The first step is to download Anaconda from Continuum Analytics' website (http://continuum.io/downloads). This is actually not the easiest part since several versions are available. Three properties define a particular version:

Once you have found the right link for your OS and Python 3 64-bit, you can download the package. You should then find it in your downloads directory (depending on your OS and your browser's settings).

The Anaconda installer comes in different flavors depending on your OS, as follows:

Then, follow the instructions to install Anaconda on your computer. Here are a few remarks:

Before you get started with Anaconda, there are a few things you need to know:

You can skip this section if you already know how to do these things.

To test Anaconda once it has been installed, open a terminal and type python. This opens a Python console, not to be confused with the OS terminal. The Python console is identified with a >>> prompt string, whereas the OS terminal is identified with a $ (Linux/OS X) or > (Windows) prompt string. These strings are displayed in the terminal, often preceded by your computer's name, your login, and the current directory (for example, yourname@computer:~$ on Linux or PS C:\Users\YourName> on Windows). You can type commands after the prompt string. After typing python, you should see something like the following:

$ python
Python 3.4.3 |Anaconda 2.3.0 (64-bit)| (default, Jun  4 2015, 15:29:08) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

What matters is that Anaconda or Continuum Analytics is mentioned here. Otherwise, typing python might have launched your system's default Python, which is not the one you want to use in this book.

If you have this problem, you may need to add the path to the Anaconda executables to your system path. For example, this path will be ~/anaconda3/bin if you chose to install Anaconda in ~/anaconda3. The bin directory contains Anaconda executables including python.

If you have any problem installing and testing Anaconda, you can ask for help on the mailing list (see the link in the References section under the Installing Python with Anaconda section of this chapter).

Next, exit the Python prompt by typing exit() and pressing Enter.

Anaconda lets you create different isolated Python environments. For example, you can have a Python 2 distribution for the rare cases where you need to temporarily switch to Python 2.

To create a new environment for Python 2, type the following command in an OS terminal:

$ conda create -n py2 anaconda python=2.7

This will create a new isolated environment named py2 based on the original Anaconda distribution, but with Python 2.7. You could also use the command conda env: type conda env -h to see the details.

You can now activate your py2 environment by typing the following command in a terminal:

Now, you should see a (py2) prefix in front of your terminal prompt. Typing python in your terminal with the py2 environment activated will open a Python 2 interpreter.

Type deactivate on Windows or source deactivate on Linux/OS X to deactivate the environment in the terminal.

Here is a list of common commands:

Some commands ask for confirmation (you need to press y to confirm). You can also use the -y option to avoid the confirmation prompt.

If conda install somepackage fails, you can try pip install somepackage instead. This will use the Python Package Index (PyPI) instead of Anaconda. Many scientific Anaconda packages are easier to install than the corresponding PyPI packages because they are precompiled for your platform. However, many packages are available on PyPI but not on Anaconda.

Here are some references:

Here are a few references about Anaconda:

All of this book's code is available on GitHub as notebooks. We recommend that you download the notebooks and experiment with them as you're working through the book.

Here is how to download the book's notebooks:

This will download the very latest version of the code into a minibook subdirectory in your home directory. You can also choose another directory.

From this directory, you can update to the latest version at any time by typing git pull.

To run the IPython console, type ipython in an OS terminal. There, you can write Python commands and see the results instantly. Here is a screenshot:

IPython console

The IPython console is most convenient when you have a command-line-based workflow and you want to execute some quick Python commands.

You can exit the IPython console by typing exit.

To run the Jupyter Notebook, open an OS terminal, go to ~/minibook/ (or into the directory where you've downloaded the book's notebooks), and type jupyter notebook. This will start the Jupyter server and open a new window in your browser (if that's not the case, go to the following URL: http://localhost:8888). Here is a screenshot of Jupyter's entry point, the Notebook dashboard:

The Notebook dashboard

The Notebook is most convenient when you start a complex analysis project that will involve a substantial amount of interactive experimentation with your code. Other common use-cases include keeping track of your interactive session (like a lab notebook), or writing technical documents that involve code, equations, and figures.

In the rest of this section, we will focus on the Notebook interface.

The dashboard contains several tabs:

A notebook is an interactive document containing code, text, and other elements. A notebook is saved in a file with the .ipynb extension. This file is a plain text file storing a JSON data structure.

A kernel is a process running an interactive session. When using IPython, this kernel is a Python process. There are kernels in many languages other than Python.

In Jupyter, notebooks and kernels are strongly separated. A notebook is a file, whereas a kernel is a process. The kernel receives snippets of code from the Notebook interface, executes them, and sends the outputs and possible errors back to the Notebook interface. Thus, in general, the kernel has no notion of a Notebook. A notebook is persistent (it's a file), whereas a kernel may be closed at the end of an interactive session and it is therefore not persistent. When a notebook is re-opened, it needs to be re-executed.

In general, no more than one Notebook interface can be connected to a given kernel. However, several IPython consoles can be connected to a given kernel.

To create a new notebook, click on the New button, and select Notebook (Python 3). A new browser tab opens and shows the Notebook interface as follows:

A new notebook

Here are the main components of the interface, from top to bottom:

There are two main types of cells: Markdown cells and code cells, and they are described as follows:

You can change the type of a cell by first clicking on a cell to select it, and then choosing the cell's type in the toolbar's dropdown menu showing Markdown or Code.

Markdown cells

Here is a screenshot of a Markdown cell:

A Markdown cell

The top panel shows the cell in edit mode, while the bottom one shows it in render mode. The edit mode lets you edit the text, while the render mode lets you display the rendered cell. We will explain the differences between these modes in greater detail in the following section.

Code cells

Here is a screenshot of a complex code cell:

Structure of a code cell

This code cell contains several parts, as follows:

• The Prompt number shows the cell's number. This number increases every time you run the cell. Since you can run cells of a notebook out of order, nothing guarantees that code numbers are linearly increasing in a given notebook.
• The Input area contains a multiline text editor that lets you write one or several lines of code with syntax highlighting.
• The Widget area may contain graphical controls; here, it displays a slider.
• The Output area can contain multiple outputs, here:
  • Standard output (text in black)
  • Error output (text with a red background)
  • Rich output (an HTML table and an image here)

The Notebook implements a modal interface similar to some text editors such as vim. Mastering this interface may represent a small learning curve for some users.

Keyboard shortcuts are available in the Notebook interface. Type h to show them. We review here the most common ones (for Windows and Linux; shortcuts for OS X may be slightly different).

Here are a few references:

Open a new notebook and type the following in the first cell:

In [1]: print("Hello world!")
Out[1]: Hello world!

Here is a screenshot:

"Hello world" in the Notebook

Congratulations! You are now a Python programmer.

Let's use Python as a calculator.

In [2]: 2 * 2
Out[2]: 4

Here, 2 * 2 is an expression statement. This operation is performed, the result is returned, and IPython displays it in the notebook cell's output.

Other built-in mathematical operators include +, -, ** for the exponentiation, and others. You will find more details at https://docs.python.org/3/reference/expressions.html#the-power-operator.

Variables form a fundamental concept of any programming language. A variable has a name and a value. Here is how to create a new variable in Python:

In [3]: a = 2

And here is how to use an existing variable:

In [4]: a * 3
Out[4]: 6

Several variables can be defined at once (this is called unpacking):

In [5]: a, b = 2, 6

There are different types of variables. Here, we have used a number (more precisely, an integer). Other important types include floating-point numbers to represent real numbers, strings to represent text, and booleans to represent True/False values. Here are a few examples:

In [6]: somefloat = 3.1415
        sometext = 'pi is about'  # You can also use double quotes.
        print(sometext, somefloat)  # Display several variables.
Out[6]: pi is about 3.1415

Note how we used the # character to write comments. Whereas Python discards the comments completely, adding comments in the code is important when the code is to be read by other humans (including yourself in the future).

String escaping refers to the ability to insert special characters in a string. For example, how can you insert ' and ", given that these characters are used to delimit a string in Python code? The backslash \ is the go-to escape character in Python (and in many other languages too). Here are a few examples:

In [7]: print("Hello \"world\"")
        print("A list:\n* item 1\n* item 2")
        print("C:\\path\\on\\windows")
        print(r"C:\path\on\windows")
Out[7]: Hello "world"
        A list:
        * item 1
        * item 2
        C:\path\on\windows
        C:\path\on\windows

The special character \n is the new line (or line feed) character. To insert a backslash, you need to escape it, which explains why it needs to be doubled as \\.

You can also disable escaping by using raw literals with a r prefix before the string, like in the last example above. In this case, backslashes are considered as normal characters.

This is convenient when writing Windows paths, since Windows uses backslash separators instead of forward slashes like on Unix systems. A very common error on Windows is forgetting to escape backslashes in paths: writing "C:\path" may lead to subtle errors.

You will find the list of special characters in Python at https://docs.python.org/3.4/reference/lexical_analysis.html#string-and-bytes-literals.

A list contains a sequence of items. You can concisely instruct Python to perform repeated actions on the elements of a list. Let's first create a list of numbers as follows:

In [8]: items = [1, 3, 0, 4, 1]

Note the syntax we used to create the list: square brackets [], and commas , to separate the items.

The built-in function len() returns the number of elements in a list:

In [9]: len(items)
Out[9]: 5

Now, let's compute the sum of all elements in the list. Python provides a built-in function for this:

In [10]: sum(items)
Out[10]: 9

We can also access individual elements in the list, using the following syntax:

In [11]: items[0]
Out[11]: 1
In [12]: items[-1]
Out[12]: 1

Note that indexing starts at 0 in Python: the first element of the list is indexed by 0, the second by 1, and so on. Also, -1 refers to the last element, -2 to the penultimate element, and so on.

The same syntax can be used to alter elements in the list:

In [13]: items[1] = 9
         items
Out[13]: [1, 9, 0, 4, 1]

We can access sublists with the following syntax:

In [14]: items[1:3]
Out[14]: [9, 0]

Here, 1:3 represents a slice going from element 1 included (this is the second element of the list) to element 3 excluded. Thus, we get a sublist with the second and third element of the original list. The first-included/last-excluded asymmetry leads to an intuitive treatment of overlaps between consecutive slices. Also, note that a sublist refers to a dynamic view of the original list, not a copy; changing elements in the sublist automatically changes them in the original list.

Python provides several other types of containers:

We can run through all elements of a list using a for loop:

In [19]: for item in items:
             print(item)
Out[19]: 1
         9
         0
         4
         1

There are several things to note here:

Python supports a concise syntax to perform a given operation on all elements of a list, as follows:

In [20]: squares = [item * item for item in items]
         squares
Out[20]: [1, 81, 0, 16, 1]

This is called a list comprehension. A new list is created here; it contains the squares of all numbers in the list. This concise syntax leads to highly readable and Pythonic code.

Indentation refers to the spaces that may appear at the beginning of some lines of code. This is a particular aspect of Python's syntax.

In most programming languages, indentation is optional and is generally used to make the code visually clearer. But in Python, indentation also has a syntactic meaning. Particular indentation rules need to be followed for Python code to be correct.

In general, there are two ways to indent some text: by inserting a tab character (also referred to as \t), or by inserting a number of spaces (typically, four). It is recommended to use spaces instead of tab characters. Your text editor should be configured such that the Tab key on the keyboard inserts four spaces instead of a tab character.

In the Notebook, indentation is automatically configured properly; so you shouldn't worry about this issue. The question only arises if you use another text editor for your Python code.

Finally, what is the meaning of indentation? In Python, indentation delimits coherent blocks of code, for example, the contents of a loop, a conditional branch, a function, and other objects. Where other languages such as C or JavaScript use curly braces to delimit such blocks, Python uses indentation.

Sometimes, you need to perform different operations on your data depending on some condition. For example, let's display all even numbers in our list:

In [21]: for item in items:
             if item % 2 == 0:
                 print(item)
Out[21]: 0
         4

Again, here are several things to note:

Python supports a concise syntax to select all elements in a list that satisfy certain properties. Here is how to create a sublist with only even numbers:

In [22]: even = [item for item in items if item % 2 == 0]
         even
Out[22]: [0, 4]

This is also a form of list comprehension.

Code is typically organized into functions. A function encapsulates part of your code. Functions allow you to reuse bits of functionality without copy-pasting the code. Here is a function that tells whether an integer number is even or not:

In [23]: def is_even(number):
             """Return whether an integer is even or not."""
             return number % 2 == 0

There are several things to note here:

Once a function is defined, it can be called like this:

In [24]: is_even(3)
Out[24]: False
In [25]: is_even(4)
Out[25]: True

Here, 3 and 4 are successively passed as arguments to the function.

A Python function can accept an arbitrary number of arguments, called positional arguments. It can also accept optional named arguments, called keyword arguments. Here is an example:

In [26]: def remainder(number, divisor=2):
             return number % divisor

The second argument of this function, divisor, is optional. If it is not provided by the caller, it will default to the number 2, as shown here:

In [27]: remainder(5)
Out[27]: 1

There are two equivalent ways of specifying a keyword argument when calling a function. They are as follows:

In [28]: remainder(5, 3)
Out[28]: 2
In [29]: remainder(5, divisor=3)
Out[29]: 2

In the first case, 3 is understood as the second argument, divisor. In the second case, the name of the argument is given explicitly by the caller. This second syntax is clearer and less error-prone than the first one.

Functions can also accept arbitrary sets of positional and keyword arguments, using the following syntax:

In [30]: def f(*args, **kwargs):
             print("Positional arguments:", args)
             print("Keyword arguments:", kwargs)
In [31]: f(1, 2, c=3, d=4)
Out[31]: Positional arguments: (1, 2)
         Keyword arguments: {'c': 3, 'd': 4}

Inside the function, args is a tuple containing positional arguments, and kwargs is a dictionary containing keyword arguments.

When passing a parameter to a Python function, a reference to the object is actually passed (passage by assignment):

Here is an example:

In [32]: my_list = [1, 2]

         def add(some_list, value):
             some_list.append(value)

         add(my_list, 3)
         my_list
Out[32]: [1, 2, 3]

The add() function modifies an object defined outside it (in this case, the object my_list); we say this function has side-effects. A function with no side-effects is called a pure function: it doesn't modify anything in the outer context, and it deterministically returns the same result for any given set of inputs. Pure functions are to be preferred over functions with side-effects.

Knowing this can help you spot out subtle bugs. There are further related concepts that are useful to know, including function scopes, naming, binding, and more. Here are a couple of links:

Let's talk about errors in Python. As you learn, you will inevitably come across errors and exceptions. The Python interpreter will most of the time tell you what the problem is, and where it occurred. It is important to understand the vocabulary used by Python so that you can more quickly find and correct your errors.

Let's see the following example:

In [33]: def divide(a, b):
             return a / b
In [34]: divide(1, 0)
Out[34]: ---------------------------------------------------------
         ZeroDivisionError       Traceback (most recent call last)
         <ipython-input-2-b77ebb6ac6f6> in <module>()
         ----> 1 divide(1, 0)

         <ipython-input-1-5c74f9fd7706> in divide(a, b)
               1 def divide(a, b):
         ----> 2     return a / b

         ZeroDivisionError: division by zero

Here, we defined a divide() function, and called it to divide 1 by 0. Dividing a number by 0 is an error in Python. Here, a ZeroDivisionError exception was raised. An exception is a particular type of error that can be raised at any point in a program. It is propagated from the innards of the code up to the command that launched the code. It can be caught and processed at any point. You will find more details about exceptions at https://docs.python.org/3/tutorial/errors.html, and common exception types at https://docs.python.org/3/library/exceptions.html#bltin-exceptions.

The error message you see contains the stack trace, the exception type, and the exception message. The stack trace shows all function calls between the raised exception and the script calling point.

The top frame, indicated by the first arrow ---->, shows the entry point of the code execution. Here, it is divide(1, 0), which was called directly in the Notebook. The error occurred while this function was called.

The next and last frame is indicated by the second arrow. It corresponds to line 2 in our function divide(a, b). It is the last frame in the stack trace: this means that the error occurred there.

We will see later in this chapter how to debug such errors interactively in IPython and in the Jupyter Notebook. Knowing how to navigate up and down in the stack trace is critical when debugging complex Python code.

Object-oriented programming (OOP) is a relatively advanced topic. Although we won't use it much in this book, it is useful to know the basics. Also, mastering OOP is often essential when you start to have a large code base.

In Python, everything is an object. A number, a string, or a function is an object. An object is an instance of a type (also known as class). An object has attributes and methods, as specified by its type. An attribute is a variable bound to an object, giving some information about it. A method is a function that applies to the object.

For example, the object 'hello' is an instance of the built-in str type (string). The type() function returns the type of an object, as shown here:

In [35]: type('hello')
Out[35]: str

There are native types, like str or int (integer), and custom types, also called classes, that can be created by the user.

In IPython, you can discover the attributes and methods of any object with the dot syntax and tab completion. For example, typing 'hello'.u and pressing Tab automatically shows us the existence of the upper() method:

In [36]: 'hello'.upper()
Out[36]: 'HELLO'

Here, upper() is a method available to all str objects; it returns an uppercase copy of a string.

A useful string method is format(). This simple and convenient templating system lets you generate strings dynamically, as shown in the following example:

In [37]: 'Hello {0:s}!'.format('Python')
Out[37]: Hello Python!

The {0:s} syntax means "replace this with the first argument of format(), which should be a string". The variable type after the colon is especially useful for numbers, where you can specify how to display the number (for example, .3f to display three decimals). The 0 makes it possible to replace a given value several times in a given string. You can also use a name instead of a position—for example 'Hello {name}!'.format(name='Python').

Some methods are prefixed with an underscore _; they are private and are generally not meant to be used directly. IPython's tab completion won't show you these private attributes and methods unless you explicitly type _ before pressing Tab.

In practice, the most important thing to remember is that appending a dot . to any Python object and pressing Tab in IPython will show you a lot of functionality pertaining to that object.

Python is a multi-paradigm language; it notably supports imperative, object-oriented, and functional programming models. Python functions are objects and can be handled like other objects. In particular, they can be passed as arguments to other functions (also called higher-order functions). This is the essence of functional programming.

Decorators provide a convenient syntax construct to define higher-order functions. Here is an example using the is_even() function from the previous Functions section:

In [38]: def show_output(func):
             def wrapped(*args, **kwargs):
                 output = func(*args, **kwargs)
                 print("The result is:", output)
             return wrapped

The show_output() function transforms an arbitrary function func() to a new function, named wrapped(), that displays the result of the function, as follows:

In [39]: f = show_output(is_even)
         f(3)
Out[39]: The result is: False

Equivalently, this higher-order function can also be used with a decorator, as follows:

In [40]: @show_output
         def square(x):
             return x * x
In [41]: square(3)
Out[41]: The result is: 9

You can find more information about Python decorators at https://en.wikipedia.org/wiki/Python_syntax_and_semantics#Decorators and at http://www.thecodeship.com/patterns/guide-to-python-function-decorators/.

Let's finish this section with a few notes about Python 2 and Python 3 compatibility issues.

There are still some Python 2 code and libraries that are not compatible with Python 3. Therefore, it is sometimes useful to be aware of the differences between the two versions. One of the most obvious differences is that print is a statement in Python 2, whereas it is a function in Python 3. Therefore, print "Hello" (without parentheses) works in Python 2 but not in Python 3, while print("Hello") works in both Python 2 and Python 3.

There are several non-mutually exclusive options to write portable code that works with both versions:

Here are a few references:

You now know the fundamentals of Python, the bare minimum that you will need in this book. As you can imagine, there is much more to say about Python.

Following are a few further basic concepts that are often useful and that we cannot cover here, unfortunately. You are highly encouraged to have a look at them in the references given at the end of this section:

Here are some slightly more advanced concepts that you might find useful if you want to strengthen your Python skills:

Finally, here are a few references: