Building a Bottle Application Part 1

gistfile1.txt

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      "#Building a Bottle Application Part 1<br/>\n",
      "#============================\n",
      "##Intro to SQLite3 and Building a DataBase From .csv\n",
      "##++++++++++++++++++++++++++++++++++++++\n",
      "\n",
      "\n",
      "**Author**: Rory Creedon (rcreedon@poverty-action.org)<br/>\n",
      "**Date**: May 2014<br/>\n",
      "**Purpose**: To demonstrate how to create an SQLite database to be used in a micro-web framework application. \n",
      "\n",
      "\n",
      "This notebook is the third in a series called \"A Guide to Building Web Applications With Bottle\".\n",
      "\n",
      "This is the first notebook in which we will begin to build a simple web application. \n",
      "\n",
      "The core task of this notebook is to build a SQL database upon which the application will be based. There will be some examples of some of the (basic) SQLite functionality along the way. These notebooks are primarily for those within IPA who may want to undertake similar tasks. Knowledge of python is essential. \n",
      "\n",
      "Note the use of the sqlite magic extension. Details can be found here:\n",
      "\n",
      "https://github.com/ipython/ipython/wiki/Extensions-Index\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "Image(r'C:\\Users\\rcreedon\\Dropbox\\Rory Notes\\Notes\\Bottle\\Intro_To_Bottle\\Beerbottles.jpg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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PeIvk5b7RT/AFHp0+Bm6Nx707S72m3B1enTlTsXHbP24dpVWZ2vuDI5Sh3M2R3Bic3S1O0MhkMV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\n",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Image at 0x34b9070>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###A Note on the Data<br/>\n",
      "The data that are used in this notebook for creating the database is a list of factories in Bangladesh, their addresses, phone numbers and a contact name. Please note that this is public information available from here: http://www.bangladeshaccord.org/wp-content/uploads/Accord-Public-Disclosure-Report-1-May-2014.pdf. Of the total list of more than 1000 I randomly chose c.200 to be in the .csv file. \n",
      "\n",
      "However, the names were randomly generated. \n",
      "\n",
      "All files and data can be downloaded from here: https://www.dropbox.com/sh/j4u608mlc3fhfm3/AABDk-_MnPOFGuchkaybAUbka"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Contents:\n",
      "\n",
      "+ The Application\n",
      "+ Getting Data From a CSV File\n",
      "+ Creating the Database\n",
      "+ Some Basic SQL Functionality"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#The Application<br/>\n",
      "#----------------------\n",
      "\n",
      "The application that we are going to create is a *test case* application for one that I am really going to be creating as part of my work in Myanmar. This test case application will take data about firms fromo a csv file, create an SQL database of those firms, and then in a micro web framework created ussing Bottle, a front end user tool will be created which will display information about those firms and allow the user to enter 'orders' to create a separate database of orders. It is therefore primarily a structured data collection tool."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Getting Data From CSV File<br/>\n",
      "#-------------------------------------\n",
      "\n",
      "In my case the infomration about firms is in a csv file. The file contains a UID, the firm name, the firm address a conctact number and a contact name. When creating an SQL database, records are entered by passing tuples of data. Therefore the first step is to get the .csv file into such a format that it is a list of tuples. This is achieved using the python `csv` module: https://docs.python.org/2/library/csv.html\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import statements\n",
      "import sqlite3, os, csv\n",
      "# IPython extension to display cursor obejects\n",
      "%load_ext sqlitemagic \n",
      "from IPython.core.display import Image"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Getting data from .csv is really quite simple. Use the python built in `open()` function (https://docs.python.org/2/library/functions.html#open)  to return a file object and then use the `csv.reader()` function to get the data from the csv file. In my case the data were from an Excel .csv so I use the 'excel' dialect option.\n",
      "\n",
      "The `csv.reader` contains lists of the rows of data found in the .csv file, so each element of those lists is a data point. Using this information it is simple to construct a list of tuples from the `csv.reader` object. Note that in the csv file I am using the address column contains multiple line breaks and other issues which result in line break symbols ('\\r\\n') clogging up the address data when it is read. Therefore I strip out those values using the `str.replace()` method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_tuples = [] # container for my tuples\n",
      "\n",
      "with open('contact_list.csv', 'rb') as csvfile:\n",
      "\tcsvreader = csv.reader(csvfile, dialect = 'excel')\n",
      "\tfor line in csvreader:\n",
      "\t\tdata_tuples.append(tuple([entry.replace('\\r', \"\").replace('\\n', '').replace('\\xa0', '').upper() \n",
      "                                  for entry in line]))\n",
      "\n",
      "data_tuples[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[('M001',\n",
        "  'SQ CELSIUS LTD',\n",
        "  'BERAIDERCHALA, KEOWA, MAONA,SREEPUR, GAZIPUR CHITTAGONG',\n",
        "  '1162762276',\n",
        "  'ELWOOD OZUNA '),\n",
        " ('M002',\n",
        "  'GENETIC FASHION LIMITED',\n",
        "  'ZIRABO (NAMAPARA) P.O.ZIRABO P.S. ASULIA, DIST DHAKA DHAKA- BANGLADESH',\n",
        "  '1190277927',\n",
        "  'AGNES AUZENNE '),\n",
        " ('M003',\n",
        "  'THE SHANIN CORP.LTD.',\n",
        "  'THE SHANIN CORP.LTD. 964 SHAWRAPARA, ROKEYA SHARONI, MIRPUR, DHAKA-1216 BANGLADESH',\n",
        "  '1190697138',\n",
        "  'FELISA PEREYRA '),\n",
        " ('M004',\n",
        "  'VISUAL KNITWARES LTD.',\n",
        "  '295, SHAFI COMPLEX,HATAZARI ROAD, JALALABAD',\n",
        "  '1195162481',\n",
        "  'EDEN BADE '),\n",
        " ('M005',\n",
        "  'ANANTA HUAXIANG LTD.',\n",
        "  'PLOT # 222-223, H2, H3, H4 ADAMJEE EPZ',\n",
        "  '1611456770',\n",
        "  'JOSEFINE ECHAVARRIA ')]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are five columns of data in the csv file: UID, Factory, Adress, Number, Contact Name (although note that there are no actual column headers. To verify the success of the above it is prudent to check that all tuples are of length 5, and that there are no Null Values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Test that all tuples are len 5\n",
      "all([len(tup) == 5 for tup in data_tuples])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Test that there are no None values\n",
      "all(test for test in [val is not None for tup in data_tuples for val in tup])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Creating the Database<br/>\n",
      "##----------------------------------\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url = 'http://media.giphy.com/media/INAQqvSYTZyyQ/giphy.gif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://media.giphy.com/media/INAQqvSYTZyyQ/giphy.gif\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<IPython.core.display.Image at 0x34b9090>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The database will be an SQL database created using the python module sqlite3 (https://docs.python.org/2/library/sqlite3.html).\n",
      "\n",
      "The syntax is quite simple to learn and getting going quickly with sqlite3 seems possible. This section will go through the steps to create the database, and the next section will look at some SQL syntax. Incidentally, the syntax is not python, the syntax is SQL syntax executed inside a python wrapper. \n",
      "\n",
      "This means that in the statments below the syntax in inverted commas is SQL syntax. SQL syntax is pure text only. The syntax is not case sensitive, but it is standard to put SQL syntax in CAPS and objects in lower case to distinguish. This paradigm will be applied throughout.\n",
      "\n",
      "The steps are as follows:\n",
      "\n",
      "+ Set the working directory\n",
      "+ Create a database by creating a connection\n",
      "+ Create a table inside that database\n",
      "+ Populate the table with data\n",
      "+ 'Save' the changes to the database\n",
      "+ Close the connection. \n",
      "\n",
      "Below where the Table is Created, the NOT NULL and PRIMARY KEY statements are called 'contraints'. These are discussed in the next section.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Set the working directory:\n",
      "os.chdir(r'C:\\Users\\rcreedon\\Dropbox\\Myanmar\\dev')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Create a database by establishing a connection\n",
      "conn = sqlite3.connect('factories.db')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Create a table. The words in brackets become the table columns\n",
      "conn.execute(\"CREATE TABLE factories \\\n",
      "            (uid TEXT PRIMARY KEY, factory TEXT NOT NULL, address TEXT NOT NULL, contact TEXT, name TEXT NOT NULL)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<sqlite3.Cursor at 0x34ab6e0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Populate the table with data\n",
      "conn.executemany('INSERT INTO factories VALUES (?,?,?,?,?)', data_tuples)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<sqlite3.Cursor at 0x35547a0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 'Save' the chnages by committing\n",
      "conn.commit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Close the connection\n",
      "conn.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above creates a connection object `conn` and then uses the execute method of that object to pass SQL commands which are passed in iverted commas. This is very handy as it means that commands have not been 'translated' into python, but rather you can pass SQL commands in SQL syntax. You can of course also pass python variables as seen when the `data_tuples` object is passed. \n",
      "\n",
      "The `conn.execute()` method executes one statement, whereas the `conn.executemany()` allows you to execute a number of statmenets. So to insert a single record you could use `execute` and pass the tuples of values to be inserted into the table. Whereas passing multiple tuples as above you use `executemany`. \n",
      "\n",
      "Changes must be 'saved' by commiting."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Some Basic SQL Functionality<br/>\n",
      "##-----------------------------------------\n",
      "\n",
      "As this is my first foray into SQL I am going to look at some of the basic syntax which will come in handy later on.\n",
      "\n",
      "**Disclaimer**: The material here is drawn quite directly from www.w3schools.com/sql\n",
      "\n",
      "Note that creating a connection to an existing database allows you to work with that database. Creating a connection to a non-existant database creates a new database."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn = sqlite3.connect('factories.db')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###SELECT\n",
      "\n",
      "The way to access data in the database is by using the SELECT syntax, which follows the following pattern:\n",
      "\n",
      "`SELECT column_name,column_name(s) FROM table_name`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"SELECT factory, name FROM factories\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<sqlite3.Cursor at 0x3554860>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This returns an object that is itself an SQL table. This can easily be converted to a list to look at the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(conn.execute(\"SELECT factory, name FROM factories\"))[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[(u'SQ CELSIUS LTD', u'ELWOOD OZUNA '),\n",
        " (u'GENETIC FASHION LIMITED', u'AGNES AUZENNE '),\n",
        " (u'THE SHANIN CORP.LTD.', u'FELISA PEREYRA '),\n",
        " (u'VISUAL KNITWARES LTD.', u'EDEN BADE '),\n",
        " (u'ANANTA HUAXIANG LTD.', u'JOSEFINE ECHAVARRIA ')]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you write `SELECT * FROM....` then all columns of data are in the resulting table"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###SELECT DISTINCT\n",
      "\n",
      "This allows you to select only the unqiue records in the database. The syntax is:\n",
      "\n",
      "`SELECT DISTINCT column_name(s) FROM table_name`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"SELECT DISTINCT address FROM factories\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<sqlite3.Cursor at 0x35548a0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###SELECT WHERE\n",
      "\n",
      "Select records that fulfil a criterion. the syntax is:\n",
      "\n",
      "`SELECT column_name,column_name FROM table_name WHERE column_name operator value`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the following, do not be confused by the `%sqlite_show` statement. This is not SQL it is an IPython extension that allows for the printing of SQL tables. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M015 = conn.execute(\"SELECT * FROM factories WHERE uid = 'M015'\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%sqlite_show M015"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M015           SUCCESS        DHAKA          1.674e+09      SHENITA      \n",
        "               FASHIONS       COMPLEX,                      DELUNA       \n",
        "               (PVT) LTD.     1/GA,                                      \n",
        "                              CENTRAL                                    \n",
        "                              BASABOO,                                   \n",
        "                              SABUZBAG,                                  \n",
        "                              DHAKA, BD                                  \n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The operators recognised by SLQ are as follows:\n",
      "\n",
      "|**Operator**    | **Meaning**  |\n",
      "|---|---|\n",
      "| = |Equal|\n",
      "| <>\t| Not equal. Note: In some versions of SQL this operator may be written as !=|\n",
      "| > | Greater than|\n",
      "| < |\tLess than|\n",
      "| >= |\tGreater than or equal|\n",
      "| <=\t|Less than or equal|\n",
      "| BETWEEN |\tBetween an inclusive range|\n",
      "| LIKE\t|Search for a pattern|\n",
      "| IN\t|To specify multiple possible values for a column|\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you want to capture the data in the resulting table and assing to variables you can cast the result to tuple and unpack it in the usual manner:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M015_tup = tuple(conn.execute(\"SELECT * FROM factories WHERE uid = 'M015'\"))\n",
      "a, b, c, d, e = M015_tup[0]\n",
      "print a, b, c, d, e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M015 SUCCESS FASHIONS (PVT) LTD. DHAKA COMPLEX, 1/GA, CENTRAL BASABOO, SABUZBAG, DHAKA, BD 1674138320 SHENITA DELUNA \n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If the value being tested is numerical then the '' surrounding the value in the above type statment should be omitted.\n",
      "\n",
      "Note that if the no record matches the condition then no exception is raised, only an empty table is returned. Also, if you are converting it to tuple, then nothing is returned, i.e. not an empty tuple. \n",
      "\n",
      "The \"IN\" operator is useful for specifying multiple values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multi = conn.execute(\"SELECT * FROM factories WHERE uid IN ('M001', 'M017', 'M99')\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%sqlite_show multi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M001           SQ CELSIUS     BERAIDERCHAL   1.163e+09      ELWOOD OZUNA \n",
        "               LTD            A, KEOWA, MA                               \n",
        "                              ONA,SREEPUR,                               \n",
        "                              GAZIPUR                                    \n",
        "                              CHITTAGONG                                 \n",
        "M017           MENS           1351 SULTAN    1.676e+09      ANNABEL      \n",
        "               APPARELS       CHAWK                         GRINDER      \n",
        "               LIMITED        MARKET,                                    \n",
        "                              ATURAR DIPU,                               \n",
        "                              CHITTAGONG                                 \n",
        "                              WEST                                       \n",
        "                              SHOLASHAR                                  \n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note above that M99 does not exist. The tuple form of this query looks like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tuple(conn.execute(\"SELECT * FROM factories WHERE uid IN ('M001', 'M017', 'M99')\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "((u'M001',\n",
        "  u'SQ CELSIUS LTD',\n",
        "  u'BERAIDERCHALA, KEOWA, MAONA,SREEPUR, GAZIPUR CHITTAGONG',\n",
        "  u'1162762276',\n",
        "  u'ELWOOD OZUNA '),\n",
        " (u'M017',\n",
        "  u'MENS APPARELS LIMITED',\n",
        "  u'1351 SULTAN CHAWK MARKET, ATURAR DIPU, CHITTAGONG WEST SHOLASHAR',\n",
        "  u'1676482992',\n",
        "  u'ANNABEL GRINDER '))"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that as previously states, there is no empty tuple where the record that did not match 'M99' with any existing records. This means you need to be careful about assuming that records exist when they do not. \n",
      "\n",
      "The 'AND' and 'OR' operators can be used as you would expect:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M015_M013 = tuple(conn.execute(\"SELECT * FROM factories\\\n",
      "\t\t\t\t\t\t\t    WHERE uid = 'M015' OR uid = 'M013'\"))\n",
      "%sqlite_show M015_M013"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M013           SAMSON         104-105        1.672e+09      JANN BARDIN  \n",
        "               SWEATERS       NOWABPUR                                   \n",
        "               LTD.           ROAD, DHAKA                                \n",
        "M015           SUCCESS        DHAKA          1.674e+09      SHENITA      \n",
        "               FASHIONS       COMPLEX,                      DELUNA       \n",
        "               (PVT) LTD.     1/GA,                                      \n",
        "                              CENTRAL                                    \n",
        "                              BASABOO,                                   \n",
        "                              SABUZBAG,                                  \n",
        "                              DHAKA, BD                                  \n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The LIKE operator allows you to search for a particular pattern in a column. The syntax is:\n",
      "\n",
      "`ELECT column_name(s) FROM table_name WHERE column_name LIKE pattern`\n",
      "\n",
      "So to find factories from the Mirpur area:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Mirpur = conn.execute(\"SELECT * FROM factories WHERE address LIKE '%Mirpur%'\")\n",
      "%sqlite_show Mirpur"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M003           THE SHANIN     THE SHANIN     1.191e+09      FELISA       \n",
        "               CORP.LTD.      CORP.LTD.                     PEREYRA      \n",
        "                              964                                        \n",
        "                              SHAWRAPARA,                                \n",
        "                              ROKEYA                                     \n",
        "                              SHARONI,                                   \n",
        "                              MIRPUR,                                    \n",
        "                              DHAKA-1216                                 \n",
        "                              BANGLADESH                                 \n",
        "M024           ORCHID         MOLLIK         1.678e+09      TONISHA RUX  \n",
        "               STYLES LTD.    TOWER, 11TH                                \n",
        "                              FLOOR, PLOT                                \n",
        "                              13-14, ZOO                                 \n",
        "                              ROAD, MIRPUR                               \n",
        "M039           AMITY DESIGN   H-7 & 9,       1.712e+09      JONE CALMES  \n",
        "               LTD.           R-9, BLOCK                                 \n",
        "                              D,                                         \n",
        "                              MIRPUR-11,                                 \n",
        "                              DHAKA                                      \n",
        "M045           DEKKO          M/4, ROAD 7,   1.712e+09      TAMAR        \n",
        "               APPARELS       SECTION 7                     TEITELBAUM   \n",
        "               LTD.           MIRPUR,                                    \n",
        "                              DHAKA                                      \n",
        "M101           BELKUCHI       3 SUJAT        1.713e+09      ANTONINA     \n",
        "               KNITWEAR       NAGAR,                        GILLUM       \n",
        "               LTD.           SULTAN                                     \n",
        "                              MANSION,                                   \n",
        "                              SECTION-12,                                \n",
        "                              MIRPUR,                                    \n",
        "                              DHAKA                                      \n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a number of wildcard characters that can be used with LIKE:\n",
      "\n",
      "|**Wildcard** | **Meaning** |\n",
      "|---|---|\n",
      "|%|\tA substitute for zero or more characters|\n",
      "|_\t|A substitute for a single character|\n",
      "|[charlist]\t| Sets and ranges of characters to match|\n",
      "|[^charlist] or [!charlist]|\tMatches only a character NOT specified within the brackets|\n",
      "\n",
      "These are not demonstrated further.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##ORDER BY\n",
      "\n",
      "The ORDER BY option will sort the results of the query, by default this is ascending. The syntax is:\n",
      "\n",
      "`SELECT column_name,column_name FROM table_name ORDER BY column_name,column_name ASC|DESC`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ordered = conn.execute(\"SELECT * FROM factories WHERE uid IN ('M001', 'M002', 'M003', 'M004') ORDER BY factory\")\n",
      "%sqlite_show ordered"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M002           GENETIC        ZIRABO         1.190e+09      AGNES        \n",
        "               FASHION        (NAMAPARA)                    AUZENNE      \n",
        "               LIMITED        P.O.ZIRABO                                 \n",
        "                              P.S. ASULIA,                               \n",
        "                              DIST DHAKA                                 \n",
        "                              DHAKA-                                     \n",
        "                              BANGLADESH                                 \n",
        "M001           SQ CELSIUS     BERAIDERCHAL   1.163e+09      ELWOOD OZUNA \n",
        "               LTD            A, KEOWA, MA                               \n",
        "                              ONA,SREEPUR,                               \n",
        "                              GAZIPUR                                    \n",
        "                              CHITTAGONG                                 \n",
        "M003           THE SHANIN     THE SHANIN     1.191e+09      FELISA       \n",
        "               CORP.LTD.      CORP.LTD.                     PEREYRA      \n",
        "                              964                                        \n",
        "                              SHAWRAPARA,                                \n",
        "                              ROKEYA                                     \n",
        "                              SHARONI,                                   \n",
        "                              MIRPUR,                                    \n",
        "                              DHAKA-1216                                 \n",
        "                              BANGLADESH                                 \n",
        "M004           VISUAL         295, SHAFI C   1.195e+09      EDEN BADE    \n",
        "               KNITWARES      OMPLEX,HATAZ                               \n",
        "               LTD.           ARI ROAD,                                  \n",
        "                              JALALABAD                                  \n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##INSERT INTO\n",
      "\n",
      "Create a new record in a table. The syntax is one of two things:\n",
      "\n",
      "`INSERT INTO table_name VALUES (value1,value2,value3,...)` or <br/>\n",
      "`INSERT INTO table_name (column1,column2,column3,...)VALUES (value1,value2,value3,...)`\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#First syntax\n",
      "conn.execute(\"INSERT INTO factories VALUES ('M196', 'LIFE TEXTILEs PVT LIMITED',\\\n",
      "                                            'BSCIS Industrial Estate, Dhaka', '0219877654', 'Alamgir Hossain')\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<sqlite3.Cursor at 0x35549e0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new = conn.execute(\"SELECT * FROM factories WHERE uid = 'M196'\")\n",
      "%sqlite_show new"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M196           LIFE           BSCIS          2.199e+08      Alamgir      \n",
        "               TEXTILEs PVT   Industrial                    Hossain      \n",
        "               LIMITED        Estate,                                    \n",
        "                              Dhaka                                      \n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Second syntax\n",
      "conn.execute(\"INSERT INTO factories (uid, factory, address, name) VALUES ('M197', 'SUPASOX LIMITED',\\\n",
      "                                                                          '8th Floor, 23/1, Panthapath',\\\n",
      "                                                                          'Delowar Hossain')\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<sqlite3.Cursor at 0x35545a0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new2 = conn.execute(\"SELECT * FROM factories WHERE uid = 'M197'\")\n",
      "%sqlite_show new2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M197           SUPASOX        8th Floor,     None           Delowar      \n",
        "               LIMITED        23/1,                         Hossain      \n",
        "                              Panthapath                                 \n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that I did not pass a values to 'contact' and as a result None has been inserted in its place. \n",
      "\n",
      "Of course as has already been seen, values inside python objects can be inserted:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M198 = ('M198', 'AB MART FASHION WEAR LIMITED', '13/3 PURBO PARA, MUDAFA, TONGI-1710, GAZIPUR', \n",
      "        '281402379', 'Happy Begum')\n",
      "conn.execute(\"INSERT INTO factories VALUES (?,?,?,?,?)\", M198)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<sqlite3.Cursor at 0x35545e0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the above the ? marks are placeholders for values that are in fact being passed by the python object. They are not optional. There must be one for each value being passed. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##UPDATE\n",
      "\n",
      "UPDATE allows you to update an existing record. The syntax is:\n",
      "\n",
      "`UPDATE table_name SET column1=value1,column2=value2,...WHERE some_column=some_value`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"UPDATE factories SET name = 'Rory Creedon' WHERE uid = 'M001'\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<sqlite3.Cursor at 0x3554ca0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rory = conn.execute(\"SELECT * FROM factories WHERE uid = 'M001'\")\n",
      "%sqlite_show rory"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M001           SQ CELSIUS     BERAIDERCHAL   1.163e+09      Rory Creedon \n",
        "               LTD            A, KEOWA, MA                               \n",
        "                              ONA,SREEPUR,                               \n",
        "                              GAZIPUR                                    \n",
        "                              CHITTAGONG                                 \n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###DELETE\n",
      "\n",
      "DELETE allows you to remove entire rows. The syntax is:\n",
      "\n",
      "`DELETE FROM table_name WHERE some_column=some_value`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"DELETE from factories WHERE uid = 'M056'\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<sqlite3.Cursor at 0x3554d20>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that no exception is thrown if no such record exists!:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"DELETE from factories WHERE uid = 'M0567'\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<sqlite3.Cursor at 0x3554c20>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Injection\n",
      "\n",
      "Injection refers to when users working on the web (and in our micro-web application) input data in order to retrieve records. Since SQL statements are text only, it is easy to dynamically change SQL statements to provide the user with selected data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "User_choice = 'M003'\n",
      "user_data = conn.execute(\"SELECT * FROM factories WHERE uid = \" + \"'\" + User_choice + \"'\")\n",
      "%sqlite_show user_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M003           THE SHANIN     THE SHANIN     1.191e+09      FELISA       \n",
        "               CORP.LTD.      CORP.LTD.                     PEREYRA      \n",
        "                              964                                        \n",
        "                              SHAWRAPARA,                                \n",
        "                              ROKEYA                                     \n",
        "                              SHARONI,                                   \n",
        "                              MIRPUR,                                    \n",
        "                              DHAKA-1216                                 \n",
        "                              BANGLADESH                                 \n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"SELECT * FROM factories WHERE uid = \" + \"'\" + User_choice + \"'\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "\"SELECT * FROM factories WHERE uid = 'M003'\""
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Constraints\n",
      "\n",
      "SQL constraints are used to specify rules for the data in a table. Constraints are specified when the table is created (as you can see above). The following are the avaialable constriants and their meaning:\n",
      "\n",
      "|**Contraint**| **Meaning**|\n",
      "|---|---|\n",
      "| NOT NULL | Indicates that a column cannot store NULL value| \n",
      "| UNIQUE | Ensures that each row for a column must have a unique value| \n",
      "| PRIMARY KEY | A combination of a NOT NULL and UNIQUE. Ensures that a column (or combination of two or more columns) have a unique identity which helps to find a particular record in a table more easily and quickly| \n",
      "| FOREIGN KEY |  Ensure the referential integrity of the data in one table to match values in another table| \n",
      "| CHECK |  Ensures that the value in a column meets a specific condition| \n",
      "| DEFAULT |  Specifies a default value when specified none for this column| \n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Where NOT NULL is specified a record cannot be updated or created without adding a value to this field. We already saw above that we were able to not insert a record into the contact column, but for the other columns this is not possible as I specified NOT NULL. So for example we will not be able to do the following:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conn.execute(\"INSERT INTO factories (uid, factory, address, contact) VALUES \\\n",
      "            ('M057', 'MyFactory', 'Number 1 Dhaka', '02-1112223')\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "IntegrityError",
       "evalue": "factories.name may not be NULL",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIntegrityError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-37-dd019cdf579b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"INSERT INTO factories (uid, factory, address, contact) VALUES             ('M057', 'MyFactory', 'Number 1 Dhaka', '02-1112223')\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mIntegrityError\u001b[0m: factories.name may not be NULL"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As can be seen, an integerity exception is thrown. \n",
      "\n",
      "We can use NULL to look for values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nulls = conn.execute(\"SELECT * FROM factories WHERE contact IS NULL\")\n",
      "%sqlite_show nulls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M197           SUPASOX        8th Floor,     None           Delowar      \n",
        "               LIMITED        23/1,                         Hossain      \n",
        "                              Panthapath                                 \n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that NOT NULL is also avaialable.\n",
      "\n",
      "UNIQUE ensures that duplicate records cannot be inserted, and PRIMARY KEY does the same but has NOT NULL also built into it. \n",
      "\n",
      "FOREIGN KEY in one table points to the PRIMARY KEY in another table. This ensures actions cannot be taken that would destroy this link. This will be very useful when we create the Orders table in our application. The syntax will be demonstrated at that stage. \n",
      "\n",
      "CHECK is a constraint that allows the records to be checked as being within a certain range before being entered. \n",
      "\n",
      "DEFAULT allows you to specify a default value to be in a record if no other value is passed. \n",
      "\n",
      "These are nto explored further here, but will be used in the application at some stages. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#I am not going to commit those changes\n",
      "conn.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**That's all for now. In the next book we will see how to use this database in a bottle application**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url = 'http://media.giphy.com/media/upg0i1m4DLe5q/giphy.gif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://media.giphy.com/media/upg0i1m4DLe5q/giphy.gif\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<IPython.core.display.Image at 0x357e870>"
       ]
      }
     ],
     "prompt_number": 40
    }
   ],
   "metadata": {}
  }
 ]
}