track_id VARCHAR(255) NOT NULL,\n",
        "     spotify_track_duration_ms FLOAT(24) NOT NULL,\n",
        "     spotify_track_explicit FLOAT(24) NOT NULL,\n",
        "     danceability FLOAT(24) NOT NULL,\n",
        "     energy FLOAT(24) NOT NULL,\n",
        "     key FLOAT(24) NOT NULL,\n",
        "     loudness FLOAT(24) NOT NULL,\n",
        "     mode FLOAT(24) NOT NULL,\n",
        "     speechiness FLOAT(24) NOT NULL,\n",
        "     acousticness FLOAT(24) NOT NULL,\n",
        "     instrumentalness FLOAT(24) NOT NULL,\n",
        "     liveness FLOAT(24) NOT NULL,\n",
        "     valence FLOAT(24) NOT NULL,\n",
        "     tempo FLOAT(24) NOT NULL,\n",
        "     time_signature FLOAT(24) NOT NULL,\n",
        "     spotify_track_popularity FLOAT(24) NOT NULL\n",
        "     );\"\"\"\n",
        "\n",
        "# Get our connection and cursor and assign to conn and cur\n",
        "conn, cur = get_conn_cur()\n",
        "\n",
        "# execute our query we wrote above\n",
        "cur.execute(tq)\n",
        "\n",
        "# commit\n",
        "conn.commit()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-bs42XC4tYW0"
      },
      "outputs": [],
      "source": [
        "# make column order match AWS table\n",
        "spotifyInfo = spotifyInfo[['SongID', 'spotify_track_id', 'spotify_track_duration_ms',\n",
        "       'spotify_track_explicit', 'danceability', 'energy', 'key', 'loudness',\n",
        "       'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness',\n",
        "       'valence', 'tempo', 'time_signature', 'spotify_track_popularity']]\n",
        "\n",
        "spotifyInfo_np = spotifyInfo.to_numpy();\n",
        "spotifyInfo_tups = [tuple(x) for x in spotifyInfo_np]\n",
        "\n",
        "iq = \"\"\"INSERT INTO spotifyInfo(SongID,spotify_track_id,spotify_track_duration_ms,\n",
        "       spotify_track_explicit,danceability,energy,key,loudness,\n",
        "       mode,speechiness,acousticness,instrumentalness,liveness,\n",
        "       valence,tempo,time_signature,spotify_track_popularity) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);\"\"\"\n",
        "\n",
        "# Upload data\n",
        "conn, cur = get_conn_cur()\n",
        "cur.executemany(iq, spotifyInfo_tups)\n",
        "conn.commit()\n",
        "conn.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32ybs61MwsGH"
      },
      "source": [
        "#SQL Queries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7EpS8EH9uXr"
      },
      "source": [
        "##Average Tempo Query"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNkd-7NrzsWr",
        "outputId": "ab0c00be-f55c-4783-e305-5c46ab128092"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('hot5',), ('songs',), ('spotifyinfo',)]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_table_names()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN9VObep0oUj",
        "outputId": "fbe29e22-5aab-4021-b51d-4e5800e1e781"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('songid',), ('song',), ('performer',), ('spotify_genre',)]"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('songs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NPBG3gTq1ZwP",
        "outputId": "1a1bee41-b8d9-4624-b410-5685f7828024"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('spotify_track_popularity',),\n",
              " ('tempo',),\n",
              " ('time_signature',),\n",
              " ('spotify_track_duration_ms',),\n",
              " ('spotify_track_explicit',),\n",
              " ('danceability',),\n",
              " ('energy',),\n",
              " ('key',),\n",
              " ('loudness',),\n",
              " ('mode',),\n",
              " ('speechiness',),\n",
              " ('acousticness',),\n",
              " ('instrumentalness',),\n",
              " ('liveness',),\n",
              " ('valence',),\n",
              " ('spotify_track_id',),\n",
              " ('songid',)]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('spotifyinfo')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bf2ALNaXwyou",
        "outputId": "202077ae-72cd-487b-a136-7ba058b9b999"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-47-829af43c36e2>:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  avgtempo = pd.read_sql(\"\"\"SELECT date_part('year', hot5.weekid) as year, AVG(spotifyinfo.tempo) as avg_tempo FROM spotifyinfo\n"
          ]
        }
      ],
      "source": [
        "#Query gets the average tempo per year\n",
        "#I used pd.read_sql in order to get it into a pandas dataframe so I can graph it\n",
        "avgtempo = pd.read_sql(\"\"\"SELECT date_part('year', hot5.weekid) as year, AVG(spotifyinfo.tempo) as avg_tempo FROM spotifyinfo\n",
        "        LEFT JOIN hot5 ON spotifyinfo.songid = hot5.songid\n",
        "        GROUP BY year\n",
        "        HAVING date_part('year', hot5.weekid) < 2021\n",
        "        ORDER BY year ASC\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "                host=\"\",\n",
        "                database=\"\",\n",
        "                user=\"\",\n",
        "                password=\"\",\n",
        "                port=''))\n",
        "\n",
        "### Filter less than 2021 because 2021 only goes until June 1st of 2021 so the average is skewed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9KHf-NEE1f8z"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "YFmlICrX_fbg",
        "outputId": "c040ad78-baf0-49e1-c054-e9a5cdd18f75"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Create scatterplot of average tempos over the year\n",
        "plt.scatter(avgtempo['year'], avgtempo['avg_tempo'])\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Average Tempo')\n",
        "plt.xlim(1950, 2020)\n",
        "plt.ylim(0, 140)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhvD0j0U-EhI"
      },
      "source": [
        "##Top Songs in 2010 Query"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1csLfAYwQyu",
        "outputId": "a9e8a501-4772-4249-e744-de14bc5427a4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('songid',), ('song',), ('performer',), ('spotify_genre',)]"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('songs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adSBFA-X_69g",
        "outputId": "a3cab35d-b840-440e-c299-c4d9ce2a3006"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('week_position',),\n",
              " ('peak_position',),\n",
              " ('weeks_on_chart',),\n",
              " ('weekid',),\n",
              " ('url',),\n",
              " ('songid',),\n",
              " ('song_week_key',)]"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('hot5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4E0KtN1Mv_-_",
        "outputId": "5f637678-ebf4-4a49-a25d-27185caea0a7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-53-397da6a4f78e>:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  songs_2010 = pd.read_sql(\"\"\"SELECT hot5.weekid as date, songs.song FROM songs\n"
          ]
        }
      ],
      "source": [
        "#Query that gets the number one songs from every week of 2010\n",
        "songs_2010 = pd.read_sql(\"\"\"SELECT hot5.weekid as date, songs.song FROM songs\n",
        "        LEFT JOIN hot5 ON songs.songid = hot5.songid\n",
        "        WHERE date_part('year', hot5.weekid) = 2010.0\n",
        "        AND hot5.week_position = 1\n",
        "        ORDER BY date DESC\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0Qfa7Pl2xK0o",
        "outputId": "d257ace3-6bc9-4f50-ebb7-53f1f69d05eb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-43ad086f-9985-47f7-bd52-a5b5e141376c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>song</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-12-25</td>\n",
              "      <td>Firework</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-12-18</td>\n",
              "      <td>Firework</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-12-11</td>\n",
              "      <td>Raise Your Glass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-12-04</td>\n",
              "      <td>Only Girl (In The World)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-11-27</td>\n",
              "      <td>Like A G6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2010-11-20</td>\n",
              "      <td>What's My Name?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2010-11-13</td>\n",
              "      <td>We R Who We R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2010-11-06</td>\n",
              "      <td>Like A G6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2010-10-30</td>\n",
              "      <td>Like A G6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2010-10-23</td>\n",
              "      <td>Just The Way You Are</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2010-10-16</td>\n",
              "      <td>Just The Way You Are</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2010-10-09</td>\n",
              "      <td>Just The Way You Are</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2010-10-02</td>\n",
              "      <td>Just The Way You Are</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2010-09-25</td>\n",
              "      <td>Teenage Dream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2010-09-18</td>\n",
              "      <td>Teenage Dream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2010-09-11</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2010-09-04</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2010-08-28</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2010-08-21</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2010-08-14</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2010-08-07</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2010-07-31</td>\n",
              "      <td>Love The Way You Lie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2010-06-12</td>\n",
              "      <td>OMG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2010-06-05</td>\n",
              "      <td>OMG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2010-05-29</td>\n",
              "      <td>OMG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2010-05-22</td>\n",
              "      <td>Not Afraid</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2010-05-15</td>\n",
              "      <td>OMG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2010-05-08</td>\n",
              "      <td>Nothin' On You</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2010-05-01</td>\n",
              "      <td>Nothin' On You</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2010-04-24</td>\n",
              "      <td>Rude Boy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2010-04-17</td>\n",
              "      <td>Rude Boy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2010-04-10</td>\n",
              "      <td>Rude Boy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2010-04-03</td>\n",
              "      <td>Rude Boy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2010-03-27</td>\n",
              "      <td>Rude Boy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2010-03-20</td>\n",
              "      <td>Break Your Heart</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2010-03-13</td>\n",
              "      <td>Imma Be</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2010-03-06</td>\n",
              "      <td>Imma Be</td>\n",
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            ],
            "text/plain": [
              "         date                      song\n",
              "0  2010-12-25                  Firework\n",
              "1  2010-12-18                  Firework\n",
              "2  2010-12-11          Raise Your Glass\n",
              "3  2010-12-04  Only Girl (In The World)\n",
              "4  2010-11-27                 Like A G6\n",
              "5  2010-11-20           What's My Name?\n",
              "6  2010-11-13             We R Who We R\n",
              "7  2010-11-06                 Like A G6\n",
              "8  2010-10-30                 Like A G6\n",
              "9  2010-10-23      Just The Way You Are\n",
              "10 2010-10-16      Just The Way You Are\n",
              "11 2010-10-09      Just The Way You Are\n",
              "12 2010-10-02      Just The Way You Are\n",
              "13 2010-09-25             Teenage Dream\n",
              "14 2010-09-18             Teenage Dream\n",
              "15 2010-09-11      Love The Way You Lie\n",
              "16 2010-09-04      Love The Way You Lie\n",
              "17 2010-08-28      Love The Way You Lie\n",
              "18 2010-08-21      Love The Way You Lie\n",
              "19 2010-08-14      Love The Way You Lie\n",
              "20 2010-08-07      Love The Way You Lie\n",
              "21 2010-07-31      Love The Way You Lie\n",
              "22 2010-06-12                       OMG\n",
              "23 2010-06-05                       OMG\n",
              "24 2010-05-29                       OMG\n",
              "25 2010-05-22                Not Afraid\n",
              "26 2010-05-15                       OMG\n",
              "27 2010-05-08            Nothin' On You\n",
              "28 2010-05-01            Nothin' On You\n",
              "29 2010-04-24                  Rude Boy\n",
              "30 2010-04-17                  Rude Boy\n",
              "31 2010-04-10                  Rude Boy\n",
              "32 2010-04-03                  Rude Boy\n",
              "33 2010-03-27                  Rude Boy\n",
              "34 2010-03-20          Break Your Heart\n",
              "35 2010-03-13                   Imma Be\n",
              "36 2010-03-06                   Imma Be"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "songs_2010"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MR-xMEWAzbZ"
      },
      "source": [
        "##Top 10 Danceable Songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoG8OJDjA6gg",
        "outputId": "6850b05a-9df0-42b9-b084-146e4a91317f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('spotify_track_popularity',),\n",
              " ('tempo',),\n",
              " ('time_signature',),\n",
              " ('spotify_track_duration_ms',),\n",
              " ('spotify_track_explicit',),\n",
              " ('danceability',),\n",
              " ('energy',),\n",
              " ('key',),\n",
              " ('loudness',),\n",
              " ('mode',),\n",
              " ('speechiness',),\n",
              " ('acousticness',),\n",
              " ('instrumentalness',),\n",
              " ('liveness',),\n",
              " ('valence',),\n",
              " ('spotify_track_id',),\n",
              " ('songid',)]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('spotifyinfo')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98rHqn4DBqJV",
        "outputId": "c71a90b1-c960-4bda-8d40-2f714c5692e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('week_position',),\n",
              " ('peak_position',),\n",
              " ('weeks_on_chart',),\n",
              " ('weekid',),\n",
              " ('url',),\n",
              " ('songid',),\n",
              " ('song_week_key',)]"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('hot5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kGfmgvJA3JN",
        "outputId": "a79e686f-360d-4611-9930-bad21f828002"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-57-ba707f8e1242>:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  top_dance = pd.read_sql(\"\"\"SELECT songs.song, danceability FROM spotifyinfo\n"
          ]
        }
      ],
      "source": [
        "#Query that gets the top 5 dancability songs\n",
        "top_dance = pd.read_sql(\"\"\"SELECT songs.song, danceability FROM spotifyinfo\n",
        "        LEFT JOIN songs ON spotifyinfo.songid = songs.songid\n",
        "        ORDER BY danceability DESC\n",
        "        LIMIT 10\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "N883kGjKD5eG",
        "outputId": "653bfafa-e898-4cb3-a742-9ef26b4a7092"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                         song  danceability\n",
              "0           Funky Cold Medina         0.988\n",
              "1              State of Shock         0.980\n",
              "2                Ice Ice Baby         0.978\n",
              "3               Give It To Me         0.973\n",
              "4                    SexyBack         0.967\n",
              "5                Hot In Herre         0.965\n",
              "6                    Anaconda         0.964\n",
              "7                  Girlfriend         0.963\n",
              "8  Another One Bites The Dust         0.951\n",
              "9                 Temperature         0.951"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "top_dance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYEexbKvGtRL"
      },
      "source": [
        "##Top Energy Songs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BiKId0QjG89j",
        "outputId": "a550e216-a4f2-433a-cbf4-e4a6d37c9294"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('songid',), ('song',), ('performer',), ('spotify_genre',)]"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('songs')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zpHTnP4zGwqb",
        "outputId": "c40212e5-a2c1-4b08-9fd0-36dac03f8775"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-60-6353d1309cdb>:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  top_energy = pd.read_sql(\"\"\"SELECT songs.song, songs.performer as artist, energy FROM spotifyinfo\n"
          ]
        }
      ],
      "source": [
        "#Query that gets the songs with the highest energy\n",
        "top_energy = pd.read_sql(\"\"\"SELECT songs.song, songs.performer as artist, energy FROM spotifyinfo\n",
        "        LEFT JOIN songs ON spotifyinfo.songid = songs.songid\n",
        "        ORDER BY energy DESC\n",
        "        LIMIT 5\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 206
        },
        "id": "LekP-O-XG1zp",
        "outputId": "764fa66e-77da-467d-9684-bd48dd26385d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>song</th>\n",
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              "      <td>We Got The Beat</td>\n",
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              "      <th>1</th>\n",
              "      <td>When Doves Cry</td>\n",
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              "      <td>0.989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Paradise City</td>\n",
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              "      <td>0.980</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>Promiscuous</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
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              "                                                     [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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              song                             artist  energy\n",
              "0  We Got The Beat                            Go-Go's   0.994\n",
              "1   When Doves Cry                             Prince   0.989\n",
              "2    Paradise City                      Guns N' Roses   0.980\n",
              "3      Promiscuous  Nelly Furtado Featuring Timbaland   0.977\n",
              "4               If                      Janet Jackson   0.976"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "top_energy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA65-MDDHSOj"
      },
      "source": [
        "##Highest Songs Across 3 Catergories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrE11MFEHa0p",
        "outputId": "ac042e74-065d-4d81-876c-8f7465761352"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('spotify_track_popularity',),\n",
              " ('tempo',),\n",
              " ('time_signature',),\n",
              " ('spotify_track_duration_ms',),\n",
              " ('spotify_track_explicit',),\n",
              " ('danceability',),\n",
              " ('energy',),\n",
              " ('key',),\n",
              " ('loudness',),\n",
              " ('mode',),\n",
              " ('speechiness',),\n",
              " ('acousticness',),\n",
              " ('instrumentalness',),\n",
              " ('liveness',),\n",
              " ('valence',),\n",
              " ('spotify_track_id',),\n",
              " ('songid',)]"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('spotifyinfo')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdfMcbqbHVMN",
        "outputId": "f4bc88f7-f594-4ca7-98ef-fb22b610e358"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-63-f2cf2bf74fbb>:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  top = pd.read_sql(\"\"\"SELECT songs.song, songs.performer as artist, energy, danceability,  valence FROM spotifyinfo\n"
          ]
        }
      ],
      "source": [
        "#Based on the last two queries I was curious what songs were the highest across 3 categories\n",
        "top = pd.read_sql(\"\"\"SELECT songs.song, songs.performer as artist, energy, danceability,  valence FROM spotifyinfo\n",
        "        LEFT JOIN songs ON spotifyinfo.songid = songs.songid\n",
        "        WHERE energy > 0.9\n",
        "        AND danceability > 0.8\n",
        "        AND valence > 0.9\n",
        "        ORDER BY energy DESC\n",
        "        LIMIT 5\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))"
      ]
    },
    {
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        },
        "id": "uz8Q1rMhIYZz",
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              "      <th>1</th>\n",
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              "      <td>Get The Party Started</td>\n",
              "      <td>P!nk</td>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                           song          artist  energy  danceability  valence\n",
              "0                      Hot Line     The Sylvers   0.967         0.831    0.966\n",
              "1  C'mon N' Ride It (The Train)  Quad City DJ's   0.959         0.843    0.923\n",
              "2             Papa Don't Preach         Madonna   0.906         0.807    0.971\n",
              "3         Get The Party Started            P!nk   0.904         0.802    0.961"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "top"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1A_DVtGtJueb"
      },
      "source": [
        "##Average Energy Across the Years"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2-AJawFJ1R-",
        "outputId": "16bd4322-567f-4819-ac14-8cb56200cfb5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-65-95dc226179a5>:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  avgEnergy = pd.read_sql(\"\"\"SELECT date_part('year', hot5.weekid) as year, AVG(spotifyinfo.energy) as avg_energy FROM spotifyinfo\n"
          ]
        }
      ],
      "source": [
        "#Query that gets the average energy from every year to see how it has changed over the years\n",
        "avgEnergy = pd.read_sql(\"\"\"SELECT date_part('year', hot5.weekid) as year, AVG(spotifyinfo.energy) as avg_energy FROM spotifyinfo\n",
        "        LEFT JOIN hot5 ON spotifyinfo.songid = hot5.songid\n",
        "        GROUP BY year\n",
        "        ORDER BY year ASC\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 424
        },
        "id": "2Yxne_Q8KRf3",
        "outputId": "fe0874f7-b761-4ecd-bc55-d019eac64a35"
      },
      "outputs": [
        {
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              "\n",
              "\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>59</th>\n",
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              "      <td>0.625804</td>\n",
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              "<p>64 rows × 2 columns</p>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-493fc157-2240-41ba-9235-c811282ef619 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-493fc157-2240-41ba-9235-c811282ef619');\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"
            ],
            "text/plain": [
              "      year  avg_energy\n",
              "0   1958.0    0.463525\n",
              "1   1959.0    0.477279\n",
              "2   1960.0    0.439790\n",
              "3   1961.0    0.506680\n",
              "4   1962.0    0.510462\n",
              "..     ...         ...\n",
              "59  2017.0    0.657502\n",
              "60  2018.0    0.627682\n",
              "61  2019.0    0.555055\n",
              "62  2020.0    0.673686\n",
              "63  2021.0    0.625804\n",
              "\n",
              "[64 rows x 2 columns]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avgEnergy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "9vJUrGf_KY1Q",
        "outputId": "5f1dd588-4101-4c41-a77e-614e97dfacad"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(avgEnergy['year'], avgEnergy['avg_energy'])\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Average Energy')\n",
        "plt.xlim(1950, 2022)\n",
        "plt.ylim(0, 1)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9GE88m8KzcW"
      },
      "source": [
        "## Artists in the Top 5 the Most Times"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dcKJ-aW6K6Nv",
        "outputId": "87ef2ac3-948e-4a24-e022-dcda39811af8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('week_position',),\n",
              " ('peak_position',),\n",
              " ('weeks_on_chart',),\n",
              " ('weekid',),\n",
              " ('url',),\n",
              " ('songid',),\n",
              " ('song_week_key',)]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_column_names('hot5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CRMNGpzK3h4",
        "outputId": "f9b31c63-c05c-4aad-e794-fd51f2b84f8c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-69-f1b19ddf18a2>:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
            "  TopArtist = pd.read_sql(\"\"\"SELECT songs.performer, COUNT(hot5.songid) as times_in_top_5 FROM hot5\n"
          ]
        }
      ],
      "source": [
        "#Query to find the top artists across the billboard hot 100. Uses the hot5 table in order to get artists\n",
        "#who may have had songs in the top 5 for more than one week, allowing me to get the total count\n",
        "TopArtist = pd.read_sql(\"\"\"SELECT songs.performer, COUNT(hot5.songid) as times_in_top_5 FROM hot5\n",
        "        LEFT JOIN songs ON hot5.songid = songs.songid\n",
        "        GROUP BY songs.performer\n",
        "        ORDER BY times_in_top_5 DESC\n",
        "        LIMIT 10\n",
        "        ;\"\"\",\n",
        "        con = psycopg2.connect(\n",
        "        host=\"\",\n",
        "        database=\"\",\n",
        "        user=\"\",\n",
        "        password=\"\",\n",
        "        port=''))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "je3J-HsBNQRs",
        "outputId": "9e503dce-5c11-4bd2-beb2-322acbc0f1d2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-18c4d4de-bdd3-4fbd-b594-86c729aab06f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>times_in_top_5</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>performer</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mariah Carey</th>\n",
              "      <td>154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Beatles</th>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Madonna</th>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bruno Mars</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Usher</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Michael Jackson</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Whitney Houston</th>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taylor Swift</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Weeknd</th>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Janet Jackson</th>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18c4d4de-bdd3-4fbd-b594-86c729aab06f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <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",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26d7481f-3db2-494f-8a09-7f77efebf7a6')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "  quickchartButtonEl.style.display =\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-18c4d4de-bdd3-4fbd-b594-86c729aab06f 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-18c4d4de-bdd3-4fbd-b594-86c729aab06f');\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"
            ],
            "text/plain": [
              "                 times_in_top_5\n",
              "performer                      \n",
              "Mariah Carey                154\n",
              "The Beatles                 140\n",
              "Madonna                     136\n",
              "Bruno Mars                   96\n",
              "Usher                        93\n",
              "Michael Jackson              91\n",
              "Whitney Houston              85\n",
              "Taylor Swift                 84\n",
              "The Weeknd                   83\n",
              "Janet Jackson                83"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "TopArtist.set_index('performer')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.15"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}