1_The Art of Analyzing Big Data- The Data Scientist’s Toolbox.ipynb

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      "name": "Working with Structured Data - SQL.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fq8BHLiYCyE",
        "colab_type": "text"
      },
      "source": [
        "# Homework Assignment 1\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYvqtKSOYCyG",
        "colab_type": "text"
      },
      "source": [
        "For this homework you will need to write code that analyzes real-world datasets. The code needs to be written in Python using the [sqlite3](https://docs.python.org/2/library/sqlite3.html) package. \n",
        "\n",
        "**Please note:** You need to answer only the questions that match your ID first digit.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AML-gkV-YCyI",
        "colab_type": "text"
      },
      "source": [
        "# 1. Babies Names Dataset (35pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvkBrt7SYCyK",
        "colab_type": "text"
      },
      "source": [
        "**Task 1 (for everyone):** Write a code that uses the  [**babies names dataset**](https://catalog.data.gov/dataset/baby-names-from-social-security-card-applications-data-by-state-and-district-of-#topic=developers_navigation) and creates a table named (Names) with the dataset data and the following columns: 'State', 'Gender', 'Name', 'Number', and 'Year' (5pt)\n",
        "**Bonus:** Load the data using a Batch INSERT SQL Query (2pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFSvVqY7ehGc",
        "colab_type": "text"
      },
      "source": [
        "## (A) Download babies names dataset using Bash\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8ktCaRcUIGs",
        "colab_type": "code",
        "outputId": "eb00afea-522c-49aa-ab18-6fb3622cafe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# clean main folders\n",
        "!rm -r *\n",
        "!ls\n",
        "\n",
        "# Creating a dataset directory. \n",
        "# -p Will also create any intermediate directories that don't exist\n",
        "!mkdir -p ./datasets/us-baby-name/zip\n",
        "!mkdir -p ./datasets/us-baby-name/data\n",
        "\n",
        "# download the dataset from SSA and unzip it\n",
        "!wget 'https://www.ssa.gov/oact/babynames/state/namesbystate.zip' -O \"./datasets/us-baby-name/zip/namesbystate.zip\"\n",
        "!unzip {\"./datasets/us-baby-name/zip/namesbystate.zip\"} -d {\"./datasets/us-baby-name/data\"}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-24 22:20:21--  https://www.ssa.gov/oact/babynames/state/namesbystate.zip\n",
            "Resolving www.ssa.gov (www.ssa.gov)... 137.200.39.19, 2001:1930:d07::aaaa\n",
            "Connecting to www.ssa.gov (www.ssa.gov)|137.200.39.19|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 21974087 (21M) [application/zip]\n",
            "Saving to: ‘./datasets/us-baby-name/zip/namesbystate.zip’\n",
            "\n",
            "./datasets/us-baby- 100%[===================>]  20.96M   344KB/s    in 63s     \n",
            "\n",
            "2020-03-24 22:21:26 (340 KB/s) - ‘./datasets/us-baby-name/zip/namesbystate.zip’ saved [21974087/21974087]\n",
            "\n",
            "Archive:  ./datasets/us-baby-name/zip/namesbystate.zip\n",
            "  inflating: ./datasets/us-baby-name/data/AK.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/AL.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/AR.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/AZ.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/CA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/CO.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/CT.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/DC.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/DE.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/FL.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/GA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/HI.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/IA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/ID.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/IL.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/IN.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/KS.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/KY.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/LA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MD.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/ME.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MI.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MN.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MO.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MS.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/MT.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NC.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/ND.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NE.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NH.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NJ.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NM.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NV.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/NY.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/OH.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/OK.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/OR.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/PA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/RI.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/SC.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/SD.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/StateReadMe.pdf  \n",
            "  inflating: ./datasets/us-baby-name/data/TN.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/TX.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/UT.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/VA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/VT.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/WA.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/WI.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/WV.TXT  \n",
            "  inflating: ./datasets/us-baby-name/data/WY.TXT  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-b9bCgYIe4X5",
        "colab_type": "text"
      },
      "source": [
        "## (B) Test the Download and the Unzip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkBBP19baAmH",
        "colab_type": "code",
        "outputId": "8c543c5d-625c-45e2-fc9e-44c4d16bb529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# only for test\n",
        "!ls 'datasets/us-baby-name/data'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AK.TXT\tCT.TXT\tIA.TXT\tLA.TXT\tMO.TXT\tNH.TXT\tOK.TXT\tStateReadMe.pdf  WA.TXT\n",
            "AL.TXT\tDC.TXT\tID.TXT\tMA.TXT\tMS.TXT\tNJ.TXT\tOR.TXT\tTN.TXT\t\t WI.TXT\n",
            "AR.TXT\tDE.TXT\tIL.TXT\tMD.TXT\tMT.TXT\tNM.TXT\tPA.TXT\tTX.TXT\t\t WV.TXT\n",
            "AZ.TXT\tFL.TXT\tIN.TXT\tME.TXT\tNC.TXT\tNV.TXT\tRI.TXT\tUT.TXT\t\t WY.TXT\n",
            "CA.TXT\tGA.TXT\tKS.TXT\tMI.TXT\tND.TXT\tNY.TXT\tSC.TXT\tVA.TXT\n",
            "CO.TXT\tHI.TXT\tKY.TXT\tMN.TXT\tNE.TXT\tOH.TXT\tSD.TXT\tVT.TXT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BertGootfLLJ",
        "colab_type": "text"
      },
      "source": [
        "## (C) Create dataframe using the TXT files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vrdQhaLYCyL",
        "colab_type": "code",
        "outputId": "80004a7a-b141-4c06-da77-4e2010223bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import glob\n",
        "import pandas as pd\n",
        "\n",
        "path = 'datasets/us-baby-name/data'\n",
        "file_list = glob.glob(f'{path}/*.TXT')\n",
        "all_name_df = None\n",
        "for file_path in file_list:\n",
        "  df_file = pd.read_csv(file_path, sep=\",\", header=None)\n",
        "  df_file.columns = ['State', 'Gender',  'Year', 'Name', 'Number']\n",
        "  if all_name_df is None:\n",
        "    all_name_df = df_file\n",
        "  else:\n",
        "    all_name_df = all_name_df.append(df_file)\n",
        "all_name_df = all_name_df[['State', 'Gender', 'Name', 'Number', 'Year']]  # reagrrange the columns\n",
        "print(all_name_df.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  State Gender    Name  Number  Year\n",
            "0    TX      F    Mary     895  1910\n",
            "1    TX      F    Ruby     314  1910\n",
            "2    TX      F   Annie     277  1910\n",
            "3    TX      F  Willie     260  1910\n",
            "4    TX      F    Ruth     252  1910\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcUgTbXIfUml",
        "colab_type": "text"
      },
      "source": [
        "# (D) Create SQL DB using the Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Cc4aK5YdZws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir -p ./datasets/us-baby-name/sql\n",
        "import sqlite3\n",
        "DB_PATH_BABY = 'datasets/us-baby-name/sql/database.sqlite'\n",
        "!rm -f ./datasets/us-baby-name/sql/database.sqlite\n",
        "to_db = list(all_name_df.itertuples(index=False))\n",
        "con = sqlite3.connect(DB_PATH_BABY)\n",
        "cur = con.cursor()\n",
        "# creates a table named (Names) with the dataset data and the following columns:\n",
        "# 'State', 'Gender', 'Name', 'Number', and 'Year' (5pt)\n",
        "cur.execute(\"CREATE TABLE babyName (State, Gender, Name, Number, Year);\")\n",
        "\n",
        "#  ***Bonus***: Load the data using a Batch INSERT SQL Query (2pt):\n",
        "cur.executemany(\"INSERT INTO babyName (State, Gender, Name, Number, Year) VALUES (?, ?, ?, ?, ?);\", to_db)\n",
        "con.commit()\n",
        "con.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVNaJineeVBy",
        "colab_type": "text"
      },
      "source": [
        "# (E) Test the DB with all baby names"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jd9WedZPe0_A",
        "colab_type": "code",
        "outputId": "6f4a8210-269d-4577-ec36-920acec0a5df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# only for test\n",
        "!ls datasets/us-baby-name/sql/\n",
        "conn = sqlite3.connect(DB_PATH_BABY) # connecting to the database\n",
        "c = conn.cursor() # creating a cursor object\n",
        "print(\"The number of names in the dataset: %s\" % c.execute(\"SELECT count(*) FROM babyName\" ).fetchone()) # execute a query & fetch the results\n",
        "c.close() # close the cursor"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "database.sqlite\n",
            "The number of names in the dataset: 6028151\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEbvkQhCYCyP",
        "colab_type": "text"
      },
      "source": [
        "**Task 2 (for everyone):** Write a query that returns the statistics for the name William (5pt). Use the [the timeit package](https://docs.python.org/3/library/timeit.html) to measure the time it takes the query to run (5pt). **Bonus:** [Create an index](https://www.w3schools.com/sql/sql_create_index.asp)  on the _Name_ column and use the [the timeit package](https://docs.python.org/3/library/timeit.html) to measure the time it takes the query to run with the index (5pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBCHPAOzYCyR",
        "colab_type": "code",
        "outputId": "b2751267-7264-4ec3-e6c0-3964a3a327e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "import timeit\n",
        "import sqlite3\n",
        "!ls datasets/us-baby-name/sql/\n",
        "conn = sqlite3.connect(DB_PATH_BABY) # connecting to the database\n",
        "c = conn.cursor() # creating a cursor object\n",
        "#  Write a query that returns the statistics for the name William (5pt)\n",
        "# Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "query_for_stat = \"SELECT Gender, sum(Number) FROM babyName WHERE Name == 'William' GROUP BY Gender\"\n",
        "print(f\"The number of Female (F) and Male (M) named Williams in the dataset: {c.execute(query_for_stat).fetchall()}\")\n",
        "conn.commit()\n",
        "conn.close() # close the cursor\n",
        "\n",
        "# Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "num_of_runs = 100\n",
        "without_time = timeit.timeit(f'c.execute(\"{query_for_stat}\").fetchone();', setup='import sqlite3; conn = sqlite3.connect(\\'datasets/us-baby-name/sql/database.sqlite\\'); c = conn.cursor();', number=num_of_runs)\n",
        "print(f'Time to run query without indeices (average of {num_of_runs} runs): {without_time/num_of_runs:.5f}')\n",
        "\n",
        "# ***Bonus***: Create an index on the Name column and use the the timeit package to measure the time it takes the query to run with the index (5pt)\n",
        "conn = sqlite3.connect(DB_PATH_BABY) # connecting to the database\n",
        "c = conn.cursor() # creating a cursor object\n",
        "# c.execute(\"DROP INDEX name_idx;\")\n",
        "c.execute(\"CREATE INDEX name_idx ON babyName (Name)\")\n",
        "conn.commit()\n",
        "conn.close() # close the cursor\n",
        "\n",
        "\n",
        "# Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "with_time = timeit.timeit(f'c.execute(\"{query_for_stat}\").fetchone();', setup='import sqlite3; conn = sqlite3.connect(\\'datasets/us-baby-name/sql/database.sqlite\\'); c = conn.cursor();', number=num_of_runs)\n",
        "print(f'Time to run query with indeices (average of {num_of_runs} runs): {with_time/num_of_runs:.5f}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "database.sqlite\n",
            "The number of Female (F) and Male (M) named Williams in the dataset: [('F', 10211), ('M', 3890923)]\n",
            "Time to run query without indeices (average of 100 runs): 0.41991\n",
            "Time to run query with indeices (average of 100 runs): 0.01263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dClZxXjYCyU",
        "colab_type": "text"
      },
      "source": [
        "### <span style=\"color:red\"> Please answer only **one** of the following questions according to your ID number (use the formula **<YOUR_ID> mod 4 +1**) </span>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDpqwgwQYCyV",
        "colab_type": "code",
        "outputId": "20f5ca3d-2962-47e7-a79b-06aac7916b0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# which question to answer - put your ID number and run the code \n",
        "your_id  = \"\"\n",
        "q = int(your_id) % 4 + 1\n",
        "print(\"You need to answer question number %s\" % q)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You need to answer question number 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGU71KQ8YCyZ",
        "colab_type": "text"
      },
      "source": [
        "***Question 1:*** Write a function that returns how many babies were born in a given state in a given year.\n",
        "Use it to calculate the number of babies born in LA in 1950 (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RB3GjaaYCya",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "510Wry5WYCye",
        "colab_type": "text"
      },
      "source": [
        "***Question 2:*** Write a function that returns how many male babies were born between a given range of years.\n",
        "Use it to calculate how many babies were born between 1970 and 1975  (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5wOKWsBYCyf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x721dD1NYCyi",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:** Write a function that returns the most common female name in a given state. Use it to calculate the most common female name in Wasington in 1987 (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3t_ouDoYCyj",
        "colab_type": "code",
        "outputId": "1afd88b8-64f9-4f62-fff2-8ae0b30abcda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "def get_common_female_name(state, year):\n",
        "  conn = sqlite3.connect(DB_PATH_BABY) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "\n",
        "  #  Write a query that returns the statistics for the name William (5pt)\n",
        "  # Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "  query_for_stat = f\"SELECT Name, Number FROM babyName WHERE Gender == 'F' AND State == '{state}' AND Year == {year} GROUP BY Name ORDER BY Number desc;\"\n",
        "  female_num = c.execute(query_for_stat).fetchone()\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "  return female_num\n",
        "\n",
        "female_num = get_common_female_name('WA','1987')\n",
        "print(f\"The most common female name in Wasington in 1987:\")\n",
        "print(f\"Name: {female_num[0]} | Frequent: {female_num[1]}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The most common female name in Wasington in 1987:\n",
            "Name: Jessica | Frequent: 884\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRyUoBvhYCyl",
        "colab_type": "text"
      },
      "source": [
        "**Question 4:** Write a function that returns how many male babies named _William_ where born in a given state in a given year. Use it to find the state in which the highest number of babies _William_ where born in 1999 (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lz1q1mvPYCym",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THz0vDHHYCyo",
        "colab_type": "text"
      },
      "source": [
        "**Question (for everyone):** For the state of NY write code that calculates the second most popular female/male names in each decade (10pt). **Bonus**: Visualize it somehow using Matplotlib (5pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVRTQGOS_3G9",
        "colab_type": "code",
        "outputId": "acb26edf-e31f-4a3c-e113-78afaace7211",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "def find_second_most_popular_in_decade(State, Gender):\n",
        "  conn = sqlite3.connect(DB_PATH_BABY) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "  #  Write a query that returns the statistics for the name William (5pt)\n",
        "  # Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "  new_table_query = f\"SELECT Name, CAST(Year/10 AS int)*10 AS StartYear, CAST(Year/10 AS int)*10 + 9 AS EndYear, sum(Number) AS Total FROM babyName WHERE State == '{State}' AND Gender == '{Gender}' GROUP BY CAST(Year/10 AS int)*10, Name\"\n",
        "  new_table_query2 = f\"SELECT Name, CAST(Year/10 AS int)*10 AS SY, CAST(Year/10 AS int)*10 + 9 AS EndYear, sum(Number) AS Total FROM babyName WHERE State == '{State}' AND Gender == '{Gender}' GROUP BY CAST(Year/10 AS int)*10, Name\"\n",
        "  query_for_stat = f\"SELECT Name, StartYear, EndYear, Max(Total) FROM ({new_table_query}) WHERE Total < (SELECT maxT FROM (SELECT SY, MAX(Total) as maxT FROM ({new_table_query2}) WHERE StartYear == SY)) GROUP BY StartYear\"\n",
        "  list_to_return = c.execute(query_for_stat).fetchall()\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "  return list_to_return\n",
        "\n",
        "print(\"For the state of NY write, the second most popular female names in each decade\")\n",
        "female_ny = find_second_most_popular_in_decade('NY', 'F')\n",
        "for decade in female_ny:\n",
        "  print(f'Decade: [{decade[1]}-{decade[2]+1}) | Second most popular female names: {decade[0]} | Frequent: {decade[3]}')\n",
        "print(\"For the state of NY write, the second most popular male names in each decade\")\n",
        "male_ny = find_second_most_popular_in_decade('NY', 'M')\n",
        "for decade in male_ny:\n",
        "  print(f'Decade: [{decade[1]}-{decade[2]+1}) | Second most popular female names: {decade[0]} | Frequent: {decade[3]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For the state of NY write, the second most popular female names in each decade\n",
            "Decade: [1910-1920) | Second most popular female names: Helen | Frequent: 29923\n",
            "Decade: [1920-1930) | Second most popular female names: Dorothy | Frequent: 31655\n",
            "Decade: [1930-1940) | Second most popular female names: Joan | Frequent: 33005\n",
            "Decade: [1940-1950) | Second most popular female names: Patricia | Frequent: 43340\n",
            "Decade: [1950-1960) | Second most popular female names: Susan | Frequent: 47749\n",
            "Decade: [1960-1970) | Second most popular female names: Mary | Frequent: 31467\n",
            "Decade: [1970-1980) | Second most popular female names: Lisa | Frequent: 19555\n",
            "Decade: [1980-1990) | Second most popular female names: Jessica | Frequent: 31551\n",
            "Decade: [1990-2000) | Second most popular female names: Jessica | Frequent: 19645\n",
            "Decade: [2000-2010) | Second most popular female names: Isabella | Frequent: 10749\n",
            "Decade: [2010-2020) | Second most popular female names: Olivia | Frequent: 10846\n",
            "For the state of NY write, the second most popular male names in each decade\n",
            "Decade: [1910-1920) | Second most popular female names: Joseph | Frequent: 30473\n",
            "Decade: [1920-1930) | Second most popular female names: Robert | Frequent: 49144\n",
            "Decade: [1930-1940) | Second most popular female names: John | Frequent: 56851\n",
            "Decade: [1940-1950) | Second most popular female names: John | Frequent: 77177\n",
            "Decade: [1950-1960) | Second most popular female names: John | Frequent: 85358\n",
            "Decade: [1960-1970) | Second most popular female names: John | Frequent: 76309\n",
            "Decade: [1970-1980) | Second most popular female names: Christopher | Frequent: 35673\n",
            "Decade: [1980-1990) | Second most popular female names: Christopher | Frequent: 38220\n",
            "Decade: [1990-2000) | Second most popular female names: Christopher | Frequent: 29808\n",
            "Decade: [2000-2010) | Second most popular female names: Matthew | Frequent: 19333\n",
            "Decade: [2010-2020) | Second most popular female names: Michael | Frequent: 11063\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aUsOAXAzq1U",
        "colab_type": "code",
        "outputId": "645df069-9a92-414b-ecda-2871e5fd4bca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "# Visualize it somehow using Matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "names_female = []\n",
        "dates = []\n",
        "values_female = []\n",
        "for female_num_tuple in female_ny:\n",
        "  name = female_num_tuple[0]\n",
        "  names_female.append(name)\n",
        "  start_time = female_num_tuple[1]\n",
        "  end_time = female_num_tuple[2]\n",
        "  dates.append(f'[{start_time}-{end_time+1})')\n",
        "  count =  female_num_tuple[3]\n",
        "  values_female.append(count)\n",
        "\n",
        "names_male = []\n",
        "dates = []\n",
        "values_male = []\n",
        "for male_num_tuple in male_ny:\n",
        "  name = male_num_tuple[0]\n",
        "  names_male.append(name)\n",
        "  start_time = male_num_tuple[1]\n",
        "  end_time = male_num_tuple[2]\n",
        "  dates.append(f'[{start_time}-{end_time+1})')\n",
        "  count =  male_num_tuple[3]\n",
        "  values_male.append(count)\n",
        "\n",
        "N = len(dates)\n",
        "ind = np.arange(N)  # the x locations for the groups\n",
        "width = 0.3       # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 8))\n",
        "\n",
        "yvals = values_male\n",
        "rects1 = ax.bar(ind, yvals, width, color='blue')\n",
        "zvals = values_female\n",
        "rects2 = ax.bar(ind+width, zvals, width, color='pink')\n",
        "\n",
        "ax.set_ylabel('Frequent')\n",
        "ax.set_xlabel('Decades')\n",
        "ax.set_xticks(ind+(width/2))\n",
        "ax.set_xticklabels( tuple(dates) )\n",
        "ax.legend( (rects1[0], rects2[0]), ('Male', 'Female') )\n",
        "\n",
        "def autolabel(rects, names):\n",
        "    for i in range(len(rects)):\n",
        "        rect = rects[i]\n",
        "        name = names[i]\n",
        "        h = rect.get_height()\n",
        "        rotation = 90\n",
        "        space = 1.04*h\n",
        "        acutal_num = f'{int(h):,}'\n",
        "        if int(h) > 70000:\n",
        "          rotation = 0\n",
        "          space = 1.01*h\n",
        "        ax.text(rect.get_x()+rect.get_width()/2.,\n",
        "                space,\n",
        "                f'{name} ({acutal_num})',\n",
        "                ha='center', va='bottom', rotation=rotation)\n",
        "          \n",
        "\n",
        "autolabel(rects1, names_male)\n",
        "autolabel(rects2, names_female)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RGRlJxYoVady4sbN906ZN+Pr60qtXr1zXqlOnDlOmTOG+++7L0d6+fXvn61esWIGvry+d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\n4P8BNg55tsYoAEUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gmdQrLL9YCyp",
        "colab_type": "text"
      },
      "source": [
        "# 2. Flavors of Cacao Dataset (15pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTpNKH7UYCyq",
        "colab_type": "text"
      },
      "source": [
        "Using the [Flavors of Cacao](https://www.kaggle.com/rombikuboktaeder/choco-flavors) dataset, answer the following questions:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcRl8In_T-CX",
        "colab_type": "text"
      },
      "source": [
        "## (A) Connect to Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYCIkZPvNxUt",
        "colab_type": "code",
        "outputId": "51809101-6795-432a-f307-ea4d4992d0aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!mkdir /root/.kaggle/\n",
        "# Installing the Kaggle package\n",
        "!pip install kaggle \n",
        "\n",
        "import json\n",
        "import os\n",
        "#TODO: REMOVE THIS LINE\n",
        "#Important Note: complete this with your own key - after running this for the first time remmember to **remove** your API_KEY\n",
        "# api_token = {\"username\":\"<Insert Your Kaggle User Name>\",\"key\":\"<Insert Your Kaggle API key>\"}\n",
        "\n",
        "# creating kaggle.json file with the personal API-Key details \n",
        "# You can also put this file on your Google Drive\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "  json.dump(api_token, file)\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.6)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2019.11.28)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.12.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.0.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.21.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.38.0)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.8)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0DbHSrgUCT5",
        "colab_type": "text"
      },
      "source": [
        "## (B) Search for the Choco dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnHQBMBfQR7_",
        "colab_type": "code",
        "outputId": "839bab2e-18eb-46ee-e30f-4cfad6a67792",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# searching for the dataset\n",
        "!rm -r ./datasets/choco_flavors/\n",
        "!mkdir -p ./datasets/choco_flavors/data\n",
        "!mkdir -p ./datasets/choco_flavors/zip\n",
        "!ls datasets/choco_flavors/data\n",
        "\n",
        "!kaggle datasets list -s choco_flavors"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove './datasets/choco_flavors/': No such file or directory\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "ref                             title          size  lastUpdated          downloadCount  \n",
            "------------------------------  -------------  ----  -------------------  -------------  \n",
            "rombikuboktaeder/choco-flavors  choco_flavors  30KB  2018-04-01 04:36:29            512  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQUwhRDtUOW0",
        "colab_type": "text"
      },
      "source": [
        "## (C) Download choco_flavors using Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u1DdMC_QTfv",
        "colab_type": "code",
        "outputId": "a80259a5-d901-4745-f9b2-bc368e3d9aec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# download the dataset from Kaggle and unzip it\n",
        "!kaggle datasets download rombikuboktaeder/choco-flavors -p ./datasets/choco_flavors/zip\n",
        "!unzip ./datasets/choco_flavors/zip/*.zip -d ./datasets/choco_flavors/data\n",
        "!ls ./datasets/choco_flavors/zip/\n",
        "!ls ./datasets/choco_flavors/data/\n",
        "!ls ./datasets/choco_flavors/sql/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading choco-flavors.zip to ./datasets/choco_flavors/zip\n",
            "\r  0% 0.00/30.3k [00:00<?, ?B/s]\n",
            "\r100% 30.3k/30.3k [00:00<00:00, 58.5MB/s]\n",
            "Archive:  ./datasets/choco_flavors/zip/choco-flavors.zip\n",
            "  inflating: ./datasets/choco_flavors/data/flavors_of_cacao.csv  \n",
            "choco-flavors.zip\n",
            "flavors_of_cacao.csv\n",
            "ls: cannot access './datasets/choco_flavors/sql/': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgVvOQ2hUaCO",
        "colab_type": "text"
      },
      "source": [
        "## (D) Convert CSV to SQLITE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMi3CQM4UZBj",
        "colab_type": "code",
        "outputId": "331596ea-6ece-44c3-d585-c099033b8715",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "!rm -r datasets/choco_flavors/sql/\n",
        "!mkdir -p ./datasets/choco_flavors/sql\n",
        "import pandas as pd\n",
        "DATASET_PATH = 'datasets/choco_flavors/data/flavors_of_cacao.csv'\n",
        "csv_data = pd.read_csv(DATASET_PATH)\n",
        "csv_data.columns = (\"Company\", \"Bean Origin\", \"REF\", \"Review Date\", \"Cocoa Percent\", \"Company Location\", \"Rating\", \"Bean Type\", \"Broad Bean Origin\")\n",
        "csv_data[\"Bean Type\"].replace(r'^\\s*$', np.nan, regex=True, inplace=True)\n",
        "csv_data = csv_data.dropna()\n",
        "print(csv_data.head(5))\n",
        "\n",
        "import sqlite3\n",
        "DB_PATH_choco = 'datasets/choco_flavors/sql/database.sqlite'\n",
        "to_db = list(csv_data.itertuples(index=False))\n",
        "con = sqlite3.connect(DB_PATH_choco)\n",
        "cur = con.cursor()\n",
        "# creates a table named (Names) with the dataset data and the following columns:\n",
        "columns_str = '(Company, BeanOrigin, REF, ReviewDate, CocoaPercent, CompanyLocation, Rating, BeanType, BroadBeanOrigin)'\n",
        "cur.execute(f'DROP TABLE IF EXISTS choco')\n",
        "cur.execute(f\"CREATE TABLE choco {columns_str};\")\n",
        "#  ***Bonus***: Load the data using a Batch INSERT SQL Query (2pt):\n",
        "cur.executemany(f\"INSERT INTO choco {columns_str} VALUES (?,?,?,?,?,?,?,?,?);\", to_db)\n",
        "con.commit()\n",
        "con.close()\n",
        "print('Done')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'datasets/choco_flavors/sql/': No such file or directory\n",
            "     Company     Bean Origin   REF  ...  Rating   Bean Type Broad Bean Origin\n",
            "5   A. Morin        Carenero  1315  ...    2.75     Criollo         Venezuela\n",
            "7   A. Morin    Sur del Lago  1315  ...    3.50     Criollo         Venezuela\n",
            "8   A. Morin  Puerto Cabello  1319  ...    3.75     Criollo         Venezuela\n",
            "11  A. Morin      Madagascar  1011  ...    3.00     Criollo        Madagascar\n",
            "17  A. Morin           Chuao  1015  ...    4.00  Trinitario         Venezuela\n",
            "\n",
            "[5 rows x 9 columns]\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbg9gkVtYCyr",
        "colab_type": "text"
      },
      "source": [
        "**Question 1:** Write a function that returns the number of bars manufactured where the bars' BroadBean Origin is a given country. Use the function to calculate the number of bars where BroadBean Origin is 'Fiji' (15pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5FWfEiaYCys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GCvKxWtYCyu",
        "colab_type": "text"
      },
      "source": [
        "**Question 2:** Write a function that returns the maximal and average cocoa percentage in a bar manufactured by a company in a specific country. Use the function to calculate the maximal and average cocoa percentage in bars manufactured by a Swiss company (15pt)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZJXxtwWYCyv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlYqUA1xYCyx",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:** Calculate the second most common bean type(s) and the most rare bean type(s) (15\n",
        "pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kk0qIAiwYCyx",
        "colab_type": "code",
        "outputId": "c6a1abc5-5203-4b94-f056-ed10cc3abcd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "def second_most_common_bean_type():\n",
        "  conn = sqlite3.connect(DB_PATH_choco) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "  query_for_stat = f\"SELECT BeanType, count(BeanType) AS frequent FROM (choco) GROUP BY BeanType ORDER BY frequent DESC LIMIT 1 OFFSET 1;\"\n",
        "  list_to_return = c.execute(query_for_stat).fetchall()\n",
        "  max_value = list_to_return[0][1]\n",
        "  query_for_stat = f\"SELECT BeanType, Frequent FROM (SELECT BeanType, count(BeanType) as Frequent FROM (choco) GROUP BY BeanType) WHERE frequent == {max_value}\"\n",
        "  list_to_return = c.execute(query_for_stat).fetchall()\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "  return list_to_return\n",
        "\n",
        "def most_rare_bean_type():\n",
        "  conn = sqlite3.connect(DB_PATH_choco) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "  query_for_stat = f\"SELECT BeanType, count(BeanType) AS frequent FROM (choco) GROUP BY BeanType ORDER BY frequent LIMIT 1;\"\n",
        "  list_to_return = c.execute(query_for_stat).fetchall()\n",
        "  min_value = list_to_return[0][1]\n",
        "  query_for_stat = f\"SELECT BeanType, Frequent FROM (SELECT BeanType, count(BeanType) as Frequent FROM (choco) GROUP BY BeanType) WHERE frequent == {min_value}\"\n",
        "  list_to_return = c.execute(query_for_stat).fetchall()\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "  return list_to_return\n",
        "\n",
        "print(\"Please note: I removed rows with empty values\")\n",
        "delimiter = '-' * 10\n",
        "print(f'{delimiter} Calculate the second most common bean type(s): {delimiter}')\n",
        "second_most_common = second_most_common_bean_type()\n",
        "for common_value_tuple in second_most_common:\n",
        "  print(f'Name: {common_value_tuple[0]}, Fequent:{common_value_tuple[1]}')\n",
        "\n",
        "print(f'{delimiter} Calculate the most rare bean type(s): {delimiter}')\n",
        "rare = most_rare_bean_type()\n",
        "for rare_tuple in rare:\n",
        "  print(f'Name: {rare_tuple[0]}, Fequent:{rare_tuple[1]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please note: I removed rows with empty values\n",
            "---------- Calculate the second most common bean type(s): ----------\n",
            "Name: Criollo, Fequent:153\n",
            "---------- Calculate the most rare bean type(s): ----------\n",
            "Name: Amazon, Fequent:1\n",
            "Name: Blend-Forastero,Criollo, Fequent:1\n",
            "Name: CCN51, Fequent:1\n",
            "Name: Criollo (Ocumare 67), Fequent:1\n",
            "Name: Criollo (Ocumare 77), Fequent:1\n",
            "Name: Criollo (Ocumare), Fequent:1\n",
            "Name: Criollo (Wild), Fequent:1\n",
            "Name: Criollo, +, Fequent:1\n",
            "Name: Forastero (Amelonado), Fequent:1\n",
            "Name: Forastero (Arriba) ASSS, Fequent:1\n",
            "Name: Forastero(Arriba, CCN), Fequent:1\n",
            "Name: Forastero, Trinitario, Fequent:1\n",
            "Name: Trinitario (Amelonado), Fequent:1\n",
            "Name: Trinitario (Scavina), Fequent:1\n",
            "Name: Trinitario, Nacional, Fequent:1\n",
            "Name: Trinitario, TCGA, Fequent:1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOg-NK_lYCyz",
        "colab_type": "text"
      },
      "source": [
        "**Question 4:** Calculate the number of reviews and the average rating in each year. Calculate the number of reviews and the average rating of each company in each year (15pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8qwHAldYCy0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eKx7_9vyYCy1",
        "colab_type": "text"
      },
      "source": [
        "# 3. Kickstarter Projects Dataset (25pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HXua9B5YCy2",
        "colab_type": "text"
      },
      "source": [
        "Using the [Kickstarter Projects Dataset](https://www.kaggle.com/kemical/kickstarter-projects#ks-projects-201801.csv), answer the following questions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zHfxv6E6pZp",
        "colab_type": "code",
        "outputId": "7f8a5175-878b-4427-bc8b-116b21cc3ba0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "# searching for the dataset\n",
        "!rm -r ./datasets/kickstarter/\n",
        "!mkdir -p ./datasets/kickstarter/data\n",
        "!mkdir -p ./datasets/kickstarter/zip\n",
        "!mkdir -p ./datasets/kickstarter/sql\n",
        "!ls datasets/kickstarter/data\n",
        "\n",
        "!kaggle datasets list -s kickstarter"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove './datasets/kickstarter/': No such file or directory\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "ref                                              title                                        size  lastUpdated          downloadCount  \n",
            "-----------------------------------------------  ------------------------------------------  -----  -------------------  -------------  \n",
            "kemical/kickstarter-projects                     Kickstarter Projects                         37MB  2018-02-08 09:02:30          35037  \n",
            "socathie/kickstarter-project-statistics          Kickstarter Project Statistics                1MB  2019-11-14 06:38:31           5336  \n",
            "codename007/funding-successful-projects          Funding Successful Projects on Kickstarter   20MB  2017-06-20 17:37:38           2419  \n",
            "yashkantharia/kickstarter-campaigns              Kickstarter Campaigns                        12MB  2019-03-03 13:46:08            442  \n",
            "oscarvilla/kickstarter-nlp                       kickstarter NLP                              11MB  2018-08-09 01:38:57            206  \n",
            "wood2174/mapkickstarter                          Kickstarter                                  18MB  2018-03-07 00:01:12            212  \n",
            "toshimelonhead/400000-kickstarter-projects       400,000 Kickstarter Projects                   0B  2019-07-23 01:23:31            145  \n",
            "parienza/kickstarter                             kickstarter                                   4MB  2017-12-27 02:42:30            219  \n",
            "alancharred/kickstarter                          kickstarter                                 242KB  2018-12-13 06:20:25             25  \n",
            "tonyplaysguitar/steam-spy-data-from-api-request  Kickstarter videogames released on Steam      1MB  2018-01-21 23:54:08            231  \n",
            "tayoaki/kickstarter-dataset                      Kickstarter dataset                           2MB  2018-05-09 00:49:56            247  \n",
            "markusschmitz/kickstarter                        kickstarter                                  21MB  2019-05-23 05:34:44             40  \n",
            "iamsajanbhagat/kickstarter                       Kickstarter                                  20MB  2018-01-22 06:31:09             90  \n",
            "uysalah/archived-kickstarter-projects            Archived Kickstarter Projects                 1MB  2019-05-10 04:33:22            125  \n",
            "antonionoca/kickstarter2018nlp                   kickstarter-2018-nlp                         35MB  2019-04-23 22:02:34             65  \n",
            "ukveteran/kickstarter                            Kickstarter                                  20MB  2020-01-03 20:27:30             11  \n",
            "huseinzol05/dear-genie-kickstarter               dear genie kickstarter                       24MB  2018-01-08 05:48:10             28  \n",
            "amirvahid/kickstarter-project                    Kickstarter Project                           5MB  2019-06-14 00:59:55             15  \n",
            "officerbribe/yks-six-pack                        YKS Six Pack                                147KB  2020-03-05 17:49:58             55  \n",
            "cravingsformeow/kickstarter-us-2018-cleansed     Kickstarter US 2018 Cleansed                  5MB  2020-03-24 17:36:42              0  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-00ivhk87LUu",
        "colab_type": "code",
        "outputId": "f16bbc99-1b01-4433-aca0-65745096f2f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# download the dataset from Kaggle and unzip it\n",
        "!kaggle datasets download kemical/kickstarter-projects -p ./datasets/kickstarter/zip\n",
        "!unzip ./datasets/kickstarter/zip/*.zip -d ./datasets/kickstarter/data\n",
        "!ls ./datasets/kickstarter/zip/\n",
        "!ls ./datasets/kickstarter/data/\n",
        "!ls ./datasets/kickstarter/sql/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading kickstarter-projects.zip to ./datasets/kickstarter/zip\n",
            " 46% 17.0M/36.8M [00:00<00:00, 106MB/s]\n",
            "100% 36.8M/36.8M [00:00<00:00, 146MB/s]\n",
            "Archive:  ./datasets/kickstarter/zip/kickstarter-projects.zip\n",
            "  inflating: ./datasets/kickstarter/data/ks-projects-201612.csv  \n",
            "  inflating: ./datasets/kickstarter/data/ks-projects-201801.csv  \n",
            "kickstarter-projects.zip\n",
            "ks-projects-201612.csv\tks-projects-201801.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cgoBvra-VhW",
        "colab_type": "code",
        "outputId": "a9e0df36-f2d8-46f2-e0c7-ede2d10f6e3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "import pandas as pd  # create dataframe only from one file\n",
        "df = pd.read_csv('./datasets/kickstarter/data/ks-projects-201801.csv')\n",
        "df.fillna(0)\n",
        "print(df.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           ID  ... usd_goal_real\n",
            "0  1000002330  ...       1533.95\n",
            "1  1000003930  ...      30000.00\n",
            "2  1000004038  ...      45000.00\n",
            "3  1000007540  ...       5000.00\n",
            "4  1000011046  ...      19500.00\n",
            "\n",
            "[5 rows x 15 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NGgJ1dyeZiFH",
        "colab_type": "text"
      },
      "source": [
        "**Task 1 (for everyone):** Load the dataset to SQLite DB using [PonyORM](https://ponyorm.org) (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLhhxMDDb2nN",
        "colab_type": "code",
        "outputId": "a503f1a2-ed1d-4d29-afe7-ab942603ae07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "!pip install pony\n",
        "from pony.orm import *\n",
        "# Creating a new database\n",
        "!rm -f /content/datasets/kickstarter/sql/kickstarter.pony.db\n",
        "!ls /content/datasets/kickstarter/sql/\n",
        "db = Database()\n",
        "db.bind(provider='sqlite', filename='/content/datasets/kickstarter/sql/kickstarter.pony.db', create_db=True) # notice we can easily use other type of databases instead of SQLite\n",
        "\n",
        "class Category(db.Entity):\n",
        "    cat_name = Required(str)\n",
        "    projects = Set('Project')\n",
        "\n",
        "class Project(db.Entity):\n",
        "    proj_id = Required(int)\n",
        "    proj_name = Required(str)\n",
        "    backers = Required(int)\n",
        "    category = Required('Category')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pony\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/e4/45fa6185e86edfa97eef5a4fbe3f29f537de7f36c032ff7c54676310dcb1/pony-0.7.13.tar.gz (284kB)\n",
            "\r\u001b[K     |█▏                              | 10kB 18.3MB/s eta 0:00:01\r\u001b[K     |██▎                             | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |███▌                            | 30kB 2.5MB/s eta 0:00:01\r\u001b[K     |████▋                           | 40kB 1.7MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |███████                         | 61kB 2.4MB/s eta 0:00:01\r\u001b[K     |████████                        | 71kB 2.8MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 81kB 3.2MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 92kB 3.6MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 102kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 112kB 2.7MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 122kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 133kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 143kB 2.7MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 153kB 2.7MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 163kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 174kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 184kB 2.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████▉          | 194kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 204kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 215kB 2.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 225kB 2.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 235kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 245kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 256kB 2.7MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 266kB 2.7MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 276kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 286kB 2.7MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pony\n",
            "  Building wheel for pony (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pony: filename=pony-0.7.13-cp36-none-any.whl size=345363 sha256=e8d4f308c18a952e0ac3883a808cfe0fb498976169afd394a9a7e71f077fb608\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/c7/66/a9192d0ea4bcb17a25164284ec89b4563a6afbf7333947c0ea\n",
            "Successfully built pony\n",
            "Installing collected packages: pony\n",
            "Successfully installed pony-0.7.13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hynzXMXA7pfW",
        "colab_type": "code",
        "outputId": "5d9c4f85-9321-4078-90be-6390814c8d80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "show(Project)\n",
        "show(Category)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class Project(Entity):\n",
            "    id = PrimaryKey(int, auto=True)\n",
            "    proj_id = Required(int)\n",
            "    proj_name = Required(str)\n",
            "    backers = Required(int)\n",
            "    category = Required(Category)\n",
            "class Category(Entity):\n",
            "    id = PrimaryKey(int, auto=True)\n",
            "    cat_name = Required(str)\n",
            "    projects = Set(Project)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcv42ZJsEBoU",
        "colab_type": "code",
        "outputId": "d5beb5db-e5ff-4db3-c6c4-7108b42622d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "set_sql_debug(True) # helps to see what SQL commands are running\n",
        "db.generate_mapping(create_tables=True) # create tables"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GET CONNECTION FROM THE LOCAL POOL\n",
            "PRAGMA foreign_keys = false\n",
            "BEGIN IMMEDIATE TRANSACTION\n",
            "CREATE TABLE \"Category\" (\n",
            "  \"id\" INTEGER PRIMARY KEY AUTOINCREMENT,\n",
            "  \"cat_name\" TEXT NOT NULL\n",
            ")\n",
            "\n",
            "CREATE TABLE \"Project\" (\n",
            "  \"id\" INTEGER PRIMARY KEY AUTOINCREMENT,\n",
            "  \"proj_id\" INTEGER NOT NULL,\n",
            "  \"proj_name\" TEXT NOT NULL,\n",
            "  \"backers\" INTEGER NOT NULL,\n",
            "  \"category\" INTEGER NOT NULL REFERENCES \"Category\" (\"id\") ON DELETE CASCADE\n",
            ")\n",
            "\n",
            "CREATE INDEX \"idx_project__category\" ON \"Project\" (\"category\")\n",
            "\n",
            "SELECT \"Category\".\"id\", \"Category\".\"cat_name\"\n",
            "FROM \"Category\" \"Category\"\n",
            "WHERE 0 = 1\n",
            "\n",
            "SELECT \"Project\".\"id\", \"Project\".\"proj_id\", \"Project\".\"proj_name\", \"Project\".\"backers\", \"Project\".\"category\"\n",
            "FROM \"Project\" \"Project\"\n",
            "WHERE 0 = 1\n",
            "\n",
            "COMMIT\n",
            "PRAGMA foreign_keys = true\n",
            "CLOSE CONNECTION\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-omHXLlB7seW",
        "colab_type": "code",
        "outputId": "a62a9632-c38d-48f8-b03f-84be6583bd11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "cat_dict = {}\n",
        "for idx, row in df.iterrows():\n",
        "    cat_name = row['category']\n",
        "    cat = None\n",
        "    if cat_name in cat_dict:\n",
        "      cat = cat_dict[cat_name]\n",
        "    else:\n",
        "      cat = Category(cat_name=cat_name)\n",
        "      cat_dict[cat_name] = cat\n",
        "    Project(proj_id=row['ID'], proj_name=str(row['name']), backers=row['backers'], category=cat)\n",
        "commit()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "[999667180, 'VARIANT WARS - The Prisoners', 7, 33]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999668351, 'CAT in WATER', 123, 94]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999669816, 'My project is to complete my CD. From studio to market.', 2, 3]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999673386, 'The Diagram Decades of the New York City Subway Map', 55, 17]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999682338, \"Let's Pretend We're Christians and Play in the Snow\", 4, 10]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999685327, 'Just be MEan Holiday Cards!', 7, 8]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999687927, 'Reese and Reeves 2: A new novel', 47, 33]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999696476, 'FREE GOVERNOR SIEGLEMAN (Canceled)', 0, 94]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999696660, 'Objects Attack!', 79, 2]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999701844, 'Eggbeater- An Independant Horror/Comedy Film', 1, 98]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999704525, 'NARCOS: Webseries / TV Pilot', 19, 4]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999709270, 'Doozy: The EP', 15, 35]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999712771, 'Claude-Philippe Benoit Monograph', 0, 17]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999715332, 'Help Us Launch Our New Custom Boot Line!', 1, 125]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999721430, 'The Panic of 2008 documentary', 4, 9]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999725304, 'The Mangled (Re-launch)', 87, 2]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999741441, 'CORPOREAL', 14, 57]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999753943, 'Parti Bash Mobile App & Technology', 2, 50]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999754150, '31•13: Thesis Exhibition', 121, 93]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999762532, 'Traverse City Dance Project 2012', 75, 57]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999763489, 'Kronos: Under 30 Project / #5', 162, 88]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999764111, 'Bully For You!!!  Giving Birth to the \"Awww\" Factor!', 13, 8]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99977040, 'County Blues', 1, 52]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99977482, 'Language of Love -  a new CD from Lisa Deveaux', 31, 11]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999778598, 'Michael & Jeanne \"Peace and Sunshine\"', 1, 11]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999780798, 'Green City Times', 3, 40]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99978792, \"Join the Fearless Movement, Andre Leneal's Debut Album\", 78, 29]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999796570, 'M A V A R I', 215, 52]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999799441, 'The Hollywood Kills On Tour', 78, 35]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999800713, 'BETTER DESPERATE', 5, 115]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999801167, 'Eternal Warfare Records Festival 2013 (Canceled)', 36, 35]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999801508, 'Competition Finalist', 14, 18]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999810803, 'The Finger (Canceled)', 1, 9]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999811847, '\"100 PHOTOGRAPHS FOR OUR LIBERTIES\"', 37, 68]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999814706, 'Pug Life - Inspired by the Pug', 0, 52]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999814955, 'Collinwood Needs Coffee!!!', 42, 5]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999829547, 'PookieCat rune cards - funny oracle cards for all ages', 39, 27]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999832803, 'MONSTROCARDS- A Creative Drawing Game of Horrific Hilarity', 413, 70]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99984851, '\"Loreto\"', 0, 54]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999865831, \"STATE OF G - Men's Blazer Jacket & Women's Cardigan\", 97, 52]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999867839, 'Transcaucasia', 37, 28]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99987261, \"You're Not Cis, You Just Think You Are: My Enby Epiphany\", 5, 10]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999877902, \"Little Saturday's Debut Album\", 78, 91]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999878813, 'Ways to Look at the Moon', 123, 47]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999880710, 'Terminal Uniqueness', 5, 10]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999884445, 'Superette Tea - Boldly Caffeinated Loose Leaf Tea', 103, 7]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999893629, 'Ciridian Helix (Canceled)', 2, 33]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99991751, 'Here is the oasis for the youngest in the world.', 0, 8]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999929142, 'ÉPOUVANTAILS : 28mm Figurines de jeux pour KINGS OF WAR', 35, 14]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999931480, 'Google Glass Shield (Canceled)', 0, 126]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999934908, 'The Manual Bar Blade', 120, 8]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999941823, 'The Chronicles of Erinoth - A Graphic Novel', 38, 21]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999943841, 'The Dog Coffee Book', 31, 74]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999950812, 'The Cease is Increase', 16, 10]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999955533, 'Cards and Chit Network Season 2! (Canceled)', 5, 14]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999963618, 'Colorado International Activism Film Festival', 6, 2]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[99996661, 'Date Seat - Boston iOS App', 0, 50]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999969812, 'AT THE BEACH', 78, 88]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999971898, 'Beach Wrestling Documentary', 36, 9]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999972264, 'Islanda', 1, 9]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999975836, 'Homemade fresh dog food, Cleveland OH', 4, 100]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999976312, \"Angela's Poetry (Canceled)\", 0, 1]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999976400, 'ChknTruk Nationwide Charity Drive 2014 (Canceled)', 1, 9]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999977640, 'The Tribe', 5, 2]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999986353, 'Walls of Remedy- New lesbian Romantic Comedy feature unlike any other!!', 1, 2]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999987933, 'BioDefense Education Kit', 6, 58]\n",
            "\n",
            "INSERT INTO \"Project\" (\"proj_id\", \"proj_name\", \"backers\", \"category\") VALUES (?, ?, ?, ?)\n",
            "[999988282, 'Nou Renmen Ayiti!  We Love Haiti!', 17, 76]\n",
            "\n",
            "COMMIT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1K5VTLoYCy3",
        "colab_type": "text"
      },
      "source": [
        "### <span style=\"color:red\"> Please answer only **one** of the following questions according to your ID number (use the formula **<YOUR_ID> mod 3 +1**) </span>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwlOZtVHYCy3",
        "colab_type": "code",
        "outputId": "21fe1229-833f-4bff-d77d-31c7d0fd6b45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# which question to answer - put your ID number and run the code \n",
        "your_id  = \"\"\n",
        "q = int(your_id) % 3 + 1\n",
        "print(\"You need to answer question number %s\" % q)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You need to answer question number 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkVN8OnxYCy5",
        "colab_type": "text"
      },
      "source": [
        "**Question 1:** On average which project category received the highest number of backers? (15 pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkhb156oYCy6",
        "colab_type": "code",
        "outputId": "3a728ab1-a051-4d4d-a504-ee12b1ae9c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import statistics\n",
        "import operator\n",
        "dict_cat = {}\n",
        "for c in Category.select():\n",
        "  list_of_backerts = list(c.projects.backers)\n",
        "  dict_cat[c.cat_name] = statistics.mean(list_of_backerts)\n",
        "rel_key = max(dict_cat.items(), key=operator.itemgetter(1))[0]\n",
        "print(f'Category: {rel_key}, Number of Backers: {dict_cat[rel_key]}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BEGIN IMMEDIATE TRANSACTION\n",
            "SELECT \"c\".\"id\", \"c\".\"cat_name\"\n",
            "FROM \"Category\" \"c\"\n",
            "\n",
            "Category: Chiptune, Number of Backers: 443.9428571428571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTk5XnNGYCy7",
        "colab_type": "text"
      },
      "source": [
        "[**Question 2:** On average which project category received the highest pledged USD? (15 pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfiXdIneYCy8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wM5KvHE1YCy9",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:** In which month occurred the highest number of projects? (15 pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DTIStGMYCy-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ResaA3kxYCy_",
        "colab_type": "text"
      },
      "source": [
        "## 4. Oscars Datasets (10pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYQasQOKYCzA",
        "colab_type": "text"
      },
      "source": [
        "Using the [Oscars Dataset](https://www.kaggle.com/theacademy/academy-awards), please answer only one of the following questions (you can chose):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DHJJSI25Wiy",
        "colab_type": "code",
        "outputId": "1e9c0004-90f7-4c78-8b6f-42f458594318",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "# searching for the dataset\n",
        "!rm -r ./datasets/academy-awards/\n",
        "!mkdir -p ./datasets/academy-awards/data\n",
        "!mkdir -p ./datasets/academy-awards/zip\n",
        "!mkdir -p ./datasets/academy-awards/sql\n",
        "!ls datasets/academy-awards/data\n",
        "\n",
        "!kaggle datasets list -s academy-awards"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove './datasets/academy-awards/': No such file or directory\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "ref                                                     title                                                size  lastUpdated          downloadCount  \n",
            "------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  \n",
            "theacademy/academy-awards                               The Academy Awards, 1927-2015                       185KB  2017-02-13 17:30:48           4866  \n",
            "fmejia21/demographics-of-academy-awards-oscars-winners  Demographics of Academy Awards (Oscars) Winners      20KB  2020-02-04 17:38:26           2132  \n",
            "unanimad/golden-globe-awards                            Golden Globe Awards, 1944 - 2020                    117KB  2020-01-06 16:19:01           1550  \n",
            "unanimad/the-oscar-award                                The Oscar Award, 1927 - 2020                        191KB  2020-02-19 15:45:30            552  \n",
            "madhurinani/oscars-2017-tweets                          2017 #Oscars Tweets                                  11MB  2017-03-17 19:50:40            357  \n",
            "unanimad/grammy-awards                                  Grammy Awards                                       185KB  2019-12-26 13:15:21            118  \n",
            "alexis333/academy-awards-cleanup                        Academy Awards Cleanup                              178KB  2019-03-25 18:19:58             39  \n",
            "unanimad/bafta-awards                                   British Academy of Film and Television Arts Awards   74KB  2020-02-04 11:35:02             35  \n",
            "pmagda/primetime-emmy-awards                            Primetime Emmy Awards, 1949-2017                    235KB  2017-09-19 16:15:31            367  \n",
            "unanimad/emmy-awards                                    Emmy Awards                                         904KB  2020-01-02 23:54:37             46  \n",
            "cerosdotcom/oscars-speeches                             Oscars speeches since 1939                          523KB  2018-03-02 16:07:32            220  \n",
            "ardenthira/hollywood-black-list-20082017                Hollywood Black List, 2008-2017                     257KB  2018-05-15 15:30:54             69  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP3p41Fx5nVd",
        "colab_type": "code",
        "outputId": "926b6b91-88fc-46a1-8ec1-521113b16108",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# download the dataset from Kaggle and unzip it\n",
        "!kaggle datasets download theacademy/academy-awards  -p ./datasets/academy-awards/zip\n",
        "!unzip ./datasets/academy-awards/zip/*.zip -d ./datasets/academy-awards/data\n",
        "!ls ./datasets/academy-awards/zip/\n",
        "!ls ./datasets/academy-awards/data/\n",
        "!ls ./datasets/academy-awards/sql/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading academy-awards.zip to ./datasets/academy-awards/zip\n",
            "\r  0% 0.00/185k [00:00<?, ?B/s]\n",
            "\r100% 185k/185k [00:00<00:00, 71.9MB/s]\n",
            "Archive:  ./datasets/academy-awards/zip/academy-awards.zip\n",
            "  inflating: ./datasets/academy-awards/data/database.csv  \n",
            "academy-awards.zip\n",
            "database.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZybnLTm6Eql",
        "colab_type": "code",
        "outputId": "6530855a-4e13-41bb-d61f-5cb4507e41cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import pandas as pd\n",
        "DATASET_PATH = 'datasets/academy-awards/data/database.csv'\n",
        "csv_data = pd.read_csv(DATASET_PATH)\n",
        "print(csv_data.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Year  Ceremony    Award  Winner                 Name              Film\n",
            "0  1927/1928         1    Actor     NaN  Richard Barthelmess         The Noose\n",
            "1  1927/1928         1    Actor     1.0        Emil Jannings  The Last Command\n",
            "2  1927/1928         1  Actress     NaN       Louise Dresser   A Ship Comes In\n",
            "3  1927/1928         1  Actress     1.0         Janet Gaynor        7th Heaven\n",
            "4  1927/1928         1  Actress     NaN       Gloria Swanson    Sadie Thompson\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23_X1XkuiMaa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sqlite3\n",
        "DB_PATH_academy = 'datasets/academy-awards/sql/database.sqlite'\n",
        "!rm -f ./datasets/academy-awards/sql/database.sqlite\n",
        "to_db = list(csv_data.itertuples(index=False))\n",
        "con = sqlite3.connect(DB_PATH_academy)\n",
        "cur = con.cursor()\n",
        "# creates a table named (Names) with the dataset data and the following columns:\n",
        "columns_str = '(Year, Ceremony, Award, Winner, Name, Film)'\n",
        "cur.execute(f\"CREATE TABLE award {columns_str};\")\n",
        "#  ***Bonus***: Load the data using a Batch INSERT SQL Query (2pt):\n",
        "cur.executemany(f\"INSERT INTO award {columns_str} VALUES (?,?,?,?,?,?);\", to_db)\n",
        "con.commit()\n",
        "con.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGDmLdbKYCzA",
        "colab_type": "text"
      },
      "source": [
        "**Question 1:** Who is the female actress with the most Oscar nominees? (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qipeHl6AYCzB",
        "colab_type": "code",
        "outputId": "87b7ca31-5434-42f8-82a9-a012f2e093dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "  conn = sqlite3.connect(DB_PATH_academy) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "\n",
        "  #  Write a query that returns the statistics for the name William (5pt)\n",
        "  # Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "  print(\"Please Note! It's not clear if you meant to the specific female actress or actress in general. I've attached both answers below.\")\n",
        "  query_for_stat = f\"SELECT Name, count(*) FROM award WHERE Award == 'Actress' GROUP BY Name ORDER BY count(Name) DESC LIMIT 1\"\n",
        "  female_num = c.execute(query_for_stat).fetchall()\n",
        "  print(f'FACT 1: *{female_num[0][0]}* is the female actress with the most Oscar nominees (*{female_num[0][1]}* nominees)')\n",
        "  query_for_stat = f\"SELECT Name, count(*) FROM award WHERE Award LIKE 'Actress%' GROUP BY Name ORDER BY count(Name) DESC LIMIT 1\"\n",
        "  female_num = c.execute(query_for_stat).fetchall()\n",
        "  print(f'FACT 2: *{female_num[0][0]}* is the female actress (including actress supporting or actress leading role) with the most Oscar nominees (*{female_num[0][1]}* nominees)')\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Please Note! It's not clear if you meant to the specific female actress or actress in general. I've attached both answers below.\n",
            "FACT 1: *Bette Davis* is the female actress with the most Oscar nominees (*11* nominees)\n",
            "FACT 2: *Meryl Streep* is the female actress (including actress supporting or actress leading role) with the most Oscar nominees (*19* nominees)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BR33wDkbYCzD",
        "colab_type": "text"
      },
      "source": [
        "**Question 2:** Who is the male director with the most Oscar nominees? (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jm9HeghYCzD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Va320K09YCzE",
        "colab_type": "text"
      },
      "source": [
        "**Question 3:** Which top-10 movies received the highest number of Oscar nominees? (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9gVwnHVYCzF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AF15wiyqYCzH",
        "colab_type": "text"
      },
      "source": [
        "**Question 4:** Write a function that receives an actor's name and returns the actor’s number of Oscar nominees. Use the function to calculate the number of times Leonardo DiCaprio was a nominee (10pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZdwQJCWYCzH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW1N-PBiYCzJ",
        "colab_type": "text"
      },
      "source": [
        "## 5. Select a Dataset (15pt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me79ek_mYCzJ",
        "colab_type": "text"
      },
      "source": [
        "**Open Question:** Select an interesting dataset and use SQL to discover something interesting (15pt). **Bonus:** Use BigQuery (2pt)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXIIUhgccGWw",
        "colab_type": "code",
        "outputId": "793aaed5-0695-431f-9346-751f85422593",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "# searching for the dataset\n",
        "!rm -r ./datasets/spotify/\n",
        "!mkdir -p ./datasets/spotify/data\n",
        "!mkdir -p ./datasets/spotify/zip\n",
        "!mkdir -p ./datasets/spotify/sql\n",
        "!ls datasets/spotify/data\n",
        "\n",
        "!kaggle datasets list -s spotify"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove './datasets/spotify/': No such file or directory\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "ref                                                   title                                            size  lastUpdated          downloadCount  \n",
            "----------------------------------------------------  ----------------------------------------------  -----  -------------------  -------------  \n",
            "leonardopena/top50spotify2019                         Top 50 Spotify Songs - 2019                       2KB  2019-08-08 16:39:46          12463  \n",
            "geomack/spotifyclassification                         Spotify Song Attributes                          98KB  2017-08-04 21:05:17           9184  \n",
            "nadintamer/top-spotify-tracks-of-2018                 Top Spotify Tracks of 2018                        7KB  2019-02-07 06:04:33           9152  \n",
            "nadintamer/top-tracks-of-2017                         Top Spotify Tracks of 2017                        7KB  2017-12-20 06:04:48           7643  \n",
            "zaheenhamidani/ultimate-spotify-tracks-db             Spotify Tracks DB                                15MB  2019-07-23 04:26:23           3493  \n",
            "leonardopena/top-spotify-songs-from-20102019-by-year  Top Spotify songs from 2010-2019 - BY YEAR       19KB  2019-12-26 15:22:06           2142  \n",
            "edalrami/19000-spotify-songs                          19,000 Spotify Songs                              1MB  2018-11-23 00:23:37           1645  \n",
            "tomigelo/spotify-audio-features                       Spotify Audio Features                           17MB  2019-04-14 18:02:24           1619  \n",
            "edumucelli/spotifys-worldwide-daily-song-ranking      Spotify's Worldwide Daily Song Ranking           43MB  2018-01-12 20:32:57           8510  \n",
            "leonardopena/top-50-spotify-songs-by-each-country     Top 50 Spotify songs BY EACH COUNTRY             25KB  2019-12-26 15:31:33            556  \n",
            "cnic92/spotify-past-decades-songs-50s10s              Spotify Past Decades Songs Attributes            28KB  2020-01-28 17:41:12            433  \n",
            "theoverman/the-spotify-hit-predictor-dataset          The Spotify Hit Predictor Dataset (1960-2019)     3MB  2020-02-01 08:51:44            332  \n",
            "ehcall/spotify-artists                                Spotify Artists                                   2MB  2017-11-08 18:42:50            256  \n",
            "danielgrijalvas/steven-wilson-analysis                Steven Wilson detector                          166KB  2017-11-26 21:46:49            251  \n",
            "prasertk/spotify-global-2019-moststreamed-tracks      Spotify global 2019 most-streamed tracks        193KB  2020-01-14 07:16:46            129  \n",
            "aniruddhaachar/audio-features                         Audio Features for Playlist Creation            306KB  2017-03-03 06:23:55            808  \n",
            "iamsumat/spotify-top-2000s-mega-dataset               Spotify - All Time Top 2000s Mega Dataset        65KB  2020-02-04 10:05:27            229  \n",
            "saraclay/my-spotify-streaming-history                 My Spotify Listening History                     68KB  2020-02-18 00:50:01              1  \n",
            "grasslover/spotify-music-genre-list                   Spotify music genre list and 80k songs/tracks.   10MB  2019-04-30 14:52:53            747  \n",
            "snapcrack/the-billboard-200-acoustic-data             The Billboard 200 acoustic data                  49MB  2019-04-09 01:08:27            204  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukSwEoo1b9es",
        "colab_type": "code",
        "outputId": "a75cdf22-5267-41fb-ee6a-40959d9c2953",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# download the dataset from Kaggle and unzip it\n",
        "!kaggle datasets download zaheenhamidani/ultimate-spotify-tracks-db  -p ./datasets/spotify/zip\n",
        "!unzip ./datasets/spotify/zip/*.zip -d ./datasets/spotify/data\n",
        "!ls ./datasets/spotify/zip/\n",
        "!ls ./datasets/spotify/data/\n",
        "!ls ./datasets/spotify/sql/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading ultimate-spotify-tracks-db.zip to ./datasets/spotify/zip\n",
            " 32% 5.00M/15.5M [00:00<00:01, 10.4MB/s]\n",
            "100% 15.5M/15.5M [00:00<00:00, 26.4MB/s]\n",
            "Archive:  ./datasets/spotify/zip/ultimate-spotify-tracks-db.zip\n",
            "  inflating: ./datasets/spotify/data/SpotifyFeatures.csv  \n",
            "ultimate-spotify-tracks-db.zip\n",
            "SpotifyFeatures.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6Bygh9rcTLE",
        "colab_type": "code",
        "outputId": "9b8e0bcb-ecf6-484a-8e33-e127f78be18a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "import pandas as pd\n",
        "DATASET_PATH_SPOTIFY = 'datasets/spotify/data/SpotifyFeatures.csv'\n",
        "csv_data = pd.read_csv(DATASET_PATH_SPOTIFY)\n",
        "print(csv_data.head(5))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   genre        artist_name  ... time_signature valence\n",
            "0  Movie     Henri Salvador  ...            4/4   0.814\n",
            "1  Movie  Martin & les fées  ...            4/4   0.816\n",
            "2  Movie    Joseph Williams  ...            5/4   0.368\n",
            "3  Movie     Henri Salvador  ...            4/4   0.227\n",
            "4  Movie       Fabien Nataf  ...            4/4   0.390\n",
            "\n",
            "[5 rows x 18 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PGVT5pwcbtG",
        "colab_type": "code",
        "outputId": "8731878f-a1a8-46ad-fd74-8397027cd64d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import sqlite3\n",
        "DB_PATH_SPOTIFY = 'datasets/spotify/sql/database.sqlite'\n",
        "!rm -r ./datasets/spotify/sql/database.sqlite\n",
        "to_db = list(csv_data.itertuples(index=False))\n",
        "con = sqlite3.connect(DB_PATH_SPOTIFY)\n",
        "cur = con.cursor()\n",
        "# creates a table named (Names) with the dataset data and the following columns:\n",
        "columns_str = '(genre, artist_name, track_name, track_id, popularity, acousticness, danceability, duration_ms, energy, instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time_signature, valence)'\n",
        "cur.execute(f'DROP TABLE IF EXISTS spotify')\n",
        "cur.execute(f\"CREATE TABLE spotify {columns_str};\")\n",
        "#  ***Bonus***: Load the data using a Batch INSERT SQL Query (2pt):\n",
        "cur.executemany(f\"INSERT INTO spotify {columns_str} VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?);\", to_db)\n",
        "con.commit()\n",
        "con.close()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove './datasets/spotify/sql/database.sqlite': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpxEYIzEk50c",
        "colab_type": "text"
      },
      "source": [
        "# TOP FIVE artists with the most average of ______ songs from Spotify\n",
        "Dataset from Kaggle: [Spotify Tracks DB](https://www.kaggle.com/zaheenhamidani/ultimate-spotify-tracks-db)\n",
        "\n",
        "There are 26 genres and a total of 232,725 tracks.\n",
        "\n",
        "This is the explanation of every measure: https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/\n",
        "\n",
        "***For example:***\n",
        "\n",
        "**Danceability** describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqe59hv0dFta",
        "colab_type": "code",
        "outputId": "dfb3deb3-3f0b-48ba-f6b4-0e8fdb014ba2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from google.cloud import bigquery\n",
        "import os\n",
        "!mkdir /root/.bigquery/\n",
        "\n",
        "def find_mean_feature(num, feature_name):\n",
        "  conn = sqlite3.connect(DB_PATH_SPOTIFY) # connecting to the database\n",
        "  c = conn.cursor() # creating a cursor object\n",
        "\n",
        "  #  Write a query that returns the statistics for the name William (5pt)\n",
        "  # Use the the timeit package to measure the time it takes the query to run (5pt).\n",
        "  query_for_stat = f\"SELECT artist_name, AVG({feature_name}) FROM spotify GROUP BY artist_name ORDER BY AVG({feature_name}) DESC LIMIT {num}\"\n",
        "  female_num = c.execute(query_for_stat).fetchall()\n",
        "  print(f'----- Top {num} artists with highest average of {feature_name} songs -----')\n",
        "  for art_num, artist in enumerate(female_num):\n",
        "    print(f'{art_num+1}. Artist: {artist[0]} | Average {feature_name}: {artist[1]}')\n",
        "  print()\n",
        "  conn.commit()\n",
        "  conn.close() # close the cursor\n",
        "\n",
        "find_mean_feature(num=5, feature_name='popularity')\n",
        "find_mean_feature(num=5, feature_name='energy')\n",
        "find_mean_feature(num=5, feature_name='danceability')\n",
        "find_mean_feature(num=5, feature_name='acousticness')\n",
        "find_mean_feature(num=5, feature_name='duration_ms')\n",
        "find_mean_feature(num=5, feature_name='instrumentalness')\n",
        "find_mean_feature(num=5, feature_name='liveness')\n",
        "find_mean_feature(num=5, feature_name='loudness')\n",
        "find_mean_feature(num=5, feature_name='speechiness')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----- Top 5 artists with highest average of popularity songs -----\n",
            "1. Artist: Pedro Capó | Average popularity: 87.0\n",
            "2. Artist: Mario Bautista | Average popularity: 85.0\n",
            "3. Artist: Mau y Ricky | Average popularity: 83.0\n",
            "4. Artist: Kris Kross Amsterdam | Average popularity: 82.0\n",
            "5. Artist: Ninho | Average popularity: 82.0\n",
            "\n",
            "----- Top 5 artists with highest average of energy songs -----\n",
            "1. Artist: Caramell | Average energy: 0.9990000000000001\n",
            "2. Artist: Culture Beat | Average energy: 0.997\n",
            "3. Artist: Flat Duo Jets | Average energy: 0.997\n",
            "4. Artist: Like Moths To Flames | Average energy: 0.997\n",
            "5. Artist: Vektor | Average energy: 0.995\n",
            "\n",
            "----- Top 5 artists with highest average of danceability songs -----\n",
            "1. Artist: Vanilla Ice | Average danceability: 0.978\n",
            "2. Artist: Mr. Symarip | Average danceability: 0.976\n",
            "3. Artist: [dunkelbunt] | Average danceability: 0.9750000000000001\n",
            "4. Artist: Bankroll Fresh | Average danceability: 0.9690000000000001\n",
            "5. Artist: Biboulakis | Average danceability: 0.9640000000000001\n",
            "\n",
            "----- Top 5 artists with highest average of acousticness songs -----\n",
            "1. Artist: Eric Neveux | Average acousticness: 0.996\n",
            "2. Artist: Jacques Ibert | Average acousticness: 0.996\n",
            "3. Artist: Mamie Smith | Average acousticness: 0.996\n",
            "4. Artist: Max Reger | Average acousticness: 0.996\n",
            "5. Artist: Ned Rorem | Average acousticness: 0.996\n",
            "\n",
            "----- Top 5 artists with highest average of duration_ms songs -----\n",
            "1. Artist: La Mesa Reñoña | Average duration_ms: 5191750.5\n",
            "2. Artist: Sultan Khan | Average duration_ms: 3195440.0\n",
            "3. Artist: DJ Luigi | Average duration_ms: 2783306.4\n",
            "4. Artist: Jamie Llewellyn | Average duration_ms: 2701742.5\n",
            "5. Artist: Roy Dotrice | Average duration_ms: 2186285.4166666665\n",
            "\n",
            "----- Top 5 artists with highest average of instrumentalness songs -----\n",
            "1. Artist: Academy of Ancient Music | Average instrumentalness: 0.987\n",
            "2. Artist: Kurt Weill | Average instrumentalness: 0.983\n",
            "3. Artist: Jeff Danna | Average instrumentalness: 0.982\n",
            "4. Artist: Richard Wells | Average instrumentalness: 0.9790000000000001\n",
            "5. Artist: Celer | Average instrumentalness: 0.9740000000000001\n",
            "\n",
            "----- Top 5 artists with highest average of liveness songs -----\n",
            "1. Artist: Ramsey Lewis Trio | Average liveness: 0.9620000000000001\n",
            "2. Artist: John Petrucci | Average liveness: 0.9590000000000001\n",
            "3. Artist: Adoração Central | Average liveness: 0.955\n",
            "4. Artist: Los3 | Average liveness: 0.955\n",
            "5. Artist: Alastair Greene | Average liveness: 0.953\n",
            "\n",
            "----- Top 5 artists with highest average of loudness songs -----\n",
            "1. Artist: Coachwhips | Average loudness: 1.949\n",
            "2. Artist: The Infested | Average loudness: 0.8415\n",
            "3. Artist: Mosaic.Wav | Average loudness: 0.037000000000000005\n",
            "4. Artist: Lil Texas | Average loudness: -0.6288571428571429\n",
            "5. Artist: Buxxi & Jack Style | Average loudness: -0.754\n",
            "\n",
            "----- Top 5 artists with highest average of speechiness songs -----\n",
            "1. Artist: Dan St. Paul | Average speechiness: 0.956\n",
            "2. Artist: Mónica Escobedo | Average speechiness: 0.9490000000000001\n",
            "3. Artist: Lachlan Patterson | Average speechiness: 0.948\n",
            "4. Artist: Eddie Ifft | Average speechiness: 0.9475\n",
            "5. Artist: Kathy Griffin | Average speechiness: 0.9465\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}