python-01-crash-course.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programming with Python and the Jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lesson is a very brief introduction to Python and the Jupyter notebook, aiming to give you the bare essentials needed to follow our tutorial on cobrapy, escher and cameo. It is not meant as a general introduction to Python, for that there are plenty of other resources, you can for example start with [learnpython.org](https://www.learnpython.org/).\n",
    "\n",
    "We assume that you have Jupyter installed and running, or using it via our online interactive server, please look elsewhere for getting to this point, e.g. [this comprehensive tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook#gs.mVNYmI4)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving around and make new cells (3 + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook is consists of 'cells' which are either python code or markdown (like this one).\n",
    "\n",
    "- If you press “esc” and “return” alternately, the outer border of your code cell will change from gray to green. The difference in color is subtle.\n",
    "  - These are the control (gray) and edit (green) modes of your notebook.\n",
    "- If you use the “esc” and “return” keys to make the surround gray and then press the “h” key, a list of all the shortcut keys will appear.\n",
    "- When in control mode (esc/gray),\n",
    "  - The “b” key will make a new cell below the currently selected cell.\n",
    "  - The “a” key will make one above.\n",
    "  - The “x” key will delete the current cell.\n",
    "- You can undo actions with Ctrl + z in edit mode (green) and just \"z\" in control mode (gray).\n",
    "- There are lots of shortcuts you can try out and most actions can be done with the menus at the top of the page if you forget the shortcuts.\n",
    "- If you remember the “esc” and “h” shortcut, you will be able to find out all the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you press \"B\" and you get a new cell below the current one, preceded by a \"In [ ]\". This indicates that you have a \"code\" cell. If you hit \"esc\" and \"m\" you convert the cell to a markdown cell. In such cells you can write text as you like and use formatting with regular characters\n",
    "\n",
    "- A leading # makes a heading\n",
    "- Bullet lists starts with -\n",
    "- \\*\\*fat\\*\\* **fat**\n",
    "- \\*italic\\* *italic*\n",
    "\n",
    "Plenty more formatting such as tables and math notation but we won't go into that here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make this text a headline\n",
    "\n",
    "Make this a sub-headline\n",
    "\n",
    "Write some text in a new cell and add some formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables, functions and assignment (5 + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A variable is like a box in which you can put a value. \n",
    "- You can make a new variable by inventing a name and assigning a value to it.\n",
    "- Variables names can be almost anything (but must not start with a number). Names are case-sensitive so `Age` is not the same as `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age = 42\n",
    "last_name = 'Wang'\n",
    "shoe_size = 10.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Wang'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Functions are just like in math, something that takes a value and spits out another. In addition they may have a side-effect, like printing something to the screen. `print` is such a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mr Wang is 42 years old and wears size 10.5 boots.\n"
     ]
    }
   ],
   "source": [
    "print('Mr', last_name, 'is', age, 'years old and wears size', shoe_size, 'boots.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making new functions is done using the `def` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def one_plus(x):\n",
    "    return 1 + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_plus(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to code cells and fill in the blanks"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "name = ____\n",
    "____ = 42\n",
    "print(____)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "def age_in_five_years(____):\n",
    "    print(_____)\n",
    "\n",
    "age_in_five_years(42)\n",
    "# 47"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data types (3 + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every value has a type.\n",
    "\n",
    "- The three most common types are strings, like `last_name`, integer (any whole number), like `age` and float (any real value number), like `shoe_size`.\n",
    "- Notice the difference between a variable name, `last_name` and the value `'Wang'`. One does not have quotation marks, the other does.\n",
    "- What you can do with variables depend on their type. Adding two numbers makes sense, so can adding two strings. But subtracting one string from another does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 2\n",
    "y = 3\n",
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdef'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 'abc'\n",
    "y = 'def'\n",
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# but what this should do is not easy to say and consequently also not allowed\n",
    "# x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "<class 'int'>\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "x = 'abc'\n",
    "y = 3\n",
    "z = 3.14156\n",
    "print(type(x))\n",
    "print(type(y))\n",
    "print(type(z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correct the code below"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "'1.2' + 2 == 2.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting help (1 + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Jupyter, you can read documentation for functions by typing a question mark after (or before) an object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and you can get help on parameters (i.e. what you put inside the brackets) for functions by shift-tab, or just tab for a shorter version."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What does the built-in function `len` do? Try using it.\n",
    "- Try tab and shift-tab to see information about parameters for `print`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error messages (2 + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you learn to program in Python you will see lots of error messages. An error, aka exception, arises when you try to do something that is ill-defined, either by obvious mistakes, when you pass the wrong types of arguments (perhaps due to poor documentation..), or when the program you are trying to run is buggy. Error messages can look scary and incomprehensible but more often than not they are actually useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Forgot to close quotation marks around the string\n",
    "# name = 'Kiyofumi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Forgot closing parenthesis\n",
    "# print('hello worls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Wrong indentation\n",
    "# name = 'Kiyofumi'\n",
    "#   age = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "- Uncomment the code above and try to understand the error messages. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More data types (2 + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many data types in Python and as you will see a bit further on, it is easy to make new ones. In our further notebooks we make frequent use of two particular types, `list` and `set`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `list` is exactly that, an ordered collection of items. You can create a list with values as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "the_abc = ['a', 'b', 'c']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get access the elements in the list by using indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_abc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "the_abc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(the_abc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `set` is also a collection of items, but, they don't have a particular order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "joy_division = {'ian', 'peter', 'bernard', 'stephen'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the cells below to code and fill in the blanks."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "___(joy_division) == 4"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "the_abc + ____ == ['a', 'b', 'c', 'd', 'e', 'f']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For loops (2 + 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to do an action for every item in a list, then it would be tedious to write it like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n"
     ]
    }
   ],
   "source": [
    "print(the_abc[0])\n",
    "print(the_abc[1])\n",
    "print(the_abc[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Particularly if we wanted to use a list with thousands of elements.. For this we can instead use a for loop. Note the indentation which says that everything below the `for`, and immediately one level to the right, should be done once for every item in the iterable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "c\n"
     ]
    }
   ],
   "source": [
    "for letter in the_abc: # the_abc is our iterable here.\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the cell below to code and fill in the blanks."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "cumsum = 0\n",
    "for i in ____:\n",
    "    cumsum = ____\n",
    "cumsum\n",
    "# 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classes and object-orientation (5 + 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In cobrapy, cameo and escher, we use many objects that are more complicated than just strings and numbers. For this we make use of something called *classes*. A class describes what properties and methods an object of its class shall have. Think for example of a tree. A tree class could be written like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Tree(object):\n",
    "    def __init__(self, height=0, species = 'oak'):\n",
    "        self.height = height\n",
    "        self.species = species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make a new tree like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree = Tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And inspect how tall it is and what species using the '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My newly planted tree is an oak and it is 0 meters tall\n"
     ]
    }
   ],
   "source": [
    "print('My newly planted tree is an', tree.species, 'and it is', tree.height, 'meters tall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could add a method to the class like this. Note the indentation, because its is one level to the right of the `class Tree:` it becomes part of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Tree(object):\n",
    "    def __init__(self, height=0, species = 'oak'):\n",
    "        self.height = height\n",
    "        self.species = species\n",
    "\n",
    "    def grow(self, meters):\n",
    "        self.height += meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree = Tree()\n",
    "tree.grow(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My 2 year old tree is an oak and it is 1 meters tall\n"
     ]
    }
   ],
   "source": [
    "print('My 2 year old tree is an', tree.species, 'and it is', tree.height, 'meters tall')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By convention, class names are capitalized, variables and functions are lower-case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the cell below to code and fill in the blanks."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "class Wallet(____):\n",
    "    def __init__(self, content=0):\n",
    "        self.content = content\n",
    "\n",
    "    def earn(self, amount):\n",
    "        self.content __ amount\n",
    "    \n",
    "    def spend(____, ____):\n",
    "        ____\n",
    "\n",
    "wallet = Wallet()\n",
    "wallet.earn(10)\n",
    "wallet.spend(3)\n",
    "wallet.content\n",
    "# 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages and modules (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the useful functions and class you will use reside in a package, which is a collection of modules. cobrapy, cameo and escher are examples of packages. To use one of these you have to import them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cobra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can have access to members of that package. E.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8.1'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cobra.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within cobrapy, there are lots of modules. We can import from the top-level "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cobra import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or from lower-level modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cobra.flux_analysis import flux_variability_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't want to type that long name? Tab is your friend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data frames (5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the pandas package, we get access to the extremely useful class `DataFrame` and since we use that on numerous occasions in this tutorial let's have a look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = pandas.read_csv('data/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.0000</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>150.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.8433</td>\n",
       "      <td>3.0540</td>\n",
       "      <td>3.7587</td>\n",
       "      <td>1.1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.8281</td>\n",
       "      <td>0.4336</td>\n",
       "      <td>1.7644</td>\n",
       "      <td>0.7632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.3000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.1000</td>\n",
       "      <td>2.8000</td>\n",
       "      <td>1.6000</td>\n",
       "      <td>0.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.8000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>4.3500</td>\n",
       "      <td>1.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.4000</td>\n",
       "      <td>3.3000</td>\n",
       "      <td>5.1000</td>\n",
       "      <td>1.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.9000</td>\n",
       "      <td>4.4000</td>\n",
       "      <td>6.9000</td>\n",
       "      <td>2.5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count      150.0000     150.0000      150.0000     150.0000\n",
       "mean         5.8433       3.0540        3.7587       1.1987\n",
       "std          0.8281       0.4336        1.7644       0.7632\n",
       "min          4.3000       2.0000        1.0000       0.1000\n",
       "25%          5.1000       2.8000        1.6000       0.3000\n",
       "50%          5.8000       3.0000        4.3500       1.3000\n",
       "75%          6.4000       3.3000        5.1000       1.8000\n",
       "max          7.9000       4.4000        6.9000       2.5000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data frames is kind of table that has rows and columns and indices. You can access values by the column or row index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal_length, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['sepal_length']\n",
    "# is the same as\n",
    "iris.sepal_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length       5.1\n",
       "sepal_width        3.5\n",
       "petal_length       1.4\n",
       "petal_width        0.2\n",
       "species         setosa\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also make more complex queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length  sepal_width  petal_length  petal_width     species\n",
       "50           7.0          3.2           4.7          1.4  versicolor\n",
       "52           6.9          3.1           4.9          1.5  versicolor\n",
       "58           6.6          2.9           4.6          1.3  versicolor\n",
       "65           6.7          3.1           4.4          1.4  versicolor\n",
       "75           6.6          3.0           4.4          1.4  versicolor\n",
       "76           6.8          2.8           4.8          1.4  versicolor\n",
       "77           6.7          3.0           5.0          1.7  versicolor\n",
       "86           6.7          3.1           4.7          1.5  versicolor"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[(iris.sepal_length > 6.5) & (iris.species == 'versicolor')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc33e598f98>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc33e598320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "iris.plot.scatter(x='sepal_length', y='sepal_width')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may expect there are tons of things you can do with a data frame but this is all you need to know for this tutorial."
   ]
  }
 ],
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2+"
  },
  "toc": {
   "colors": {
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   "toc_section_display": "block",
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 },
 "nbformat": 4,
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}