keyword unpacking syntax

.gitignore

@@ -1,3 +1,5 @@
+.DS_Store
+*.DS_Store
 *.pyc
 *.o
 *.so

README.md

@@ -1,7 +1,7 @@
-python_reference
+Python Tutorials and References
 ================
 
-Syntax examples for useful Python functions, methods, and modules
+Useful functions, tutorials, and other Python-related things
 
 
 ###Links to view the IPython Notebooks
@@ -9,4 +9,9 @@ Syntax examples for useful Python functions, methods, and modules
 - [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
 - [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)
 - [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)
+- [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
 
+### Links to Markdown files
+- [A thorough guide to SQLite database operations in Python](./sqlite3_howto/README.md)
+- [Unit testing in Python - Why we want to make it a habit](./tutorials/unit_testing.md)
+- [Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks](./tutorials/installing_scientific_packages.md)

not_so_obvious_python_stuff.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9a07a78204a51f0faab65e52657f0446cd604ed470627f9c6af1ba74c047fe23"
+  "signature": "sha256:a8a72a5c6b66d788ab0b916d2b29992525bb5d6cdc24653f5c043abac522a5bb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,7 +81,7 @@
       "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
       "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
       "- [Assigning types to variables as values](#variable_types)\n",
-      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)"
+      "- [Only the first clause of generators is evaluated immediately](#generator_rhs)"
      ]
     },
     {
@@ -275,13 +275,13 @@
      "collapsed": false,
      "input": [
       "a_list = []\n",
-      "print('ID:',id(a_list))\n",
+      "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n",
       "\n",
       "a_list.append(1)\n",
-      "print('ID (append):',id(a_list))\n",
+      "print(a_list, '\\nID (append):',id(a_list), '\\n')\n",
       "\n",
-      "a_list.append(2)\n",
-      "print('ID (extend):',id(a_list))"
+      "a_list.extend([2])\n",
+      "print(a_list, '\\nID (extend):',id(a_list))"
      ],
      "language": "python",
      "metadata": {},
@@ -290,13 +290,18 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "ID: 4366495544\n",
-        "ID (append): 4366495544\n",
-        "ID (extend): 4366495544\n"
+        "[] \n",
+        "ID (initial): 140704077653128 \n",
+        "\n",
+        "[1] \n",
+        "ID (append): 140704077653128 \n",
+        "\n",
+        "[1, 2] \n",
+        "ID (extend): 140704077653128\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -2980,6 +2985,102 @@
      ],
      "prompt_number": 4
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name='generator_rhs'>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Only the first clause of generators is evaluated immediately"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n",
+      "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gen_fails = (i for i in 1/0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ZeroDivisionError",
+       "evalue": "division by zero",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gen_succeeds = (i for i in range(5) for j in 1/0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('But obviously fails when we iterate ...')\n",
+      "for i in gen_succeeds:\n",
+      "    print(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ZeroDivisionError",
+       "evalue": "division by zero",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "But obviously fails when we iterate ...\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3034,7 +3135,7 @@
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -3062,12 +3163,12 @@
        "stream": "stdout",
        "text": [
         "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'d': 4, 'c': 3, 'b': 2, 'a': 1}\n",
+        "kwargs contents:  {'b': 2, 'a': 1, 'c': 3, 'd': 4}\n",
         "value of argument a: 1\n"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -3097,7 +3198,7 @@
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -3133,18 +3234,9 @@
       "\n",
       "#### 04/27/2014\n",
       "- minor fixes of typos  \n",
-      "- single- vs. double-underscore clarification in the private class section."
+      "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
     {
      "cell_type": "code",
      "collapsed": false,

sqlite3/README.md

@@ -1,4 +0,0 @@
-sqlite3_examples
-================
-
-Syntax examples for working with SQLite databases via the sqlite3 module in Python

sqlite3/create_db.py

@@ -1,30 +0,0 @@
-# 10/28/2013 Sebastian Raschka
-# Syntax basics for creating sqlite3 data bases
-
-import sqlite3
-
-# create new db and make connection
-conn = sqlite3.connect('zinc_db1.db')    
-c = conn.cursor()
-
-# create table
-c.execute('''CREATE TABLE zinc_db1
-             (zinc_id PRIMARY KEY, purchasable TEXT, non_rot_bonds INT)''')
-
-# Insert one row of data
-c.execute("INSERT INTO zinc_db1 VALUES ('ZINC00895032','YES', 4)")
-
-# Insert multiple lines of data
-multi_lines =[ ('ZINC00895033','YES', 1),
-	       ('ZINC00895034','NO', 0),
-	       ('ZINC00895035','YES', 3),
-	       ('ZINC00895036','YES', 9),
-	       ('ZINC00895037','YES', 10) 
-	      ]
-c.executemany('INSERT INTO zinc_db1 VALUES (?,?,?)', multi_lines)
-
-# Save (commit) the changes
-conn.commit()
-
-# close connection
-conn.close()

sqlite3/query_db.py

@@ -1,28 +0,0 @@
-# 10/28/2013 Sebastian Raschka
-# Syntax basics for querying sqlite3 data bases
-
-import sqlite3
-
-# open existing database
-conn = sqlite3.connect('zinc_db1.db')    
-c = conn.cursor()
-
-# print all lines ordered by number of non_rot_bonds
-for row in c.execute('SELECT * FROM zinc_db1 ORDER BY non_rot_bonds'):
-    print row
-
-# print all lines that are purchasable and have <= 7 rotatable bonds
-t = ('YES',7,)
-for row in c.execute('SELECT * FROM zinc_db1 WHERE purchasable=? AND non_rot_bonds <= ?', t):
-	print row
-
-# print all lines that are purchasable and have <= 7 rotatable bonds
-t = ('YES',7,)
-c.execute('SELECT * FROM zinc_db1 WHERE purchasable=? AND non_rot_bonds <= ?', t)
-rows = c.fetchall()
-for r in rows:
-    print r
-
-# close connection
-conn.close()
-

sqlite3/update_db.py

@@ -1,46 +0,0 @@
-# 10/28/2013 Sebastian Raschka
-# Syntax basics for updating sqlite3 data bases
-
-import sqlite3
-
-# make connection to existing db
-conn = sqlite3.connect('zinc_db1.db')
-c = conn.cursor()
-
-# update field (no insert if id doesn't exist)
-t = ('NO', 'ZINC00895033', )
-c.execute("UPDATE zinc_db1 SET purchasable=? WHERE zinc_id=?", t)
-print "Total number of rows changed:", conn.total_changes
-
-
-# update, or insert when id does not exist
-# here: updates rotatable bonds if record with primary key zinc_id exists,<br>  
-#     else inserts new record an sets purchasable to 0
-c.execute("""INSERT OR REPLACE INTO zinc_db1 (zinc_id, rotatable_bonds, purchasable)
-              VALUES ( 'ZINC123456798', 
-                3, 
-                COALESCE((SELECT purchasable from zinc_db1 WHERE zinc_id = 'ZINC123456798'), 0)
-              )""")
-
-
-
-# delete rows
-t = ('NO', )
-c.execute("DELETE FROM zinc_db1 WHERE purchasable=?", t)
-print "Total number of rows deleted: ", conn.total_changes
-
-# add column
-c.execute("ALTER TABLE zinc_db1 ADD COLUMN 'keto_oxy' TEXT")
-
-# save changes
-conn.commit()
-
-# print column names
-c.execute("SELECT * FROM zinc_db1")
-col_name_list = [tup[0] for tup in c.description]
-print col_name_list
-
-
-
-# close connection
-conn.close()

sqlite3_howto/LICENSE

@@ -0,0 +1,674 @@
+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
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+
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sqlite3_howto/README.md

@@ -0,0 +1,784 @@
+## A thorough guide to SQLite database operations in Python
+
+_\-- written by Sebastian Raschka_ on March 7, 2014
+
+
+  
+  
+
+![sqlite_python_logo.png](../Images/sqlite_python_logo.png)
+
+  
+  
+
+* * *
+
+#### Sections
+
+• Connecting to an SQLite database  
+• Creating a new SQLite database  
+      - Overview of SQLite data types  
+      - A quick word on PRIMARY KEYS:  
+• Adding new columns  
+• Inserting and updating rows  
+• Creating unique indexes  
+• Querying the database - Selecting rows  
+• Security and injection attacks  
+• Date and time operations  
+• Printing a database summary  
+• Conclusion  
+
+The complete Python code that I am using in this tutorial can be downloaded
+from my GitHub repository: <https://github.com/rasbt/python_sqlite_code>  
+  
+
+* * *
+
+  
+
+## Connecting to an SQLite database
+
+The sqlite3 that we will be using throughout this tutorial is part of the
+Python Standard Library and is a nice and easy interface to SQLite databases:
+There are no server processes involved, no configurations required, and no
+other obstacles we have to worry about.
+
+In general, the only thing that needs to be done before we can perform any
+operation on a SQLite database via Python's `sqlite3` module, is to open a
+connection to an SQLite database file:
+
+    
+    
+    import sqlite3
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+
+where the database file (`sqlite_file`) can reside anywhere on our disk, e.g.,
+
+    
+    
+    sqlite_file = '/Users/Sebastian/Desktop/my_db.sqlite' 
+    
+
+Conveniently, a new database file (`.sqlite` file) will be created
+automatically the first time we try to connect to a database. However, we have
+to be aware that it won't have a table, yet. In the following section, we will
+take a look at some example code of how to create a new SQLite database files
+with tables for storing some data.
+
+To round up this section about connecting to a SQLite database file, there are
+two more operations that are worth mentioning. If we are finished with our
+operations on the database file, we have to close the connection via the
+`.close()` method:
+
+    
+    
+    conn.close()
+    
+
+And if we performed any operation on the database other than sending queries,
+we need to commit those changes via the `.commit()` method before we close the
+connection:
+
+    
+    
+    conn.commit()
+    conn.close()
+    
+
+  
+
+## Creating a new SQLite database
+
+Let us have a look at some example code to create a new SQLite database file
+with two tables: One with and one without a PRIMARY KEY column (don't worry,
+there is more information about PRIMARY KEYs further down in this section).
+
+    
+    
+    mport sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+    table_name1 = 'my_table_1'  # name of the table to be created
+    table_name2 = 'my_table_2'  # name of the table to be created
+    new_field = 'my_1st_column' # name of the column
+    field_type = 'INTEGER'  # column data type
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # Creating a new SQLite table with 1 column
+    c.execute('CREATE TABLE {tn} ({nf} {ft})'\
+            .format(tn=table_name1, nf=new_field, ft=field_type))
+    
+    # Creating a second table with 1 column and set it as PRIMARY KEY
+    # note that PRIMARY KEY column must consist of unique values!
+    c.execute('CREATE TABLE {tn} ({nf} {ft} PRIMARY KEY)'\
+            .format(tn=table_name2, nf=new_field, ft=field_type))
+    
+    # Committing changes and closing the connection to the database file
+    conn.commit()
+    conn.close()
+    
+
+Download the script: [create_new_db.py](https://raw.github.com/rasbt/python_sq
+lite_code/master/code/create_new_db.py)
+
+* * *
+
+**Tip:** A handy tool to visualize and access SQLite databases is the free FireFox [SQLite Manager](https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/?src) add-on. Throughout this article, I will use this tool to provide screenshots of the database structures that we created below the corresponding code sections.
+
+* * *
+
+  
+  
+
+![1_sqlite3_init_db.png](../Images/1_sqlite3_init_db.png)  
+  
+Using the code above, we created a new `.sqlite` database file with 2 tables.
+Each table consists of currently one column only, which is of type INTEGER.
+
+  
+
+* * *
+
+**Here is a quick overview of all data types that are supported by SQLite 3:**
+
+  * INTEGER: A signed integer up to 8 bytes depending on the magnitude of the value.
+  * REAL: An 8-byte floating point value.
+  * TEXT: A text string, typically UTF-8 encoded (depending on the database encoding).
+  * BLOB: A blob of data (binary large object) for storing binary data.
+  * NULL: A NULL value, represents missing data or an empty cell.
+
+* * *
+
+Looking at the table above, You might have noticed that SQLite 3 has no
+designated Boolean data type. However, this should not be an issue, since we
+could simply re-purpose the INTEGER type to represent Boolean values (0 =
+false, 1 = true).
+
+  
+
+**A quick word on PRIMARY KEYS:**  
+In our example code above, we set our 1 column in the second table to PRIMARY
+KEY. The advantage of a PRIMARY KEY index is a significant performance gain if
+we use the PRIMARY KEY column as query for accessing rows in the table. Every
+table can only have max. 1 PRIMARY KEY (single or multiple column(s)), and the
+values in this column MUST be unique! But more on column indexing in the a
+later section.
+
+  
+
+## Adding new columns
+
+If we want to add a new column to an existing SQLite database table, we can
+either leave the cells for each row empty (NULL value), or we can set a
+default value for each cell, which is pretty convenient for certain
+applications.  
+Let's have a look at some code:
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+    table_name = 'my_table_2'   # name of the table to be created
+    id_column = 'my_1st_column' # name of the PRIMARY KEY column
+    new_column1 = 'my_2nd_column'  # name of the new column
+    new_column2 = 'my_3nd_column'  # name of the new column
+    column_type = 'TEXT' # E.g., INTEGER, TEXT, NULL, REAL, BLOB
+    default_val = 'Hello World' # a default value for the new column rows
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # A) Adding a new column without a row value
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct}"\
+            .format(tn=table_name, cn=new_column1, ct=column_type))
+    
+    # B) Adding a new column with a default row value
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct} DEFAULT '{df}'"\
+            .format(tn=table_name, cn=new_column2, ct=column_type, df=default_val))
+    
+    # Committing changes and closing the connection to the database file
+    conn.commit()
+    conn.close()
+    
+
+Download the script: [add_new_column.py](https://raw.github.com/rasbt/python_s
+qlite_code/master/code/add_new_column.py)
+
+  
+  
+![2_sqlite3_add_col.png](../Images/2_sqlite3_add_col.png)  
+  
+
+We just added 2 more columns (`my_2nd_column` and `my_3rd_column`) to
+`my_table_2` of our SQLite database next to the PRIMARY KEY column
+`my_1st_column`.  
+The difference between the two new columns is that we initialized
+`my_3rd_column` with a default value (here:'Hello World'), which will be
+inserted for every existing cell under this column and for every new row that
+we are going to add to the table if we don't insert or update it with a
+different value.
+
+  
+
+## Inserting and updating rows
+
+Inserting and updating rows into an existing SQLite database table - next to
+sending queries - is probably the most common database operation. The
+Structured Query Language has a convenient `UPSERT` function, which is
+basically just a merge between UPDATE and INSERT: It inserts new rows into a
+database table with a value for the PRIMARY KEY column if it does not exist
+yet, or updates a row for an existing PRIMARY KEY value. Unfortunately, this
+convenient syntax is not supported by the more compact SQLite database
+implementation that we are using here. However, there are some workarounds.
+But let us first have a look at the example code:
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'
+    table_name = 'my_table_2'
+    id_column = 'my_1st_column'
+    column_name = 'my_2nd_column'
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # A) Inserts an ID with a specific value in a second column 
+    try:
+        c.execute("INSERT INTO {tn} ({idf}, {cn}) VALUES (123456, 'test')".\
+            format(tn=table_name, idf=id_column, cn=column_name))
+    except sqlite3.IntegrityError:
+        print('ERROR: ID already exists in PRIMARY KEY column {}'.format(id_column))
+    
+    # B) Tries to insert an ID (if it does not exist yet)
+    # with a specific value in a second column 
+    c.execute("INSERT OR IGNORE INTO {tn} ({idf}, {cn}) VALUES (123456, 'test')".\
+            format(tn=table_name, idf=id_column, cn=column_name))
+    
+    # C) Updates the newly inserted or pre-existing entry            
+    c.execute("UPDATE {tn} SET {cn}=('Hi World') WHERE {idf}=(123456)".\
+            format(tn=table_name, cn=column_name, idf=id_column))
+    
+    conn.commit()
+    conn.close()
+    
+
+Download the script: [update_or_insert_records.py](https://raw.github.com/rasb
+t/python_sqlite_code/master/code/update_or_insert_records.py)  
+  
+![3_sqlite3_insert_update.png](../Images/3_sqlite3_insert_update.png)  
+  
+Both A) `INSERT` and B) `INSERT OR IGNORE` have in common that they append new
+rows to the database if a given PRIMARY KEY does not exist in the database
+table, yet. However, if we'd try to append a PRIMARY KEY value that is not
+unique, a simple `INSERT` would raise an `sqlite3.IntegrityError` exception,
+which can be either captured via a try-except statement (case A) or
+circumvented by the SQLite call `INSERT OR IGNORE` (case B). This can be
+pretty useful if we want to construct an `UPSERT` equivalent in SQLite. E.g.,
+if we want to add a dataset to an existing database table that contains a mix
+between existing and new IDs for our PRIMARY KEY column.
+
+  
+
+## Creating unique indexes
+
+Just like hashtable-datastructures, indexes function as direct pointers to our
+data in a table for a particular column (i.e., the indexed column). For
+example, the PRIMARY KEY column would have such an index by default. The
+downside of indexes is that every row value in the column must be unique.
+However, it is recommended and pretty useful to index certain columns if
+possible, since it rewards us with a significant performance gain for the data
+retrieval.  
+The example code below shows how to add such an unique index to an existing
+column in an SQLite database table. And if we should decide to insert non-
+unique values into a indexed column later, there is also a convenient way to
+drop the index, which is also shown in the code below.
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+    table_name = 'my_table_2'   # name of the table to be created
+    id_column = 'my_1st_column' # name of the PRIMARY KEY column
+    new_column = 'unique_names'  # name of the new column
+    column_type = 'TEXT' # E.g., INTEGER, TEXT, NULL, REAL, BLOB
+    index_name = 'my_unique_index'  # name for the new unique index
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # Adding a new column and update some record
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct}"\
+            .format(tn=table_name, cn=new_column, ct=column_type))
+    c.execute("UPDATE {tn} SET {cn}='sebastian_r' WHERE {idf}=123456".\
+            format(tn=table_name, idf=id_column, cn=new_column))
+    
+    # Creating an unique index
+    c.execute('CREATE INDEX {ix} on {tn}({cn})'\
+            .format(ix=index_name, tn=table_name, cn=new_column))
+    
+    # Dropping the unique index
+    # E.g., to avoid future conflicts with update/insert functions
+    c.execute('DROP INDEX {ix}'.format(ix=index_name))
+    
+    # Committing changes and closing the connection to the database file
+    conn.commit()
+    conn.close()
+    
+
+Download the script: [create_unique_index.py](https://raw.github.com/rasbt/pyt
+hon_sqlite_code/master/code/create_unique_index.py)  
+  
+![4_sqlite3_unique_index.png](../Images/4_sqlite3_unique_index.png)  
+  
+
+  
+
+## Querying the database - Selecting rows
+
+After we learned about how to create and modify SQLite databases, it's about
+time for some data retrieval. The code below illustrates how we can retrieve
+row entries for all or some columns if they match certain criteria.
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+    table_name = 'my_table_2'   # name of the table to be queried
+    id_column = 'my_1st_column'
+    some_id = 123456
+    column_2 = 'my_2nd_column'
+    column_3 = 'my_3rd_column'    
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # 1) Contents of all columns for row that match a certain value in 1 column
+    c.execute('SELECT * FROM {tn} WHERE {cn}="Hi World"'.\
+            format(tn=table_name, cn=column_2))
+    all_rows = c.fetchall()
+    print('1):', all_rows)
+    
+    # 2) Value of a particular column for rows that match a certain value in column_1 
+    c.execute('SELECT ({coi}) FROM {tn} WHERE {cn}="Hi World"'.\
+            format(coi=column_2, tn=table_name, cn=column_2))
+    all_rows = c.fetchall()
+    print('2):', all_rows)
+    
+    # 3) Value of 2 particular columns for rows that match a certain value in 1 column
+    c.execute('SELECT {coi1},{coi2} FROM {tn} WHERE {coi1}="Hi World"'.\
+            format(coi1=column_2, coi2=column_3, tn=table_name, cn=column_2))
+    all_rows = c.fetchall()
+    print('3):', all_rows)
+    
+    # 4) Selecting only up to 10 rows that match a certain value in 1 column
+    c.execute('SELECT * FROM {tn} WHERE {cn}="Hi World" LIMIT 10'.\
+            format(tn=table_name, cn=column_2))
+    ten_rows = c.fetchall()
+    print('4):', ten_rows)
+    
+    # 5) Check if a certain ID exists and print its column contents
+    c.execute("SELECT * FROM {tn} WHERE {idf}={my_id}".\
+            format(tn=table_name, cn=column_2, idf=id_column, my_id=some_id))
+    id_exists = c.fetchone()
+    if id_exists:
+        print('5): {}'.format(id_exists))
+    else:
+        print('5): {} does not exist'.format(some_id))
+    
+    # Closing the connection to the database file
+    conn.close()
+    
+
+Download the script: [selecting_entries.py](https://raw.github.com/rasbt/pytho
+n_sqlite_code/master/code/selecting_entries.py)  
+  
+
+![4_sqlite3_unique_index.png](../Images/4_sqlite3_unique_index.png)  
+  
+if we use the `.fetchall()` method, we return a list of tuples from the
+database query, where each tuple represents one row entry. The print output
+for the 5 different cases shown in the code above would look like this (note
+that we only have a table with 1 row here):  
+  
+![6_sqlite3_print_selecting_rows.png](../Images/6_sqlite3_print_selecting_rows
+.png)  
+  
+
+  
+
+## Security and injection attacks
+
+So far, we have been using Python's string formatting method to insert
+parameters like table and column names into the `c.execute()` functions. This
+is fine if we just want to use the database for ourselves. However, this
+leaves our database vulnerable to injection attacks. For example, if our
+database would be part of a web application, it would allow hackers to
+directly communicate with the database in order to bypass login and password
+verification and steal data.  
+In order to prevent this, it is recommended to use `?` place holders in the
+SQLite commands instead of the `%` formatting expression or the `.format()`
+method, which we have been using in this tutorial.  
+For example, instead of using
+
+    
+    
+    # 5) Check if a certain ID exists and print its column contents
+    c.execute("SELECT * FROM {tn} WHERE {idf}={my_id}".\
+            format(tn=table_name, cn=column_2, idf=id_column, my_id=some_id))
+    
+
+in the Querying the database - Selecting rows section above, we would want to
+use the `?` placeholder for the queried column value and include the
+variable(s) (here: `123456`), which we want to insert, as tuple at the end of
+the `c.execute()` string.
+
+    
+    
+    # 5) Check if a certain ID exists and print its column contents
+    c.execute("SELECT * FROM {tn} WHERE {idf}=?".\
+            format(tn=table_name, cn=column_2, idf=id_column), (123456,))   
+    
+
+However, the problem with this approach is that it would only work for values,
+not for column or table names. So what are we supposed to do with the rest of
+the string if we want to protect ourselves from injection attacks? The easy
+solution would be to refrain from using variables in SQLite queries whenever
+possible, and if it cannot be avoided, we would want to use a function that
+strips all non-alphanumerical characters from the stored content of the
+variable, e.g.,
+
+    
+    
+    def clean_name(some_var):
+        return ''.join(char for char in some_var if char.isalnum())
+    
+
+  
+
+## Date and time operations
+
+SQLite inherited the convenient date and time operations from SQL, which are
+one of my favorite features of the Structured Query Language: It does not only
+allow us to insert dates and times in various different formats, but we can
+also perform simple `+` and `-` arithmetic, for example to look up entries
+that have been added xxx days ago.
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+    table_name = 'my_table_3'   # name of the table to be created
+    id_field = 'id' # name of the ID column
+    date_col = 'date' # name of the date column
+    time_col = 'time'# name of the time column
+    date_time_col = 'date_time' # name of the date & time column
+    field_type = 'TEXT'  # column data type
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # Creating a new SQLite table with 1 column
+    c.execute('CREATE TABLE {tn} ({fn} {ft} PRIMARY KEY)'\
+            .format(tn=table_name, fn=id_field, ft=field_type))
+    
+    # A) Adding a new column to save date insert a row with the current date 
+    # in the following format: YYYY-MM-DD
+    # e.g., 2014-03-06
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+             .format(tn=table_name, cn=date_col))
+    # insert a new row with the current date and time, e.g., 2014-03-06
+    c.execute("INSERT INTO {tn} ({idf}, {cn}) VALUES('some_id1', DATE('now'))"\
+             .format(tn=table_name, idf=id_field, cn=date_col))
+    
+    # B) Adding a new column to save date and time and update with the current time
+    # in the following format: HH:MM:SS
+    # e.g., 16:26:37
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+             .format(tn=table_name, cn=time_col))
+    # update row for the new current date and time column, e.g., 2014-03-06 16:26:37
+    c.execute("UPDATE {tn} SET {cn}=TIME('now') WHERE {idf}='some_id1'"\
+             .format(tn=table_name, idf=id_field, cn=time_col))
+    
+    # C) Adding a new column to save date and time and update with current date-time
+    # in the following format: YYYY-MM-DD HH:MM:SS
+    # e.g., 2014-03-06 16:26:37
+    c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+             .format(tn=table_name, cn=date_time_col))
+    # update row for the new current date and time column, e.g., 2014-03-06 16:26:37
+    c.execute("UPDATE {tn} SET {cn}=(CURRENT_TIMESTAMP) WHERE {idf}='some_id1'"\
+             .format(tn=table_name, idf=id_field, cn=date_time_col))
+    
+    # The database should now look like this:
+    # id         date           time        date_time
+    # "some_id1" "2014-03-06"   "16:42:30"  "2014-03-06 16:42:30"
+    
+    # 4) Retrieve all IDs of entries between 2 date_times
+    c.execute("SELECT {idf} FROM {tn} WHERE {cn} BETWEEN '2013-03-06 10:10:10' AND '2015-03-06 10:10:10'".\
+        format(idf=id_field, tn=table_name, cn=date_time_col))
+    all_date_times = c.fetchall()
+    print('4) all entries between ~2013 - 2015:', all_date_times)
+    
+    # 5) Retrieve all IDs of entries between that are older than 1 day and 12 hrs
+    c.execute("SELECT {idf} FROM {tn} WHERE DATE('now') - {dc} >= 1 AND DATE('now') - {tc} >= 12".\
+        format(idf=id_field, tn=table_name, dc=date_col, tc=time_col))
+    all_1day12hrs_entries = c.fetchall()
+    print('5) entries older than 1 day:', all_1day12hrs_entries)
+    
+    # Committing changes and closing the connection to the database file
+    conn.commit()
+    conn.close()
+    
+
+Download the script: [date_time_ops.py](https://raw.github.com/rasbt/python_sq
+lite_code/master/code/date_time_ops.py)
+
+  
+  
+![5_sqlite3_date_time.png](../Images/5_sqlite3_date_time.png)  
+  
+
+Some of the really convenient functions that return the current time and date
+are:
+
+* * *
+    
+    
+    DATE('now') # returns current date, e.g., 2014-03-06
+    TIME('now') # returns current time, e.g., 10:10:10
+    CURRENT_TIMESTAMP # returns current date and time, e.g., 2014-03-06 16:42:30
+    #  (or alternatively: DATETIME('now'))
+    
+
+* * *
+
+The screenshot below shows the print outputs of the code that we used to query
+for entries that lie between a specified date interval using
+
+    
+    
+    BETWEEN '2013-03-06 10:10:10' AND '2015-03-06 10:10:10'
+    
+
+and entries that are older than 1 day via
+
+    
+    
+    WHERE DATE('now') - some_date
+    
+
+Note that we don't have to provide the complete time stamps here, the same
+syntax applies to simple dates or simple times only, too.  
+  
+![5_sqlite3_date_time_2.png](../Images/5_sqlite3_date_time_2.png)  
+  
+
+  
+
+#### Update Mar 16, 2014:
+
+  
+If'd we are interested to calulate the hours between two `DATETIME()`
+timestamps, we can could use the handy `STRFTIME()` function like this  
+  
+
+    
+    
+    SELECT (STRFTIME('%s','2014-03-14 14:51:00') - STRFTIME('%s','2014-03-16 14:51:00'))  
+     / -3600
+    
+
+  
+which would calculate the difference in hours between two dates in this
+particular example above (here: `48`) in this case.  
+And to calculate the difference in hours between the current `DATETIME` and a
+given `DATETIME` string, we could use the following SQLite syntax:  
+  
+
+    
+    
+    SELECT (STRFTIME('%s',DATETIME('now')) - STRFTIME('%s','2014-03-15 14:51:00')) / 3600
+    
+
+  
+
+## Retrieving column names
+
+In the previous two sections we have seen how we query SQLite databases for
+data contents. Now let us have a look at how we retrieve its metadata (here:
+column names):
+
+    
+    
+    import sqlite3
+    
+    sqlite_file = 'my_first_db.sqlite'
+    table_name = 'my_table_3'
+    
+    # Connecting to the database file
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    
+    # Retrieve column information
+    # Every column will be represented by a tuple with the following attributes:
+    # (id, name, type, notnull, default_value, primary_key)
+    c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+    
+    # collect names in a list
+    names = [tup[1] for tup in c.fetchall()]
+    print(names)
+    # e.g., ['id', 'date', 'time', 'date_time']
+    
+    # Closing the connection to the database file
+    conn.close()
+    
+
+Download the script: [get_columnnames.py](https://raw.github.com/rasbt/python_
+sqlite_code/master/code/get_columnnames.py)  
+  
+![7_sqlite3_get_colnames_1.png](../Images/7_sqlite3_get_colnames_1.png)  
+  
+Since we haven't created a PRIMARY KEY column for `my_table_3`, SQLite
+automatically provides an indexed `rowid` column with unique ascending integer
+values, which will be ignored in our case. Using the `PRAGMA TABLE_INFO()`
+function on our table, we return a list of tuples, where each tuple contains
+the following information about every column in the table: `(id, name, type,
+notnull, default_value, primary_key)`.  
+So, in order to get the names of every column in our table, we only have to
+grab the 2nd value in each tuple of the returned list, which can be done by
+
+    
+    
+    names = [tup[1] for tup in c.fetchall()]
+
+after the `PRAGMA TABLE_INFO()` call. If we would print the contents of the
+variable `names` now, the output would look like this:  
+  
+![7_sqlite3_get_colnames_2.png](../Images/7_sqlite3_get_colnames_2.png)  
+  
+
+  
+
+## Printing a database summary
+
+I hope we covered most of the basics about SQLite database operations in the
+previous sections, and by now we should be well equipped to get some serious
+work done using SQLite in Python.  
+Let me conclude this tutorial with an obligatory "last but not least" and a
+convenient script to print a nice overview of SQLite database tables:
+
+    
+    
+    import sqlite3
+    
+    def connect(sqlite_file):
+        """ Make connection to an SQLite database file """
+        conn = sqlite3.connect(sqlite_file)
+        c = conn.cursor()
+        return conn, c
+    
+    def close(conn):
+        """ Commit changes and close connection to the database """
+        # conn.commit()
+        conn.close()
+    
+    def total_rows(cursor, table_name, print_out=False):
+        """ Returns the total number of rows in the database """
+        c.execute('SELECT COUNT(*) FROM {}'.format(table_name))
+        count = c.fetchall()
+        if print_out:
+            print('\nTotal rows: {}'.format(count[0][0]))
+        return count[0][0]
+    
+    def table_col_info(cursor, table_name, print_out=False):
+        """ 
+           Returns a list of tuples with column informations:
+          (id, name, type, notnull, default_value, primary_key)
+        
+        """
+        c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+        info = c.fetchall()
+        
+        if print_out:
+            print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey")
+            for col in info:
+                print(col)
+        return info
+    
+    def values_in_col(cursor, table_name, print_out=True):
+        """ Returns a dictionary with columns as keys and the number of not-null 
+            entries as associated values.
+        """
+        c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+        info = c.fetchall()
+        col_dict = dict()
+        for col in info:
+            col_dict[col[1]] = 0
+        for col in col_dict:
+            c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name))
+            # In my case this approach resulted in a better performance than using COUNT
+            number_rows = len(c.fetchall())
+            col_dict[col] = number_rows
+        if print_out:
+            print("\nNumber of entries per column:")
+            for i in col_dict.items():
+                print('{}: {}'.format(i[0], i[1]))
+        return col_dict
+    
+    
+    if __name__ == '__main__':
+    
+        sqlite_file = 'my_first_db.sqlite'
+        table_name = 'my_table_3'
+    
+        conn, c = connect(sqlite_file)
+        total_rows(c, table_name, print_out=True)
+        table_col_info(c, table_name, print_out=True)
+        values_in_col(c, table_name, print_out=True) # slow on large data bases
+        
+        close(conn)
+    
+
+Download the script: [print_db_info.py](https://raw.github.com/rasbt/python_sq
+lite_code/master/code/print_db_info.py)  
+  
+![8_sqlite3_print_db_info_1.png](../Images/8_sqlite3_print_db_info_1.png)  
+  
+![8_sqlite3_print_db_info_2.png](../Images/8_sqlite3_print_db_info_2.png)
+
+  
+
+## Conclusion
+
+I really hope this tutorial was helpful to you to get started with SQLite
+database operations via Python. I have been using the `sqlite3` module a lot
+recently, and it has found its way into most of my programs for larger data
+analyses.  
+Currently, I am working on a novel drug screening software that requires me to
+store 3D structures and other functional data for ~13 million chemical
+compounds, and SQLite has been an invaluable part of my program to quickly
+store, query, analyze, and share my data.  
+Another smaller project that uses `sqlite3` in Python would be smilite, a
+module to retrieve and compare SMILE strings of chemical compounds from the
+free ZINC online database. If you are interested, you can check it out at:
+<https://github.com/rasbt/smilite>.  
+  
+If you have any suggestions or questions, please don't hesitate to write me an
+[ email](mailto:se.raschka@gmail.com) or leave a comment in the comment
+section below! I am looking forward to your opinions and ideas, and I hope I
+can improve and extend this tutorial in future.

sqlite3_howto/code/add_new_column.py

@@ -0,0 +1,28 @@
+# Sebastian Raschka, 2014
+# Adding a new column to an existing SQLite database
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+table_name = 'my_table_2'	# name of the table to be created
+id_column = 'my_1st_column' # name of the PRIMARY KEY column
+new_column1 = 'my_2nd_column'  # name of the new column
+new_column2 = 'my_3rd_column'  # name of the new column
+column_type = 'TEXT' # E.g., INTEGER, TEXT, NULL, REAL, BLOB
+default_val = 'Hello World' # a default value for the new column rows
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# A) Adding a new column without a row value
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct}"\
+        .format(tn=table_name, cn=new_column1, ct=column_type))
+
+# B) Adding a new column with a default row value
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct} DEFAULT '{df}'"\
+        .format(tn=table_name, cn=new_column2, ct=column_type, df=default_val))
+
+# Committing changes and closing the connection to the database file
+conn.commit()
+conn.close()

sqlite3_howto/code/create_new_db.py

@@ -0,0 +1,27 @@
+# Sebastian Raschka, 2014
+# Creating a new SQLite database
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+table_name1 = 'my_table_1'	# name of the table to be created
+table_name2 = 'my_table_2'	# name of the table to be created
+new_field = 'my_1st_column' # name of the column
+field_type = 'INTEGER'  # column data type
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# Creating a new SQLite table with 1 column
+c.execute('CREATE TABLE {tn} ({nf} {ft})'\
+        .format(tn=table_name1, nf=new_field, ft=field_type))
+
+# Creating a second table with 1 column and set it as PRIMARY KEY
+# note that PRIMARY KEY column must consist of unique values!
+c.execute('CREATE TABLE {tn} ({nf} {ft} PRIMARY KEY)'\
+        .format(tn=table_name2, nf=new_field, ft=field_type))
+
+# Committing changes and closing the connection to the database file
+conn.commit()
+conn.close()

sqlite3_howto/code/create_unique_index.py

@@ -0,0 +1,34 @@
+# Sebastian Raschka, 2014
+# Creating an index on a column with unique! values
+# Boosts performance for data base operations.
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+table_name = 'my_table_2'	# name of the table to be created
+id_column = 'my_1st_column' # name of the PRIMARY KEY column
+new_column = 'unique_names'  # name of the new column
+column_type = 'TEXT' # E.g., INTEGER, TEXT, NULL, REAL, BLOB
+index_name = 'my_unique_index'  # name for the new unique index
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# Adding a new column and update some record
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}' {ct}"\
+        .format(tn=table_name, cn=new_column, ct=column_type))
+c.execute("UPDATE {tn} SET {cn}='sebastian_r' WHERE {idf}=123456".\
+        format(tn=table_name, idf=id_column, cn=new_column))
+
+# Creating an unique index
+c.execute('CREATE INDEX {ix} on {tn}({cn})'\
+        .format(ix=index_name, tn=table_name, cn=new_column))
+
+# Dropping the unique index
+# E.g., to avoid future conflicts with update/insert functions
+c.execute('DROP INDEX {ix}'.format(ix=index_name))
+
+# Committing changes and closing the connection to the database file
+conn.commit()
+conn.close()

sqlite3_howto/code/date_time_ops.py

@@ -0,0 +1,69 @@
+# Sebastian Raschka, 03/2014
+# Date and Time operations in sqlite3
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+table_name = 'my_table_3'	# name of the table to be created
+id_field = 'id' # name of the ID column
+date_col = 'date' # name of the date column
+time_col = 'time'# name of the time column
+date_time_col = 'date_time' # name of the date & time column
+field_type = 'TEXT'  # column data type
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# Creating a new SQLite table with 1 column
+c.execute('CREATE TABLE {tn} ({fn} {ft} PRIMARY KEY)'\
+        .format(tn=table_name, fn=id_field, ft=field_type))
+
+
+# 1) Adding a new column to save date insert a row with the current date 
+# in the following format: YYYY-MM-DD
+# e.g., 2014-03-06
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+         .format(tn=table_name, cn=date_col))
+# insert a new row with the current date and time, e.g., 2014-03-06
+c.execute("INSERT INTO {tn} ({idf}, {cn}) VALUES('some_id1', DATE('now'))"\
+         .format(tn=table_name, idf=id_field, cn=date_col))
+
+
+# 2) Adding a new column to save date and time and update with the current time
+# in the following format: HH:MM:SS
+# e.g., 16:26:37
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+         .format(tn=table_name, cn=time_col))
+# update row for the new current date and time column, e.g., 2014-03-06 16:26:37
+c.execute("UPDATE {tn} SET {cn}=TIME('now') WHERE {idf}='some_id1'"\
+         .format(tn=table_name, idf=id_field, cn=time_col))
+
+# 3) Adding a new column to save date and time and update with current date-time
+# in the following format: YYYY-MM-DD HH:MM:SS
+# e.g., 2014-03-06 16:26:37
+c.execute("ALTER TABLE {tn} ADD COLUMN '{cn}'"\
+         .format(tn=table_name, cn=date_time_col))
+# update row for the new current date and time column, e.g., 2014-03-06 16:26:37
+c.execute("UPDATE {tn} SET {cn}=(CURRENT_TIMESTAMP) WHERE {idf}='some_id1'"\
+         .format(tn=table_name, idf=id_field, cn=date_time_col))
+
+# Database should now look like this:
+# id         date           time        date_time
+# "some_id1" "2014-03-06"   "16:42:30"  "2014-03-06 16:42:30"
+
+# 4) Retrieve all IDs of entries between 2 date_times
+c.execute("SELECT {idf} FROM {tn} WHERE {cn} BETWEEN '2013-03-06 10:10:10' AND '2015-03-06 10:10:10'".\
+    format(idf=id_field, tn=table_name, cn=date_time_col))
+all_date_times = c.fetchall()
+print('4) all entries between ~2013 - 2015:', all_date_times)
+
+# 5) Retrieve all IDs of entries between that are older than 1 day and 12 hrs
+c.execute("SELECT {idf} FROM {tn} WHERE DATE('now') - {dc} >= 1 AND DATE('now') - {tc} >= 12".\
+    format(idf=id_field, tn=table_name, dc=date_col, tc=time_col))
+all_1day12hrs_entries = c.fetchall()
+print('5) entries older than 1 day:', all_1day12hrs_entries)
+
+# Committing changes and closing the connection to the database file
+conn.commit()
+conn.close()

sqlite3_howto/code/get_columnnames.py

@@ -0,0 +1,24 @@
+# Sebastian Raschka, 2014
+# Getting column names of an SQLite database table
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'
+table_name = 'my_table_3'
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# Retrieve column information
+# Every column will be represented by a tuple with the following attributes:
+# (id, name, type, notnull, default_value, primary_key)
+c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+
+# collect names in a list
+names = [tup[1] for tup in c.fetchall()]
+print(names)
+# e.g., ['id', 'date', 'time', 'date_time']
+
+# Closing the connection to the database file
+conn.close()

sqlite3_howto/code/print_db_info.py

@@ -0,0 +1,91 @@
+# Sebastian Raschka 2014
+# Prints Information of a SQLite database.
+
+# E.g.,
+#
+"""
+Total rows: 1
+
+Column Info:
+ID, Name, Type, NotNull, DefaultVal, PrimaryKey
+(0, 'id', 'TEXT', 0, None, 1)
+(1, 'date', '', 0, None, 0)
+(2, 'time', '', 0, None, 0)
+(3, 'date_time', '', 0, None, 0)
+
+Number of entries per column:
+date: 1
+date_time: 1
+id: 1
+time: 1
+"""
+
+import sqlite3
+
+def connect(sqlite_file):
+    """ Make connection to an SQLite database file """
+    conn = sqlite3.connect(sqlite_file)
+    c = conn.cursor()
+    return conn, c
+
+def close(conn):
+    """ Commit changes and close connection to the database """
+    #conn.commit()
+    conn.close()
+
+def total_rows(cursor, table_name, print_out=False):
+    """ Returns the total number of rows in the database """
+    c.execute('SELECT COUNT(*) FROM {}'.format(table_name))
+    count = c.fetchall()
+    if print_out:
+        print('\nTotal rows: {}'.format(count[0][0]))
+    return count[0][0]
+
+def table_col_info(cursor, table_name, print_out=False):
+    """ 
+       Returns a list of tuples with column informations:
+      (id, name, type, notnull, default_value, primary_key)
+    
+    """
+    c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+    info = c.fetchall()
+    
+    if print_out:
+        print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey")
+        for col in info:
+            print(col)
+    return info
+
+def values_in_col(cursor, table_name, print_out=True):
+    """ Returns a dictionary with columns as keys and the number of not-null 
+        entries as associated values.
+    """
+    c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+    info = c.fetchall()
+    col_dict = dict()
+    for col in info:
+        col_dict[col[1]] = 0
+    for col in col_dict:
+        c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name))
+        # In my case this approach resulted in a better performance than using COUNT
+        number_rows = len(c.fetchall())
+        col_dict[col] = number_rows
+    if print_out:
+        print("\nNumber of entries per column:")
+        for i in col_dict.items():
+            print('{}: {}'.format(i[0], i[1]))
+    return col_dict
+
+
+if __name__ == '__main__':
+
+    sqlite_file = 'my_first_db.sqlite'
+    table_name = 'my_table_3'
+
+    conn, c = connect(sqlite_file)
+    total_rows(c, table_name, print_out=True)
+    table_col_info(c, table_name, print_out=True)
+    values_in_col(c, table_name, print_out=True) # slow on large data bases
+    
+    close(conn)
+

sqlite3_howto/code/selecting_entries.py

@@ -0,0 +1,51 @@
+# Sebastian Raschka, 2014
+# Selecting rows from an existing SQLite database
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
+table_name = 'my_table_2'	# name of the table to be queried
+id_column = 'my_1st_column'
+some_id = 123456
+column_2 = 'my_2nd_column'
+column_3 = 'my_3rd_column'    
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+# 1) Contents of all columns for row that match a certain value in 1 column
+c.execute('SELECT * FROM {tn} WHERE {cn}="Hi World"'.\
+        format(tn=table_name, cn=column_2))
+all_rows = c.fetchall()
+print('1):', all_rows)
+
+# 2) Value of a particular column for rows that match a certain value in column_1 
+c.execute('SELECT ({coi}) FROM {tn} WHERE {cn}="Hi World"'.\
+        format(coi=column_2, tn=table_name, cn=column_2))
+all_rows = c.fetchall()
+print('2):', all_rows)
+
+# 3) Value of 2 particular columns for rows that match a certain value in 1 column
+c.execute('SELECT {coi1},{coi2} FROM {tn} WHERE {coi1}="Hi World"'.\
+        format(coi1=column_2, coi2=column_3, tn=table_name, cn=column_2))
+all_rows = c.fetchall()
+print('3):', all_rows)
+
+# 4) Selecting only up to 10 rows that match a certain value in 1 column
+c.execute('SELECT * FROM {tn} WHERE {cn}="Hi World" LIMIT 10'.\
+        format(tn=table_name, cn=column_2))
+ten_rows = c.fetchall()
+print('4):', ten_rows)
+
+# 5) Check if a certain ID exists and print its column contents
+c.execute("SELECT * FROM {tn} WHERE {idf}=?".\
+        format(tn=table_name, cn=column_2, idf=id_column), (123456,))
+id_exists = c.fetchone()
+if id_exists:
+    print('5): {}'.format(id_exists))
+else:
+    print('5): {} does not exist'.format(some_id))
+
+# Closing the connection to the database file
+conn.close()

sqlite3_howto/code/update_or_insert_records.py

@@ -0,0 +1,35 @@
+# Sebastian Raschka, 2014
+# Update records or insert them if they don't exist.
+# Note that this is a workaround to accomodate for missing
+# SQL features in SQLite.
+
+import sqlite3
+
+sqlite_file = 'my_first_db.sqlite'
+table_name = 'my_table_2'
+id_column = 'my_1st_column'
+column_name = 'my_2nd_column'
+
+# Connecting to the database file
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+
+# A) Inserts an ID with a specific value in a second column 
+try:
+    c.execute("INSERT INTO {tn} ({idf}, {cn}) VALUES (123456, 'test')".\
+        format(tn=table_name, idf=id_column, cn=column_name))
+except sqlite3.IntegrityError:
+    print('ERROR: ID already exists in PRIMARY KEY column {}'.format(id_column))
+
+# B) Tries to insert an ID (if it does not exist yet)
+# with a specific value in a second column 
+c.execute("INSERT OR IGNORE INTO {tn} ({idf}, {cn}) VALUES (123456, 'test')".\
+        format(tn=table_name, idf=id_column, cn=column_name))
+
+# C) Updates the newly inserted or pre-existing entry            
+c.execute("UPDATE {tn} SET {cn}=('Hi World') WHERE {idf}=(123456)".\
+        format(tn=table_name, cn=column_name, idf=id_column))
+
+conn.commit()
+conn.close()

sqlite3_howto/code/updating_rows.py

@@ -0,0 +1,50 @@
+# Sebastian Raschka, 2014
+# Updating rows in an existing SQLite database
+
+import sqlite3
+
+sqlite_file = ''
+table_name = ''
+column_name_1 = ''  
+column_name_2 = ''
+column_name_3 = ''
+value_1 = 'hello world'
+value_2 = 12345
+
+conn = sqlite3.connect(sqlite_file)
+c = conn.cursor()
+
+
+# A.1) Updating all rows for a single column
+
+c.execute('UPDATE {dn} SET {cn1}={v1}'.\
+        format(dn=table_name, cn1=column_name_1, v1=value1)
+
+
+# A.2) Updating all rows for 2 columns (same for multiple columns)
+
+c.execute('UPDATE {dn} SET {cn1}={v1}, {cn2}={v2}'.\
+        format(dn=table_name, cn1=column_name_1, cn2=column_name_2,
+               v1=value1, v2=value2)
+
+
+
+
+# B.1) Updating specific rows that meet a certain criterion
+# here: update column_1 with value_1 if row has value_2 in column_2
+
+c.execute('UPDATE {dn} SET {cn1}={v1} WHERE {cn2}={v2}'.\
+        format(dn=table_name, cn1=column_name_1, v1=value1)
+
+
+# B.2) Updating specific rows that meet multiple criteria
+# here: update column_1 with value_1 
+#    if row has value_2 in column_2
+#    and if row has value = 1 in column_3
+
+c.execute('UPDATE {dn} SET {cn1}={v1} WHERE {cn2}={v2} AND {cn3}=1'.\
+        format(dn=table_name, cn1=column_name_1, v1=value1, cn3=column_name_3)
+
+
+conn.commit()
+conn.close()

sqlite3_howto/code/write_from_sqlite.py

@@ -0,0 +1,102 @@
+import sqlite3
+
+def create_col_index(db_name, table_name, column_name, index_name):
+    '''
+    Creates a column index on a SQLite table.
+    
+    Keyword arguments:
+        db_name (str): Path of the .sqlite database file.
+        table_name (str): Name of the target table in the SQLite file.
+        condition (str): Condition for querying the SQLite database table.
+        column_name (str): Name of the column for which the index is created.
+
+    '''
+
+    # Connecting to the database file
+    conn = sqlite3.connect(db_name)
+    c = conn.cursor()
+
+    # Creating the index
+    c.execute('CREATE INDEX {} ON {} ({})'.format(index_name, table_name, column_name))
+
+    # Save index and close the connection to the database
+    conn.commit()
+    conn.close()
+
+
+
+def drop_col_index(db_name, index_name):
+    '''
+    Drops a column index from a SQLite table.
+    
+    Keyword arguments:
+        db_name (str): Path of the .sqlite database file.
+        table_name (str): Name of the target table in the SQLite file.
+        condition (str): Condition for querying the SQLite database table.
+        column_name (str): Name of the column for which the index is dropped.
+
+    '''
+
+    # Connecting to the database file
+    conn = sqlite3.connect(db_name)
+    c = conn.cursor()
+
+    # Drops the index
+    c.execute('DROP INDEX {}'.format(index_name))
+
+    # Save index and close the connection to the database
+    conn.commit()
+    conn.close()
+
+
+
+def write_from_query(db_name, table_name, condition, content_column, out_file, fetchmany=False):
+    '''
+    Writes contents from a SQLite database column to an output file
+    
+    Keyword arguments:
+        db_name (str): Path of the .sqlite database file.
+        table_name (str): Name of the target table in the SQLite file.
+        condition (str): Condition for querying the SQLite database table.
+        content_column (str): Name of the column that contains the content for the output file.
+        out_file (str): Path of the output file that will be written.
+
+    '''
+    # Connecting to the database file
+    conn = sqlite3.connect(db_name)
+    c = conn.cursor()
+
+    # Querying the database and writing the output file
+
+
+    # A) using .fetchmany(); recommended for larger databases
+    if fetchmany:
+        c.execute('SELECT ({}) FROM {} WHERE {}'.format(content_column, table_name, condition))
+        with open(out_file, 'w') as outf:
+            results = c.fetchmany(fetchmany)
+            while results:
+                for row in results:
+                    outf.write(row[0])
+                results = c.fetchmany(fetchmany)
+
+    # B) simple .execute() loop
+    else:
+        c.execute('SELECT ({}) FROM {} WHERE {}'.format(content_column, table_name, condition))
+        with open(out_file, 'w') as outf:
+            for row in c:
+                outf.write(row[0])
+
+    # Closing the connection to the database
+    conn.close()
+
+if __name__ == '__main__':
+    write_from_query(
+        db_name='my_db.sqlite',
+        table_name='my_table',
+        condition='variable1=1 AND variable2<=5 AND variable3="Zinc_Plus"',
+        content_column='variable4',
+        out_file='sqlite_out.txt'
+    )
+        
+
+

tutorials/installing_scientific_packages.md

@@ -0,0 +1,321 @@
+
+
+## Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks
+
+_\-- written by Sebastian Raschka_ on March 13, 2014
+
+
+  
+  
+
+![](../Images/python_sci_pack_ing.png)
+
+* * *
+
+#### Sections
+
+• Anaconda and Miniconda  
+• Consider a virtual environment  
+• Installing pip  
+• Installing NumPy  
+• Installing SciPy  
+• Installing matplotlib  
+• Installing IPython  
+• Updating installed packages  
+
+  
+
+* * *
+
+  
+
+  
+  
+
+## Anaconda and Miniconda
+
+  
+
+Alternatively, instead of going through all the manual steps listed in the
+following sections, there is the [Anaconda Python
+distribution](https://store.continuum.io/cshop/anaconda/) for scientific
+computing. Although Anaconda is distributed by Continuum Analytics, it is
+completely free and includes more than 125 packages for science and data
+analysis.  
+The installation procedure is nicely summarized here:
+<http://docs.continuum.io/anaconda/install.html>
+
+If this is too much, the [Miniconda](http://repo.continuum.io/miniconda/)
+might be right for you. Miniconda is basically just a Python distribution with
+the Conda package manager, which let's us install a list of Python packages
+into a specified conda environment.
+
+    
+    
+    $[bash]> conda create -n myenv python=3
+    $[bash]> conda install -n myenv numpy scipy matplotlib ipython
+    
+
+Note: environments will be created in `ROOT_DIR/envs` by default, you can use
+the `-p` instead of the `-n` flag in the conda commands above in order to
+specify a custom path.
+
+If you we decided pro Anaconda or Miniconda, we are basically done at this
+point. The following sections are explaining a more (semi)-manual approach to
+install the packages individually using `pip`.
+
+  
+  
+
+## Consider a virtual environment
+
+  
+In order to not mess around with our system packages, we should consider
+setting up a virtual environment when we want to install the additional
+scientific packages.  
+To set up a new virtual environment, we can use the following command
+
+    
+    
+    $[bash]> python3 -m venv /path_to/my_virtual_env
+    
+
+and activate it via
+
+    
+    
+    $[bash]> source /path_to/my_virtual_env/bin/activate
+    
+
+  
+  
+
+## Installing pip
+
+  
+`pip` is a tool for installing and managing Python packages. It makes the
+installation process for Python packages a lot easier, since they don't have
+to be downloaded manually.  
+If you haven't installed the `pip` package for your version of Python, yet,
+I'd suggest to download it from <https://pypi.python.org/pypi/pip>, unzip it,
+and install it from the unzipped directory via
+
+    
+    
+    $[bash]> python3 setup.py install
+    
+
+  
+  
+
+## Installing NumPy
+
+  
+Installing NumPy should be straight forward now using `pip`
+
+    
+    
+    $[bash]> python3 -m pip install numpy
+    
+
+The installation will probably take a few minutes due to the source files that
+have to be compiled for your machine. Once it is installed, `NumPy` should be
+available in Python via
+
+    
+    
+    >> import numpy
+    
+
+If you want to see a few examples of how to operate with NumPy arrays, you can
+check out my [Matrix Cheatsheet for Moving from MATLAB matrices to NumPy
+arrays](http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html)
+
+  
+  
+
+## Installing SciPy
+
+  
+While the `clang` compiler worked fine for compiling the C source code for
+`numpy`, we now need an additional Fortran compiler in order to install
+`scipy`.
+
+  
+
+#### Installing a Fortran Compiler
+
+Unfortunately, MacOS 10.9 Mavericks doesn't come with a Fortran compiler, but
+it is pretty easy to download and install one.  
+For example, `gfortran` for MacOS 10.9 can be downloaded from
+<http://coudert.name/software/gfortran-4.8.2-Mavericks.dmg>
+
+Just double-click on the downloaded .DMG container and follow the familiar
+MacOS X installation procedure. Once it is installed, the `gfortran` compiler
+should be available from the command line,. We can test it by typing
+
+    
+    
+    $[bash]> gfortran -v
+    
+
+Among other information, we will see the current version, e.g.,
+
+    
+    
+    gcc version 4.8.2 (GCC)
+
+  
+
+#### Installing SciPy
+
+Now, we should be good to go to install `SciPy` using `pip`.
+
+    
+    
+    $[bash]> python3 -m pip install scipy
+    
+
+After it was successfully installed - might also take a couple of minutes due
+to the source code compilation - it should be available in Python via
+
+    
+    
+    >> import scipy
+    
+
+  
+  
+
+## Installing matplotlib
+
+  
+The installation process for matplotlib should go very smoothly using `pip`, I
+haven't encountered any hurdles here.
+
+    
+    
+    $[bash]> python3 -m pip install matplotlib
+    
+
+After successful installation, it can be imported in Python via
+
+    
+    
+    >> import matplotlib
+    
+
+The `matplotlib` library has become my favorite data plotting tool recently,
+you can check out some examples in my little matplotlib-gallery on GitHub:
+<https://github.com/rasbt/matplotlib_gallery>
+
+  
+  
+
+## Installing IPython
+
+  
+
+#### Installing pyzmq
+
+The IPython kernel requires the `pyzmq` package to run, `pyzmq` contains
+Python bindings for ØMQ, which is a lightweight and fast messaging
+implementation. It can be installed via `pip`.
+
+    
+    
+    $[bash]> python3 -m pip install pyzmq
+    
+
+  
+
+#### Installing pyside
+
+When I was trying to install the `pyside` package, I had it complaining about
+the missing `cmake`. It can be downloaded from:
+
+<http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Darwin64-universal.dmg>
+
+Just as we did with `gfortran` in the Installing SciPy section, double-click
+on the downloaded .DMG container and follow the familiar MacOS X installation
+procedure.  
+We can confirm that it was successfully installed by typing
+
+    
+    
+    $[bash]> cmake --version
+    
+
+into the command line where it would print something like
+
+    
+    
+    cmake version 2.8.12.2
+
+  
+
+#### Installing IPython
+
+Now, we should finally be able to install IPython with all its further
+dependencies (pygments, Sphinx, jinja2, docutils, markupsafe) via
+
+    
+    
+    $[bash]> python3 -m pip install ipython[all]
+    
+
+By doing this, we would install IPython to a custom location, e.g.,
+`/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-
+packages/IPython`.
+
+You can find the path to this location by importing IPython in Python and then
+print its path:
+
+    
+    
+    >> import IPython
+    >> IPython.__path__
+    
+
+Finally, we can set an `alias` in our `.bash_profile` or `.bash_rc` file to
+conviniently run IPython from the console. E.g.,
+
+    
+    
+    alias ipython3="python3 /Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/IPython/terminal/ipapp.py"
+
+(Don't forget to `source` the `.bash_rc` or `.bash_profile` file afterwards)
+
+Now we can run
+
+    
+    
+    $[bash]> ipython3
+    
+
+from you shell terminal to launch the interactive IPython shell, and
+
+    
+    
+    $[bash]> ipython3 notebook
+    
+
+to bring up the awesome IPython notebook in our browser, respectively.
+
+  
+  
+
+## Updating installed packages
+
+  
+Finally, if we want to keep our freshly installed packages up to date, we'd
+run `pip` with the `\--upgrade` flag, for example
+
+    
+    
+    $[bash]> python3 -m pip install numpy --upgrade 
+    
+
+  
+
+

tutorials/unit_testing.md

@@ -0,0 +1,290 @@
+
+## Unit testing in Python - Why we want to make it a habit
+
+
+
+#### Sections
+
+Advantages of unit testing  
+Main components a typical unit test  
+The different unit test frameworks in Python  
+Installing py.test)  
+A py.test example walkthrough  
+     Writing some code we want to test  
+     Creating a "test" file  
+     Testing edge cases and refining our code  
+
+* * *
+
+  
+
+## Advantages of unit testing
+
+Traditionally, for every piece of code we write (let it be a single function
+or class method), we would feed it some arbitrary inputs to make sure that it
+works the way we have expected. And this might sound like a reasonable
+approach given that everything works as it should and if we do not plan to
+make any changes to the code until the end of days. Of course, this is rarely
+the case.  
+Suppose we want to modify our code by refactoring it, or by tweaking it for
+improved efficiency: Do we really want to manually type the previous test
+cases all over again to make sure we didn't break anything? Or suppose we are
+planning to pass our code along to our co-workers: What reason do they have to
+trust it? How can we make their life easier by providing evidence that
+everything was tested and is supposed to work properly?  
+Surely, no one wants to spend hours or even days of mundane work to test code
+that was inherited before it can be put to use in good conscience.  
+There must be a cleverer way, an automated and more systematic approach…  
+This is where unit tests come into play. Once we designed the interface
+(_here:_ the in- and outputs of our functions and methods), we can write down
+several test cases and let them be checked every time we make changes to our
+code - without the tedious work of typing everything all over again, and
+without the risk of forgetting anything or by omitting crucial tests simply
+due to laziness.  
+**This is especially important in scientific research, where your whole project depends on the correct analysis and assessment of any data - and there is probably no more convenient way to convince both you and the rightly skeptical reviewer that you just made a(nother) groundbreaking discovery.**
+
+  
+  
+
+## Main components a typical unit test
+
+In principle, unit testing is really no more than a more systematic way to
+automate code testing process. Where the term "unit" is typically defined as
+an isolated test case that consists of a the following components:  
+  
+\- a so-called "fixture" (e.g., a function, a class or class method, or even a
+data file)  
+\- an action on the fixture (e.g., calling a function with a particular input)  
+\- an expected outcome (e.g., the expected return value of a function)  
+\- the actual outcome (e.g., the actual return value of a function call)  
+\- a verification message (e.g., a report whether the actual return value
+matches the expected return value or not)
+
+  
+  
+
+## The different unit test frameworks in Python
+
+In Python, we have the luxury to be able to choose from a variety of good and
+capable unit testing frameworks. Probably, the most popular and most widely
+used ones are:  
+  
+\- the [unittest](http://docs.python.org/3.3/library/unittest.html) module -
+part of the Python Standard Library  
+\- [nose](https://nose.readthedocs.org/en/latest/index.html)  
+\- [py.test](http://pytest.org/latest/index.html)  
+  
+All of them work very well, and they are all sufficient for basic unit
+testing. Some people might prefer to use _nose_ over the more "basic"
+_unittest_ module. And many people are moving to the more recent _py.test_
+framework, since it offers some nice extensions and even more advanced and
+useful features. However, it shall not be the focus of this tutorial to
+discuss all the details of the different unit testing frameworks and weight
+them against each other. The screenshot below shows how the simple execution
+of _py.test_ and _nose_ may look like. To provide you with a little bit more
+background information: Both _nose_ and _py.test_ are crawling a subdirectory
+tree while looking for Python script files that start with the naming prefix
+"test". If those script files contain functions, classes, and class methods
+that also start with the prefix "test", the contained code will be executed by
+the unit testing frameworks.  
+  
+![../Images/pytest_01.png](../Images/pytest_01.png)
+
+  
+
+* * *
+
+Command line syntax:  
+`py.test <file/directory>` \- default unit testing with detailed report  
+`py.test -q <file/directory>` \- default unit testing with summarized report
+(quiet mode)  
+`nosetests` \- default unit testing with summarized report  
+`nosetests -v` \- default unit testing with detailed report (verbose mode)  
+
+* * *
+
+  
+
+For the further sections of this tutorial, we will be using _py.test_, but
+everything is also compatible to the _nose_ framework, and for the simple
+examples below it would not matter which framework we picked.  
+However, there is one little difference in the default behavior, though, and
+it might also answer the question: "How does the framework know where to find
+the test code to execute?"  
+By default, _py.test_ descends into all subdirectories (from the current
+working directory or a particular folder that you provided as additional
+argument) looking for Python scripts that start with the prefix "test". If
+there are functions, classes, or class methods contained in these scripts that
+also start with the prefix "test", those will be executed by the unit testing
+framework. The basic behavior of _nose_ is quite similar, but in contrast to
+browsing through all subdirectories, it will only consider those that start
+with the prefix "test" to look for the respective Python unit test code. Thus,
+it is a good habit to put all your test code under a directory starting with
+the prefix "test" even if you use _py.test_ \- your _nose_ colleagues will
+thank you!  
+The figure below shows how the _nose_ and _py.test_ unit test frameworks would
+descend the subdirectory tree looking for Python script files that start with
+the prefix "test".  
+![../Images/pytest_02.png](../Images/pytest_02.png)
+
+_Note: Interestingly,_ nose _seems to be twice as fast as_ py.test _in the
+example above, and I was curious if it is due to the fact that_ py.test
+_searches all subdirectories (_ nose _only searches those that start with
+"test"). Although there is a tiny speed difference when I specify the test
+code containing folder directly,_ nose _still seems to be faster. However, I
+don't know how it scales, and it might be an interesting experiment to test
+for much larger projects._  
+  
+![../Images/pytest_02_2.png](../Images/pytest_02_2.png)
+
+  
+  
+
+## Installing py.test
+
+Installing py.test is pretty straightforward. We can install it directly from
+the command line via  
+
+    
+    
+    pip install -U pytest 
+    
+
+  
+or  
+
+    
+    
+    easy_install -U pytest
+    
+
+  
+If this doesn't work for you, you can visit the _py.test_ website
+(<http://pytest.org/latest/>), download the package, and try to install it
+"manually":  
+
+    
+    
+    ~/Desktop/pytest-2.5.0> python3 setup.py install
+    
+
+  
+If it was installed correctly, we can now run _py.test_ in any directory from
+the command line via  
+
+    
+    
+    py.test <file or directory> 
+    
+
+  
+or  
+
+    
+    
+    python -m pytest <file or directory>
+    
+
+  
+
+  
+  
+
+## A py.test example walkthrough
+
+For the following example we will be using _py.test_, however, _nose_ works
+pretty similarly, and as I mentioned in the previous section, I only want to
+focus on the essentials of unit testing here. Note that _py.test_ has a lot of
+advanced and useful features to offer that we won't touch in this tutorial,
+e.g., setting break points for debugging, etc. (if you want to learn more,
+please take a look at the complete _py.test_ documentation:
+<http://pytest.org/latest/contents.html#toc>).
+
+  
+
+### Writing some code we want to test
+
+Assume we wrote two very simple functions that we want to test, either as
+small scripts or part of a larger package. The first function,
+"multiple_of_three", is supposed to check whether a number is a multiple of
+the number 3 or not. We want the function to return the boolean value True if
+this is the case, and else it should return False. The second function,
+"filter_multiples_of_three", takes a list as input argument and is supposed to
+return a subset of the input list containing only those numbers that are
+multiples of 3.  
+  
+![../Images/pytest_03.png](../Images/pytest_03.png)
+
+  
+
+### Creating a "test" file
+
+Next, we write a small unit test to check if our function works for some
+simple input cases:  
+  
+  
+![../Images/pytest_04.png](../Images/pytest_04.png)  
+  
+Great, when we run our py.test unit testing framework, we see that everything
+works as expected!  
+  
+  
+![../Images/pytest_05.png](../Images/pytest_05.png)  
+  
+But what about edge cases?
+
+  
+  
+
+### Testing edge cases and refining our code
+
+In order to check if our function is yet robust enough to handle special
+cases, e.g., 0 as input, we extend our unit test code. Here, assume that we
+don't want 0 to evaluate to True, since we don't consider 3 to be a factor of
+0.  
+  
+![../Images/pytest_06.png](../Images/pytest_06.png)
+![../Images/pytest_07.png](../Images/pytest_07.png)  
+  
+As we can see from the _py.test report_, our test just failed. So let us go
+back and fix our code to handle this special case.  
+  
+![../Images/pytest_08.png](../Images/pytest_08.png)  
+  
+So far so good, when we execute _py.test_ again (image not shown) we see that
+our codes handles 0 correctly now. Let us add some more edge cases: Negative
+integers, decimal floating-point numbers, and large integers.
+
+![../Images/pytest_09.png](../Images/pytest_09.png)
+![../Images/pytest_10.png](../Images/pytest_10.png)
+
+According to the unit test report, we face another problem here: Our code
+considers 3 as a factor of -9 (negative 9). For the sake of this example,
+let's assume that we don't want this to happen: We'd like to consider only
+positive numbers to be multiples of 3. In order to account for those cases, we
+need to make another small modification to our code by changing `!=0` to `>0`
+in the if-statement.  
+  
+![../Images/pytest_11.png](../Images/pytest_11.png)
+
+After running the _py.test_ utility again, we are certain that our code can
+also handle negative numbers correctly now. And once we are satisfied with the
+general behavior of our current code, we can move on to testing the next
+function "filter_multiples_of_three", which depends on the correctness of
+"multiple_of_three".
+
+![../Images/pytest_12.png](../Images/pytest_12.png)
+![../Images/pytest_13.png](../Images/pytest_13.png)
+
+This time, our test seems to be "bug"-free, and we are confident that it can
+handle all the scenarios we could currently think of. If we plan to make any
+further modifications to the code in future, nothing can be more convenient to
+just re-run our previous tests in order to make sure that we didn't break
+anything.
+
+If you have any questions or need more explanations, you are welcome to
+provide feedback in the comment section below.
+
+  
+  
+