Introduction_to_Databases_in_Python_solutions.ipynb

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  "metadata": {
    "colab": {
      "name": "Introduction to Databases in Python - solutions.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "3cYb7L3zEoej",
        "Qdw8MJ41FUk6",
        "cYEY8XR2FbK8",
        "8NKkInzyFl7j"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwGIrTt0EcMh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://www.datacamp.com/courses/introduction-to-relational-databases-in-python"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPxBA6DwzHni",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODX3BXVHmTvi",
        "colab_type": "code",
        "outputId": "594207af-adc8-41ab-b6d7-08507a867f51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "! pip install pymysql"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pymysql\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/39/15045ae46f2a123019aa968dfcba0396c161c20f855f11dea6796bcaae95/PyMySQL-0.9.3-py2.py3-none-any.whl (47kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 3.3MB/s \n",
            "\u001b[?25hInstalling collected packages: pymysql\n",
            "Successfully installed pymysql-0.9.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7x-pF-ImMmAb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pymysql\n",
        "\n",
        "from sqlalchemy import create_engine, Table, MetaData\n",
        "from sqlalchemy import Table, Column, String, Integer, Float, Boolean\n",
        "from sqlalchemy import select, and_, desc, func\n",
        "from sqlalchemy import case, cast, insert, update, delete"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cYb7L3zEoej",
        "colab_type": "text"
      },
      "source": [
        "## 1. Basics of Relational Databases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bASoDhioG95r",
        "colab_type": "text"
      },
      "source": [
        "#### Introduction to Databases\n",
        "\n",
        "Tables,  Columns, Rows, and Relationships are all part of the relational model. Dimensions are not. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qMU0WkM7HA_k",
        "colab_type": "text"
      },
      "source": [
        "#### Connecting to your Database\n",
        "\n",
        "An engine is just a common interface to a database, and the information it requires to connect to one is contained in a connection string, such as sqlite:///census_nyc.sqlite. Here, sqlite is the database driver, while census_nyc.sqlite is a SQLite file contained in the local directory.\n",
        "\n",
        "Your job in this exercise is to create an engine that connects to a local SQLite file named census.sqlite. Then, print the names of the tables it contains using the .table_names() method. Note that when you just want to print the table names, you do not need to use engine.connect() after creating the engine."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PotX4QjlFUZa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import create_engine\n",
        "from sqlalchemy import create_engine, Table"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QD3R9954HDWS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create an engine that connects to the census.sqlite file: engine\n",
        "engine = create_engine('sqlite:///census.sqlite')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcphn0I6OD3J",
        "colab_type": "code",
        "outputId": "d49c2423-e4dc-4a9d-b8e1-b14c16649e66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Print table names\n",
        "print(engine.table_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['census', 'state_fact']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ya2i3djO9ag",
        "colab_type": "text"
      },
      "source": [
        "**Autoloading Tables from a Database**\n",
        "\n",
        "SQLAlchemy can be used to automatically load tables from a database using something called reflection. Reflection is the process of reading the database and building the metadata based on that information. It's the opposite of creating a Table by hand and is very useful for working with existing databases. \n",
        "\n",
        "To perform reflection, you need to import the Table object from the SQLAlchemy package. Then, you use this Table object to read your table from the engine and autoload the columns. Using the Table object in this manner is a lot like passing arguments to a function. For example, to autoload the columns with the engine, you have to specify the keyword arguments autoload=True and autoload_with=engine to Table().\n",
        "\n",
        "In this exercise, your job is to reflect the census table available on your engine into a variable called census. The metadata has already been loaded for you using MetaData() and is available in the variable metadata."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFbmV8P2PfDY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import Table\n",
        "from sqlalchemy import Table, MetaData"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZUa-NFrG13y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metadata = MetaData(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXur2nH4FNDE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reflect census table from the engine: census\n",
        "census = Table('census', metadata, autoload=True, autoload_with=engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mmb_yMfPr0w",
        "colab_type": "code",
        "outputId": "9846b809-9450-4237-88c8-d99c06085fc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print census table metadata\n",
        "print(repr(census))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('census', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('state', VARCHAR(length=30), table=<census>), Column('sex', VARCHAR(length=1), table=<census>), Column('age', INTEGER(), table=<census>), Column('pop2000', INTEGER(), table=<census>), Column('pop2008', INTEGER(), table=<census>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWTCA1y2HDk_",
        "colab_type": "text"
      },
      "source": [
        "**Viewing Table Details**\n",
        "\n",
        "It is important to get an understanding of your database by examining the column names. This can be done by using the .columns attribute and accessing the .keys() method. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YK8wRSHmJfoB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reflect the census table from the engine: census\n",
        "census = Table('census', metadata, autoload=True, autoload_with=engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UE_c4l3JmIL",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Print a list of column names of the census table by applying the .keys() method to census.columns.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vR_pxTxWJujO",
        "colab_type": "code",
        "outputId": "73bcbabf-fc12-486b-9dbb-45eb9a1e3820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the column names\n",
        "print(census.columns.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['state', 'sex', 'age', 'pop2000', 'pop2008']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zP2RADDwJupJ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Print the details of the census table using the metadata.tables dictionary along with the repr() function. To do this, first access the 'census' key of the metadata.tables dictionary, and place this inside the provided repr() function.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pReSXKodJw4O",
        "colab_type": "code",
        "outputId": "ceb08062-ac60-428d-e3a3-97721ff7708b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print full table metadata\n",
        "print(repr(metadata.tables['census']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('census', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('state', VARCHAR(length=30), table=<census>), Column('sex', VARCHAR(length=1), table=<census>), Column('age', INTEGER(), table=<census>), Column('pop2000', INTEGER(), table=<census>), Column('pop2008', INTEGER(), table=<census>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XltPoWFcHDeT",
        "colab_type": "text"
      },
      "source": [
        "#### Introduction to SQL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3nOn-w_J422",
        "colab_type": "text"
      },
      "source": [
        "**Selecting data from a Table: raw SQL**\n",
        "\n",
        "Using what we just learned about SQL and applying the .execute() method on our connection, we can leverage a raw SQL query to query all the records in our census table. The object returned by the .execute() method is a ResultProxy. On this ResultProxy, we can then use the .fetchall() method to get our results - that is, the ResultSet."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9fYCMuJ9NF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build select statement for census table: stmt\n",
        "stmt = 'SELECT * FROM census'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erzCme10KtpM",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Use the .execute() and .fetchall() methods on connection and store the result in results. Remember that .execute() comes before .fetchall() and that stmt needs to be passed to .execute().\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj_HxFXSPkpI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and fetch the results: results\n",
        "connection = engine.connect()\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IK0RuYCKw7x",
        "colab_type": "code",
        "outputId": "33a3de2a-1903-4498-b7a6-ad0c7f88a497",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print results\n",
        "print(results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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('Illinois', 'F', 22, 81888, 88671), ('Illinois', 'F', 23, 83219, 88442), ('Illinois', 'F', 24, 82301, 88009), ('Illinois', 'F', 25, 84817, 92928), ('Illinois', 'F', 26, 82888, 90322), ('Illinois', 'F', 27, 85594, 89980), ('Illinois', 'F', 28, 89682, 90458), ('Illinois', 'F', 29, 94496, 86087), ('Illinois', 'F', 30, 96361, 85098), ('Illinois', 'F', 31, 90025, 86881), ('Illinois', 'F', 32, 89414, 83797), ('Illinois', 'F', 33, 89087, 85142), ('Illinois', 'F', 34, 91233, 81825), ('Illinois', 'F', 35, 98151, 84362), ('Illinois', 'F', 36, 98588, 87900), ('Illinois', 'F', 37, 99155, 91106), ('Illinois', 'F', 38, 100045, 93812), ('Illinois', 'F', 39, 102074, 87686), ('Illinois', 'F', 40, 104762, 87678), ('Illinois', 'F', 41, 99919, 86734), ('Illinois', 'F', 42, 100474, 88419), ('Illinois', 'F', 43, 98601, 95458), ('Illinois', 'F', 44, 95485, 95101), ('Illinois', 'F', 45, 95859, 96963), ('Illinois', 'F', 46, 90902, 97582), ('Illinois', 'F', 47, 88623, 98107), ('Illinois', 'F', 48, 85539, 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168912), ('Texas', 'F', 36, 167597, 174075), ('Texas', 'F', 37, 168463, 179467), ('Texas', 'F', 38, 168421, 178232), ('Texas', 'F', 39, 169355, 168167), ('Texas', 'F', 40, 171412, 160986), ('Texas', 'F', 41, 164244, 159286), ('Texas', 'F', 42, 163809, 162595), ('Texas', 'F', 43, 162822, 171490), ('Texas', 'F', 44, 155226, 174336), ('Texas', 'F', 45, 155427, 174769), ('Texas', 'F', 46, 149105, 173570), ('Texas', 'F', 47, 144081, 173569), ('Texas', 'F', 48, 136873, 175589), ('Texas', 'F', 49, 133610, 167009), ('Texas', 'F', 50, 133121, 166679), ('Texas', 'F', 51, 127211, 165361), ('Texas', 'F', 52, 125058, 157127), ('Texas', 'F', 53, 129694, 156170), ('Texas', 'F', 54, 99379, 150187), ('Texas', 'F', 55, 100403, 145009), ('Texas', 'F', 56, 97778, 137342), ('Texas', 'F', 57, 95755, 133928), ('Texas', 'F', 58, 87189, 132764), ('Texas', 'F', 59, 82764, 125965), ('Texas', 'F', 60, 79048, 123963), ('Texas', 'F', 61, 75160, 127832), ('Texas', 'F', 62, 74358, 97210), ('Texas', 'F', 63, 70332, 98081), ('Texas', 'F', 64, 70089, 94872), ('Texas', 'F', 65, 71266, 92604), ('Texas', 'F', 66, 65074, 83347), ('Texas', 'F', 67, 64383, 78672), ('Texas', 'F', 68, 63639, 74319), ('Texas', 'F', 69, 62713, 70485), ('Texas', 'F', 70, 64996, 68972), ('Texas', 'F', 71, 59894, 64274), ('Texas', 'F', 72, 58527, 63227), ('Texas', 'F', 73, 57708, 63683), ('Texas', 'F', 74, 56446, 57435), ('Texas', 'F', 75, 55989, 56080), ('Texas', 'F', 76, 52656, 54423), ('Texas', 'F', 77, 48993, 52482), ('Texas', 'F', 78, 47681, 53907), ('Texas', 'F', 79, 44609, 48607), ('Texas', 'F', 80, 42132, 46399), ('Texas', 'F', 81, 35378, 44418), ('Texas', 'F', 82, 33852, 41838), ('Texas', 'F', 83, 30076, 40489), ('Texas', 'F', 84, 27961, 36821), ('Texas', 'F', 85, 171538, 223439)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KxNqILTK1Nx",
        "colab_type": "text"
      },
      "source": [
        "**Selecting data from a Table with SQLAlchemy**\n",
        "\n",
        "SQLAlchemy provides a nice \"Pythonic\" way of interacting with databases. \n",
        "\n",
        "In this exercise, you'll once again build a statement to query all records from the census table. This time, however, you'll make use of the select() function of the sqlalchemy module. This function requires a list of tables or columns as the only required argument."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6uGi8QMHFu6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import select\n",
        "from sqlalchemy import select"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODyluyutL5zA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reflect census table via engine: census\n",
        "census = Table('census', metadata, autoload=True, autoload_with=engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcq8hOE9L7JK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build select statement for census table: stmt\n",
        "stmt = select([census]) # pass a list to select()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4xfBR4jL8QD",
        "colab_type": "code",
        "outputId": "8d5b4472-6ba7-4a0b-e5f8-0b7d7840387d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Print the emitted statement to see the SQL emitted\n",
        "print(stmt)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SELECT census.state, census.sex, census.age, census.pop2000, census.pop2008 \n",
            "FROM census\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_aYqMBnL9OJ",
        "colab_type": "code",
        "outputId": "e200afd2-16d0-4ff3-f7c4-b1e1dfe0f3a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Execute the statement and print the results\n",
        "print(connection.execute(stmt).fetchall())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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98081), ('Texas', 'F', 64, 70089, 94872), ('Texas', 'F', 65, 71266, 92604), ('Texas', 'F', 66, 65074, 83347), ('Texas', 'F', 67, 64383, 78672), ('Texas', 'F', 68, 63639, 74319), ('Texas', 'F', 69, 62713, 70485), ('Texas', 'F', 70, 64996, 68972), ('Texas', 'F', 71, 59894, 64274), ('Texas', 'F', 72, 58527, 63227), ('Texas', 'F', 73, 57708, 63683), ('Texas', 'F', 74, 56446, 57435), ('Texas', 'F', 75, 55989, 56080), ('Texas', 'F', 76, 52656, 54423), ('Texas', 'F', 77, 48993, 52482), ('Texas', 'F', 78, 47681, 53907), ('Texas', 'F', 79, 44609, 48607), ('Texas', 'F', 80, 42132, 46399), ('Texas', 'F', 81, 35378, 44418), ('Texas', 'F', 82, 33852, 41838), ('Texas', 'F', 83, 30076, 40489), ('Texas', 'F', 84, 27961, 36821), ('Texas', 'F', 85, 171538, 223439)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wxH_z_uL_n0",
        "colab_type": "text"
      },
      "source": [
        "**Handling a ResultSet**\n",
        "\n",
        "Recall the differences between a ResultProxy and a ResultSet:\n",
        "\n",
        "\n",
        "\n",
        "*   ResultProxy: \n",
        "\n",
        "The object returned by the .execute() method. It can be used in a variety of ways to get the data returned by the query.\n",
        "\n",
        "*   ResultSet: \n",
        "\n",
        "The actual data asked for in the query when using a fetch method such as .fetchall() on a ResultProxy.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAaCqfAwMIW2",
        "colab_type": "code",
        "outputId": "f6548855-4b1e-4e46-9788-39434c848445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17034
        }
      },
      "source": [
        "results"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " ('Illinois', 'M', 3, 88868, 90037),\n",
              " ('Illinois', 'M', 4, 91947, 91111),\n",
              " ('Illinois', 'M', 5, 93894, 89802),\n",
              " ('Illinois', 'M', 6, 93676, 88931),\n",
              " ('Illinois', 'M', 7, 94818, 90940),\n",
              " ('Illinois', 'M', 8, 95035, 86943),\n",
              " ('Illinois', 'M', 9, 96436, 86055),\n",
              " ('Illinois', 'M', 10, 97280, 86565),\n",
              " ('Illinois', 'M', 11, 94029, 86606),\n",
              " ('Illinois', 'M', 12, 92402, 89596),\n",
              " ('Illinois', 'M', 13, 89926, 91661),\n",
              " ('Illinois', 'M', 14, 90717, 91256),\n",
              " ('Illinois', 'M', 15, 92178, 92729),\n",
              " ('Illinois', 'M', 16, 90587, 93083),\n",
              " ('Illinois', 'M', 17, 92782, 94541),\n",
              " ('Illinois', 'M', 18, 90997, 100253),\n",
              " ('Illinois', 'M', 19, 89629, 96588),\n",
              " ('Illinois', 'M', 20, 91040, 95460),\n",
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              " ('New Jersey', 'M', 0, 56983, 58756),\n",
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              " ('New Jersey', 'M', 22, 48353, 55026),\n",
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              " ('Florida', 'M', 71, 67055, 65238),\n",
              " ('Florida', 'M', 72, 66345, 62768),\n",
              " ('Florida', 'M', 73, 64748, 62531),\n",
              " ('Florida', 'M', 74, 61089, 57165),\n",
              " ('Florida', 'M', 75, 60053, 57284),\n",
              " ('Florida', 'M', 76, 56904, 56721),\n",
              " ('Florida', 'M', 77, 53383, 56186),\n",
              " ('Florida', 'M', 78, 51014, 55313),\n",
              " ('Florida', 'M', 79, 47526, 51480),\n",
              " ('Florida', 'M', 80, 42814, 49612),\n",
              " ('Florida', 'M', 81, 36996, 46701),\n",
              " ('Florida', 'M', 82, 34263, 42593),\n",
              " ('Florida', 'M', 83, 29757, 40759),\n",
              " ('Florida', 'M', 84, 25681, 36309),\n",
              " ('Florida', 'M', 85, 113951, 199180),\n",
              " ('Florida', 'F', 0, 92803, 113205),\n",
              " ('Florida', 'F', 1, 90743, 113133),\n",
              " ('Florida', 'F', 2, 91961, 112272),\n",
              " ('Florida', 'F', 3, 92395, 109917),\n",
              " ('Florida', 'F', 4, 94072, 107962),\n",
              " ('Florida', 'F', 5, 96113, 106341),\n",
              " ('Florida', 'F', 6, 98135, 107067),\n",
              " ('Florida', 'F', 7, 99759, 109771),\n",
              " ('Florida', 'F', 8, 101879, 103109),\n",
              " ('Florida', 'F', 9, 105664, 101034),\n",
              " ('Florida', 'F', 10, 108858, 102544),\n",
              " ('Florida', 'F', 11, 104588, 103409),\n",
              " ('Florida', 'F', 12, 102981, 105172),\n",
              " ('Florida', 'F', 13, 101314, 107018),\n",
              " ('Florida', 'F', 14, 100981, 109340),\n",
              " ('Florida', 'F', 15, 101253, 111069),\n",
              " ('Florida', 'F', 16, 98627, 113005),\n",
              " ('Florida', 'F', 17, 97885, 117383),\n",
              " ('Florida', 'F', 18, 97981, 113733),\n",
              " ('Florida', 'F', 19, 97213, 106850),\n",
              " ('Florida', 'F', 20, 96827, 108456),\n",
              " ('Florida', 'F', 21, 90585, 105653),\n",
              " ('Florida', 'F', 22, 89417, 109238),\n",
              " ('Florida', 'F', 23, 89858, 113527),\n",
              " ('Florida', 'F', 24, 87412, 114759),\n",
              " ('Florida', 'F', 25, 91740, 113190),\n",
              " ('Florida', 'F', 26, 90374, 116802),\n",
              " ('Florida', 'F', 27, 95229, 121723),\n",
              " ('Florida', 'F', 28, 102176, 113799),\n",
              " ('Florida', 'F', 29, 108211, 111734),\n",
              " ('Florida', 'F', 30, 110640, 107973),\n",
              " ('Florida', 'F', 31, 104048, 106631),\n",
              " ('Florida', 'F', 32, 104856, 103243),\n",
              " ('Florida', 'F', 33, 107537, 107256),\n",
              " ('Florida', 'F', 34, 112617, 106743),\n",
              " ('Florida', 'F', 35, 122702, 110527),\n",
              " ('Florida', 'F', 36, 126477, 118067),\n",
              " ('Florida', 'F', 37, 125632, 123676),\n",
              " ('Florida', 'F', 38, 125797, 126120),\n",
              " ('Florida', 'F', 39, 127771, 118436),\n",
              " ('Florida', 'F', 40, 129871, 118364),\n",
              " ('Florida', 'F', 41, 124022, 120533),\n",
              " ('Florida', 'F', 42, 124375, 125231),\n",
              " ('Florida', 'F', 43, 123613, 135509),\n",
              " ('Florida', 'F', 44, 118578, 138328),\n",
              " ('Florida', 'F', 45, 119679, 136657),\n",
              " ('Florida', 'F', 46, 114659, 136722),\n",
              " ('Florida', 'F', 47, 111078, 138381),\n",
              " ('Florida', 'F', 48, 107713, 139760),\n",
              " ('Florida', 'F', 49, 106500, 133818),\n",
              " ('Florida', 'F', 50, 107077, 133607),\n",
              " ('Florida', 'F', 51, 104708, 132320),\n",
              " ('Florida', 'F', 52, 105209, 127850),\n",
              " ('Florida', 'F', 53, 111282, 129149),\n",
              " ('Florida', 'F', 54, 87062, 124079),\n",
              " ('Florida', 'F', 55, 88525, 120377),\n",
              " ('Florida', 'F', 56, 88897, 117778),\n",
              " ('Florida', 'F', 57, 94181, 117201),\n",
              " ('Florida', 'F', 58, 84424, 118163),\n",
              " ('Florida', 'F', 59, 80948, 116460),\n",
              " ('Florida', 'F', 60, 81804, 118791),\n",
              " ('Florida', 'F', 61, 78651, 120818),\n",
              " ('Florida', 'F', 62, 79473, 97150),\n",
              " ('Florida', 'F', 63, 77436, 99112),\n",
              " ('Florida', 'F', 64, 77929, 99557),\n",
              " ('Florida', 'F', 65, 79455, 103739),\n",
              " ('Florida', 'F', 66, 74290, 92972),\n",
              " ('Florida', 'F', 67, 77147, 88221),\n",
              " ('Florida', 'F', 68, 77115, 86622),\n",
              " ('Florida', 'F', 69, 78932, 81565),\n",
              " ('Florida', 'F', 70, 80857, 80187),\n",
              " ('Florida', 'F', 71, 79541, 75722),\n",
              " ('Florida', 'F', 72, 79608, 74602),\n",
              " ('Florida', 'F', 73, 78667, 74443),\n",
              " ('Florida', 'F', 74, 76018, 68411),\n",
              " ('Florida', 'F', 75, 76193, 69805),\n",
              " ('Florida', 'F', 76, 73371, 68461),\n",
              " ('Florida', 'F', 77, 69115, 68956),\n",
              " ('Florida', 'F', 78, 66656, 68699),\n",
              " ('Florida', 'F', 79, 63795, 66050),\n",
              " ('Florida', 'F', 80, 58715, 64696),\n",
              " ('Florida', 'F', 81, 51299, 62457),\n",
              " ('Florida', 'F', 82, 48680, 58314),\n",
              " ('Florida', 'F', 83, 43118, 57062),\n",
              " ('Florida', 'F', 84, 39522, 52826),\n",
              " ('Florida', 'F', 85, 221202, 322186),\n",
              " ('Maryland', 'M', 0, 35923, 38474),\n",
              " ('Maryland', 'M', 1, 35456, 38494),\n",
              " ('Maryland', 'M', 2, 35575, 38042),\n",
              " ('Maryland', 'M', 3, 36124, 37396),\n",
              " ('Maryland', 'M', 4, 37130, 37767),\n",
              " ('Maryland', 'M', 5, 38179, 37230),\n",
              " ('Maryland', 'M', 6, 38365, 37345),\n",
              " ('Maryland', 'M', 7, 39879, 37546),\n",
              " ('Maryland', 'M', 8, 41023, 36258),\n",
              " ('Maryland', 'M', 9, 41744, 35861),\n",
              " ('Maryland', 'M', 10, 43013, 35837),\n",
              " ('Maryland', 'M', 11, 41074, 36416),\n",
              " ('Maryland', 'M', 12, 39958, 37451),\n",
              " ('Maryland', 'M', 13, 39172, 38609),\n",
              " ('Maryland', 'M', 14, 38446, 38676),\n",
              " ('Maryland', 'M', 15, 38417, 40011),\n",
              " ('Maryland', 'M', 16, 37373, 41047),\n",
              " ('Maryland', 'M', 17, 36980, 41657),\n",
              " ('Maryland', 'M', 18, 35004, 45175),\n",
              " ('Maryland', 'M', 19, 33029, 38869),\n",
              " ('Maryland', 'M', 20, 32505, 37411),\n",
              " ('Maryland', 'M', 21, 30244, 36602),\n",
              " ('Maryland', 'M', 22, 29463, 36399),\n",
              " ('Maryland', 'M', 23, 29156, 37326),\n",
              " ('Maryland', 'M', 24, 28962, 36809),\n",
              " ('Maryland', 'M', 25, 29936, 39310),\n",
              " ('Maryland', 'M', 26, 28873, 35190),\n",
              " ('Maryland', 'M', 27, 31191, 37003),\n",
              " ('Maryland', 'M', 28, 33560, 37557),\n",
              " ('Maryland', 'M', 29, 36745, 35750),\n",
              " ('Maryland', 'M', 30, 38834, 34753),\n",
              " ('Maryland', 'M', 31, 37038, 33509),\n",
              " ('Maryland', 'M', 32, 37652, 33731),\n",
              " ('Maryland', 'M', 33, 38209, 34332),\n",
              " ('Maryland', 'M', 34, 39891, 32184),\n",
              " ('Maryland', 'M', 35, 43531, 34396),\n",
              " ('Maryland', 'M', 36, 44369, 36143),\n",
              " ('Maryland', 'M', 37, 43213, 38023),\n",
              " ('Maryland', 'M', 38, 43616, 40400),\n",
              " ('Maryland', 'M', 39, 44396, 38484),\n",
              " ('Maryland', 'M', 40, 45453, 38148),\n",
              " ('Maryland', 'M', 41, 44077, 38831),\n",
              " ('Maryland', 'M', 42, 43383, 40664),\n",
              " ('Maryland', 'M', 43, 42670, 43526),\n",
              " ('Maryland', 'M', 44, 40687, 44487),\n",
              " ('Maryland', 'M', 45, 41132, 43178),\n",
              " ('Maryland', 'M', 46, 38832, 43373),\n",
              " ('Maryland', 'M', 47, 37984, 43995),\n",
              " ('Maryland', 'M', 48, 36792, 44756),\n",
              " ('Idaho', 'M', 0, 10208, 12708),\n",
              " ('Idaho', 'M', 1, 10112, 12719),\n",
              " ('Idaho', 'M', 2, 9974, 12463),\n",
              " ('Idaho', 'M', 3, 9928, 12209),\n",
              " ('Idaho', 'M', 4, 9948, 12261),\n",
              " ('Idaho', 'M', 5, 9992, 11855),\n",
              " ('Idaho', 'M', 6, 10032, 11670),\n",
              " ('Idaho', 'M', 7, 10368, 11764),\n",
              " ('Idaho', 'M', 8, 10600, 11505),\n",
              " ('Idaho', 'M', 9, 10780, 11367),\n",
              " ('Idaho', 'M', 10, 11034, 11214),\n",
              " ('Idaho', 'M', 11, 10624, 11218),\n",
              " ('Idaho', 'M', 12, 10729, 11251),\n",
              " ('Idaho', 'M', 13, 10592, 11263),\n",
              " ('Idaho', 'M', 14, 11012, 11241),\n",
              " ('Idaho', 'M', 15, 11323, 11460),\n",
              " ('Idaho', 'M', 16, 11421, 11729),\n",
              " ('Idaho', 'M', 17, 11563, 11858),\n",
              " ('Idaho', 'M', 18, 11761, 11818),\n",
              " ('Idaho', 'M', 19, 10224, 9483),\n",
              " ('Idaho', 'M', 20, 9434, 9026),\n",
              " ('Idaho', 'M', 21, 10161, 10409),\n",
              " ('Idaho', 'M', 22, 10067, 11712),\n",
              " ('Idaho', 'M', 23, 9774, 11692),\n",
              " ('Idaho', 'M', 24, 8838, 11556),\n",
              " ('Idaho', 'M', 25, 8812, 11528),\n",
              " ('Idaho', 'M', 26, 8423, 11243),\n",
              " ('Idaho', 'M', 27, 8378, 12454),\n",
              " ('Idaho', 'M', 28, 8517, 11917),\n",
              " ('Idaho', 'M', 29, 9060, 11717),\n",
              " ('Idaho', 'M', 30, 8976, 10144),\n",
              " ('Idaho', 'M', 31, 8598, 10617),\n",
              " ('Idaho', 'M', 32, 8251, 9594),\n",
              " ('Idaho', 'M', 33, 8303, 9821),\n",
              " ('Idaho', 'M', 34, 8432, 9232),\n",
              " ('Idaho', 'M', 35, 8844, 9611),\n",
              " ('Idaho', 'M', 36, 9111, 9572),\n",
              " ('Idaho', 'M', 37, 9479, 10434),\n",
              " ('Idaho', 'M', 38, 9789, 10187),\n",
              " ('Idaho', 'M', 39, 9839, 9444),\n",
              " ('Idaho', 'M', 40, 10287, 9284),\n",
              " ('Idaho', 'M', 41, 9519, 9184),\n",
              " ('Idaho', 'M', 42, 9776, 9613),\n",
              " ('Idaho', 'M', 43, 9914, 9823),\n",
              " ('Idaho', 'M', 44, 9526, 9940),\n",
              " ('Idaho', 'M', 45, 9772, 10255),\n",
              " ('Idaho', 'M', 46, 9586, 10628),\n",
              " ('Idaho', 'M', 47, 9398, 10642),\n",
              " ('Idaho', 'M', 48, 9120, 10893),\n",
              " ('Idaho', 'M', 49, 8672, 10298),\n",
              " ('Idaho', 'M', 50, 8521, 10165),\n",
              " ('Idaho', 'M', 51, 8270, 10479),\n",
              " ('Idaho', 'M', 52, 8217, 10055),\n",
              " ('Idaho', 'M', 53, 8481, 10189),\n",
              " ('Idaho', 'M', 54, 6402, 9903),\n",
              " ('Idaho', 'M', 55, 6424, 9760),\n",
              " ('Idaho', 'M', 56, 6293, 9465),\n",
              " ('Idaho', 'M', 57, 6376, 9029),\n",
              " ('Idaho', 'M', 58, 5720, 8864),\n",
              " ('Idaho', 'M', 59, 5392, 8571),\n",
              " ('Idaho', 'M', 60, 5256, 8667),\n",
              " ('Idaho', 'M', 61, 5070, 8742),\n",
              " ('Idaho', 'M', 62, 4693, 6670),\n",
              " ('Idaho', 'M', 63, 4685, 6631),\n",
              " ('Idaho', 'M', 64, 4245, 6643),\n",
              " ('Idaho', 'M', 65, 4402, 6500),\n",
              " ('Idaho', 'M', 66, 3938, 5766),\n",
              " ('Idaho', 'M', 67, 3860, 5428),\n",
              " ('Idaho', 'M', 68, 3774, 5120),\n",
              " ('Idaho', 'M', 69, 3493, 4907),\n",
              " ('Idaho', 'M', 70, 3726, 4374),\n",
              " ('Idaho', 'M', 71, 3458, 4408),\n",
              " ('Idaho', 'M', 72, 3348, 3896),\n",
              " ('Idaho', 'M', 73, 3224, 3926),\n",
              " ('Idaho', 'M', 74, 3052, 3493),\n",
              " ('Idaho', 'M', 75, 2980, 3342),\n",
              " ('Idaho', 'M', 76, 2868, 3240),\n",
              " ('Idaho', 'M', 77, 2561, 2848),\n",
              " ('Idaho', 'M', 78, 2545, 3001),\n",
              " ('Idaho', 'M', 79, 2390, 2672),\n",
              " ('Idaho', 'M', 80, 2137, 2517),\n",
              " ('Idaho', 'M', 81, 1880, 2400),\n",
              " ('Idaho', 'M', 82, 1814, 2159),\n",
              " ('Idaho', 'M', 83, 1568, 1985),\n",
              " ('Idaho', 'M', 84, 1379, 1855),\n",
              " ('Idaho', 'M', 85, 6090, 9243),\n",
              " ('Idaho', 'F', 0, 9755, 12063),\n",
              " ('Idaho', 'F', 1, 9579, 12063),\n",
              " ('Idaho', 'F', 2, 9244, 11925),\n",
              " ('Idaho', 'F', 3, 9537, 11796),\n",
              " ('Idaho', 'F', 4, 9614, 11539),\n",
              " ('Idaho', 'F', 5, 9387, 11181),\n",
              " ('Idaho', 'F', 6, 9626, 11337),\n",
              " ('Idaho', 'F', 7, 9766, 11365),\n",
              " ('Idaho', 'F', 8, 9880, 10965),\n",
              " ('Idaho', 'F', 9, 10152, 10850),\n",
              " ('Idaho', 'F', 10, 10362, 10389),\n",
              " ('Idaho', 'F', 11, 10316, 10683),\n",
              " ('Idaho', 'F', 12, 10033, 10819),\n",
              " ('Idaho', 'F', 13, 10181, 10496),\n",
              " ('Idaho', 'F', 14, 10323, 10642),\n",
              " ('Idaho', 'F', 15, 10729, 10774),\n",
              " ('Idaho', 'F', 16, 10575, 10859),\n",
              " ('Idaho', 'F', 17, 10788, 11139),\n",
              " ('Idaho', 'F', 18, 11254, 11279),\n",
              " ('Idaho', 'F', 19, 11515, 10804),\n",
              " ('Idaho', 'F', 20, 10428, 9489),\n",
              " ('Idaho', 'F', 21, 9229, 9011),\n",
              " ('Idaho', 'F', 22, 8733, 9548),\n",
              " ('Idaho', 'F', 23, 8784, 10010),\n",
              " ('Idaho', 'F', 24, 8070, 10159),\n",
              " ('Idaho', 'F', 25, 8078, 10061),\n",
              " ('Idaho', 'F', 26, 7911, 10443),\n",
              " ('Idaho', 'F', 27, 7837, 11216),\n",
              " ('Idaho', 'F', 28, 8181, 11349),\n",
              " ('Idaho', 'F', 29, 8662, 11684),\n",
              " ('Idaho', 'F', 30, 8646, 9996),\n",
              " ('Idaho', 'F', 31, 8428, 10347),\n",
              " ('Idaho', 'F', 32, 7875, 9167),\n",
              " ('Idaho', 'F', 33, 8039, 9414),\n",
              " ('Idaho', 'F', 34, 8218, 9162),\n",
              " ('Idaho', 'F', 35, 8720, 9121),\n",
              " ('Idaho', 'F', 36, 9132, 9568),\n",
              " ('Idaho', 'F', 37, 9556, 9926),\n",
              " ('Idaho', 'F', 38, 9532, 9735),\n",
              " ('Idaho', 'F', 39, 9867, 9458),\n",
              " ('Idaho', 'F', 40, 9879, 9080),\n",
              " ('Idaho', 'F', 41, 9801, 9138),\n",
              " ('Idaho', 'F', 42, 9876, 9286),\n",
              " ('Idaho', 'F', 43, 9928, 9743),\n",
              " ('Idaho', 'F', 44, 9619, 9967),\n",
              " ('Idaho', 'F', 45, 9817, 10458),\n",
              " ('Idaho', 'F', 46, 9580, 10454),\n",
              " ('Idaho', 'F', 47, 9325, 10848),\n",
              " ('Idaho', 'F', 48, 8941, 10523),\n",
              " ('Idaho', 'F', 49, 8574, 10493),\n",
              " ('Idaho', 'F', 50, 8514, 10535),\n",
              " ('Idaho', 'F', 51, 8077, 10495),\n",
              " ('Idaho', 'F', 52, 8052, 10318),\n",
              " ('Idaho', 'F', 53, 8083, 10331),\n",
              " ('Idaho', 'F', 54, 6290, 9993),\n",
              " ('Idaho', 'F', 55, 6379, 9773),\n",
              " ('Idaho', 'F', 56, 6430, 9638),\n",
              " ('Idaho', 'F', 57, 6448, 9137),\n",
              " ('Idaho', 'F', 58, 5677, 9024),\n",
              " ('Idaho', 'F', 59, 5390, 8673),\n",
              " ('Idaho', 'F', 60, 5221, 8700),\n",
              " ('Idaho', 'F', 61, 4920, 8570),\n",
              " ('Idaho', 'F', 62, 4806, 6788),\n",
              " ('Idaho', 'F', 63, 4509, 6797),\n",
              " ('Idaho', 'F', 64, 4431, 6811),\n",
              " ('Idaho', 'F', 65, 4476, 6681),\n",
              " ('Idaho', 'F', 66, 4106, 5924),\n",
              " ('Idaho', 'F', 67, 3991, 5502),\n",
              " ('Idaho', 'F', 68, 4138, 5296),\n",
              " ('Idaho', 'F', 69, 3983, 4916),\n",
              " ('Idaho', 'F', 70, 4043, 4718),\n",
              " ('Idaho', 'F', 71, 3782, 4374),\n",
              " ('Idaho', 'F', 72, 3827, 4269),\n",
              " ('Idaho', 'F', 73, 3791, 4311),\n",
              " ('Idaho', 'F', 74, 3589, 3922),\n",
              " ('Idaho', 'F', 75, 3699, 3756),\n",
              " ('Idaho', 'F', 76, 3552, 3808),\n",
              " ('Idaho', 'F', 77, 3416, 3597),\n",
              " ('Idaho', 'F', 78, 3361, 3593),\n",
              " ('Idaho', 'F', 79, 3192, 3306),\n",
              " ('Idaho', 'F', 80, 3052, 3310),\n",
              " ('Idaho', 'F', 81, 2777, 3153),\n",
              " ('Idaho', 'F', 82, 2572, 2847),\n",
              " ('Idaho', 'F', 83, 2348, 2890),\n",
              " ('Idaho', 'F', 84, 2135, 2639),\n",
              " ('Idaho', 'F', 85, 12208, 16258),\n",
              " ('Massachusetts', 'M', 0, 40436, 40194),\n",
              " ('Massachusetts', 'M', 1, 40124, 39518),\n",
              " ('Massachusetts', 'M', 2, 40332, 38736),\n",
              " ('Massachusetts', 'M', 3, 40618, 38298),\n",
              " ('Massachusetts', 'M', 4, 41584, 38987),\n",
              " ('Massachusetts', 'M', 5, 42643, 39180),\n",
              " ('Massachusetts', 'M', 6, 42952, 38893),\n",
              " ('Massachusetts', 'M', 7, 44354, 39833),\n",
              " ('Massachusetts', 'M', 8, 44301, 39112),\n",
              " ('Massachusetts', 'M', 9, 46045, 39015),\n",
              " ('Massachusetts', 'M', 10, 47023, 39467),\n",
              " ('Massachusetts', 'M', 11, 44534, 39847),\n",
              " ('Massachusetts', 'M', 12, 44373, 40818),\n",
              " ('Massachusetts', 'M', 13, 43043, 41969),\n",
              " ('Massachusetts', 'M', 14, 43046, 42278),\n",
              " ('Massachusetts', 'M', 15, 42547, 43736),\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZX6LVibOVV2U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the first row of the results by using an index: first_row\n",
        "first_row = results[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfqOhBC_Vv-w",
        "colab_type": "code",
        "outputId": "30c74a92-2ebc-4d7b-a20c-98b0e8e9fb36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the first row of the results\n",
        "print(first_row)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Illinois', 'M', 0, 89600, 95012)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypc5B4vgVxuX",
        "colab_type": "code",
        "outputId": "ce3ad743-7dd2-4e98-8eaf-f8143ede889b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the first column of the first row by using an index\n",
        "print(first_row[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Illinois\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkILmqSwVzTO",
        "colab_type": "code",
        "outputId": "4d30119a-d309-428b-80a6-227c1ab8e294",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the 'state' column of the first row by using its name\n",
        "print(first_row['state'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Illinois\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qdw8MJ41FUk6",
        "colab_type": "text"
      },
      "source": [
        "## 2. Applying Filtering, Ordering and Grouping to Queries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXjVqKtjHMwD",
        "colab_type": "text"
      },
      "source": [
        "#### Filtering and Targeting Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlLlAoIZV6qr",
        "colab_type": "text"
      },
      "source": [
        "**Connecting to a PostgreSQL Database**\n",
        "\n",
        "Let's begin by connecting to a PostgreSQL database. When connecting to a PostgreSQL database, many prefer to use the psycopg2 database driver as it supports practically all of PostgreSQL's features efficiently and is the standard dialect for PostgreSQL in SQLAlchemy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Tt32FR6WKJL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import create_engine function\n",
        "from sqlalchemy import create_engine"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "se3lXnsEXDR0",
        "colab_type": "code",
        "outputId": "a824804e-fa6b-471e-a937-46475e401219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# Create an engine to the census database\n",
        "engine = create_engine(\n",
        "    'postgresql+psycopg2://' +\n",
        "    'student:datacamp' +\n",
        "    '@postgresql.csrrinzqubik.us-east-1.rds.amazonaws.com' +\n",
        "    ':5432/census')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
            "  \"\"\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4gErH8QXEfI",
        "colab_type": "code",
        "outputId": "93891b62-edc2-4769-c236-032c5ce248ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Use the .table_names() method on the engine to print the table names\n",
        "print(engine.table_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['census', 'state_fact', 'vrska', 'census1', 'data', 'data1', 'employees3', 'users', 'employees', 'employees_2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4axn8KQTXHBT",
        "colab_type": "text"
      },
      "source": [
        "**Filter data selected from a Table - Simple**\n",
        "\n",
        "As mentioned in the video, a where() clause is used to filter the data that a statement returns. \n",
        "\n",
        "For example, to select all the records from the census table where the sex is Female (or 'F') we would do the following:\n",
        "\n",
        "select([census]).where(census.columns.sex == 'F')\n",
        "\n",
        "In addition to == we can use basically any python comparison operator (such as <=, !=, etc) in the where() clause."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUKJ4wzUXWyw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a select query: stmt\n",
        "stmt = select([census])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqDws96_YU3K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add a where clause to filter the results to only those for New York\n",
        "stmt = stmt.where(census.columns.state == 'New York')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiO6VJjVYWJt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query to retrieve all the data returned: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuiqJB9mYXF0",
        "colab_type": "code",
        "outputId": "b744c807-3d4e-455a-cf6c-a76e2df0a584",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2941
        }
      },
      "source": [
        "# Loop over the results and print the age, sex, and pop2008\n",
        "for result in results:\n",
        "    print(result.age, result.sex, result.pop2008)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 M 128088\n",
            "1 M 125649\n",
            "2 M 121615\n",
            "3 M 120580\n",
            "4 M 122482\n",
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            "0 F 122194\n",
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            "63 F 94667\n",
            "64 F 90185\n",
            "65 F 97321\n",
            "66 F 83336\n",
            "67 F 77404\n",
            "68 F 77802\n",
            "69 F 71850\n",
            "70 F 71451\n",
            "71 F 66625\n",
            "72 F 65037\n",
            "73 F 65719\n",
            "74 F 58818\n",
            "75 F 58722\n",
            "76 F 57584\n",
            "77 F 56907\n",
            "78 F 58456\n",
            "79 F 54136\n",
            "80 F 52932\n",
            "81 F 50693\n",
            "82 F 48206\n",
            "83 F 47777\n",
            "84 F 43454\n",
            "85 F 273476\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjeRjI82Yd0O",
        "colab_type": "text"
      },
      "source": [
        "**Filter data selected from a Table - Expressions**\n",
        "\n",
        "Along with in_, you can also use methods like and_ any_ to create more powerful where() clauses."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTCUBtHRFZqi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a query for the census table: stmt\n",
        "stmt = select([census])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CeZASzKRZrf9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "states = ['New York', 'California', 'Texas']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mz8svXBAZw-E",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Append a where clause to return all the records with a state in the states list. Use in_(states) on census.columns.state to do this.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6zrXFlbZjA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to match all the states in_ the list states\n",
        "stmt = stmt.where(census.columns.state.in_(states))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3JfYVbEZv0M",
        "colab_type": "code",
        "outputId": "607ef915-71e4-4453-b221-51e3114b4288",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8789
        }
      },
      "source": [
        "# Loop over the ResultProxy and print the state and its population in 2000\n",
        "for result in connection.execute(stmt):\n",
        "    print(result.state, result.pop2000)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New York 126237\n",
            "New York 124008\n",
            "New York 124725\n",
            "New York 126697\n",
            "New York 131357\n",
            "New York 133095\n",
            "New York 134203\n",
            "New York 137986\n",
            "New York 139455\n",
            "New York 142454\n",
            "New York 145621\n",
            "New York 138746\n",
            "New York 135565\n",
            "New York 132288\n",
            "New York 132388\n",
            "New York 131959\n",
            "New York 130189\n",
            "New York 132566\n",
            "New York 132672\n",
            "New York 133654\n",
            "New York 132121\n",
            "New York 126166\n",
            "New York 123215\n",
            "New York 121282\n",
            "New York 118953\n",
            "New York 123151\n",
            "New York 118727\n",
            "New York 122359\n",
            "New York 128651\n",
            "New York 140687\n",
            "New York 149558\n",
            "New York 139477\n",
            "New York 138911\n",
            "New York 139031\n",
            "New York 145440\n",
            "New York 156168\n",
            "New York 153840\n",
            "New York 152078\n",
            "New York 150765\n",
            "New York 152606\n",
            "New York 159345\n",
            "New York 148628\n",
            "New York 147892\n",
            "New York 144195\n",
            "New York 139354\n",
            "New York 141953\n",
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            "New York 128767\n",
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            "New York 125955\n",
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            "New York 124418\n",
            "New York 95025\n",
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            "New York 90096\n",
            "New York 95340\n",
            "New York 83273\n",
            "New York 77213\n",
            "New York 77054\n",
            "New York 72212\n",
            "New York 70967\n",
            "New York 66461\n",
            "New York 64361\n",
            "New York 64385\n",
            "New York 58819\n",
            "New York 58176\n",
            "New York 57310\n",
            "New York 57057\n",
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            "New York 51263\n",
            "New York 48440\n",
            "New York 46702\n",
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            "New York 40730\n",
            "New York 37950\n",
            "New York 35774\n",
            "New York 32453\n",
            "New York 26803\n",
            "California 252494\n",
            "California 247978\n",
            "California 250644\n",
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            "California 266855\n",
            "California 272801\n",
            "California 274899\n",
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            "California 262671\n",
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            "California 290332\n",
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            "New York 25041\n",
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            "New York 94346\n",
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            "New York 88932\n",
            "New York 82899\n",
            "New York 82172\n",
            "New York 77171\n",
            "New York 76032\n",
            "New York 76498\n",
            "New York 70465\n",
            "New York 71088\n",
            "New York 70847\n",
            "New York 71377\n",
            "New York 74378\n",
            "New York 70611\n",
            "New York 70513\n",
            "New York 69156\n",
            "New York 68042\n",
            "New York 68410\n",
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            "New York 37436\n",
            "New York 226378\n",
            "Texas 172223\n",
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            "Texas 70089\n",
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            "Texas 35378\n",
            "Texas 33852\n",
            "Texas 30076\n",
            "Texas 27961\n",
            "Texas 171538\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDtfIZZmZ27h",
        "colab_type": "text"
      },
      "source": [
        "**Filter data selected from a Table - Advanced**\n",
        "\n",
        "SQLAlchemy also allows users to use conjunctions such as and_(), or_(), and not_() to build more complex filtering. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWUiO8JIZ9eV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import and_\n",
        "from sqlalchemy import and_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Oe6ljm6ZkMI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query for the census table: stmt\n",
        "stmt = select([census])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTCrsYhoaQgg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to select only non-male records from California using and_\n",
        "stmt = stmt.where(\n",
        "    # The state of California with a non-male sex\n",
        "    and_(census.columns.state == 'California',\n",
        "         census.columns.sex != 'M'\n",
        "         )\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEa_oTawaRyt",
        "colab_type": "code",
        "outputId": "bd4f8a3f-7e77-4214-81b6-b844af6dca5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1479
        }
      },
      "source": [
        "# Loop over the ResultProxy printing the age and sex\n",
        "for result in connection.execute(stmt):\n",
        "    print(result.age, result.sex)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 F\n",
            "1 F\n",
            "2 F\n",
            "3 F\n",
            "4 F\n",
            "5 F\n",
            "6 F\n",
            "7 F\n",
            "8 F\n",
            "9 F\n",
            "10 F\n",
            "11 F\n",
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            "35 F\n",
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            "37 F\n",
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            "45 F\n",
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            "73 F\n",
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            "76 F\n",
            "77 F\n",
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            "80 F\n",
            "81 F\n",
            "82 F\n",
            "83 F\n",
            "84 F\n",
            "85 F\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQrZ15fuHQHj",
        "colab_type": "text"
      },
      "source": [
        "#### Overview of Ordering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nwDZpgSLaYiW",
        "colab_type": "text"
      },
      "source": [
        "**Ordering by a Single Column**\n",
        "\n",
        "To sort the result output by a field, we use the .order_by() method. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMVWBN60agTi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select the state column: stmt\n",
        "stmt = select([census.columns.state])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4HTc0XLbA-0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Order stmt by the state column\n",
        "stmt = stmt.order_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88D5u3RobCYV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the results: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqDYEBJ9bDYC",
        "colab_type": "code",
        "outputId": "5fbd111a-2ec9-49d7-d15c-a018d497fd37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print the first 10 results\n",
        "print(results[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',), ('Alabama',)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PGPX6Efcy5o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import desc\n",
        "from sqlalchemy import desc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jle_y6AYc2ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select the state column: stmt\n",
        "stmt = select([census.columns.state])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uwVvZDzc3wv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Order stmt by state in descending order: rev_stmt\n",
        "rev_stmt = stmt.order_by(desc(census.columns.state))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYT-MUgzc424",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the results: rev_results\n",
        "rev_results = connection.execute(rev_stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6FqKEVPc6IQ",
        "colab_type": "code",
        "outputId": "d3fd0579-30dd-490e-9e27-97af8bce400e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print the first 10 rev_results\n",
        "print(rev_results[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',), ('Wyoming',)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQCCHv5wc98u",
        "colab_type": "text"
      },
      "source": [
        "**Ordering by Multiple Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_XCUJmrdqus",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select state and age: stmt\n",
        "stmt = select([census.columns.state, census.columns.age])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsDMoP7Ldr0N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append order by to ascend by state and descend by age\n",
        "stmt = stmt.order_by(census.columns.state, desc(census.columns.age))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFj_MH8TdtYa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and store all the records: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3eelhCBduzZ",
        "colab_type": "code",
        "outputId": "79929039-da33-494a-ebe5-3774f4f80a63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print the first 20 results\n",
        "print(results[:20])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Alabama', 85), ('Alabama', 85), ('Alabama', 84), ('Alabama', 84), ('Alabama', 83), ('Alabama', 83), ('Alabama', 82), ('Alabama', 82), ('Alabama', 81), ('Alabama', 81), ('Alabama', 80), ('Alabama', 80), ('Alabama', 79), ('Alabama', 79), ('Alabama', 78), ('Alabama', 78), ('Alabama', 77), ('Alabama', 77), ('Alabama', 76), ('Alabama', 76)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4_uKUbsHSjL",
        "colab_type": "text"
      },
      "source": [
        "#### Counting, Summing and Grouping Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRB8FQbVd_Q7",
        "colab_type": "text"
      },
      "source": [
        "**Counting Distinct Data**\n",
        "\n",
        "As mentioned in the video, SQLAlchemy's func module provides access to built-in SQL functions that can make operations like counting and summing faster and more efficient.\n",
        "\n",
        "\n",
        "\n",
        "*   func.sum()\n",
        "\n",
        "*   func.count()\n",
        "*   ~ .distinct()\n",
        "\n",
        "\n",
        "*   ~ .fetchall()\n",
        "\n",
        "\n",
        "*   ~ .first()\n",
        "*   ~ .scalar()\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RenG3iBYekSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sqlalchemy import func"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfOlqWzHeTwc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to count the distinct states values: stmt\n",
        "stmt = select([func.count(census.columns.state.distinct())])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpEK_-wpHSX7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the scalar result: distinct_state_count\n",
        "distinct_state_count = connection.execute(stmt).scalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8JzN9aNesAN",
        "colab_type": "code",
        "outputId": "90ae8af9-d31c-445a-aba6-108b77f2b42f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the distinct_state_count\n",
        "print(distinct_state_count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHeoS85qeu2m",
        "colab_type": "text"
      },
      "source": [
        "**Count of Records by State**\n",
        "\n",
        "Often, we want to get a count for each record with a particular value in another column. The .group_by() method helps answer this type of query. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rwh92HFLe4YS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import func\n",
        "from sqlalchemy import func"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60VFeOiFhe_m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select the state and count of ages by state: stmt\n",
        "stmt = select([census.columns.state, func.count(census.columns.age)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oAvF3I-hgaU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group stmt by state\n",
        "stmt = stmt.group_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-z8Z0xdhhkz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and store all the records: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBfqJcYXhixw",
        "colab_type": "code",
        "outputId": "c33d8eb9-87f2-48eb-dd7a-b62e199d3ab0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print results\n",
        "print(results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Alabama', 172), ('Alaska', 172), ('Arizona', 172), ('Arkansas', 172), ('California', 172), ('Colorado', 172), ('Connecticut', 172), ('Delaware', 172), ('District of Columbia', 172), ('Florida', 172), ('Georgia', 172), ('Hawaii', 172), ('Idaho', 172), ('Illinois', 172), ('Indiana', 172), ('Iowa', 172), ('Kansas', 172), ('Kentucky', 172), ('Louisiana', 172), ('Maine', 172), ('Maryland', 172), ('Massachusetts', 172), ('Michigan', 172), ('Minnesota', 172), ('Mississippi', 172), ('Missouri', 172), ('Montana', 172), ('Nebraska', 172), ('Nevada', 172), ('New Hampshire', 172), ('New Jersey', 172), ('New Mexico', 172), ('New York', 172), ('North Carolina', 172), ('North Dakota', 172), ('Ohio', 172), ('Oklahoma', 172), ('Oregon', 172), ('Pennsylvania', 172), ('Rhode Island', 172), ('South Carolina', 172), ('South Dakota', 172), ('Tennessee', 172), ('Texas', 172), ('Utah', 172), ('Vermont', 172), ('Virginia', 172), ('Washington', 172), ('West Virginia', 172), ('Wisconsin', 172), ('Wyoming', 172)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8Ar5xXVhj_D",
        "colab_type": "code",
        "outputId": "3c49a039-0e29-40d4-bc6d-e57111863736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the keys/column names of the results returned\n",
        "print(results[0].keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['state', 'count_1']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZI88Fv4xhlw8",
        "colab_type": "text"
      },
      "source": [
        "**Determining the Population Sum by State**\n",
        "\n",
        "To avoid confusion with query result column names like count_1, we can use the .label() method to provide a name for the resulting column. This gets appendedto the function method we are using, and its argument is the name we want to use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5mMEWL4Hev6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import func\n",
        "from sqlalchemy import func"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHGG-6THiWUD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build an expression to calculate the sum of pop2008 labeled as population\n",
        "pop2008_sum = func.sum(census.columns.pop2008).label('population')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-y1nVvCtiX4w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select the state and sum of pop2008: stmt\n",
        "stmt = select([census.columns.state, pop2008_sum])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0LBpCR8iZEm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group stmt by state\n",
        "stmt = stmt.group_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1W5YVX7iaDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and store all the records: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkLlA-E2ibA2",
        "colab_type": "code",
        "outputId": "6770d6b4-4095-4a6b-c698-70a710a4ef0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print results\n",
        "print(results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('Alabama', 4649367), ('Alaska', 664546), ('Arizona', 6480767), ('Arkansas', 2848432), ('California', 36609002), ('Colorado', 4912947), ('Connecticut', 3493783), ('Delaware', 869221), ('District of Columbia', 588910), ('Florida', 18257662), ('Georgia', 9622508), ('Hawaii', 1250676), ('Idaho', 1518914), ('Illinois', 12867077), ('Indiana', 6373299), ('Iowa', 3000490), ('Kansas', 2782245), ('Kentucky', 4254964), ('Louisiana', 4395797), ('Maine', 1312972), ('Maryland', 5604174), ('Massachusetts', 6492024), ('Michigan', 9998854), ('Minnesota', 5215815), ('Mississippi', 2922355), ('Missouri', 5891974), ('Montana', 963802), ('Nebraska', 1776757), ('Nevada', 2579387), ('New Hampshire', 1314533), ('New Jersey', 8670204), ('New Mexico', 1974993), ('New York', 19465159), ('North Carolina', 9121606), ('North Dakota', 634282), ('Ohio', 11476782), ('Oklahoma', 3620620), ('Oregon', 3786824), ('Pennsylvania', 12440129), ('Rhode Island', 1046535), ('South Carolina', 4438870), ('South Dakota', 800997), ('Tennessee', 6202407), ('Texas', 24214127), ('Utah', 2730919), ('Vermont', 620602), ('Virginia', 7648902), ('Washington', 6502019), ('West Virginia', 1812879), ('Wisconsin', 5625013), ('Wyoming', 529490)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5iobyhhicQp",
        "colab_type": "code",
        "outputId": "f63cd19d-7e40-43ae-f7e1-bba0e045fb42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the keys/column names of the results returned\n",
        "print(results[0].keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['state', 'population']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUXphOksHgir",
        "colab_type": "text"
      },
      "source": [
        "#### Let's use Pandas and Matplotlib to visualize our Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsHxbK7AHlVq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import pandas\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScCiTHq4inKS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a DataFrame from the results: df\n",
        "df = pd.DataFrame(results)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-xCD_ASioJv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set column names\n",
        "df.columns = results[0].keys()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MT7nvbkMipdS",
        "colab_type": "code",
        "outputId": "afe68ea4-38f3-44a0-a46a-f27e5736649b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        }
      },
      "source": [
        "# Print the Dataframe\n",
        "print(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   state  population\n",
            "0                Alabama     4649367\n",
            "1                 Alaska      664546\n",
            "2                Arizona     6480767\n",
            "3               Arkansas     2848432\n",
            "4             California    36609002\n",
            "5               Colorado     4912947\n",
            "6            Connecticut     3493783\n",
            "7               Delaware      869221\n",
            "8   District of Columbia      588910\n",
            "9                Florida    18257662\n",
            "10               Georgia     9622508\n",
            "11                Hawaii     1250676\n",
            "12                 Idaho     1518914\n",
            "13              Illinois    12867077\n",
            "14               Indiana     6373299\n",
            "15                  Iowa     3000490\n",
            "16                Kansas     2782245\n",
            "17              Kentucky     4254964\n",
            "18             Louisiana     4395797\n",
            "19                 Maine     1312972\n",
            "20              Maryland     5604174\n",
            "21         Massachusetts     6492024\n",
            "22              Michigan     9998854\n",
            "23             Minnesota     5215815\n",
            "24           Mississippi     2922355\n",
            "25              Missouri     5891974\n",
            "26               Montana      963802\n",
            "27              Nebraska     1776757\n",
            "28                Nevada     2579387\n",
            "29         New Hampshire     1314533\n",
            "30            New Jersey     8670204\n",
            "31            New Mexico     1974993\n",
            "32              New York    19465159\n",
            "33        North Carolina     9121606\n",
            "34          North Dakota      634282\n",
            "35                  Ohio    11476782\n",
            "36              Oklahoma     3620620\n",
            "37                Oregon     3786824\n",
            "38          Pennsylvania    12440129\n",
            "39          Rhode Island     1046535\n",
            "40        South Carolina     4438870\n",
            "41          South Dakota      800997\n",
            "42             Tennessee     6202407\n",
            "43                 Texas    24214127\n",
            "44                  Utah     2730919\n",
            "45               Vermont      620602\n",
            "46              Virginia     7648902\n",
            "47            Washington     6502019\n",
            "48         West Virginia     1812879\n",
            "49             Wisconsin     5625013\n",
            "50               Wyoming      529490\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EPI5HWwi2KI",
        "colab_type": "code",
        "outputId": "fbb9df42-c4e1-4774-fcab-a37b59e74fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Plot the DataFrame\n",
        "df.plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IbvRdieqnlruISAYpuIuIZJCCu4hIBim4i4hkUNHgbmZ3mdkaM1tRYLuZ2c1mttrMlpnZ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\n3EVEMkjBXUQkgxTcRUQySMFdRCSD/j+3T4HUtsXBqgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cYEY8XR2FbK8",
        "colab_type": "text"
      },
      "source": [
        "## 3. Advanced SQLAlchemy Queries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpVgIqF6Ho6b",
        "colab_type": "text"
      },
      "source": [
        "#### Calculating Values in a Query"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5yqvbLYjHPx",
        "colab_type": "text"
      },
      "source": [
        "**Connecting to a MySQL Database**\n",
        "\n",
        "Now, you'll connect to a MySQL database, for which many prefer to use the pymysql database driver, which, like psycopg2 for PostgreSQL, you have to install prior to use.\n",
        "\n",
        "This connection string is going to start with 'mysql+pymysql://', indicating which dialect and driver you're using to establish the connection. The dialect block is followed by the 'username:password' combo. Next, you specify the host and port with the following '@host:port/'. Finally, you wrap up the connection string with the 'database_name'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VU7-4_ZPlsmW",
        "colab_type": "text"
      },
      "source": [
        "Create an engine to the census database by concatenating the following strings and passing them to create_engine():\n",
        "\n",
        "\n",
        "\n",
        "*   'mysql+pymysql://' (the dialect and driver).\n",
        "\n",
        "*   'student:datacamp' (the username and password).\n",
        "*   '@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:3306/' (the host and port).\n",
        "\n",
        "\n",
        "*   'census' (the database name).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4o25Slgrl0OA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import create_engine function\n",
        "from sqlalchemy import create_engine"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-i_uQLuJmOZj",
        "colab_type": "code",
        "outputId": "bc0b9b25-8c43-4f6b-c8e7-55888f7b0f67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "! pip install pymysql"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pymysql\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/39/15045ae46f2a123019aa968dfcba0396c161c20f855f11dea6796bcaae95/PyMySQL-0.9.3-py2.py3-none-any.whl (47kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 1.7MB/s \n",
            "\u001b[?25hInstalling collected packages: pymysql\n",
            "Successfully installed pymysql-0.9.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4RsIz2RmVUi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pymysql"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpQNWNGimCJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create an engine to the census database\n",
        "engine = create_engine(\n",
        "    'mysql+pymysql://' +\n",
        "    'student:datacamp' +\n",
        "    '@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:3306/' +\n",
        "    'census'\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4j09Cz9rFge7",
        "colab_type": "code",
        "outputId": "ff87c607-84ae-4242-8543-d9c2f783cbf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the table names\n",
        "print(engine.table_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['census', 'state_fact']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zx8Fb2dCme9G",
        "colab_type": "text"
      },
      "source": [
        "**Calculating a Difference between Two Columns**\n",
        "\n",
        "For math operations on numbers, the operators in SQLAlchemy work the same way as they do in Python.\n",
        "\n",
        "You can use these operators to perform addition (+), subtraction (-), multiplication (*), division (/), and modulus (%) operations. Note: They behave differently when used with non-numeric column types.\n",
        "\n",
        "Define a select statement called stmt to return:\n",
        "\n",
        "\n",
        "\n",
        "1.    The state column of the census table (census.columns.state).\n",
        "2.    The difference in population count between 2008 (census.columns.pop2008) and 2000 (census.columns.pop2000) labeled as 'pop_change'.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1Gdpf88pPxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build query to return state names by population difference from 2008 to 2000: stmt\n",
        "stmt = select([census.columns.state, (census.columns.pop2008-census.columns.pop2000).label('pop_change')])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6wvfQWVpRMW",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Group the statement by census.columns.state.\n",
        "\n",
        "*   Order the statement by population change ('pop_change') in descending order. Do so by passing it desc('pop_change').\n",
        "*   Use the .limit() method on the statement to return only 5 records.\n",
        "\n",
        "\n",
        "*   Execute the statement and fetchall() the records.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Cs6BQgzpY9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append group by for the state: stmt\n",
        "stmt = stmt.group_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TnkY0oyocUr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append order by for pop_change descendingly: stmt\n",
        "stmt = stmt.order_by(desc('pop_change'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkW96OP7medn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Return only 5 results: stmt\n",
        "stmt = stmt.limit(5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ne7N2h9Kpd0k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use connection to execute the statement and fetch all results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP5x7O8spe_T",
        "colab_type": "code",
        "outputId": "0b2de6d2-5fdb-4e45-90f2-2a99f64a8f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# Print the state and population change for each record\n",
        "for result in results:\n",
        "    print('{}:{}'.format(result.state, result.pop_change))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "California:105705\n",
            "Florida:100984\n",
            "Texas:51901\n",
            "New York:47098\n",
            "Pennsylvania:42387\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXEzHvIpphQr",
        "colab_type": "text"
      },
      "source": [
        "**Determining the Overall Percentage of Females**\n",
        "\n",
        "The case() expression accepts a list of conditions to match and the column to return if the condition matches, followed by an else_ if none of the conditions match. We can wrap this entire expression in any function or math operation we like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_TQ9T3ZpnkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import case, cast and Float from sqlalchemy\n",
        "from sqlalchemy import case, cast, Float"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CO3fUpsqp-HM",
        "colab_type": "text"
      },
      "source": [
        "Build an expression female_pop2000to calculate female population in 2000. To achieve this:\n",
        "\n",
        "\n",
        "\n",
        "*   Use case() inside func.sum().\n",
        "*   The first argument of case() is a list containing a tuple of\n",
        "\n",
        "\n",
        "> *   A boolean checking that census.columns.sex is equal to 'F'.\n",
        "> *   The column census.columns.pop2000.\n",
        "\n",
        "\n",
        "\n",
        "*   The second argument is the else_ condition, which should be set to 0.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwk35Go7qMFP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build an expression to calculate female population in 2000\n",
        "female_pop2000 = func.sum(\n",
        "    case([\n",
        "        (census.columns.sex == 'F', census.columns.pop2000)\n",
        "    ], else_=0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbCoL8CIrDp7",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Calculate the total population in 2000 and use cast() to convert it to Float.\n",
        "*   Build a query to calculate the percentage of females in 2000. To do this, divide female_pop2000 by total_pop2000 and multiply by 100.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR4k9qQNq6Hv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Cast an expression to calculate total population in 2000 to Float\n",
        "total_pop2000 = cast(func.sum(census.columns.pop2000), Float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1SghIs3q7L_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to calculate the percentage of females in 2000: stmt\n",
        "stmt = select([female_pop2000 / total_pop2000* 100])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_algUafq8kv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the scalar result: percent_female\n",
        "percent_female = connection.execute(stmt).scalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeAaZPCHq93f",
        "colab_type": "code",
        "outputId": "8b59dbef-1523-4b6f-a23a-1ccd28619dce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the percentage\n",
        "print(percent_female)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51.09467432293413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZI8uJRDSHsdr",
        "colab_type": "text"
      },
      "source": [
        "#### SQL Relationships"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAF0_6brrJh8",
        "colab_type": "text"
      },
      "source": [
        "**Automatic Joins with an Established Relationship**\n",
        "\n",
        "If you have two tables that already have an established relationship, you can automatically use that relationship by just adding the columns we want from each table to the select statement. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDy3079gsK2E",
        "colab_type": "code",
        "outputId": "26a61eb1-662f-4d22-c99a-c6865c0d2d60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Create an engine that connects to the census.sqlite file: engine\n",
        "engine = create_engine('sqlite:///census.sqlite')\n",
        "metadata = MetaData(engine)\n",
        "state_fact = Table('state_fact', metadata, autoload=True, autoload_with=engine)\n",
        "print(repr(state_fact))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('state_fact', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('id', VARCHAR(length=256), table=<state_fact>), Column('name', VARCHAR(length=256), table=<state_fact>), Column('abbreviation', VARCHAR(length=256), table=<state_fact>), Column('country', VARCHAR(length=256), table=<state_fact>), Column('type', VARCHAR(length=256), table=<state_fact>), Column('sort', VARCHAR(length=256), table=<state_fact>), Column('status', VARCHAR(length=256), table=<state_fact>), Column('occupied', VARCHAR(length=256), table=<state_fact>), Column('notes', VARCHAR(length=256), table=<state_fact>), Column('fips_state', VARCHAR(length=256), table=<state_fact>), Column('assoc_press', VARCHAR(length=256), table=<state_fact>), Column('standard_federal_region', VARCHAR(length=256), table=<state_fact>), Column('census_region', VARCHAR(length=256), table=<state_fact>), Column('census_region_name', VARCHAR(length=256), table=<state_fact>), Column('census_division', VARCHAR(length=256), table=<state_fact>), Column('census_division_name', VARCHAR(length=256), table=<state_fact>), Column('circuit_court', VARCHAR(length=256), table=<state_fact>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xef0_pf9rr15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to join census and state_fact tables: stmt\n",
        "stmt = select([census.columns.pop2000, state_fact.columns.abbreviation])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V93wQadirTjg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and get the first result: result\n",
        "result = connection.execute(stmt).first()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOwkzGvvrTiQ",
        "colab_type": "code",
        "outputId": "a41bab56-868c-4a5b-97fb-72783374ef04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Loop over the keys in the result object and print the key and value\n",
        "for key in result.keys():\n",
        "    print(key, getattr(result, key))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "pop2000 89600\n",
            "abbreviation IL\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "banfi1J5wA2S",
        "colab_type": "text"
      },
      "source": [
        "**Joins**\n",
        "\n",
        "The join() takes the table object you want to join in as the first argument and a condition that indicates how the tables are related to the second argument.\n",
        "\n",
        "Finally, you use the .select_from() method on the select statement to wrap the join clause. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1UkF0QZwShf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to select ALL the columns\n",
        "# the census and state_fact tables: stmt\n",
        "stmt = select([census, state_fact])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYb6pfuhw6cq",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Append a select_from to stmt to join the census table to the state_fact table by the state column in census and the name column in the state_fact table.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e84eZseQwvgY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add a select_from clause that wraps a join for the census and state_fact\n",
        "# tables where the census state column and state_fact name column match\n",
        "stmt = stmt.select_from(\n",
        "    census.join(state_fact, census.columns.state == state_fact.columns.name))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQaxbhmbwxKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and get the first result: result\n",
        "result = connection.execute(stmt).first()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opgm_K9qHvcL",
        "colab_type": "code",
        "outputId": "3428d1fc-675e-45b7-9a94-1ed877dcc3dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "# Loop over the keys in the result object and print the key and value\n",
        "for key in result.keys():\n",
        "    print(key, getattr(result, key))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "state Illinois\n",
            "sex M\n",
            "age 0\n",
            "pop2000 89600\n",
            "pop2008 95012\n",
            "id 13\n",
            "name Illinois\n",
            "abbreviation IL\n",
            "country USA\n",
            "type state\n",
            "sort 10\n",
            "status current\n",
            "occupied occupied\n",
            "notes \n",
            "fips_state 17\n",
            "assoc_press Ill.\n",
            "standard_federal_region V\n",
            "census_region 2\n",
            "census_region_name Midwest\n",
            "census_division 3\n",
            "census_division_name East North Central\n",
            "circuit_court 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "02OJ-Z4FxBOF",
        "colab_type": "text"
      },
      "source": [
        "**More Practice with Joins**\n",
        "\n",
        "Build a statement to select:\n",
        "\n",
        "\n",
        "\n",
        "*   The state column from the census table.\n",
        "\n",
        "*   The sum of the pop2008 column from the census table.\n",
        "*   The census_division_name column from the state_fact table.\n",
        "\n",
        "\n",
        "Append a .select_from() to stmt in order to join the census and state_fact tables by the state and name columns.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "or5sMxVk0QRR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to select the state, sum of 2008 population and census\n",
        "# division name: stmt\n",
        "stmt = select([\n",
        "    census.columns.state,\n",
        "    func.sum(census.columns.pop2008),\n",
        "    state_fact.columns.census_division_name\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVp8ebA9N6dR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append select_from to join the census and state_fact tables by the census state and state_fact name columns\n",
        "stmt = stmt.select_from(\n",
        "    census.join(state_fact, census.columns.state == state_fact.columns.name)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76wJhpxmN7pz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a group by for the state_fact name column\n",
        "stmt = stmt.group_by(state_fact.columns.name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pu3_TPKZN9Mm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement and get the results: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDXPsgBDN-WO",
        "colab_type": "code",
        "outputId": "c8f7c700-725a-4905-b4f6-6b796a69a78b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "# Loop over the the results object and print each record.\n",
        "for record in results:\n",
        "    print(record)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Alabama', 4649367, 'East South Central')\n",
            "('Alaska', 664546, 'Pacific')\n",
            "('Arizona', 6480767, 'Mountain')\n",
            "('Arkansas', 2848432, 'West South Central')\n",
            "('California', 36609002, 'Pacific')\n",
            "('Colorado', 4912947, 'Mountain')\n",
            "('Connecticut', 3493783, 'New England')\n",
            "('Delaware', 869221, 'South Atlantic')\n",
            "('Florida', 18257662, 'South Atlantic')\n",
            "('Georgia', 9622508, 'South Atlantic')\n",
            "('Hawaii', 1250676, 'Pacific')\n",
            "('Idaho', 1518914, 'Mountain')\n",
            "('Illinois', 12867077, 'East North Central')\n",
            "('Indiana', 6373299, 'East North Central')\n",
            "('Iowa', 3000490, 'West North Central')\n",
            "('Kansas', 2782245, 'West North Central')\n",
            "('Kentucky', 4254964, 'East South Central')\n",
            "('Louisiana', 4395797, 'West South Central')\n",
            "('Maine', 1312972, 'New England')\n",
            "('Maryland', 5604174, 'South Atlantic')\n",
            "('Massachusetts', 6492024, 'New England')\n",
            "('Michigan', 9998854, 'East North Central')\n",
            "('Minnesota', 5215815, 'West North Central')\n",
            "('Mississippi', 2922355, 'East South Central')\n",
            "('Missouri', 5891974, 'West North Central')\n",
            "('Montana', 963802, 'Mountain')\n",
            "('Nebraska', 1776757, 'West North Central')\n",
            "('Nevada', 2579387, 'Mountain')\n",
            "('New Hampshire', 1314533, 'New England')\n",
            "('New Jersey', 8670204, 'Mid-Atlantic')\n",
            "('New Mexico', 1974993, 'Mountain')\n",
            "('New York', 19465159, 'Mid-Atlantic')\n",
            "('North Carolina', 9121606, 'South Atlantic')\n",
            "('North Dakota', 634282, 'West North Central')\n",
            "('Ohio', 11476782, 'East North Central')\n",
            "('Oklahoma', 3620620, 'West South Central')\n",
            "('Oregon', 3786824, 'Pacific')\n",
            "('Pennsylvania', 12440129, 'Mid-Atlantic')\n",
            "('Rhode Island', 1046535, 'New England')\n",
            "('South Carolina', 4438870, 'South Atlantic')\n",
            "('South Dakota', 800997, 'West North Central')\n",
            "('Tennessee', 6202407, 'East South Central')\n",
            "('Texas', 24214127, 'West South Central')\n",
            "('Utah', 2730919, 'Mountain')\n",
            "('Vermont', 620602, 'New England')\n",
            "('Virginia', 7648902, 'South Atlantic')\n",
            "('Washington', 6502019, 'Pacific')\n",
            "('West Virginia', 1812879, 'South Atlantic')\n",
            "('Wisconsin', 5625013, 'East North Central')\n",
            "('Wyoming', 529490, 'Mountain')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u92lGonNHvjT",
        "colab_type": "text"
      },
      "source": [
        "#### Working with Hierarchical Tables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZgYmXlUOBxO",
        "colab_type": "text"
      },
      "source": [
        "**Using alias to handle same table joined queries**\n",
        "\n",
        "Often, you'll have tables that contain hierarchical data, such as employees and managers who are also employees. For this reason, you may wish to join a table to itself on different columns. \n",
        "\n",
        "The .alias() method, which creates a copy of a table, helps accomplish this task. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "600Lf9HRR88R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "connection.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnOX3WT1PD97",
        "colab_type": "code",
        "outputId": "52e8ce23-8cdf-469c-e675-1c056aac31c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Create an engine that connects to the employees.sqlite file: engine\n",
        "engine1 = create_engine('sqlite:///employees.sqlite')\n",
        "connection = engine1.connect()\n",
        "\n",
        "# Print table names\n",
        "print(engine1.table_names())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['data', 'data1', 'employees']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fX_7SoxVPwPf",
        "colab_type": "code",
        "outputId": "a58d5468-387d-45b5-958b-329f99a02350",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "metadata = MetaData(engine1)\n",
        "employees = Table('employees', metadata, autoload=True, autoload_with=engine1)\n",
        "# Print employees table metadata\n",
        "print(repr(employees))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('employees', MetaData(bind=Engine(sqlite:///employees.sqlite)), Column('id', INTEGER(), table=<employees>, primary_key=True, nullable=False), Column('name', VARCHAR(length=20), table=<employees>), Column('job', VARCHAR(length=20), table=<employees>), Column('mgr', INTEGER(), table=<employees>), Column('hiredate', DATETIME(), table=<employees>), Column('sal', NUMERIC(precision=7, scale=2), table=<employees>), Column('comm', NUMERIC(precision=7, scale=2), table=<employees>), Column('dept', INTEGER(), table=<employees>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xofcqc5RHzJj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make an alias of the employees table: managers\n",
        "managers = employees.alias()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KtpNtAiRFO8",
        "colab_type": "code",
        "outputId": "7c7db437-e044-4275-f797-e150b8b30797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(repr(managers))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<sqlalchemy.sql.selectable.Alias at 0x7f4607609f60; %(139938748211040 employees)s>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLy6BFoLRTsK",
        "colab_type": "code",
        "outputId": "bc7f5438-2cf4-418b-fc29-ff63c2c367a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "employees.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlalchemy.sql.base.ImmutableColumnCollection at 0x7f46048d3798>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGgjktWUO-C_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select manager's and their employees names: stmt\n",
        "stmt = select(\n",
        "    [managers.columns.name.label('manager'),\n",
        "     employees.columns.name.label('employee')]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYIB1bTuQNZp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Match managers id with employees mgr: stmt\n",
        "stmt = stmt.where(managers.columns.id == employees.columns.mgr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwYORA5zQO-p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Order the statement by the managers name: stmt\n",
        "stmt = stmt.order_by(managers.columns.name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKn8aDDwQQCu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute statement: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghYjiCvtQRHS",
        "colab_type": "code",
        "outputId": "b0b40232-cea6-4d2a-b181-6fbc0b185213",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# Print records\n",
        "for record in results:\n",
        "    print(record)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('FILLMORE', 'GRANT')\n",
            "('FILLMORE', 'ADAMS')\n",
            "('FILLMORE', 'MONROE')\n",
            "('GARFIELD', 'JOHNSON')\n",
            "('GARFIELD', 'LINCOLN')\n",
            "('GARFIELD', 'POLK')\n",
            "('GARFIELD', 'WASHINGTON')\n",
            "('HARDING', 'TAFT')\n",
            "('HARDING', 'HOOVER')\n",
            "('JACKSON', 'HARDING')\n",
            "('JACKSON', 'GARFIELD')\n",
            "('JACKSON', 'FILLMORE')\n",
            "('JACKSON', 'ROOSEVELT')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1M4qKrBS0gb",
        "colab_type": "text"
      },
      "source": [
        "**Leveraging Functions and Group_bys with Hierarchical Data**\n",
        "\n",
        "Here, your job is to get a count of employees for each manager.\n",
        "\n",
        "\n",
        "\n",
        "*   Using a .where() clause, filter the records where the id column of the managers table and mgr column of the employees table are equal.\n",
        "*   Group the query by the name column of the managers table.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhrvJ6kVTCb9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Make an alias of the employees table: managers\n",
        "managers = employees.alias()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CA4XF3wOTZIs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to select managers and counts of their employees: stmt\n",
        "stmt = select([managers.columns.name, func.count(employees.columns.id)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dH_3WToTaXd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause that ensures the manager id and employee mgr are equal\n",
        "stmt = stmt.where(managers.columns.id == employees.columns.mgr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWyPUjbMTbUV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group by Managers Name\n",
        "stmt = stmt.group_by(managers.columns.name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "954AsUGoTcUn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute statement: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxE7iAhMTduZ",
        "colab_type": "code",
        "outputId": "5c954c6b-c057-441e-e790-d65e74af5d6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# print manager\n",
        "for record in results:\n",
        "    print(record)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('FILLMORE', 3)\n",
            "('GARFIELD', 4)\n",
            "('HARDING', 2)\n",
            "('JACKSON', 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBW-7BfHHzPy",
        "colab_type": "text"
      },
      "source": [
        "#### Dealing with Large ResultSets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScXIHpW9TgNb",
        "colab_type": "text"
      },
      "source": [
        "**Working on Blocks of Records**\n",
        "\n",
        "You can get blocks of rows from the ResultProxy by using the .fetchmany() method inside a loop. With **.fetchmany()**, give it an argument of the number of records you want. \n",
        "\n",
        "When you reach an empty list, there are no more rows left to fetch, and you have processed all the results of the query. Then you need to use the **.close()** method to close out the connection to the database."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dwf-M1WXXkl9",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Use a while loop that checks if there are more_results.\n",
        "\n",
        "*   Inside the loop, apply the method .fetchmany() to results_proxy to get 50 records at a time and store those records as partial_results.\n",
        "*   After fetching the records, if partial_results is an empty list (that is, if it is equal to []), set more_results to False.\n",
        "\n",
        "\n",
        "*   Loop over the partial_results and, if row.state is a key in the state_count dictionary, increment state_count[row.state] by 1; otherwise set state_count[row.state] to 1.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0eeZa2wZDgE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "more_results = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JYMfNNCZJvz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results_proxy = connection.execute(stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC30F1bWXvOj",
        "colab_type": "code",
        "outputId": "9be9b904-a2a8-485a-8334-7b57b8a3736f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "# Start a while loop checking for more results\n",
        "while more_results:\n",
        "    # Fetch the first 50 results from the ResultProxy: partial_results\n",
        "    partial_results = results_proxy.fetchmany(50)\n",
        "\n",
        "    # if empty list, set more_results to False\n",
        "    if partial_results == []:\n",
        "        more_results = False\n",
        "\n",
        "    # Loop over the fetched records and increment the count for the state\n",
        "    for row in partial_results:\n",
        "        if row.state in state_count:\n",
        "            state_count[row.state] += 1\n",
        "        else:\n",
        "            state_count[row.state] = 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-0d65fb454d2e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;31m# Loop over the fetched records and increment the count for the state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpartial_results\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstate_count\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m             \u001b[0mstate_count\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Could not locate column in row for column 'state'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vygO6Z9GTsDf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Close the ResultProxy, and thus the connection\n",
        "results_proxy.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PU9DgZeXH2YT",
        "colab_type": "code",
        "outputId": "a6c9027b-5b93-4ea4-ed9b-cb225db53d4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "# Print the count by state\n",
        "print(state_count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-b0079dc83bb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'state_count' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoc-uExwFgoa",
        "colab_type": "text"
      },
      "source": [
        "## 4. Creating and Manipulating your own Databases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvqdBUfJH5xC",
        "colab_type": "text"
      },
      "source": [
        "#### Creating Databases and Tables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-Nb-IOvZkiz",
        "colab_type": "text"
      },
      "source": [
        "**Creating Tables with SQLAlchemy**\n",
        "\n",
        "Previously, you used the Table object to reflect a table from an existing database, but what if you wanted to create a new table? You'd still use the Table object; however, you'd need to replace the autoload and autoload_with parameters with Column objects.\n",
        "\n",
        "After defining the table, you can create the table in the database by using the .create_all() method on metadata and supplying the engine as the only parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxoOCwK3ZmEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import Table, Column, String, Integer, Float, Boolean from sqlalchemy\n",
        "from sqlalchemy import Table, Column, String, Integer, Float, Boolean"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMMeCP96Fl0S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define a new table with a name, count, amount, and valid column: data\n",
        "data = Table('data', metadata,\n",
        "             Column('name', String(255)),\n",
        "             Column('count', Integer()),\n",
        "             Column('amount', Float()),\n",
        "             Column('valid', Boolean())\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8-bO1r7aL5a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use the metadata to create the table\n",
        "metadata.create_all(engine1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvnD__A_aOzX",
        "colab_type": "code",
        "outputId": "5313da7c-77a8-4058-ade0-d36d6ad7a764",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Print table details\n",
        "print(repr(data))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('data', MetaData(bind=Engine(sqlite:///employees.sqlite)), Column('name', String(length=255), table=<data>), Column('count', Integer(), table=<data>), Column('amount', Float(), table=<data>), Column('valid', Boolean(), table=<data>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKSwxXWMaQ0A",
        "colab_type": "text"
      },
      "source": [
        "**Constraints and Data Defaults**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvRCxLcFaaCS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define a new table with a name, count, amount, and valid column: data\n",
        "data = Table('data', metadata,\n",
        "             Column('name', String(255), unique=True),\n",
        "             Column('count', Integer(), default=1),\n",
        "             Column('amount', Float()),\n",
        "             Column('valid', Boolean(), default=False),\n",
        "             extend_existing=True\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGEhQ_7uaend",
        "colab_type": "code",
        "outputId": "df1d7cbc-fdf4-4ebc-d49d-258c6b7ca2ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Use the metadata to create the table\n",
        "metadata.create_all(engine1)\n",
        "\n",
        "# Print the table details\n",
        "print(repr(metadata.tables['data']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('data', MetaData(bind=Engine(sqlite:///employees.sqlite)), Column('name', String(length=255), table=<data>), Column('count', Integer(), table=<data>, default=ColumnDefault(1)), Column('amount', Float(), table=<data>), Column('valid', Boolean(), table=<data>, default=ColumnDefault(False)), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9fIQIpdH9MC",
        "colab_type": "text"
      },
      "source": [
        "#### Inserting Data into a Table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GNEmMyJH_hK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import insert and select from sqlalchemy\n",
        "from sqlalchemy import insert, select"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VNv_2bca1o8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build an insert statement to insert a record into the data table: stmt\n",
        "stmt = insert(data).values(name='Anna', count=1, amount=1000.00, valid=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nIS3lFhcufs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement via the connection: results\n",
        "results = connection.execute(stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSdZCJ9bcv3l",
        "colab_type": "code",
        "outputId": "595582f7-9dcc-400d-c1af-7ae0ef0b6271",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print result rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p89pl7O5cxTw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a select statement to validate the insert\n",
        "stmt = select([data]).where(data.columns.name == 'Anna')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BauqTLo-cyr7",
        "colab_type": "code",
        "outputId": "623288e8-28e6-4b15-f70d-d831621efe42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the result of executing the query.\n",
        "print(connection.execute(stmt).first())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Anna', 1, 1000.0, True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bQKI4Ifuc0w9",
        "colab_type": "text"
      },
      "source": [
        "**Inserting Multiple Records at Once**\n",
        "\n",
        "\n",
        "\n",
        "*   Build a list of dictionaries called values_list with two dictionaries. In the first dictionary set name to 'Anna', count to 1, amount to 1000.00, and valid to True. In the second dictionary of the list, set name to 'Taylor', count to 1, amount to 750.00, and valid to False.\n",
        "\n",
        "*   Build an insert statement for the data table for a multiple insert, save it as stmt.\n",
        "*   Execute stmt with the values_list via connection and store the results. Make sure values_list is the second argument to .execute().\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOTAF525dyDs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a list of dictionaries: values_list\n",
        "values_list = [\n",
        "    {'name': 'Anna', 'count': 1, 'amount': 1000.00, 'valid': True},\n",
        "    {'name': 'Taylor', 'count': 1, 'amount': 750.00, 'valid': False}\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZH71n5_fMOW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build an insert statement for the data table: stmt\n",
        "stmt = insert(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w5eQJdZ6fNu-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute stmt with the values_list: results\n",
        "results = connection.execute(stmt, values_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91iwqq0DfPWc",
        "colab_type": "code",
        "outputId": "d1e91ea4-99b9-4570-86ea-97e65580f4e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jclDR0qfmsN",
        "colab_type": "text"
      },
      "source": [
        "**Loading a CSV into a Table**\n",
        "\n",
        "\n",
        "\n",
        "*   Create a statement for bulk insert into the census table. To do this, just use insert() and census.\n",
        "\n",
        "*   Create an empty list called values_list and a variable called total_rowcount that is set to 0.\n",
        "\n",
        "Within the for loop:\n",
        "\n",
        "*   Complete the data dictionary by filling in the values for each of the keys. The values are contained in row. row[0] represents the value for 'state', row[1] represents the value for 'sex', and so on.\n",
        "*   Append data to values_list.\n",
        "\n",
        "\n",
        "*   If 51 cleanly divides into the current idx:\n",
        "Execute stmt with the values_list and save it as results.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bcgmuEAgUaa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a insert statement for census: stmt\n",
        "stmt = insert(census)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xT67GqdgXEI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create an empty list and zeroed row count: values_list, total_rowcount\n",
        "values_list = []\n",
        "total_rowcount = 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btuFpDlugj4-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgiMlZeyiKD4",
        "colab_type": "code",
        "outputId": "c0e3de9a-3ef3-4b63-d40a-dfe0f1d24b23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv('census.csv', header=None)\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>89600</td>\n",
              "      <td>95012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>88445</td>\n",
              "      <td>91829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>88729</td>\n",
              "      <td>89547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>88868</td>\n",
              "      <td>90037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>4</td>\n",
              "      <td>91947</td>\n",
              "      <td>91111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0  1  2      3      4\n",
              "0  Illinois  M  0  89600  95012\n",
              "1  Illinois  M  1  88445  91829\n",
              "2  Illinois  M  2  88729  89547\n",
              "3  Illinois  M  3  88868  90037\n",
              "4  Illinois  M  4  91947  91111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVexr72tgd-_",
        "colab_type": "code",
        "outputId": "2242e233-3a40-4161-a7e0-ba69187c4447",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "# Enumerate the rows of csv_reader\n",
        "for idx, row in enumerate(csv_reader):\n",
        "    #create data and append to values_list\n",
        "    data = {'state': row[0], 'sex': row[1], 'age': row[2], 'pop2000': row[3],\n",
        "            'pop2008': row[4]}\n",
        "    values_list.append(data)\n",
        "\n",
        "    # Check to see if divisible by 51\n",
        "    if idx % 51 == 0:\n",
        "        results = connection.execute(stmt, values_list)\n",
        "        total_rowcount += results.rowcount\n",
        "        values_list = []"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-76-93a51e7315ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m#create data and append to values_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     data = {'state': row[0], 'sex': row[1], 'age': row[2], 'pop2000': row[3],\n\u001b[0m\u001b[1;32m      4\u001b[0m             'pop2008': row[4]}\n\u001b[1;32m      5\u001b[0m     \u001b[0mvalues_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not subscriptable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKRk02I5ggnS",
        "colab_type": "code",
        "outputId": "6e89dc95-ac69-4c16-f884-b854f078f960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print total rowcount\n",
        "print(total_rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IxgUf0QH_oC",
        "colab_type": "text"
      },
      "source": [
        "#### Updating Data in a Database"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pQhEFtOtFAF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sqlalchemy import update"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_gaS0VYsXk_",
        "colab_type": "code",
        "outputId": "b7a5ca4b-cbaa-4fd8-8c0a-33b43498116f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Create an engine that connects to the census.sqlite file: engine\n",
        "engine = create_engine('sqlite:///census.sqlite')\n",
        "connection = engine.connect()\n",
        "metadata = MetaData(engine)\n",
        "state_fact = Table('state_fact', metadata, autoload=True, autoload_with=engine)\n",
        "print(repr(state_fact))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('state_fact', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('id', VARCHAR(length=256), table=<state_fact>), Column('name', VARCHAR(length=256), table=<state_fact>), Column('abbreviation', VARCHAR(length=256), table=<state_fact>), Column('country', VARCHAR(length=256), table=<state_fact>), Column('type', VARCHAR(length=256), table=<state_fact>), Column('sort', VARCHAR(length=256), table=<state_fact>), Column('status', VARCHAR(length=256), table=<state_fact>), Column('occupied', VARCHAR(length=256), table=<state_fact>), Column('notes', VARCHAR(length=256), table=<state_fact>), Column('fips_state', VARCHAR(length=256), table=<state_fact>), Column('assoc_press', VARCHAR(length=256), table=<state_fact>), Column('standard_federal_region', VARCHAR(length=256), table=<state_fact>), Column('census_region', VARCHAR(length=256), table=<state_fact>), Column('census_region_name', VARCHAR(length=256), table=<state_fact>), Column('census_division', VARCHAR(length=256), table=<state_fact>), Column('census_division_name', VARCHAR(length=256), table=<state_fact>), Column('circuit_court', VARCHAR(length=256), table=<state_fact>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p-e68TXfmCx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a select statement: select_stmt\n",
        "select_stmt = select([state_fact]).where(state_fact.columns.name == 'New York')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4OKqSgyr7Ss",
        "colab_type": "code",
        "outputId": "03d01c23-bf78-4b5e-8eac-aa0b228fb848",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the results of executing the select_stmt\n",
        "print(connection.execute(select_stmt).fetchall())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('32', 'New York', 'NY', 'USA', 'state', '10', 'current', 'occupied', '', '36', 'N.Y.', 'II', '1', 'Northeast', '2', 'Mid-Atlantic', '2')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9IvNls6sZ0a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to update the fips_state to 36: stmt\n",
        "stmt = update(state_fact).values(fips_state = 36)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ervkmz_js8Md",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to limit it to records for New York state\n",
        "stmt = stmt.where(state_fact.columns.name == 'New York')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24_zudo_tij8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement: results\n",
        "results = connection.execute(stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MA0m6ihGtkbK",
        "colab_type": "code",
        "outputId": "086e454a-1252-4d95-a772-7ccadc928861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbpRztSstlpl",
        "colab_type": "code",
        "outputId": "f078116b-0d20-46dc-f12d-8c96a43390cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Execute the select_stmt again to view the changes\n",
        "print(connection.execute(select_stmt).fetchall())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('32', 'New York', 'NY', 'USA', 'state', '10', 'current', 'occupied', '', '36', 'N.Y.', 'II', '1', 'Northeast', '2', 'Mid-Atlantic', '2')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WztVanCICJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to update the notes to 'The Wild West': stmt\n",
        "stmt = update(state_fact).values(notes='The Wild West')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6tnxZePuEP0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to match the West census region records\n",
        "stmt = stmt.where(state_fact.columns.census_region_name == 'West')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZdlKpCtuFwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement: results\n",
        "results = connection.execute(stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpwFJO_huG7-",
        "colab_type": "code",
        "outputId": "835f1395-dd38-455c-ffad-bfe26c20acf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0X-L_l4ulYf",
        "colab_type": "text"
      },
      "source": [
        "**Correlated Updates**\n",
        "\n",
        "You can also update records with data from a select statement. This is called a correlated update. It works by defining a select statement that returns the value you want to update the record with and assigning that as the value in an update statement.\n",
        "\n",
        "You'll be using a flat_census in this exercise as the target of your correlated update. The flat_census table is a summarized copy of your census table."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQ7EDTp0ug_X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to select name from state_fact: stmt\n",
        "fips_stmt = select([state_fact.columns.name])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vTDsAuRpursF",
        "colab_type": "code",
        "outputId": "8c2b0ad9-61bc-42f0-bf16-f2d1969d5ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "# Append a where clause to Match the fips_state to flat_census fips_code\n",
        "fips_stmt = fips_stmt.where(\n",
        "    state_fact.columns.fips_state == flat_census.columns.fips_code)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-43a94fe7fd39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m fips_stmt = fips_stmt.where(\n\u001b[0;32m----> 2\u001b[0;31m     state_fact.columns.fips_state == flat_census.columns.fips_code)\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'flat_census' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-Y_N6hbuucy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build an update statement to set the name to fips_stmt: update_stmt\n",
        "update_stmt = update(flat_census).values(state_name=fips_stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Orh5p717uvjQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute update_stmt: results\n",
        "results = connection.execute(update_stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5bSt95tuwkE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Print rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuZtnNpnICQj",
        "colab_type": "text"
      },
      "source": [
        "#### Removing Data From a Database"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGXojkpzfu-J",
        "colab_type": "code",
        "outputId": "19e507aa-094c-4526-e3c5-6aca9909cfbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Create an engine that connects to the census.sqlite file: engine\n",
        "engine = create_engine('sqlite:///census.sqlite')\n",
        "connection = engine.connect()\n",
        "metadata = MetaData(engine)\n",
        "census = Table('census', metadata, autoload=True, autoload_with=engine)\n",
        "print(repr(census))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('census', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('state', VARCHAR(length=30), table=<census>), Column('sex', VARCHAR(length=1), table=<census>), Column('age', INTEGER(), table=<census>), Column('pop2000', INTEGER(), table=<census>), Column('pop2008', INTEGER(), table=<census>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-Ink8bQfKYa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to count records using the sex column for Men ('M') age 36: stmt\n",
        "stmt = select([func.count(census.columns.sex)]).where(\n",
        "    and_(census.columns.sex == 'M',\n",
        "         census.columns.age == 36)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vy7MVr2fyt7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the select statement and use the scalar() fetch method to save the record count\n",
        "to_delete = connection.execute(stmt).scalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2DiP0ZPf0av",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to delete records from the census table: stmt_del\n",
        "stmt_del = delete(census)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4arOPjxXf1aZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to target Men ('M') age 36\n",
        "stmt_del = stmt_del.where(\n",
        "    and_(census.columns.sex == 'M',\n",
        "         census.columns.age == 36)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufPBrSenf2_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement: results\n",
        "results = connection.execute(stmt_del)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pajwqfHJf4B0",
        "colab_type": "code",
        "outputId": "96236434-314a-47e4-d7d6-450b10a28db4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print affected rowcount and to_delete record count, make sure they match\n",
        "print(results.rowcount, to_delete)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "51 51\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdSNzigWgFeH",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*  Drop the state_fact table by applying the method .drop() to it and passing it the argument engine (in fact, engine will be the sole argument for every function/method in this exercise!)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K6umDInUfsNn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop the state_fact table\n",
        "state_fact.drop(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb8enShfgHnS",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Check to see if state_fact exists via print. Use the .exists() method with engine as the argument.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYE-mPhigJKB",
        "colab_type": "code",
        "outputId": "adc9eec5-8700-4676-d316-da5fc7c81e66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Check to see if state_fact exists\n",
        "print(state_fact.exists(engine))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6D_9h_3gJZL",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Drop all the tables via the metadata using the .drop_all() method.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dc-iz0kTgLAE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop all tables\n",
        "metadata.drop_all(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jZgTi4BgW3K",
        "colab_type": "code",
        "outputId": "986d5218-8ac9-442e-b124-ad91906ef87c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Check to see if census exists\n",
        "print(census.exists(engine))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJs_IvzZuqTh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import delete, select\n",
        "from sqlalchemy import delete, select"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-qU0sRtxnYW",
        "colab_type": "code",
        "outputId": "dd586cab-7a8c-432c-836e-eb15f895d85d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Create an engine that connects to the census.sqlite file: engine\n",
        "engine = create_engine('sqlite:///census.sqlite')\n",
        "connection = engine.connect()\n",
        "metadata = MetaData(engine)\n",
        "census = Table('census', metadata, autoload=True, autoload_with=engine)\n",
        "print(repr(census))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Table('census', MetaData(bind=Engine(sqlite:///census.sqlite)), Column('state', VARCHAR(length=30), table=<census>), Column('sex', VARCHAR(length=1), table=<census>), Column('age', INTEGER(), table=<census>), Column('pop2000', INTEGER(), table=<census>), Column('pop2008', INTEGER(), table=<census>), schema=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lm6TS71LvCNm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to empty the census table: stmt\n",
        "stmt = delete(census)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJgSwb9CvDiZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement: results\n",
        "results = connection.execute(stmt)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CLFVQRnvFAZ",
        "colab_type": "code",
        "outputId": "8e0332aa-2908-40ab-d3a4-8737040e2719",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print affected rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQHMYK1LvGES",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to select all records from the census table\n",
        "stmt = select([census])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kt_Oq5DvHS_",
        "colab_type": "code",
        "outputId": "4ab3b084-3d90-4a7d-bfb0-c182253731b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print the results of executing the statement to verify there are no rows\n",
        "print(connection.execute(stmt).fetchall())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPVJb6yjwPRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to count records using the sex column for Men ('M') age 36: stmt\n",
        "stmt = select([func.count(census.columns.sex)]).where(\n",
        "    and_(census.columns.sex == 'M',\n",
        "         census.columns.age == 36)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0dOxcbLwQfw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the select statement and use the scalar() fetch method to save the record count\n",
        "to_delete = connection.execute(stmt).scalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fe0hB6AZwRzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a statement to delete records from the census table: stmt_del\n",
        "stmt_del = delete(census)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5TvlkgfFwS9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Append a where clause to target Men ('M') age 36\n",
        "stmt_del = stmt_del.where(\n",
        "    and_(census.columns.sex == 'M',\n",
        "         census.columns.age == 36)\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcrg3xgvwUr0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the statement: results\n",
        "results = connection.execute(stmt_del)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gIccnmMwV0S",
        "colab_type": "code",
        "outputId": "9b146c12-3398-4580-d519-60660b968a45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print affected rowcount and to_delete record count, make sure they match\n",
        "print(results.rowcount, to_delete)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVXmyo8DBzst",
        "colab_type": "text"
      },
      "source": [
        "**Deleting a Table Completely**\n",
        "\n",
        "You're now going to practice dropping individual tables from a database with the .drop() method, as well as all tables in a database with the .drop_all() method!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AhmyZ8BJB3DI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop the state_fact table\n",
        "state_fact.drop(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_f8Fz62B4dd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check to see if state_fact exists\n",
        "print(state_fact.exists(engine))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OO5EsiZrB5xQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Drop all tables\n",
        "metadata.drop_all(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBCHa5jOB6vp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check to see if census exists\n",
        "print(census.exists(engine))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5YkEc5tyAgc",
        "colab_type": "text"
      },
      "source": [
        "# Problem with section above"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NKkInzyFl7j",
        "colab_type": "text"
      },
      "source": [
        "## 5. Putting it all together"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9xhJZrlI7-f",
        "colab_type": "text"
      },
      "source": [
        "#### Census Case Study"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlBUOcN_FoEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import create_engine, MetaData\n",
        "from sqlalchemy import create_engine, MetaData"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hn-VGjn_GfvJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define an engine to connect to chapter5.sqlite: engine\n",
        "engine = create_engine('sqlite:///chapter5.sqlite')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6MPBbLNGg93",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize MetaData: metadata\n",
        "metadata = MetaData()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cy5rHt0HcJh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import Table, Column, String, and Integer\n",
        "from sqlalchemy import Table, Column, String, Integer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATteKzcRi9Mt",
        "colab_type": "text"
      },
      "source": [
        "Define a census table with the following columns:\n",
        "\n",
        "\n",
        "\n",
        "*   'state' - String - length of 30\n",
        "\n",
        "*   'sex' - String - length of 1\n",
        "\n",
        "*   'age' - Integer\n",
        "*   'pop2000' - Integer\n",
        "\n",
        "\n",
        "*   'pop2008' - Integer\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiwCdt7ijGGm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a census table: census\n",
        "census = Table('census', metadata,\n",
        "               Column('state', String(30)),\n",
        "               Column('sex', String(1)),\n",
        "               Column('age', Integer()),\n",
        "               Column('pop2000', Integer()),\n",
        "               Column('pop2008', Integer()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9LEZyNGjf7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the table in the database\n",
        "metadata.create_all(engine)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcGRMd9aJAG2",
        "colab_type": "text"
      },
      "source": [
        "#### Populating the Database"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFCP-risjhPH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create an empty list: values_list\n",
        "values_list = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMeUNNLVodOl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# csv_reader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pno52acwmHYB",
        "colab_type": "code",
        "outputId": "90a21bda-86cd-4267-db84-d9981252235e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# Iterate over the rows\n",
        "for row in csv_reader:\n",
        "    # Create a dictionary with the values\n",
        "    data = {'state': row[0], 'sex': row[1], 'age': row[2], 'pop2000': row[3],\n",
        "            'pop2008': row[4]}\n",
        "    # Append the dictionary to the values list\n",
        "    values_list.append(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-9bc40829123e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsv_reader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;31m# Create a dictionary with the values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     data = {'state': row[0], 'sex': row[1], 'age': row[2], 'pop2000': row[3],\n\u001b[1;32m      4\u001b[0m             'pop2008': row[4]}\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# Append the dictionary to the values list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyuLe-9b0rYz",
        "colab_type": "text"
      },
      "source": [
        "**Load Data from a list into the Table**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYF0bTX3JCwG",
        "colab_type": "code",
        "outputId": "3b5f4bd7-6ab3-495a-fc48-2b42e99c4883",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "df = pd.read_csv('census.csv', header=None)\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>89600</td>\n",
              "      <td>95012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>88445</td>\n",
              "      <td>91829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>88729</td>\n",
              "      <td>89547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>88868</td>\n",
              "      <td>90037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>M</td>\n",
              "      <td>4</td>\n",
              "      <td>91947</td>\n",
              "      <td>91111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0  1  2      3      4\n",
              "0  Illinois  M  0  89600  95012\n",
              "1  Illinois  M  1  88445  91829\n",
              "2  Illinois  M  2  88729  89547\n",
              "3  Illinois  M  3  88868  90037\n",
              "4  Illinois  M  4  91947  91111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr-HBjG01uMC",
        "colab_type": "code",
        "outputId": "ce2743b4-1698-426a-8690-a995af5a2c2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([0, 1, 2, 3, 4], dtype='int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFxPe2Vp1bJn",
        "colab_type": "code",
        "outputId": "f320ebd0-4bb4-4744-a5cb-2ed46adf75df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# reorder columns \n",
        "df = df.reindex([2, 3, 4, 1, 0], axis=1)\n",
        "df.columns = ['age', 'pop2000', 'pop2008', 'sex', 'state']\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>pop2000</th>\n",
              "      <th>pop2008</th>\n",
              "      <th>sex</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>89600</td>\n",
              "      <td>95012</td>\n",
              "      <td>M</td>\n",
              "      <td>Illinois</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>88445</td>\n",
              "      <td>91829</td>\n",
              "      <td>M</td>\n",
              "      <td>Illinois</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>88729</td>\n",
              "      <td>89547</td>\n",
              "      <td>M</td>\n",
              "      <td>Illinois</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>88868</td>\n",
              "      <td>90037</td>\n",
              "      <td>M</td>\n",
              "      <td>Illinois</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>91947</td>\n",
              "      <td>91111</td>\n",
              "      <td>M</td>\n",
              "      <td>Illinois</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  pop2000  pop2008 sex     state\n",
              "0    0    89600    95012   M  Illinois\n",
              "1    1    88445    91829   M  Illinois\n",
              "2    2    88729    89547   M  Illinois\n",
              "3    3    88868    90037   M  Illinois\n",
              "4    4    91947    91111   M  Illinois"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQlRtRsl2RS7",
        "colab_type": "code",
        "outputId": "0d54fe19-a876-4ee9-851d-c2847c73b5e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8772, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_GoEbEQ3weM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# change df to dictionary\n",
        "\n",
        "values_list = df.to_dict(orient='records')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnWQJR3Z5mLm",
        "colab_type": "code",
        "outputId": "39070f80-5aa3-41d0-8615-dbba58d44cc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "values_list[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': 0, 'pop2000': 89600, 'pop2008': 95012, 'sex': 'M', 'state': 'Illinois'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1JwohOr-FN8",
        "colab_type": "code",
        "outputId": "603691ef-fcfb-4abb-81c1-3d5d501c2475",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(values_list)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8772"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoMi-mSs3N7g",
        "colab_type": "code",
        "outputId": "35a22c28-008d-46b8-c2f7-a91eedc6e13f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(values_list)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQ9ob4Te2j75",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import insert\n",
        "from sqlalchemy import insert"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Qvbpk4i2-YW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build insert statement: stmt\n",
        "stmt = insert(census)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uu_dsyvv2-3t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use values_list to insert data: results\n",
        "results = connection.execute(stmt, values_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MkCEP8T-cKj",
        "colab_type": "code",
        "outputId": "e1509b61-3b3c-4fd1-c50c-58121f428911",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Print rowcount\n",
        "print(results.rowcount)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8772\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1x0I8ceSJC4X",
        "colab_type": "text"
      },
      "source": [
        "#### Example Queries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yvb3Rhq5-mHq",
        "colab_type": "text"
      },
      "source": [
        "**Build a Query to Determine the Average Age by Population**\n",
        "\n",
        "In this exercise, you will use the func.sum() and group_by() methods to first determine the average age weighted by the population in 2008, and then group by sex.\n",
        "\n",
        "Build a statement to:\n",
        "\n",
        "\n",
        "\n",
        "*   Select sex from the census table.\n",
        "*   Select the average age weighted by the population in 2008 (pop2008). See the example given in the assignment text to see how you can do this. Label this average age calculation as 'average_age'.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snS7gf41-u4G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import select\n",
        "from sqlalchemy import select"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk76jbmY1R-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calculate weighted average age: stmt\n",
        "stmt = select([census.columns.sex,\n",
        "               (func.sum(census.columns.pop2008 * census.columns.age) /\n",
        "                func.sum(census.columns.pop2008)).label('average_age')\n",
        "               ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnRUitYH1SBk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group by sex\n",
        "stmt = stmt.group_by(census.columns.sex)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHzC7syG_zJv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the results: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmubjPJ81R8L",
        "colab_type": "code",
        "outputId": "5c20ee26-bed3-41e5-ac6d-7ed3f9484f12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Print the average age by sex\n",
        "for result in results:\n",
        "    print(result.sex, result.average_age)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F 38\n",
            "M 35\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Dub8HRR_19x",
        "colab_type": "text"
      },
      "source": [
        "**Build a Query to Determine the Percentage of Population by Gender and State**\n",
        "\n",
        "In this exercise, you will write a query to determine the percentage of the population in 2000 that comprised of women. You will group this query by state."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Y6Ej1mI_73r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import case, cast and Float from sqlalchemy\n",
        "from sqlalchemy import case, cast, Float"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVNltlUu_-6_",
        "colab_type": "text"
      },
      "source": [
        "Define a statement to select state and the percentage of females in 2000.\n",
        "\n",
        "\n",
        "\n",
        "*   Inside func.sum(), use case() to select females (using the sex column) from pop2000. Remember to specify else_=0 if the sex is not 'F'.\n",
        "*   To get the percentage, divide the number of females in the year 2000 by the overall population in 2000. Cast the divisor - census.columns.pop2000 - to Float before multiplying by 100.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Scd6kc6wADZ8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build a query to calculate the percentage of females in 2000: stmt\n",
        "stmt = select([census.columns.state,\n",
        "    (func.sum(\n",
        "        case([\n",
        "            (census.columns.sex == 'F', census.columns.pop2000)\n",
        "        ], else_=0)) /\n",
        "     cast(func.sum(census.columns.pop2000), Float) * 100).label('percent_female')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3O1tGlpQA2DN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group By state\n",
        "stmt = stmt.group_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJtBP5v-A3zt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Execute the query and store the results: results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzYGuBRkADwc",
        "colab_type": "code",
        "outputId": "831e4dcf-d8e8-4ae4-a19f-ed76a048b9dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 923
        }
      },
      "source": [
        "# Print the percentage\n",
        "for result in results:\n",
        "    print(result.state, result.percent_female)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Alabama 51.832407770179465\n",
            "Alaska 49.301497893484594\n",
            "Arizona 50.22361303057914\n",
            "Arkansas 51.26992846221834\n",
            "California 50.35233214901979\n",
            "Colorado 49.84767060299562\n",
            "Connecticut 51.66816507130644\n",
            "Delaware 51.61109733558627\n",
            "District of Columbia 53.129626141738385\n",
            "Florida 51.36488001165242\n",
            "Georgia 51.11408350339436\n",
            "Hawaii 51.118011836915514\n",
            "Idaho 49.98972623903102\n",
            "Illinois 51.11224234802867\n",
            "Indiana 50.95480313297678\n",
            "Iowa 50.950398342534264\n",
            "Kansas 50.821864107754735\n",
            "Kentucky 51.32687036927168\n",
            "Louisiana 51.75351596554121\n",
            "Maine 51.50570813418951\n",
            "Maryland 51.93575549972231\n",
            "Massachusetts 51.843023571316785\n",
            "Michigan 50.97246518318712\n",
            "Minnesota 50.49332944301148\n",
            "Mississippi 51.92229481794672\n",
            "Missouri 51.46888602639692\n",
            "Montana 50.32202690728538\n",
            "Nebraska 50.8584549336086\n",
            "Nevada 49.36736361384359\n",
            "New Hampshire 50.858019844961746\n",
            "New Jersey 51.51713956125773\n",
            "New Mexico 51.0471720798335\n",
            "New York 51.83453865150073\n",
            "North Carolina 51.482262322084594\n",
            "North Dakota 50.50069363231332\n",
            "Ohio 51.46550350015544\n",
            "Oklahoma 51.11362457075227\n",
            "Oregon 50.4294670361772\n",
            "Pennsylvania 51.74043473051053\n",
            "Rhode Island 52.07343391902215\n",
            "South Carolina 51.73072129765755\n",
            "South Dakota 50.52583581373275\n",
            "Tennessee 51.430689699449275\n",
            "Texas 50.515721664207966\n",
            "Utah 49.97295275106927\n",
            "Vermont 51.018573209949466\n",
            "Virginia 51.657252447241795\n",
            "Washington 50.518565087218334\n",
            "West Virginia 51.40042318092286\n",
            "Wisconsin 50.61486452653393\n",
            "Wyoming 49.94595542648306\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rKbkuVvA6sW",
        "colab_type": "text"
      },
      "source": [
        "**Build a Query to Determine the Difference by State from the 2000 and 2008 Censuses**\n",
        "\n",
        "In this final exercise, you will write a query to calculate the states that changed the most in population. You will limit your query to display only the top 10 states."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "me3oamdpBaxh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build query to return state name and population difference from 2008 to 2000\n",
        "stmt = select([census.columns.state,\n",
        "     (census.columns.pop2008-census.columns.pop2000).label('pop_change')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvwheRXYBb6N",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "*   Group the query by census.columns.state using the .group_by() method on stmt.\n",
        "*   Order by 'pop_change' in descending order using the .order_by() method with the desc() function on 'pop_change'.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGCQxHMmBfmH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Group by State\n",
        "stmt = stmt.group_by(census.columns.state)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLJu0Ot-Bg_G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Order by Population Change\n",
        "stmt = stmt.order_by(desc('pop_change'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQ-drO4DBiJT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Limit to top 10\n",
        "stmt = stmt.limit(10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0i4QhmuBjQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use connection to execute the statement and fetch all results\n",
        "results = connection.execute(stmt).fetchall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZYA6V1KBkZh",
        "colab_type": "code",
        "outputId": "f5958c10-dc31-42ee-de9e-534d37b48c57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Print the state and population change for each record\n",
        "for result in results:\n",
        "    print('{}:{}'.format(result.state, result.pop_change))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "California:105705\n",
            "Florida:100984\n",
            "Texas:51901\n",
            "New York:47098\n",
            "Pennsylvania:42387\n",
            "Arizona:29509\n",
            "Ohio:29392\n",
            "Illinois:26221\n",
            "Michigan:25126\n",
            "North Carolina:24108\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}