# Running and Quitting

## Overview

Teaching: 15 min
Exercises: 0 min
Questions
• How can I run Python programs?

Objectives
• Launch the JupyterLab server.

• Create a new Python script.

• Create a Jupyter notebook.

• Shutdown the JupyterLab server.

• Understand the difference between a Python script and a Jupyter notebook.

• Create Markdown cells in a notebook.

• Create and run Python cells in a notebook.

Many software developers will often use an integrated development environment (IDE) or a text editor to create and edit their Python programs which can be executed through the IDE or command line directly. While this is a common approach, we are going to use the Jupyter Notebook via JupyterLab for the remainder of this workshop.

• You can easily type, edit, and copy and paste blocks of code.
• Tab complete allows you to easily access the names of things you are using and learn more about them.
• It allows you to annotate your code with links, different sized text, bullets, etc. to make it more accessible to you and your collaborators.
• It allows you to display figures next to the code that produces them to tell a complete story of the analysis.

Each notebook contains one or more cells that contain code, text, or images.

## Getting Started with JupyterLab

JupyterLab is an application with a web-based user interface from Project Jupyter that enables one to work with documents and activities such as Jupyter notebooks, text editors, terminals, and even custom components in a flexible, integrated, and extensible manner. JupyterLab requires a reasonably up-to-date browser (ideally a current version of Chrome, Safari, or Firefox); Internet Explorer versions 9 and below are not supported.

JupyterLab is included as part of the Anaconda Python distribution. If you have not already installed the Anaconda Python distribution, see the setup instructions for installation instructions.

Even though JupyterLab is a web-based application, JupyterLab runs locally on your machine and does not require an internet connection.

• The JupyterLab server does the work and the web browser renders the result.
• You will type code into the browser and see the result when the web page talks to the JupyterLab server.

## JupyterLab? What about Jupyter notebooks?

JupyterLab is the next stage in the evolution of the Jupyter Notebook. If you have prior experience working with Jupyter notebooks, then you will have a good idea of what to expect from JupyterLab.

Experienced users of Jupyter notebooks interested in a more detailed discussion of the similarities and differences between the JupyterLab and Jupyter notebook user interfaces can find more information in the JupyterLab user interface documentation.

## Starting JupyterLab

You can start the JupyterLab server through the command line or through an application called Anaconda Navigator. Anaconda Navigator is included as part of the Anaconda Python distribution.

### macOS - Command Line

To start the JupyterLab server you will need to access the command line through the Terminal. There are two ways to open Terminal on Mac.

1. In your Applications folder, open Utilities and double-click on Terminal
2. Press Command + spacebar to launch Spotlight. Type Terminal and then double-click the search result or hit Enter

After you have launched Terminal, type the command to launch the JupyterLab server.

$jupyter lab  ### Windows Users - Command Line To start the JupyterLab server you will need to access the Anaconda Prompt. Press Windows Logo Key and search for Anaconda Prompt, click the result or press enter. After you have launched the Anaconda Prompt, type the command: $ jupyter lab


### Anaconda Navigator

To start a JupyterLab server from Anaconda Navigator you must first start Anaconda Navigator (click for detailed instructions on macOS, Windows, and Linux). You can search for Anaconda Navigator via Spotlight on macOS (Command + spacebar), the Windows search function (Windows Logo Key) or opening a terminal shell and executing the anaconda-navigator executable from the command line.

After you have launched Anaconda Navigator, click the Launch button under JupyterLab. You may need to scroll down to find it.

Here is a screenshot of an Anaconda Navigator page similar to the one that should open on either macOS or Windows.

And here is a screenshot of a JupyterLab landing page that should be similar to the one that opens in your default web browser after starting the JupyterLab server on either macOS or Windows.

## The JupyterLab Interface

JupyterLab has many features found in traditional integrated development environments (IDEs) but is focused on providing flexible building blocks for interactive, exploratory computing.

The JupyterLab Interface consists of the Menu Bar, a collapsable Left Side Bar, and the Main Work Area which contains tabs of documents and activities.

The Menu Bar at the top of JupyterLab has the top-level menus that expose various actions available in JupyterLab along with their keyboard shortcuts (where applicable). The following menus are included by default.

• File: Actions related to files and directories such as New, Open, Close, Save, etc. The File menu also includes the Shut Down action used to shutdown the JupyterLab server.
• Edit: Actions related to editing documents and other activities such as Undo, Cut, Copy, Paste, etc.
• View: Actions that alter the appearance of JupyterLab.
• Run: Actions for running code in different activities such as notebooks and code consoles (discussed below).
• Kernel: Actions for managing kernels. Kernels in Jupyter will be explained in more detail below.
• Tabs: A list of the open documents and activities in the main work area.
• Settings: Common JupyterLab settings can be configured using this menu. There is also an Advanced Settings Editor option in the dropdown menu that provides more fine-grained control of JupyterLab settings and configuration options.
• Help: A list of JupyterLab and kernel help links.

## Kernels

The JupyterLab docs define kernels as “separate processes started by the server that run your code in different programming languages and environments.” When we open a Jupyter Notebook, that starts a kernel - a process - that is going to run the code. In this lesson, we’ll be using the Jupyter ipython kernel which lets us run Python 3 code interactively.

Using other Jupyter kernels for other programming languages would let us write and execute code in other programming languages in the same JupyterLab interface, like R, Java, Julia, Ruby, JavaScript, Fortran, etc.

A screenshot of the default Menu Bar is provided below.

The left sidebar contains a number of commonly used tabs, such as a file browser (showing the contents of the directory where the JupyterLab server was launched), a list of running kernels and terminals, the command palette, and a list of open tabs in the main work area. A screenshot of the default Left Side Bar is provided below.

The left sidebar can be collapsed or expanded by selecting “Show Left Sidebar” in the View menu or by clicking on the active sidebar tab.

### Main Work Area

The main work area in JupyterLab enables you to arrange documents (notebooks, text files, etc.) and other activities (terminals, code consoles, etc.) into panels of tabs that can be resized or subdivided. A screenshot of the default Main Work Area is provided below.

Drag a tab to the center of a tab panel to move the tab to the panel. Subdivide a tab panel by dragging a tab to the left, right, top, or bottom of the panel. The work area has a single current activity. The tab for the current activity is marked with a colored top border (blue by default).

## Creating a Python script

• To start writing a new Python program click the Text File icon under the Other header in the Launcher tab of the Main Work Area.
• You can also create a new plain text file by selecting the New -> Text File from the File menu in the Menu Bar.
• To convert this plain text file to a Python program, select the Save File As action from the File menu in the Menu Bar and give your new text file a name that ends with the .py extension.
• The .py extension lets everyone (including the operating system) know that this text file is a Python program.
• This is convention, not a requirement.

## Creating a Jupyter Notebook

To open a new notebook click the Python 3 icon under the Notebook header in the Launcher tab in the main work area. You can also create a new notebook by selecting New -> Notebook from the File menu in the Menu Bar.

• Notebook files have the extension .ipynb to distinguish them from plain-text Python programs.
• Notebooks can be exported as Python scripts that can be run from the command line.

Below is a screenshot of a Jupyter notebook running inside JupyterLab. If you are interested in more details, then see the official notebook documentation.

## How It’s Stored

• The notebook file is stored in a format called JSON.
• Just like a webpage, what’s saved looks different from what you see in your browser.
• But this format allows Jupyter to mix source code, text, and images, all in one file.

## Arranging Documents into Panels of Tabs

In the JupyterLab Main Work Area you can arrange documents into panels of tabs. Here is an example from the official documentation.

First, create a text file, Python console, and terminal window and arrange them into three panels in the main work area. Next, create a notebook, terminal window, and text file and arrange them into three panels in the main work area. Finally, create your own combination of panels and tabs. What combination of panels and tabs do you think will be most useful for your workflow?

## Solution

After creating the necessary tabs, you can drag one of the tabs to the center of a panel to move the tab to the panel; next you can subdivide a tab panel by dragging a tab to the left, right, top, or bottom of the panel.

## Code vs. Text

Jupyter mixes code and text in different types of blocks, called cells. We often use the term “code” to mean “the source code of software written in a language such as Python”. A “code cell” in a Notebook is a cell that contains software; a “text cell” is one that contains ordinary prose written for human beings.

## The Notebook has Command and Edit modes.

• If you press Esc and Return alternately, the outer border of your code cell will change from gray to blue.
• These are the Command (gray) and Edit (blue) modes of your notebook.
• Command mode allows you to edit notebook-level features, and Edit mode changes the content of cells.
• When in Command mode (esc/gray),
• The b key will make a new cell below the currently selected cell.
• The a key will make one above.
• The x key will delete the current cell.
• The z key will undo your last cell operation (which could be a deletion, creation, etc).
• All actions can be done using the menus, but there are lots of keyboard shortcuts to speed things up.

## Command Vs. Edit

In the Jupyter notebook page are you currently in Command or Edit mode?
Switch between the modes. Use the shortcuts to generate a new cell. Use the shortcuts to delete a cell. Use the shortcuts to undo the last cell operation you performed.

## Solution

Command mode has a grey border and Edit mode has a blue border. Use Esc and Return to switch between modes. You need to be in Command mode (Press Esc if your cell is blue). Type b or a. You need to be in Command mode (Press Esc if your cell is blue). Type x. You need to be in Command mode (Press Esc if your cell is blue). Type z.

### Use the keyboard and mouse to select and edit cells.

• Pressing the Return key turns the border blue and engages Edit mode, which allows you to type within the cell.
• Because we want to be able to write many lines of code in a single cell, pressing the Return key when in Edit mode (blue) moves the cursor to the next line in the cell just like in a text editor.
• We need some other way to tell the Notebook we want to run what’s in the cell.
• Notice that the Return and Shift keys on the right of the keyboard are right next to each other.

### The Notebook will turn Markdown into pretty-printed documentation.

• Notebooks can also render Markdown.
• A simple plain-text format for writing lists, links, and other things that might go into a web page.
• Equivalently, a subset of HTML that looks like what you’d send in an old-fashioned email.
• Turn the current cell into a Markdown cell by entering the Command mode (Esc/gray) and press the M key.
• In [ ]: will disappear to show it is no longer a code cell and you will be able to write in Markdown.
• Turn the current cell into a Code cell by entering the Command mode (Esc/gray) and press the y key.

### Markdown does most of what HTML does.

*   Use asterisks
*   to create
*   bullet lists.

• Use asterisks
• to create
• bullet lists.
1.  Use numbers
1.  to create
1.  numbered lists.

1. Use numbers
2. to create
3. numbered lists.
*  You can use indents
*  To create sublists
*  of the same type
*  Or sublists
1. Of different
1. types

• You can use indents
• To create sublists
• of the same type
• Or sublists
1. Of different
2. types
# A Level-1 Heading


## A Level-2 Heading (etc.)


Line breaks
don't matter.

But blank lines
create new paragraphs.


Line breaks don’t matter.

But blank lines create new paragraphs.

[Create links](http://software-carpentry.org) with [...](...).

[data_carpentry]: http://datacarpentry.org


Create links with [...](...). Or use named links.

## Creating Lists in Markdown

Create a nested list in a Markdown cell in a notebook that looks like this:

1. Get funding.
2. Do work.
• Design experiment.
• Collect data.
• Analyze.
3. Write up.
4. Publish.

## Solution

This challenge integrates both the numbered list and bullet list. Note that the bullet list is indented 2 spaces so that it is inline with the items of the numbered list.

1.  Get funding.
2.  Do work.
*   Design experiment.
*   Collect data.
*   Analyze.
3.  Write up.
4.  Publish.


## More Math

What is displayed when a Python cell in a notebook that contains several calculations is executed? For example, what happens when this cell is executed?

7 * 3
2 + 1


## Solution

Python returns the output of the last calculation.

3


## Change an Existing Cell from Code to Markdown

What happens if you write some Python in a code cell and then you switch it to a Markdown cell? For example, put the following in a code cell:

x = 6 * 7 + 12
print(x)


And then run it with Shift+Return to be sure that it works as a code cell. Now go back to the cell and use Esc then m to switch the cell to Markdown and “run” it with Shift+Return. What happened and how might this be useful?

## Solution

The Python code gets treated like Markdown text. The lines appear as if they are part of one contiguous paragraph. This could be useful to temporarily turn on and off cells in notebooks that get used for multiple purposes.

x = 6 * 7 + 12 print(x)


## Equations

Standard Markdown (such as we’re using for these notes) won’t render equations, but the Notebook will. Create a new Markdown cell and enter the following:

$\sum_{i=1}^{N} 2^{-i} \approx 1$


## Closing JupyterLab

• From the Menu Bar select the “File” menu and then choose “Shut Down” at the bottom of the dropdown menu. You will be prompted to confirm that you wish to shutdown the JupyterLab server (don’t forget to save your work!). Click “Shut Down” to shutdown the JupyterLab server.
• To restart the JupyterLab server you will need to re-run the following command from a shell.
$jupyter lab  ## Closing JupyterLab Practice closing and restarting the JupyterLab server. ## Key Points • Python scripts are plain text files. • Use the Jupyter Notebook for editing and running Python. • The Notebook has Command and Edit modes. • Use the keyboard and mouse to select and edit cells. • The Notebook will turn Markdown into pretty-printed documentation. • Markdown does most of what HTML does. # Variables and Assignment ## Overview Teaching: 10 min Exercises: 10 min Questions • How can I store data in programs? Objectives • Write programs that assign scalar values to variables and perform calculations with those values. • Correctly trace value changes in programs that use scalar assignment. ## Use variables to store values. • Variables are names for values. • In Python the = symbol assigns the value on the right to the name on the left. • The variable is created when a value is assigned to it. • Here, Python assigns an age to a variable age and a name in quotes to a variable first_name. age = 42 first_name = 'Ahmed'  • Variable names • can only contain letters, digits, and underscore _ (typically used to separate words in long variable names) • cannot start with a digit • are case sensitive (age, Age and AGE are three different variables) • Variable names that start with underscores like __alistairs_real_age have a special meaning so we won’t do that until we understand the convention. ## Use print to display values. • Python has a built-in function called print that prints things as text. • Call the function (i.e., tell Python to run it) by using its name. • Provide values to the function (i.e., the things to print) in parentheses. • To add a string to the printout, wrap the string in single or double quotes. • The values passed to the function are called arguments print(first_name, 'is', age, 'years old')  Ahmed is 42 years old  • print automatically puts a single space between items to separate them. • And wraps around to a new line at the end. ## Variables must be created before they are used. • If a variable doesn’t exist yet, or if the name has been mis-spelled, Python reports an error. (Unlike some languages, which “guess” a default value.) print(last_name)  --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-c1fbb4e96102> in <module>() ----> 1 print(last_name) NameError: name 'last_name' is not defined  • The last line of an error message is usually the most informative. • We will look at error messages in detail later. ## Variables Persist Between Cells Be aware that it is the order of execution of cells that is important in a Jupyter notebook, not the order in which they appear. Python will remember all the code that was run previously, including any variables you have defined, irrespective of the order in the notebook. Therefore if you define variables lower down the notebook and then (re)run cells further up, those defined further down will still be present. As an example, create two cells with the following content, in this order: print(myval)  myval = 1  If you execute this in order, the first cell will give an error. However, if you run the first cell after the second cell it will print out 1. To prevent confusion, it can be helpful to use the Kernel -> Restart & Run All option which clears the interpreter and runs everything from a clean slate going top to bottom. ## Variables can be used in calculations. • We can use variables in calculations just as if they were values. • Remember, we assigned the value 42 to age a few lines ago. age = age + 3 print('Age in three years:', age)  Age in three years: 45  ## Use an index to get a single character from a string. • The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string 'AB' is not the same as 'BA'. Because of this ordering, we can treat the string as a list of characters. • Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript. • Indices are numbered from 0. • Use the position’s index in square brackets to get the character at that position. atom_name = 'helium' print(atom_name[0])  h  ## Use a slice to get a substring. • A part of a string is called a substring. A substring can be as short as a single character. • An item in a list is called an element. Whenever we treat a string as if it were a list, the string’s elements are its individual characters. • A slice is a part of a string (or, more generally, a part of any list-like thing). • We take a slice with the notation [start:stop], where start is the integer index of the first element we want and stop is the integer index of the element just after the last element we want. • The difference between stop and start is the slice’s length. • Taking a slice does not change the contents of the original string. Instead, taking a slice returns a copy of part of the original string. atom_name = 'sodium' print(atom_name[0:3])  sod  ## Use the built-in function len to find the length of a string. print(len('helium'))  6  • Nested functions are evaluated from the inside out, like in mathematics. ## Python is case-sensitive. • Python thinks that upper- and lower-case letters are different, so Name and name are different variables. • There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now. ## Use meaningful variable names. • Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). flabadab = 42 ewr_422_yY = 'Ahmed' print(ewr_422_yY, 'is', flabadab, 'years old')  • Use meaningful variable names to help other people understand what the program does. • The most important “other person” is your future self. ## Swapping Values Fill the table showing the values of the variables in this program after each statement is executed. # Command # Value of x # Value of y # Value of swap # x = 1.0 # # # # y = 3.0 # # # # swap = x # # # # x = y # # # # y = swap # # # #  ## Solution # Command # Value of x # Value of y # Value of swap # x = 1.0 # 1.0 # not defined # not defined # y = 3.0 # 1.0 # 3.0 # not defined # swap = x # 1.0 # 3.0 # 1.0 # x = y # 3.0 # 3.0 # 1.0 # y = swap # 3.0 # 1.0 # 1.0 #  These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom. ## Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = 'left' position = initial initial = 'right'  ## Solution print(position)  left  The initial variable is assigned the value 'left'. In the second line, the position variable also receives the string value 'left'. In third line, the initial variable is given the value 'right', but the position variable retains its string value of 'left'. ## Challenge If you assign a = 123, what happens if you try to get the second digit of a via a[1]? ## Solution Numbers are not strings or sequences and Python will raise an error if you try to perform an index operation on a number. In the next lesson on types and type conversion we will learn more about types and how to convert between different types. If you want the Nth digit of a number you can convert it into a string using the str built-in function and then perform an index operation on that string. a = 123 print(a[1])  TypeError: 'int' object is not subscriptable  a = str(123) print(a[1])  2  ## Choosing a Name Which is a better variable name, m, min, or minutes? Why? Hint: think about which code you would rather inherit from someone who is leaving the lab: 1. ts = m * 60 + s 2. tot_sec = min * 60 + sec 3. total_seconds = minutes * 60 + seconds ## Solution minutes is better because min might mean something like “minimum” (and actually is an existing built-in function in Python that we will cover later). ## Slicing practice What does the following program print? atom_name = 'carbon' print('atom_name[1:3] is:', atom_name[1:3])  ## Solution atom_name[1:3] is: ar  ## Slicing concepts Given the following string: species_name = "Acacia buxifolia"  What would these expressions return? 1. species_name[2:8] 2. species_name[11:] (without a value after the colon) 3. species_name[:4] (without a value before the colon) 4. species_name[:] (just a colon) 5. species_name[11:-3] 6. species_name[-5:-3] 7. What happens when you choose a stop value which is out of range? (i.e., try species_name[0:20] or species_name[:103]) ## Solutions 1. species_name[2:8] returns the substring 'acia b' 2. species_name[11:] returns the substring 'folia', from position 11 until the end 3. species_name[:4] returns the substring 'Acac', from the start up to but not including position 4 4. species_name[:] returns the entire string 'Acacia buxifolia' 5. species_name[11:-3] returns the substring 'fo', from the 11th position to the third last position 6. species_name[-5:-3] also returns the substring 'fo', from the fifth last position to the third last 7. If a part of the slice is out of range, the operation does not fail. species_name[0:20] gives the same result as species_name[0:], and species_name[:103] gives the same result as species_name[:] ## Key Points • Use variables to store values. • Use print to display values. • Variables persist between cells. • Variables must be created before they are used. • Variables can be used in calculations. • Use an index to get a single character from a string. • Use a slice to get a substring. • Use the built-in function len to find the length of a string. • Python is case-sensitive. • Use meaningful variable names. # Data Types and Type Conversion ## Overview Teaching: 10 min Exercises: 10 min Questions • What kinds of data do programs store? • How can I convert one type to another? Objectives • Explain key differences between integers and floating point numbers. • Explain key differences between numbers and character strings. • Use built-in functions to convert between integers, floating point numbers, and strings. ## Every value has a type. • Every value in a program has a specific type. • Integer (int): represents positive or negative whole numbers like 3 or -512. • Floating point number (float): represents real numbers like 3.14159 or -2.5. • Character string (usually called “string”, str): text. • Written in either single quotes or double quotes (as long as they match). • The quote marks aren’t printed when the string is displayed. ## Use the built-in function type to find the type of a value. • Use the built-in function type to find out what type a value has. • Works on variables as well. • But remember: the value has the type — the variable is just a label. print(type(52))  <class 'int'>  fitness = 'average' print(type(fitness))  <class 'str'>  ## Types control what operations (or methods) can be performed on a given value. • A value’s type determines what the program can do to it. print(5 - 3)  2  print('hello' - 'h')  --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-2-67f5626a1e07> in <module>() ----> 1 print('hello' - 'h') TypeError: unsupported operand type(s) for -: 'str' and 'str'  ## You can use the “+” and “*” operators on strings. • “Adding” character strings concatenates them. full_name = 'Ahmed' + ' ' + 'Walsh' print(full_name)  Ahmed Walsh  • Multiplying a character string by an integer N creates a new string that consists of that character string repeated N times. • Since multiplication is repeated addition. separator = '=' * 10 print(separator)  ==========  ## Strings have a length (but numbers don’t). • The built-in function len counts the number of characters in a string. print(len(full_name))  11  • But numbers don’t have a length (not even zero). print(len(52))  --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-3-f769e8e8097d> in <module>() ----> 1 print(len(52)) TypeError: object of type 'int' has no len()  ## Must convert numbers to strings or vice versa when operating on them. • Cannot add numbers and strings. print(1 + '2')  --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-4-fe4f54a023c6> in <module>() ----> 1 print(1 + '2') TypeError: unsupported operand type(s) for +: 'int' and 'str'  • Not allowed because it’s ambiguous: should 1 + '2' be 3 or '12'? • Some types can be converted to other types by using the type name as a function. print(1 + int('2')) print(str(1) + '2')  3 12  ## Can mix integers and floats freely in operations. • Integers and floating-point numbers can be mixed in arithmetic. • Python 3 automatically converts integers to floats as needed. print('half is', 1 / 2.0) print('three squared is', 3.0 ** 2)  half is 0.5 three squared is 9.0  ## Variables only change value when something is assigned to them. • If we make one cell in a spreadsheet depend on another, and update the latter, the former updates automatically. • This does not happen in programming languages. variable_one = 1 variable_two = 5 * variable_one variable_one = 2 print('first is', variable_one, 'and second is', variable_two)  first is 2 and second is 5  • The computer reads the value of first when doing the multiplication, creates a new value, and assigns it to second. • After that, second does not remember where it came from. ## Fractions What type of value is 3.4? How can you find out? ## Solution It is a floating-point number (often abbreviated “float”). It is possible to find out by using the built-in function type(). print(type(3.4))  <class 'float'>  ## Automatic Type Conversion What type of value is 3.25 + 4? ## Solution It is a float: integers are automatically converted to floats as necessary. result = 3.25 + 4 print(result, 'is', type(result))  7.25 is <class 'float'>  ## Choose a Type What type of value (integer, floating point number, or character string) would you use to represent each of the following? Try to come up with more than one good answer for each problem. For example, in # 1, when would counting days with a floating point variable make more sense than using an integer? 1. Number of days since the start of the year. 2. Time elapsed from the start of the year until now in days. 3. Serial number of a piece of lab equipment. 4. A lab specimen’s age 5. Current population of a city. 6. Average population of a city over time. ## Solution The answers to the questions are: 1. Integer, since the number of days would lie between 1 and 365. 2. Floating point, since fractional days are required 3. Character string if serial number contains letters and numbers, otherwise integer if the serial number consists only of numerals 4. This will vary! How do you define a specimen’s age? whole days since collection (integer)? date and time (string)? 5. Choose floating point to represent population as large aggregates (eg millions), or integer to represent population in units of individuals. 6. Floating point number, since an average is likely to have a fractional part. ## Division Types In Python 3, the // operator performs integer (whole-number) floor division, the / operator performs floating-point division, and the % (or modulo) operator calculates and returns the remainder from integer division: print('5 // 3:', 5 // 3) print('5 / 3:', 5 / 3) print('5 % 3:', 5 % 3)  5 // 3: 1 5 / 3: 1.6666666666666667 5 % 3: 2  If num_subjects is the number of subjects taking part in a study, and num_per_survey is the number that can take part in a single survey, write an expression that calculates the number of surveys needed to reach everyone once. ## Solution We want the minimum number of surveys that reaches everyone once, which is the rounded up value of num_subjects/ num_per_survey. This is equivalent to performing a floor division with // and adding 1. Before the division we need to subtract 1 from the number of subjects to deal with the case where num_subjects is evenly divisible by num_per_survey. num_subjects = 600 num_per_survey = 42 num_surveys = (num_subjects - 1) // num_per_survey + 1 print(num_subjects, 'subjects,', num_per_survey, 'per survey:', num_surveys)  600 subjects, 42 per survey: 15  ## Strings to Numbers Where reasonable, float() will convert a string to a floating point number, and int() will convert a floating point number to an integer: print("string to float:", float("3.4")) print("float to int:", int(3.4))  string to float: 3.4 float to int: 3  If the conversion doesn’t make sense, however, an error message will occur. print("string to float:", float("Hello world!"))  --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-df3b790bf0a2> in <module> ----> 1 print("string to float:", float("Hello world!")) ValueError: could not convert string to float: 'Hello world!'  Given this information, what do you expect the following program to do? What does it actually do? Why do you think it does that? print("fractional string to int:", int("3.4"))  ## Solution What do you expect this program to do? It would not be so unreasonable to expect the Python 3 int command to convert the string “3.4” to 3.4 and an additional type conversion to 3. After all, Python 3 performs a lot of other magic - isn’t that part of its charm? int("3.4")  --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-ec6729dfccdc> in <module> ----> 1 int("3.4") ValueError: invalid literal for int() with base 10: '3.4'  However, Python 3 throws an error. Why? To be consistent, possibly. If you ask Python to perform two consecutive typecasts, you must convert it explicitly in code. int(float("3.4"))  3  ## Arithmetic with Different Types Which of the following will return the floating point number 2.0? Note: there may be more than one right answer. first = 1.0 second = "1" third = "1.1"  1. first + float(second) 2. float(second) + float(third) 3. first + int(third) 4. first + int(float(third)) 5. int(first) + int(float(third)) 6. 2.0 * second ## Solution Answer: 1 and 4 ## Complex Numbers Python provides complex numbers, which are written as 1.0+2.0j. If val is a complex number, its real and imaginary parts can be accessed using dot notation as val.real and val.imag. a_complex_number = 6 + 2j print(a_complex_number.real) print(a_complex_number.imag)  6.0 2.0  1. Why do you think Python uses j instead of i for the imaginary part? 2. What do you expect 1 + 2j + 3 to produce? 3. What do you expect 4j to be? What about 4 j or 4 + j? ## Solution 1. Standard mathematics treatments typically use i to denote an imaginary number. However, from media reports it was an early convention established from electrical engineering that now presents a technically expensive area to change. Stack Overflow provides additional explanation and discussion. 2. (4+2j) 3. 4j and Syntax Error: invalid syntax. In the latter cases, j is considered a variable and the statement depends on if j is defined and if so, its assigned value. ## Key Points • Every value has a type. • Use the built-in function type to find the type of a value. • Types control what operations can be done on values. • Strings can be added and multiplied. • Strings have a length (but numbers don’t). • Must convert numbers to strings or vice versa when operating on them. • Can mix integers and floats freely in operations. • Variables only change value when something is assigned to them. # Built-in Functions and Help ## Overview Teaching: 15 min Exercises: 10 min Questions • How can I use built-in functions? • How can I find out what they do? • What kind of errors can occur in programs? Objectives • Explain the purpose of functions. • Correctly call built-in Python functions. • Correctly nest calls to built-in functions. • Use help to display documentation for built-in functions. • Correctly describe situations in which SyntaxError and NameError occur. ## Use comments to add documentation to programs. # This sentence isn't executed by Python. adjustment = 0.5 # Neither is this - anything after '#' is ignored.  ## A function may take zero or more arguments. • We have seen some functions already — now let’s take a closer look. • An argument is a value passed into a function. • len takes exactly one. • int, str, and float create a new value from an existing one. • print takes zero or more. • print with no arguments prints a blank line. • Must always use parentheses, even if they’re empty, so that Python knows a function is being called. print('before') print() print('after')  before after  ## Every function returns something. • Every function call produces some result. • If the function doesn’t have a useful result to return, it usually returns the special value None. None is a Python object that stands in anytime there is no value. result = print('example') print('result of print is', result)  example result of print is None  ## Commonly-used built-in functions include max, min, and round. • Use max to find the largest value of one or more values. • Use min to find the smallest. • Both work on character strings as well as numbers. • “Larger” and “smaller” use (0-9, A-Z, a-z) to compare letters. print(max(1, 2, 3)) print(min('a', 'A', '0'))  3 0  ## Functions may only work for certain (combinations of) arguments. • max and min must be given at least one argument. • “Largest of the empty set” is a meaningless question. • And they must be given things that can meaningfully be compared. print(max(1, 'a'))  TypeError Traceback (most recent call last) <ipython-input-52-3f049acf3762> in <module> ----> 1 print(max(1, 'a')) TypeError: '>' not supported between instances of 'str' and 'int'  ## Functions may have default values for some arguments. • round will round off a floating-point number. • By default, rounds to zero decimal places. round(3.712)  4  • We can specify the number of decimal places we want. round(3.712, 1)  3.7  ## Functions attached to objects are called methods • Functions take another form that will be common in the pandas episodes. • Methods have parentheses like functions, but come after the variable. • Some methods are used for internal Python operations, and are marked with double underlines. my_string = 'Hello world!' # creation of a string object print(len(my_string)) # the len function takes a string as an argument and returns the length of the string print(my_string.swapcase()) # calling the swapcase method on the my_string object print(my_string.__len__()) # calling the internal __len__ method on the my_string object, used by len(my_string)  12 hELLO WORLD! 12  • You might even see them chained together. They operate left to right. print(my_string.isupper()) # Not all the letters are uppercase print(my_string.upper()) # This capitalizes all the letters print(my_string.upper().isupper()) # Now all the letters are uppercase  False HELLO WORLD True  ## Use the built-in function help to get help for a function. • Every built-in function has online documentation. help(round)  Help on built-in function round in module builtins: round(number, ndigits=None) Round a number to a given precision in decimal digits. The return value is an integer if ndigits is omitted or None. Otherwise the return value has the same type as the number. ndigits may be negative.  ## The Jupyter Notebook has two ways to get help. • Option 1: Place the cursor near where the function is invoked in a cell (i.e., the function name or its parameters), • Hold down Shift, and press Tab. • Do this several times to expand the information returned. • Option 2: Type the function name in a cell with a question mark after it. Then run the cell. ## Python reports a syntax error when it can’t understand the source of a program. • Won’t even try to run the program if it can’t be parsed. # Forgot to close the quote marks around the string. name = 'Feng   File "<ipython-input-56-f42768451d55>", line 2 name = 'Feng ^ SyntaxError: EOL while scanning string literal  # An extra '=' in the assignment. age = = 52   File "<ipython-input-57-ccc3df3cf902>", line 2 age = = 52 ^ SyntaxError: invalid syntax  • Look more closely at the error message: print("hello world"   File "<ipython-input-6-d1cc229bf815>", line 1 print ("hello world" ^ SyntaxError: unexpected EOF while parsing  • The message indicates a problem on first line of the input (“line 1”). • In this case the “ipython-input” section of the file name tells us that we are working with input into IPython, the Python interpreter used by the Jupyter Notebook. • The -6- part of the filename indicates that the error occurred in cell 6 of our Notebook. • Next is the problematic line of code, indicating the problem with a ^ pointer. ## Python reports a runtime error when something goes wrong while a program is executing. age = 53 remaining = 100 - aege # mis-spelled 'age'  NameError Traceback (most recent call last) <ipython-input-59-1214fb6c55fc> in <module> 1 age = 53 ----> 2 remaining = 100 - aege # mis-spelled 'age' NameError: name 'aege' is not defined  • Fix syntax errors by reading the source and runtime errors by tracing execution. ## What Happens When 1. Explain in simple terms the order of operations in the following program: when does the addition happen, when does the subtraction happen, when is each function called, etc. 2. What is the final value of radiance? radiance = 1.0 radiance = max(2.1, 2.0 + min(radiance, 1.1 * radiance - 0.5))  ## Solution 1. Order of operations: 1. 1.1 * radiance = 1.1 2. 1.1 - 0.5 = 0.6 3. min(radiance, 0.6) = 0.6 4. 2.0 + 0.6 = 2.6 5. max(2.1, 2.6) = 2.6 2. At the end, radiance = 2.6 ## Spot the Difference 1. Predict what each of the print statements in the program below will print. 2. Does max(len(rich), poor) run or produce an error message? If it runs, does its result make any sense? easy_string = "abc" print(max(easy_string)) rich = "gold" poor = "tin" print(max(rich, poor)) print(max(len(rich), len(poor)))  ## Solution print(max(easy_string))  c  print(max(rich, poor))  tin  print(max(len(rich), len(poor)))  4  max(len(rich), poor) throws a TypeError. This turns into max(4, 'tin') and as we discussed earlier a string and integer cannot meaningfully be compared. TypeError Traceback (most recent call last) <ipython-input-65-bc82ad05177a> in <module> ----> 1 max(len(rich), poor) TypeError: '>' not supported between instances of 'str' and 'int'  ## Why Not? Why is it that max and min do not return None when they are called with no arguments? ## Solution max and min return TypeErrors in this case because the correct number of parameters was not supplied. If it just returned None, the error would be much harder to trace as it would likely be stored into a variable and used later in the program, only to likely throw a runtime error. ## Last Character of a String If Python starts counting from zero, and len returns the number of characters in a string, what index expression will get the last character in the string name? (Note: we will see a simpler way to do this in a later episode.) ## Solution name[len(name) - 1] ## Explore the Python docs! The official Python documentation is arguably the most complete source of information about the language. It is available in different languages and contains a lot of useful resources. The Built-in Functions page contains a catalogue of all of these functions, including the ones that we’ve covered in this lesson. Some of these are more advanced and unnecessary at the moment, but others are very simple and useful. ## Key Points • Use comments to add documentation to programs. • A function may take zero or more arguments. • Commonly-used built-in functions include max, min, and round. • Functions may only work for certain (combinations of) arguments. • Functions may have default values for some arguments. • Use the built-in function help to get help for a function. • The Jupyter Notebook has two ways to get help. • Every function returns something. • Python reports a syntax error when it can’t understand the source of a program. • Python reports a runtime error when something goes wrong while a program is executing. • Fix syntax errors by reading the source code, and runtime errors by tracing the program’s execution. # Morning Coffee ## Overview Teaching: 0 min Exercises: 0 min Questions Objectives # Reflection exercise Over coffee, reflect on and discuss the following: • What are the different kinds of errors Python will report? • Did the code always produce the results you expected? If not, why? • Is there something we can do to prevent errors when we write code? ## Key Points # Libraries ## Overview Teaching: 10 min Exercises: 10 min Questions • How can I use software that other people have written? • How can I find out what that software does? Objectives • Explain what software libraries are and why programmers create and use them. • Write programs that import and use modules from Python’s standard library. • Find and read documentation for the standard library interactively (in the interpreter) and online. ## Most of the power of a programming language is in its libraries. • A library is a collection of files (called modules) that contains functions for use by other programs. • May also contain data values (e.g., numerical constants) and other things. • Library’s contents are supposed to be related, but there’s no way to enforce that. • The Python standard library is an extensive suite of modules that comes with Python itself. • Many additional libraries are available from PyPI (the Python Package Index). • We will see later how to write new libraries. ## Libraries and modules A library is a collection of modules, but the terms are often used interchangeably, especially since many libraries only consist of a single module, so don’t worry if you mix them. ## A program must import a library module before using it. • Use import to load a library module into a program’s memory. • Then refer to things from the module as module_name.thing_name. • Python uses . to mean “part of”. • Using math, one of the modules in the standard library: import math print('pi is', math.pi) print('cos(pi) is', math.cos(math.pi))  pi is 3.141592653589793 cos(pi) is -1.0  • Have to refer to each item with the module’s name. • math.cos(pi) won’t work: the reference to pi doesn’t somehow “inherit” the function’s reference to math. ## Use help to learn about the contents of a library module. • Works just like help for a function. help(math)  Help on module math: NAME math MODULE REFERENCE http://docs.python.org/3/library/math The following documentation is automatically generated from the Python source files. It may be incomplete, incorrect or include features that are considered implementation detail and may vary between Python implementations. When in doubt, consult the module reference at the location listed above. DESCRIPTION This module is always available. It provides access to the mathematical functions defined by the C standard. FUNCTIONS acos(x, /) Return the arc cosine (measured in radians) of x. ⋮ ⋮ ⋮  ## Import specific items from a library module to shorten programs. • Use from ... import ... to load only specific items from a library module. • Then refer to them directly without library name as prefix. from math import cos, pi print('cos(pi) is', cos(pi))  cos(pi) is -1.0  ## Create an alias for a library module when importing it to shorten programs. • Use import ... as ... to give a library a short alias while importing it. • Then refer to items in the library using that shortened name. import math as m print('cos(pi) is', m.cos(m.pi))  cos(pi) is -1.0  • Commonly used for libraries that are frequently used or have long names. • E.g., the matplotlib plotting library is often aliased as mpl. • But can make programs harder to understand, since readers must learn your program’s aliases. ## Exploring the Math Module 1. What function from the math module can you use to calculate a square root without using sqrt? 2. Since the library contains this function, why does sqrt exist? ## Solution 1. Using help(math) we see that we’ve got pow(x,y) in addition to sqrt(x), so we could use pow(x, 0.5) to find a square root. 2. The sqrt(x) function is arguably more readable than pow(x, 0.5) when implementing equations. Readability is a cornerstone of good programming, so it makes sense to provide a special function for this specific common case. Also, the design of Python’s math library has its origin in the C standard, which includes both sqrt(x) and pow(x,y), so a little bit of the history of programming is showing in Python’s function names. ## Locating the Right Module You want to select a random character from a string: bases = 'ACTTGCTTGAC'  1. Which standard library module could help you? 2. Which function would you select from that module? Are there alternatives? 3. Try to write a program that uses the function. ## Solution The random module seems like it could help. The string has 11 characters, each having a positional index from 0 to 10. You could use the random.randrange or random.randint functions to get a random integer between 0 and 10, and then select the bases character at that index: from random import randrange random_index = randrange(len(bases)) print(bases[random_index])  or more compactly: from random import randrange print(bases[randrange(len(bases))])  Perhaps you found the random.sample function? It allows for slightly less typing but might be a bit harder to understand just by reading: from random import sample print(sample(bases, 1)[0])  Note that this function returns a list of values. We will learn about lists in episode 11. The simplest and shortest solution is the random.choice function that does exactly what we want: from random import choice print(choice(bases))  ## Jigsaw Puzzle (Parson’s Problem) Programming Example Rearrange the following statements so that a random DNA base is printed and its index in the string. Not all statements may be needed. Feel free to use/add intermediate variables. bases="ACTTGCTTGAC" import math import random ___ = random.randrange(n_bases) ___ = len(bases) print("random base ", bases[___], "base index", ___)  ## Solution import math import random bases = "ACTTGCTTGAC" n_bases = len(bases) idx = random.randrange(n_bases) print("random base", bases[idx], "base index", idx)  ## When Is Help Available? When a colleague of yours types help(math), Python reports an error: NameError: name 'math' is not defined  What has your colleague forgotten to do? ## Solution Importing the math module (import math) ## Importing With Aliases 1. Fill in the blanks so that the program below prints 90.0. 2. Rewrite the program so that it uses import without as. 3. Which form do you find easier to read? import math as m angle = ____.degrees(____.pi / 2) print(____)  ## Solution import math as m angle = m.degrees(m.pi / 2) print(angle)  can be written as import math angle = math.degrees(math.pi / 2) print(angle)  Since you just wrote the code and are familiar with it, you might actually find the first version easier to read. But when trying to read a huge piece of code written by someone else, or when getting back to your own huge piece of code after several months, non-abbreviated names are often easier, except where there are clear abbreviation conventions. ## There Are Many Ways To Import Libraries! Match the following print statements with the appropriate library calls. Print commands: 1. print("sin(pi/2) =", sin(pi/2)) 2. print("sin(pi/2) =", m.sin(m.pi/2)) 3. print("sin(pi/2) =", math.sin(math.pi/2)) Library calls: 1. from math import sin, pi 2. import math 3. import math as m 4. from math import * ## Solution 1. Library calls 1 and 4. In order to directly refer to sin and pi without the library name as prefix, you need to use the from ... import ... statement. Whereas library call 1 specifically imports the two functions sin and pi, library call 4 imports all functions in the math module. 2. Library call 3. Here sin and pi are referred to with a shortened library name m instead of math. Library call 3 does exactly that using the import ... as ... syntax - it creates an alias for math in the form of the shortened name m. 3. Library call 2. Here sin and pi are referred to with the regular library name math, so the regular import ... call suffices. Note: although library call 4 works, importing all names from a module using a wildcard import is not recommended as it makes it unclear which names from the module are used in the code. In general it is best to make your imports as specific as possible and to only import what your code uses. In library call 1, the import statement explicitly tells us that the sin function is imported from the math module, but library call 4 does not convey this information. ## Importing Specific Items 1. Fill in the blanks so that the program below prints 90.0. 2. Do you find this version easier to read than preceding ones? 3. Why wouldn’t programmers always use this form of import? ____ math import ____, ____ angle = degrees(pi / 2) print(angle)  ## Solution from math import degrees, pi angle = degrees(pi / 2) print(angle)  Most likely you find this version easier to read since it’s less dense. The main reason not to use this form of import is to avoid name clashes. For instance, you wouldn’t import degrees this way if you also wanted to use the name degrees for a variable or function of your own. Or if you were to also import a function named degrees from another library. ## Reading Error Messages 1. Read the code below and try to identify what the errors are without running it. 2. Run the code, and read the error message. What type of error is it? from math import log log(0)  ## Solution --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-1-d72e1d780bab> in <module> 1 from math import log ----> 2 log(0) ValueError: math domain error  1. The logarithm of x is only defined for x > 0, so 0 is outside the domain of the function. 2. You get an error of type ValueError, indicating that the function received an inappropriate argument value. The additional message “math domain error” makes it clearer what the problem is. ## Key Points • Most of the power of a programming language is in its libraries. • A program must import a library module in order to use it. • Use help to learn about the contents of a library module. • Import specific items from a library to shorten programs. • Create an alias for a library when importing it to shorten programs. # Reading Tabular Data into DataFrames ## Overview Teaching: 10 min Exercises: 10 min Questions • How can I read tabular data? Objectives • Import the Pandas library. • Use Pandas to load a simple CSV data set. • Get some basic information about a Pandas DataFrame. ## Use the Pandas library to do statistics on tabular data. • Pandas is a widely-used Python library for statistics, particularly on tabular data. • Borrows many features from R’s dataframes. • A 2-dimensional table whose columns have names and potentially have different data types. • Load it with import pandas as pd. The alias pd is commonly used for Pandas. • Read a Comma Separated Values (CSV) data file with pd.read_csv. • Argument is the name of the file to be read. • Assign result to a variable to store the data that was read. import pandas as pd data = pd.read_csv('data/gapminder_gdp_oceania.csv') print(data)   country gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \ 0 Australia 10039.59564 10949.64959 12217.22686 1 New Zealand 10556.57566 12247.39532 13175.67800 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 \ 0 14526.12465 16788.62948 18334.19751 19477.00928 1 14463.91893 16046.03728 16233.71770 17632.41040 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 \ 0 21888.88903 23424.76683 26997.93657 30687.75473 1 19007.19129 18363.32494 21050.41377 23189.80135 gdpPercap_2007 0 34435.36744 1 25185.00911  • The columns in a dataframe are the observed variables, and the rows are the observations. • Pandas uses backslash \ to show wrapped lines when output is too wide to fit the screen. ## File Not Found Our lessons store their data files in a data sub-directory, which is why the path to the file is data/gapminder_gdp_oceania.csv. If you forget to include data/, or if you include it but your copy of the file is somewhere else, you will get a runtime error that ends with a line like this: FileNotFoundError: [Errno 2] No such file or directory: 'data/gapminder_gdp_oceania.csv'  ## Use index_col to specify that a column’s values should be used as row headings. • Row headings are numbers (0 and 1 in this case). • Really want to index by country. • Pass the name of the column to read_csv as its index_col parameter to do this. data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country') print(data)   gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \ country Australia 10039.59564 10949.64959 12217.22686 14526.12465 New Zealand 10556.57566 12247.39532 13175.67800 14463.91893 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \ country Australia 16788.62948 18334.19751 19477.00928 21888.88903 New Zealand 16046.03728 16233.71770 17632.41040 19007.19129 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 country Australia 23424.76683 26997.93657 30687.75473 34435.36744 New Zealand 18363.32494 21050.41377 23189.80135 25185.00911  ## Use the DataFrame.info() method to find out more about a dataframe. data.info()  <class 'pandas.core.frame.DataFrame'> Index: 2 entries, Australia to New Zealand Data columns (total 12 columns): gdpPercap_1952 2 non-null float64 gdpPercap_1957 2 non-null float64 gdpPercap_1962 2 non-null float64 gdpPercap_1967 2 non-null float64 gdpPercap_1972 2 non-null float64 gdpPercap_1977 2 non-null float64 gdpPercap_1982 2 non-null float64 gdpPercap_1987 2 non-null float64 gdpPercap_1992 2 non-null float64 gdpPercap_1997 2 non-null float64 gdpPercap_2002 2 non-null float64 gdpPercap_2007 2 non-null float64 dtypes: float64(12) memory usage: 208.0+ bytes  • This is a DataFrame • Two rows named 'Australia' and 'New Zealand' • Twelve columns, each of which has two actual 64-bit floating point values. • We will talk later about null values, which are used to represent missing observations. • Uses 208 bytes of memory. ## The DataFrame.columns variable stores information about the dataframe’s columns. • Note that this is data, not a method. (It doesn’t have parentheses.) • Like math.pi. • So do not use () to try to call it. • Called a member variable, or just member. print(data.columns)  Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967', 'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987', 'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'], dtype='object')  ## Use DataFrame.T to transpose a dataframe. • Sometimes want to treat columns as rows and vice versa. • Transpose (written .T) doesn’t copy the data, just changes the program’s view of it. • Like columns, it is a member variable. print(data.T)  country Australia New Zealand gdpPercap_1952 10039.59564 10556.57566 gdpPercap_1957 10949.64959 12247.39532 gdpPercap_1962 12217.22686 13175.67800 gdpPercap_1967 14526.12465 14463.91893 gdpPercap_1972 16788.62948 16046.03728 gdpPercap_1977 18334.19751 16233.71770 gdpPercap_1982 19477.00928 17632.41040 gdpPercap_1987 21888.88903 19007.19129 gdpPercap_1992 23424.76683 18363.32494 gdpPercap_1997 26997.93657 21050.41377 gdpPercap_2002 30687.75473 23189.80135 gdpPercap_2007 34435.36744 25185.00911  ## Use DataFrame.describe() to get summary statistics about data. DataFrame.describe() gets the summary statistics of only the columns that have numerical data. All other columns are ignored, unless you use the argument include='all'. print(data.describe())   gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \ count 2.000000 2.000000 2.000000 2.000000 mean 10298.085650 11598.522455 12696.452430 14495.021790 std 365.560078 917.644806 677.727301 43.986086 min 10039.595640 10949.649590 12217.226860 14463.918930 25% 10168.840645 11274.086022 12456.839645 14479.470360 50% 10298.085650 11598.522455 12696.452430 14495.021790 75% 10427.330655 11922.958888 12936.065215 14510.573220 max 10556.575660 12247.395320 13175.678000 14526.124650 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \ count 2.00000 2.000000 2.000000 2.000000 mean 16417.33338 17283.957605 18554.709840 20448.040160 std 525.09198 1485.263517 1304.328377 2037.668013 min 16046.03728 16233.717700 17632.410400 19007.191290 25% 16231.68533 16758.837652 18093.560120 19727.615725 50% 16417.33338 17283.957605 18554.709840 20448.040160 75% 16602.98143 17809.077557 19015.859560 21168.464595 max 16788.62948 18334.197510 19477.009280 21888.889030 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 count 2.000000 2.000000 2.000000 2.000000 mean 20894.045885 24024.175170 26938.778040 29810.188275 std 3578.979883 4205.533703 5301.853680 6540.991104 min 18363.324940 21050.413770 23189.801350 25185.009110 25% 19628.685413 22537.294470 25064.289695 27497.598692 50% 20894.045885 24024.175170 26938.778040 29810.188275 75% 22159.406358 25511.055870 28813.266385 32122.777857 max 23424.766830 26997.936570 30687.754730 34435.367440  • Not particularly useful with just two records, but very helpful when there are thousands. ## Reading Other Data Read the data in gapminder_gdp_americas.csv (which should be in the same directory as gapminder_gdp_oceania.csv) into a variable called americas and display its summary statistics. ## Solution To read in a CSV, we use pd.read_csv and pass the filename 'data/gapminder_gdp_americas.csv' to it. We also once again pass the column name 'country' to the parameter index_col in order to index by country. The summary statistics can be displayed with the DataFrame.describe() method. americas = pd.read_csv('data/gapminder_gdp_americas.csv', index_col='country') americas.describe()  ## Inspecting Data After reading the data for the Americas, use help(americas.head) and help(americas.tail) to find out what DataFrame.head and DataFrame.tail do. 1. What method call will display the first three rows of this data? 2. What method call will display the last three columns of this data? (Hint: you may need to change your view of the data.) ## Solution 1. We can check out the first five rows of americas by executing americas.head() (allowing us to view the head of the DataFrame). We can specify the number of rows we wish to see by specifying the parameter n in our call to americas.head(). To view the first three rows, execute: americas.head(n=3)   continent gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 \ country Argentina Americas 5911.315053 6856.856212 7133.166023 Bolivia Americas 2677.326347 2127.686326 2180.972546 Brazil Americas 2108.944355 2487.365989 3336.585802 gdpPercap_1967 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 \ country Argentina 8052.953021 9443.038526 10079.026740 8997.897412 Bolivia 2586.886053 2980.331339 3548.097832 3156.510452 Brazil 3429.864357 4985.711467 6660.118654 7030.835878 gdpPercap_1987 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 \ country Argentina 9139.671389 9308.418710 10967.281950 8797.640716 Bolivia 2753.691490 2961.699694 3326.143191 3413.262690 Brazil 7807.095818 6950.283021 7957.980824 8131.212843 gdpPercap_2007 country Argentina 12779.379640 Bolivia 3822.137084 Brazil 9065.800825  2. To check out the last three rows of americas, we would use the command, americas.tail(n=3), analogous to head() used above. However, here we want to look at the last three columns so we need to change our view and then use tail(). To do so, we create a new DataFrame in which rows and columns are switched: americas_flipped = americas.T  We can then view the last three columns of americas by viewing the last three rows of americas_flipped: americas_flipped.tail(n=3)  country Argentina Bolivia Brazil Canada Chile Colombia \ gdpPercap_1997 10967.3 3326.14 7957.98 28954.9 10118.1 6117.36 gdpPercap_2002 8797.64 3413.26 8131.21 33329 10778.8 5755.26 gdpPercap_2007 12779.4 3822.14 9065.8 36319.2 13171.6 7006.58 country Costa Rica Cuba Dominican Republic Ecuador ... \ gdpPercap_1997 6677.05 5431.99 3614.1 7429.46 ... gdpPercap_2002 7723.45 6340.65 4563.81 5773.04 ... gdpPercap_2007 9645.06 8948.1 6025.37 6873.26 ... country Mexico Nicaragua Panama Paraguay Peru Puerto Rico \ gdpPercap_1997 9767.3 2253.02 7113.69 4247.4 5838.35 16999.4 gdpPercap_2002 10742.4 2474.55 7356.03 3783.67 5909.02 18855.6 gdpPercap_2007 11977.6 2749.32 9809.19 4172.84 7408.91 19328.7 country Trinidad and Tobago United States Uruguay Venezuela gdpPercap_1997 8792.57 35767.4 9230.24 10165.5 gdpPercap_2002 11460.6 39097.1 7727 8605.05 gdpPercap_2007 18008.5 42951.7 10611.5 11415.8  This shows the data that we want, but we may prefer to display three columns instead of three rows, so we can flip it back: americas_flipped.tail(n=3).T  Note: we could have done the above in a single line of code by ‘chaining’ the commands: americas.T.tail(n=3).T  ## Reading Files in Other Directories The data for your current project is stored in a file called microbes.csv, which is located in a folder called field_data. You are doing analysis in a notebook called analysis.ipynb in a sibling folder called thesis: your_home_directory +-- field_data/ | +-- microbes.csv +-- thesis/ +-- analysis.ipynb  What value(s) should you pass to read_csv to read microbes.csv in analysis.ipynb? ## Solution We need to specify the path to the file of interest in the call to pd.read_csv. We first need to ‘jump’ out of the folder thesis using ‘../’ and then into the folder field_data using ‘field_data/’. Then we can specify the filename microbes.csv. The result is as follows: data_microbes = pd.read_csv('../field_data/microbes.csv')  ## Writing Data As well as the read_csv function for reading data from a file, Pandas provides a to_csv function to write dataframes to files. Applying what you’ve learned about reading from files, write one of your dataframes to a file called processed.csv. You can use help to get information on how to use to_csv. ## Solution In order to write the DataFrame americas to a file called processed.csv, execute the following command: americas.to_csv('processed.csv')  For help on to_csv, you could execute, for example: help(americas.to_csv)  Note that help(to_csv) throws an error! This is a subtlety and is due to the fact that to_csv is NOT a function in and of itself and the actual call is americas.to_csv. ## Key Points • Use the Pandas library to get basic statistics out of tabular data. • Use index_col to specify that a column’s values should be used as row headings. • Use DataFrame.info to find out more about a dataframe. • The DataFrame.columns variable stores information about the dataframe’s columns. • Use DataFrame.T to transpose a dataframe. • Use DataFrame.describe to get summary statistics about data. # Pandas DataFrames ## Overview Teaching: 15 min Exercises: 15 min Questions • How can I do statistical analysis of tabular data? Objectives • Select individual values from a Pandas dataframe. • Select entire rows or entire columns from a dataframe. • Select a subset of both rows and columns from a dataframe in a single operation. • Select a subset of a dataframe by a single Boolean criterion. ## Note about Pandas DataFrames/Series A DataFrame is a collection of Series; The DataFrame is the way Pandas represents a table, and Series is the data-structure Pandas use to represent a column. Pandas is built on top of the Numpy library, which in practice means that most of the methods defined for Numpy Arrays apply to Pandas Series/DataFrames. What makes Pandas so attractive is the powerful interface to access individual records of the table, proper handling of missing values, and relational-databases operations between DataFrames. ## Selecting values To access a value at the position [i,j] of a DataFrame, we have two options, depending on what is the meaning of i in use. Remember that a DataFrame provides an index as a way to identify the rows of the table; a row, then, has a position inside the table as well as a label, which uniquely identifies its entry in the DataFrame. ## Use DataFrame.iloc[..., ...] to select values by their (entry) position • Can specify location by numerical index analogously to 2D version of character selection in strings. import pandas as pd data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country') print(data.iloc[0, 0])  1601.056136  ## Use DataFrame.loc[..., ...] to select values by their (entry) label. • Can specify location by row name analogously to 2D version of dictionary keys. print(data.loc["Albania", "gdpPercap_1952"])  1601.056136  ## Use : on its own to mean all columns or all rows. • Just like Python’s usual slicing notation. print(data.loc["Albania", :])  gdpPercap_1952 1601.056136 gdpPercap_1957 1942.284244 gdpPercap_1962 2312.888958 gdpPercap_1967 2760.196931 gdpPercap_1972 3313.422188 gdpPercap_1977 3533.003910 gdpPercap_1982 3630.880722 gdpPercap_1987 3738.932735 gdpPercap_1992 2497.437901 gdpPercap_1997 3193.054604 gdpPercap_2002 4604.211737 gdpPercap_2007 5937.029526 Name: Albania, dtype: float64  • Would get the same result printing data.loc["Albania"] (without a second index). print(data.loc[:, "gdpPercap_1952"])  country Albania 1601.056136 Austria 6137.076492 Belgium 8343.105127 ⋮ ⋮ ⋮ Switzerland 14734.232750 Turkey 1969.100980 United Kingdom 9979.508487 Name: gdpPercap_1952, dtype: float64  • Would get the same result printing data["gdpPercap_1952"] • Also get the same result printing data.gdpPercap_1952 (not recommended, because easily confused with . notation for methods) ## Select multiple columns or rows using DataFrame.loc and a named slice. print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'])   gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 country Italy 8243.582340 10022.401310 12269.273780 Montenegro 4649.593785 5907.850937 7778.414017 Netherlands 12790.849560 15363.251360 18794.745670 Norway 13450.401510 16361.876470 18965.055510 Poland 5338.752143 6557.152776 8006.506993  In the above code, we discover that slicing using loc is inclusive at both ends, which differs from slicing using iloc, where slicing indicates everything up to but not including the final index. ## Result of slicing can be used in further operations. • Usually don’t just print a slice. • All the statistical operators that work on entire dataframes work the same way on slices. • E.g., calculate max of a slice. print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'].max())  gdpPercap_1962 13450.40151 gdpPercap_1967 16361.87647 gdpPercap_1972 18965.05551 dtype: float64  print(data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'].min())  gdpPercap_1962 4649.593785 gdpPercap_1967 5907.850937 gdpPercap_1972 7778.414017 dtype: float64  ## Use comparisons to select data based on value. • Comparison is applied element by element. • Returns a similarly-shaped dataframe of True and False. # Use a subset of data to keep output readable. subset = data.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972'] print('Subset of data:\n', subset) # Which values were greater than 10000 ? print('\nWhere are values large?\n', subset > 10000)  Subset of data: gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 country Italy 8243.582340 10022.401310 12269.273780 Montenegro 4649.593785 5907.850937 7778.414017 Netherlands 12790.849560 15363.251360 18794.745670 Norway 13450.401510 16361.876470 18965.055510 Poland 5338.752143 6557.152776 8006.506993 Where are values large? gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 country Italy False True True Montenegro False False False Netherlands True True True Norway True True True Poland False False False  ## Select values or NaN using a Boolean mask. • A frame full of Booleans is sometimes called a mask because of how it can be used. mask = subset > 10000 print(subset[mask])   gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 country Italy NaN 10022.40131 12269.27378 Montenegro NaN NaN NaN Netherlands 12790.84956 15363.25136 18794.74567 Norway 13450.40151 16361.87647 18965.05551 Poland NaN NaN NaN  • Get the value where the mask is true, and NaN (Not a Number) where it is false. • Useful because NaNs are ignored by operations like max, min, average, etc. print(subset[subset > 10000].describe())   gdpPercap_1962 gdpPercap_1967 gdpPercap_1972 count 2.000000 3.000000 3.000000 mean 13120.625535 13915.843047 16676.358320 std 466.373656 3408.589070 3817.597015 min 12790.849560 10022.401310 12269.273780 25% 12955.737547 12692.826335 15532.009725 50% 13120.625535 15363.251360 18794.745670 75% 13285.513523 15862.563915 18879.900590 max 13450.401510 16361.876470 18965.055510  ## Group By: split-apply-combine Pandas vectorizing methods and grouping operations are features that provide users much flexibility to analyse their data. For instance, let’s say we want to have a clearer view on how the European countries split themselves according to their GDP. 1. We may have a glance by splitting the countries in two groups during the years surveyed, those who presented a GDP higher than the European average and those with a lower GDP. 2. We then estimate a wealthy score based on the historical (from 1962 to 2007) values, where we account how many times a country has participated in the groups of lower or higher GDP mask_higher = data > data.mean() wealth_score = mask_higher.aggregate('sum', axis=1) / len(data.columns) wealth_score  country Albania 0.000000 Austria 1.000000 Belgium 1.000000 Bosnia and Herzegovina 0.000000 Bulgaria 0.000000 Croatia 0.000000 Czech Republic 0.500000 Denmark 1.000000 Finland 1.000000 France 1.000000 Germany 1.000000 Greece 0.333333 Hungary 0.000000 Iceland 1.000000 Ireland 0.333333 Italy 0.500000 Montenegro 0.000000 Netherlands 1.000000 Norway 1.000000 Poland 0.000000 Portugal 0.000000 Romania 0.000000 Serbia 0.000000 Slovak Republic 0.000000 Slovenia 0.333333 Spain 0.333333 Sweden 1.000000 Switzerland 1.000000 Turkey 0.000000 United Kingdom 1.000000 dtype: float64  Finally, for each group in the wealth_score table, we sum their (financial) contribution across the years surveyed using chained methods: data.groupby(wealth_score).sum()   gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967 \ 0.000000 36916.854200 46110.918793 56850.065437 71324.848786 0.333333 16790.046878 20942.456800 25744.935321 33567.667670 0.500000 11807.544405 14505.000150 18380.449470 21421.846200 1.000000 104317.277560 127332.008735 149989.154201 178000.350040 gdpPercap_1972 gdpPercap_1977 gdpPercap_1982 gdpPercap_1987 \ 0.000000 88569.346898 104459.358438 113553.768507 119649.599409 0.333333 45277.839976 53860.456750 59679.634020 64436.912960 0.500000 25377.727380 29056.145370 31914.712050 35517.678220 1.000000 215162.343140 241143.412730 263388.781960 296825.131210 gdpPercap_1992 gdpPercap_1997 gdpPercap_2002 gdpPercap_2007 0.000000 92380.047256 103772.937598 118590.929863 149577.357928 0.333333 67918.093220 80876.051580 102086.795210 122803.729520 0.500000 36310.666080 40723.538700 45564.308390 51403.028210 1.000000 315238.235970 346930.926170 385109.939210 427850.333420  ## Selection of Individual Values Assume Pandas has been imported into your notebook and the Gapminder GDP data for Europe has been loaded: import pandas as pd df = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')  Write an expression to find the Per Capita GDP of Serbia in 2007. ## Solution The selection can be done by using the labels for both the row (“Serbia”) and the column (“gdpPercap_2007”): print(df.loc['Serbia', 'gdpPercap_2007'])  The output is 9786.534714  ## Extent of Slicing 1. Do the two statements below produce the same output? 2. Based on this, what rule governs what is included (or not) in numerical slices and named slices in Pandas? print(df.iloc[0:2, 0:2]) print(df.loc['Albania':'Belgium', 'gdpPercap_1952':'gdpPercap_1962'])  ## Solution No, they do not produce the same output! The output of the first statement is:  gdpPercap_1952 gdpPercap_1957 country Albania 1601.056136 1942.284244 Austria 6137.076492 8842.598030  The second statement gives:  gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 country Albania 1601.056136 1942.284244 2312.888958 Austria 6137.076492 8842.598030 10750.721110 Belgium 8343.105127 9714.960623 10991.206760  Clearly, the second statement produces an additional column and an additional row compared to the first statement. What conclusion can we draw? We see that a numerical slice, 0:2, omits the final index (i.e. index 2) in the range provided, while a named slice, ‘gdpPercap_1952’:’gdpPercap_1962’, includes the final element. ## Reconstructing Data Explain what each line in the following short program does: what is in first, second, etc.? first = pd.read_csv('data/gapminder_all.csv', index_col='country') second = first[first['continent'] == 'Americas'] third = second.drop('Puerto Rico') fourth = third.drop('continent', axis = 1) fourth.to_csv('result.csv')  ## Solution Let’s go through this piece of code line by line. first = pd.read_csv('data/gapminder_all.csv', index_col='country')  This line loads the dataset containing the GDP data from all countries into a dataframe called first. The index_col='country' parameter selects which column to use as the row labels in the dataframe. second = first[first['continent'] == 'Americas']  This line makes a selection: only those rows of first for which the ‘continent’ column matches ‘Americas’ are extracted. Notice how the Boolean expression inside the brackets, first['continent'] == 'Americas', is used to select only those rows where the expression is true. Try printing this expression! Can you print also its individual True/False elements? (hint: first assign the expression to a variable) third = second.drop('Puerto Rico')  As the syntax suggests, this line drops the row from second where the label is ‘Puerto Rico’. The resulting dataframe third has one row less than the original dataframe second. fourth = third.drop('continent', axis = 1)  Again we apply the drop function, but in this case we are dropping not a row but a whole column. To accomplish this, we need to specify also the axis parameter (we want to drop the second column which has index 1). fourth.to_csv('result.csv')  The final step is to write the data that we have been working on to a csv file. Pandas makes this easy with the to_csv() function. The only required argument to the function is the filename. Note that the file will be written in the directory from which you started the Jupyter or Python session. ## Selecting Indices Explain in simple terms what idxmin and idxmax do in the short program below. When would you use these methods? data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country') print(data.idxmin()) print(data.idxmax())  ## Solution For each column in data, idxmin will return the index value corresponding to each column’s minimum; idxmax will do accordingly the same for each column’s maximum value. You can use these functions whenever you want to get the row index of the minimum/maximum value and not the actual minimum/maximum value. ## Practice with Selection Assume Pandas has been imported and the Gapminder GDP data for Europe has been loaded. Write an expression to select each of the following: 1. GDP per capita for all countries in 1982. 2. GDP per capita for Denmark for all years. 3. GDP per capita for all countries for years after 1985. 4. GDP per capita for each country in 2007 as a multiple of GDP per capita for that country in 1952. ## Solution 1: data['gdpPercap_1982']  2: data.loc['Denmark',:]  3: data.loc[:,'gdpPercap_1985':]  Pandas is smart enough to recognize the number at the end of the column label and does not give you an error, although no column named gdpPercap_1985 actually exists. This is useful if new columns are added to the CSV file later. 4: data['gdpPercap_2007']/data['gdpPercap_1952']  ## Many Ways of Access There are at least two ways of accessing a value or slice of a DataFrame: by name or index. However, there are many others. For example, a single column or row can be accessed either as a DataFrame or a Series object. Suggest different ways of doing the following operations on a DataFrame: 1. Access a single column 2. Access a single row 3. Access an individual DataFrame element 4. Access several columns 5. Access several rows 6. Access a subset of specific rows and columns 7. Access a subset of row and column ranges ## Solution 1. Access a single column: # by name data["col_name"] # as a Series data[["col_name"]] # as a DataFrame # by name using .loc data.T.loc["col_name"] # as a Series data.T.loc[["col_name"]].T # as a DataFrame # Dot notation (Series) data.col_name # by index (iloc) data.iloc[:, col_index] # as a Series data.iloc[:, [col_index]] # as a DataFrame # using a mask data.T[data.T.index == "col_name"].T  2. Access a single row: # by name using .loc data.loc["row_name"] # as a Series data.loc[["row_name"]] # as a DataFrame # by name data.T["row_name"] # as a Series data.T[["row_name"]].T as a DataFrame # by index data.iloc[row_index] # as a Series data.iloc[[row_index]] # as a DataFrame # using mask data[data.index == "row_name"]  3. Access an individual DataFrame element: # by column/row names data["column_name"]["row_name"] # as a Series data[["col_name"]].loc["row_name"] # as a Series data[["col_name"]].loc[["row_name"]] # as a DataFrame data.loc["row_name"]["col_name"] # as a value data.loc[["row_name"]]["col_name"] # as a Series data.loc[["row_name"]][["col_name"]] # as a DataFrame data.loc["row_name", "col_name"] # as a value data.loc[["row_name"], "col_name"] # as a Series. Preserves index. Column name is moved to .name. data.loc["row_name", ["col_name"]] # as a Series. Index is moved to .name. Sets index to column name. data.loc[["row_name"], ["col_name"]] # as a DataFrame (preserves original index and column name) # by column/row names: Dot notation data.col_name.row_name # by column/row indices data.iloc[row_index, col_index] # as a value data.iloc[[row_index], col_index] # as a Series. Preserves index. Column name is moved to .name data.iloc[row_index, [col_index]] # as a Series. Index is moved to .name. Sets index to column name. data.iloc[[row_index], [col_index]] # as a DataFrame (preserves original index and column name) # column name + row index data["col_name"][row_index] data.col_name[row_index] data["col_name"].iloc[row_index] # column index + row name data.iloc[:, [col_index]].loc["row_name"] # as a Series data.iloc[:, [col_index]].loc[["row_name"]] # as a DataFrame # using masks data[data.index == "row_name"].T[data.T.index == "col_name"].T  4. Access several columns: # by name data[["col1", "col2", "col3"]] data.loc[:, ["col1", "col2", "col3"]] # by index data.iloc[:, [col1_index, col2_index, col3_index]]  5. Access several rows # by name data.loc[["row1", "row2", "row3"]] # by index data.iloc[[row1_index, row2_index, row3_index]]  6. Access a subset of specific rows and columns # by names data.loc[["row1", "row2", "row3"], ["col1", "col2", "col3"]] # by indices data.iloc[[row1_index, row2_index, row3_index], [col1_index, col2_index, col3_index]] # column names + row indices data[["col1", "col2", "col3"]].iloc[[row1_index, row2_index, row3_index]] # column indices + row names data.iloc[:, [col1_index, col2_index, col3_index]].loc[["row1", "row2", "row3"]]  7. Access a subset of row and column ranges # by name data.loc["row1":"row2", "col1":"col2"] # by index data.iloc[row1_index:row2_index, col1_index:col2_index] # column names + row indices data.loc[:, "col1_name":"col2_name"].iloc[row1_index:row2_index] # column indices + row names data.iloc[:, col1_index:col2_index].loc["row1":"row2"]  ## Exploring available methods using the dir() function Python includes a dir() function that can be used to display all of the available methods (functions) that are built into a data object. In Episode 4, we used some methods with a string. But we can see many more are available by using dir(): my_string = 'Hello world!' # creation of a string object dir(my_string)  This command returns: ['__add__', ... '__subclasshook__', 'capitalize', 'casefold', 'center', ... 'upper', 'zfill']  You can use help() or Shift+Tab to get more information about what these methods do. Assume Pandas has been imported and the Gapminder GDP data for Europe has been loaded as data. Then, use dir() to find the function that prints out the median per-capita GDP across all European countries for each year that information is available. ## Solution Among many choices, dir() lists the median() function as a possibility. Thus, data.median()  ## Interpretation Poland’s borders have been stable since 1945, but changed several times in the years before then. How would you handle this if you were creating a table of GDP per capita for Poland for the entire twentieth century? ## Key Points • Use DataFrame.iloc[..., ...] to select values by integer location. • Use : on its own to mean all columns or all rows. • Select multiple columns or rows using DataFrame.loc and a named slice. • Result of slicing can be used in further operations. • Use comparisons to select data based on value. • Select values or NaN using a Boolean mask. # Plotting ## Overview Teaching: 15 min Exercises: 15 min Questions • How can I plot my data? • How can I save my plot for publishing? Objectives • Create a time series plot showing a single data set. • Create a scatter plot showing relationship between two data sets. ## matplotlib is the most widely used scientific plotting library in Python. import matplotlib.pyplot as plt  • Simple plots are then (fairly) simple to create. time = [0, 1, 2, 3] position = [0, 100, 200, 300] plt.plot(time, position) plt.xlabel('Time (hr)') plt.ylabel('Position (km)')  ## Display All Open Figures In our Jupyter Notebook example, running the cell should generate the figure directly below the code. The figure is also included in the Notebook document for future viewing. However, other Python environments like an interactive Python session started from a terminal or a Python script executed via the command line require an additional command to display the figure. Instruct matplotlib to show a figure: plt.show()  This command can also be used within a Notebook - for instance, to display multiple figures if several are created by a single cell. ## Plot data directly from a Pandas dataframe. • We can also plot Pandas dataframes. • This implicitly uses matplotlib.pyplot. • Before plotting, we convert the column headings from a string to integer data type, since they represent numerical values import pandas as pd data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country') # Extract year from last 4 characters of each column name # The current column names are structured as 'gdpPercap_(year)', # so we want to keep the (year) part only for clarity when plotting GDP vs. years # To do this we use strip(), which removes from the string the characters stated in the argument # This method works on strings, so we call str before strip() years = data.columns.str.strip('gdpPercap_') # Convert year values to integers, saving results back to dataframe data.columns = years.astype(int) data.loc['Australia'].plot()  ## Select and transform data, then plot it. data.T.plot() plt.ylabel('GDP per capita')  ## Many styles of plot are available. • For example, do a bar plot using a fancier style. plt.style.use('ggplot') data.T.plot(kind='bar') plt.ylabel('GDP per capita')  ## Data can also be plotted by calling the matplotlibplot function directly. • The command is plt.plot(x, y) • The color and format of markers can also be specified as an additional optional argument e.g., b- is a blue line, g-- is a green dashed line. ## Get Australia data from dataframe years = data.columns gdp_australia = data.loc['Australia'] plt.plot(years, gdp_australia, 'g--')  ## Can plot many sets of data together. # Select two countries' worth of data. gdp_australia = data.loc['Australia'] gdp_nz = data.loc['New Zealand'] # Plot with differently-colored markers. plt.plot(years, gdp_australia, 'b-', label='Australia') plt.plot(years, gdp_nz, 'g-', label='New Zealand') # Create legend. plt.legend(loc='upper left') plt.xlabel('Year') plt.ylabel('GDP per capita ($)')


Often when plotting multiple datasets on the same figure it is desirable to have a legend describing the data.

This can be done in matplotlib in two stages:

• Provide a label for each dataset in the figure:
plt.plot(years, gdp_australia, label='Australia')
plt.plot(years, gdp_nz, label='New Zealand')

• Instruct matplotlib to create the legend.
plt.legend()


By default matplotlib will attempt to place the legend in a suitable position. If you would rather specify a position this can be done with the loc= argument, e.g to place the legend in the upper left corner of the plot, specify loc='upper left'

• Plot a scatter plot correlating the GDP of Australia and New Zealand
• Use either plt.scatter or DataFrame.plot.scatter
plt.scatter(gdp_australia, gdp_nz)


data.T.plot.scatter(x = 'Australia', y = 'New Zealand')


## Minima and Maxima

Fill in the blanks below to plot the minimum GDP per capita over time for all the countries in Europe. Modify it again to plot the maximum GDP per capita over time for Europe.

data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
data_europe.____.plot(label='min')
data_europe.____
plt.legend(loc='best')
plt.xticks(rotation=90)


## Solution

data_europe = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')
data_europe.min().plot(label='min')
data_europe.max().plot(label='max')
plt.legend(loc='best')
plt.xticks(rotation=90)


## Correlations

Modify the example in the notes to create a scatter plot showing the relationship between the minimum and maximum GDP per capita among the countries in Asia for each year in the data set. What relationship do you see (if any)?

## Solution

data_asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')
data_asia.describe().T.plot(kind='scatter', x='min', y='max')


No particular correlations can be seen between the minimum and maximum gdp values year on year. It seems the fortunes of asian countries do not rise and fall together.

You might note that the variability in the maximum is much higher than that of the minimum. Take a look at the maximum and the max indexes:

data_asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')
data_asia.max().plot()
print(data_asia.idxmax())
print(data_asia.idxmin())


## Solution

Seems the variability in this value is due to a sharp drop after 1972. Some geopolitics at play perhaps? Given the dominance of oil producing countries, maybe the Brent crude index would make an interesting comparison? Whilst Myanmar consistently has the lowest gdp, the highest gdb nation has varied more notably.

## More Correlations

This short program creates a plot showing the correlation between GDP and life expectancy for 2007, normalizing marker size by population:

data_all = pd.read_csv('data/gapminder_all.csv', index_col='country')
data_all.plot(kind='scatter', x='gdpPercap_2007', y='lifeExp_2007',
s=data_all['pop_2007']/1e6)


Using online help and other resources, explain what each argument to plot does.

## Solution

A good place to look is the documentation for the plot function - help(data_all.plot).

kind - As seen already this determines the kind of plot to be drawn.

x and y - A column name or index that determines what data will be placed on the x and y axes of the plot

s - Details for this can be found in the documentation of plt.scatter. A single number or one value for each data point. Determines the size of the plotted points.

## Saving your plot to a file

If you are satisfied with the plot you see you may want to save it to a file, perhaps to include it in a publication. There is a function in the matplotlib.pyplot module that accomplishes this: savefig. Calling this function, e.g. with

plt.savefig('my_figure.png')


will save the current figure to the file my_figure.png. The file format will automatically be deduced from the file name extension (other formats are pdf, ps, eps and svg).

Note that functions in plt refer to a global figure variable and after a figure has been displayed to the screen (e.g. with plt.show) matplotlib will make this variable refer to a new empty figure. Therefore, make sure you call plt.savefig before the plot is displayed to the screen, otherwise you may find a file with an empty plot.

When using dataframes, data is often generated and plotted to screen in one line, and plt.savefig seems not to be a possible approach. One possibility to save the figure to file is then to

• save a reference to the current figure in a local variable (with plt.gcf)
• call the savefig class method from that variable.
data.plot(kind='bar')
fig = plt.gcf() # get current figure
fig.savefig('my_figure.png')


Whenever you are generating plots to go into a paper or a presentation, there are a few things you can do to make sure that everyone can understand your plots.

• Always make sure your text is large enough to read. Use the fontsize parameter in xlabel, ylabel, title, and legend, and tick_params with labelsize to increase the text size of the numbers on your axes.
• Similarly, you should make your graph elements easy to see. Use s to increase the size of your scatterplot markers and linewidth to increase the sizes of your plot lines.
• Using color (and nothing else) to distinguish between different plot elements will make your plots unreadable to anyone who is colorblind, or who happens to have a black-and-white office printer. For lines, the linestyle parameter lets you use different types of lines. For scatterplots, marker lets you change the shape of your points. If you’re unsure about your colors, you can use Coblis or Color Oracle to simulate what your plots would look like to those with colorblindness.

## Key Points

• matplotlib is the most widely used scientific plotting library in Python.

• Plot data directly from a Pandas dataframe.

• Select and transform data, then plot it.

• Many styles of plot are available: see the Python Graph Gallery for more options.

• Can plot many sets of data together.

# Lunch

## Overview

Teaching: 0 min
Exercises: 0 min
Questions
Objectives

Over lunch, reflect on and discuss the following:

• What sort of packages might you use in Python and why would you use them?
• How would data need to be formatted to be used in Pandas data frames? Would the data you have meet these requirements?
• What limitations or problems might you run into when thinking about how to apply what we’ve learned to your own projects or data?

# Lists

## Overview

Teaching: 10 min
Exercises: 10 min
Questions
• How can I store multiple values?

Objectives
• Explain why programs need collections of values.

• Write programs that create flat lists, index them, slice them, and modify them through assignment and method calls.

## A list stores many values in a single structure.

• Doing calculations with a hundred variables called pressure_001, pressure_002, etc., would be at least as slow as doing them by hand.
• Use a list to store many values together.
• Contained within square brackets [...].
• Values separated by commas ,.
• Use len to find out how many values are in a list.
pressures = [0.273, 0.275, 0.277, 0.275, 0.276]
print('pressures:', pressures)
print('length:', len(pressures))

pressures: [0.273, 0.275, 0.277, 0.275, 0.276]
length: 5


## Use an item’s index to fetch it from a list.

• Just like strings.
print('zeroth item of pressures:', pressures[0])
print('fourth item of pressures:', pressures[4])

zeroth item of pressures: 0.273
fourth item of pressures: 0.276


## Lists’ values can be replaced by assigning to them.

• Use an index expression on the left of assignment to replace a value.
pressures[0] = 0.265
print('pressures is now:', pressures)

pressures is now: [0.265, 0.275, 0.277, 0.275, 0.276]


## Appending items to a list lengthens it.

• Use list_name.append to add items to the end of a list.
primes = [2, 3, 5]
print('primes is initially:', primes)
primes.append(7)
print('primes has become:', primes)

primes is initially: [2, 3, 5]
primes has become: [2, 3, 5, 7]

• append is a method of lists.
• Like a function, but tied to a particular object.
• Use object_name.method_name to call methods.
• Deliberately resembles the way we refer to things in a library.
• We will meet other methods of lists as we go along.
• Use help(list) for a preview.
• extend is similar to append, but it allows you to combine two lists. For example:
teen_primes = [11, 13, 17, 19]
middle_aged_primes = [37, 41, 43, 47]
print('primes is currently:', primes)
primes.extend(teen_primes)
print('primes has now become:', primes)
primes.append(middle_aged_primes)
print('primes has finally become:', primes)

primes is currently: [2, 3, 5, 7]
primes has now become: [2, 3, 5, 7, 11, 13, 17, 19]
primes has finally become: [2, 3, 5, 7, 11, 13, 17, 19, [37, 41, 43, 47]]


Note that while extend maintains the “flat” structure of the list, appending a list to a list makes the result two-dimensional - the last element in primes is a list, not an integer.

## Use del to remove items from a list entirely.

• We use del list_name[index] to remove an element from a list (in the example, 9 is not a prime number) and thus shorten it.
• del is not a function or a method, but a statement in the language.
primes = [2, 3, 5, 7, 9]
print('primes before removing last item:', primes)
del primes[4]
print('primes after removing last item:', primes)

primes before removing last item: [2, 3, 5, 7, 9]
primes after removing last item: [2, 3, 5, 7]


## The empty list contains no values.

• Use [] on its own to represent a list that doesn’t contain any values.
• “The zero of lists.”
• Helpful as a starting point for collecting values (which we will see in the next episode).

## Lists may contain values of different types.

• A single list may contain numbers, strings, and anything else.
goals = [1, 'Create lists.', 2, 'Extract items from lists.', 3, 'Modify lists.']


## Character strings can be indexed like lists.

• Get single characters from a character string using indexes in square brackets.
element = 'carbon'
print('zeroth character:', element[0])
print('third character:', element[3])

zeroth character: c
third character: b


## Character strings are immutable.

• Cannot change the characters in a string after it has been created.
• Immutable: can’t be changed after creation.
• In contrast, lists are mutable: they can be modified in place.
• Python considers the string to be a single value with parts, not a collection of values.
element[0] = 'C'

TypeError: 'str' object does not support item assignment

• Lists and character strings are both collections.

## Indexing beyond the end of the collection is an error.

• Python reports an IndexError if we attempt to access a value that doesn’t exist.
• This is a kind of runtime error.
• Cannot be detected as the code is parsed because the index might be calculated based on data.
print('99th element of element is:', element[99])

IndexError: string index out of range


## Fill in the Blanks

Fill in the blanks so that the program below produces the output shown.

values = ____
values.____(1)
values.____(3)
values.____(5)
print('first time:', values)
values = values[____]
print('second time:', values)

first time: [1, 3, 5]
second time: [3, 5]


## Solution

values = []
values.append(1)
values.append(3)
values.append(5)
print('first time:', values)
values = values[1:]
print('second time:', values)


## How Large is a Slice?

If start and stop are both non-negative integers, how long is the list values[start:stop]?

## Solution

The list values[start:stop] has up to stop - start elements. For example, values[1:4] has the 3 elements values[1], values[2], and values[3]. Why ‘up to’? As we saw in episode 2, if stop is greater than the total length of the list values, we will still get a list back but it will be shorter than expected.

## From Strings to Lists and Back

Given this:

print('string to list:', list('tin'))
print('list to string:', ''.join(['g', 'o', 'l', 'd']))

string to list: ['t', 'i', 'n']
list to string: gold

1. What does list('some string') do?
2. What does '-'.join(['x', 'y', 'z']) generate?

## Solution

1. list('some string') converts a string into a list containing all of its characters.
2. join returns a string that is the concatenation of each string element in the list and adds the separator between each element in the list. This results in x-y-z. The separator between the elements is the string that provides this method.

## Working With the End

What does the following program print?

element = 'helium'
print(element[-1])

1. How does Python interpret a negative index?
2. If a list or string has N elements, what is the most negative index that can safely be used with it, and what location does that index represent?
3. If values is a list, what does del values[-1] do?
4. How can you display all elements but the last one without changing values? (Hint: you will need to combine slicing and negative indexing.)

## Solution

The program prints m.

1. Python interprets a negative index as starting from the end (as opposed to starting from the beginning). The last element is -1.
2. The last index that can safely be used with a list of N elements is element -N, which represents the first element.
3. del values[-1] removes the last element from the list.
4. values[:-1]

## Stepping Through a List

What does the following program print?

element = 'fluorine'
print(element[::2])
print(element[::-1])

1. If we write a slice as low:high:stride, what does stride do?
2. What expression would select all of the even-numbered items from a collection?

## Solution

The program prints

furn
eniroulf

1. stride is the step size of the slice.
2. The slice 1::2 selects all even-numbered items from a collection: it starts with element 1 (which is the second element, since indexing starts at 0), goes on until the end (since no end is given), and uses a step size of 2 (i.e., selects every second element).

## Slice Bounds

What does the following program print?

element = 'lithium'
print(element[0:20])
print(element[-1:3])


## Solution

lithium



The first statement prints the whole string, since the slice goes beyond the total length of the string. The second statement returns an empty string, because the slice goes “out of bounds” of the string.

## Sort and Sorted

What do these two programs print? In simple terms, explain the difference between sorted(letters) and letters.sort().

# Program A
letters = list('gold')
result = sorted(letters)
print('letters is', letters, 'and result is', result)

# Program B
letters = list('gold')
result = letters.sort()
print('letters is', letters, 'and result is', result)


## Solution

Program A prints

letters is ['g', 'o', 'l', 'd'] and result is ['d', 'g', 'l', 'o']


Program B prints

letters is ['d', 'g', 'l', 'o'] and result is None


sorted(letters) returns a sorted copy of the list letters (the original list letters remains unchanged), while letters.sort() sorts the list letters in-place and does not return anything.

## Copying (or Not)

What do these two programs print? In simple terms, explain the difference between new = old and new = old[:].

# Program A
old = list('gold')
new = old      # simple assignment
new[0] = 'D'
print('new is', new, 'and old is', old)

# Program B
old = list('gold')
new = old[:]   # assigning a slice
new[0] = 'D'
print('new is', new, 'and old is', old)


## Solution

Program A prints

new is ['D', 'o', 'l', 'd'] and old is ['D', 'o', 'l', 'd']


Program B prints

new is ['D', 'o', 'l', 'd'] and old is ['g', 'o', 'l', 'd']


new = old makes new a reference to the list old; new and old point towards the same object.

new = old[:] however creates a new list object new containing all elements from the list old; new and old are different objects.

## Key Points

• A list stores many values in a single structure.

• Use an item’s index to fetch it from a list.

• Lists’ values can be replaced by assigning to them.

• Appending items to a list lengthens it.

• Use del to remove items from a list entirely.

• The empty list contains no values.

• Lists may contain values of different types.

• Character strings can be indexed like lists.

• Character strings are immutable.

• Indexing beyond the end of the collection is an error.

# For Loops

## Overview

Teaching: 10 min
Exercises: 15 min
Questions
• How can I make a program do many things?

Objectives
• Explain what for loops are normally used for.

• Trace the execution of a simple (unnested) loop and correctly state the values of variables in each iteration.

• Write for loops that use the Accumulator pattern to aggregate values.

## A for loop executes commands once for each value in a collection.

• Doing calculations on the values in a list one by one is as painful as working with pressure_001, pressure_002, etc.
• A for loop tells Python to execute some statements once for each value in a list, a character string, or some other collection.
• “for each thing in this group, do these operations”
for number in [2, 3, 5]:
print(number)

• This for loop is equivalent to:
print(2)
print(3)
print(5)

• And the for loop’s output is:
2
3
5


## A for loop is made up of a collection, a loop variable, and a body.

for number in [2, 3, 5]:
print(number)

• The collection, [2, 3, 5], is what the loop is being run on.
• The body, print(number), specifies what to do for each value in the collection.
• The loop variable, number, is what changes for each iteration of the loop.
• The “current thing”.

## The first line of the for loop must end with a colon, and the body must be indented.

• The colon at the end of the first line signals the start of a block of statements.
• Python uses indentation rather than {} or begin/end to show nesting.
• Any consistent indentation is legal, but almost everyone uses four spaces.
for number in [2, 3, 5]:
print(number)

IndentationError: expected an indented block

• Indentation is always meaningful in Python.
firstName = "Jon"
lastName = "Smith"

  File "<ipython-input-7-f65f2962bf9c>", line 2
lastName = "Smith"
^
IndentationError: unexpected indent

• This error can be fixed by removing the extra spaces at the beginning of the second line.

## Loop variables can be called anything.

• As with all variables, loop variables are:
• Created on demand.
• Meaningless: their names can be anything at all.
for kitten in [2, 3, 5]:
print(kitten)


## The body of a loop can contain many statements.

• But no loop should be more than a few lines long.
• Hard for human beings to keep larger chunks of code in mind.
primes = [2, 3, 5]
for p in primes:
squared = p ** 2
cubed = p ** 3
print(p, squared, cubed)

2 4 8
3 9 27
5 25 125


## Use range to iterate over a sequence of numbers.

• The built-in function range produces a sequence of numbers.
• Not a list: the numbers are produced on demand to make looping over large ranges more efficient.
• range(N) is the numbers 0..N-1
• Exactly the legal indices of a list or character string of length N
print('a range is not a list: range(0, 3)')
for number in range(0, 3):
print(number)

a range is not a list: range(0, 3)
0
1
2


## The Accumulator pattern turns many values into one.

• A common pattern in programs is to:
1. Initialize an accumulator variable to zero, the empty string, or the empty list.
2. Update the variable with values from a collection.
# Sum the first 10 integers.
total = 0
for number in range(10):
total = total + (number + 1)
print(total)

55

• Read total = total + (number + 1) as:
• Add 1 to the current value of the loop variable number.
• Add that to the current value of the accumulator variable total.
• Assign that to total, replacing the current value.
• We have to add number + 1 because range produces 0..9, not 1..10.

## Classifying Errors

Is an indentation error a syntax error or a runtime error?

## Solution

An IndentationError is a syntax error. Programs with syntax errors cannot be started. A program with a runtime error will start but an error will be thrown under certain conditions.

## Tracing Execution

Create a table showing the numbers of the lines that are executed when this program runs, and the values of the variables after each line is executed.

total = 0
for char in "tin":
total = total + 1


## Solution

Line no Variables
1 total = 0
2 total = 0 char = ‘t’
3 total = 1 char = ‘t’
2 total = 1 char = ‘i’
3 total = 2 char = ‘i’
2 total = 2 char = ‘n’
3 total = 3 char = ‘n’

## Reversing a String

Fill in the blanks in the program below so that it prints “nit” (the reverse of the original character string “tin”).

original = "tin"
result = ____
for char in original:
result = ____
print(result)


## Solution

original = "tin"
result = ""
for char in original:
result = char + result
print(result)


## Practice Accumulating

Fill in the blanks in each of the programs below to produce the indicated result.

# Total length of the strings in the list: ["red", "green", "blue"] => 12
total = 0
for word in ["red", "green", "blue"]:
____ = ____ + len(word)
print(total)


## Solution

total = 0
for word in ["red", "green", "blue"]:
total = total + len(word)
print(total)

# List of word lengths: ["red", "green", "blue"] => [3, 5, 4]
lengths = ____
for word in ["red", "green", "blue"]:
lengths.____(____)
print(lengths)


## Solution

lengths = []
for word in ["red", "green", "blue"]:
lengths.append(len(word))
print(lengths)

# Concatenate all words: ["red", "green", "blue"] => "redgreenblue"
words = ["red", "green", "blue"]
result = ____
for ____ in ____:
____
print(result)


## Solution

words = ["red", "green", "blue"]
result = ""
for word in words:
result = result + word
print(result)


Create an acronym: Starting from the list ["red", "green", "blue"], create the acronym "RGB" using a for loop.

Hint: You may need to use a string method to properly format the acronym.

## Solution

acronym = ""
for word in ["red", "green", "blue"]:
acronym = acronym + word[0].upper()
print(acronym)


## Cumulative Sum

Reorder and properly indent the lines of code below so that they print a list with the cumulative sum of data. The result should be [1, 3, 5, 10].

cumulative.append(total)
for number in data:
cumulative = []
total = total + number
total = 0
print(cumulative)
data = [1,2,2,5]


## Solution

total = 0
data = [1,2,2,5]
cumulative = []
for number in data:
total = total + number
cumulative.append(total)
print(cumulative)


## Identifying Variable Name Errors

1. Read the code below and try to identify what the errors are without running it.
2. Run the code and read the error message. What type of NameError do you think this is? Is it a string with no quotes, a misspelled variable, or a variable that should have been defined but was not?
3. Fix the error.
4. Repeat steps 2 and 3, until you have fixed all the errors.
for number in range(10):
# use a if the number is a multiple of 3, otherwise use b
if (Number % 3) == 0:
message = message + a
else:
message = message + "b"
print(message)


## Solution

• Python variable names are case sensitive: number and Number refer to different variables.
• The variable message needs to be initialized as an empty string.
• We want to add the string "a" to message, not the undefined variable a.
message = ""
for number in range(10):
# use a if the number is a multiple of 3, otherwise use b
if (number % 3) == 0:
message = message + "a"
else:
message = message + "b"
print(message)


## Identifying Item Errors

1. Read the code below and try to identify what the errors are without running it.
2. Run the code, and read the error message. What type of error is it?
3. Fix the error.
seasons = ['Spring', 'Summer', 'Fall', 'Winter']
print('My favorite season is ', seasons[4])


## Solution

This list has 4 elements and the index to access the last element in the list is 3.

seasons = ['Spring', 'Summer', 'Fall', 'Winter']
print('My favorite season is ', seasons[3])


## Key Points

• A for loop executes commands once for each value in a collection.

• A for loop is made up of a collection, a loop variable, and a body.

• The first line of the for loop must end with a colon, and the body must be indented.

• Indentation is always meaningful in Python.

• Loop variables can be called anything (but it is strongly advised to have a meaningful name to the looping variable).

• The body of a loop can contain many statements.

• Use range to iterate over a sequence of numbers.

• The Accumulator pattern turns many values into one.

# Conditionals

## Overview

Teaching: 10 min
Exercises: 15 min
Questions
• How can programs do different things for different data?

Objectives
• Correctly write programs that use if and else statements and simple Boolean expressions (without logical operators).

• Trace the execution of unnested conditionals and conditionals inside loops.

## Use if statements to control whether or not a block of code is executed.

• An if statement (more properly called a conditional statement) controls whether some block of code is executed or not.
• Structure is similar to a for statement:
• First line opens with if and ends with a colon
• Body containing one or more statements is indented (usually by 4 spaces)
mass = 3.54
if mass > 3.0:
print(mass, 'is large')

mass = 2.07
if mass > 3.0:
print (mass, 'is large')

3.54 is large


## Conditionals are often used inside loops.

• Not much point using a conditional when we know the value (as above).
• But useful when we have a collection to process.
masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
if m > 3.0:
print(m, 'is large')

3.54 is large
9.22 is large


## Use else to execute a block of code when an if condition is not true.

• else can be used following an if.
• Allows us to specify an alternative to execute when the if branch isn’t taken.
masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
if m > 3.0:
print(m, 'is large')
else:
print(m, 'is small')

3.54 is large
2.07 is small
9.22 is large
1.86 is small
1.71 is small


## Use elif to specify additional tests.

• May want to provide several alternative choices, each with its own test.
• Use elif (short for “else if”) and a condition to specify these.
• Always associated with an if.
• Must come before the else (which is the “catch all”).
masses = [3.54, 2.07, 9.22, 1.86, 1.71]
for m in masses:
if m > 9.0:
print(m, 'is HUGE')
elif m > 3.0:
print(m, 'is large')
else:
print(m, 'is small')

3.54 is large
2.07 is small
9.22 is HUGE
1.86 is small
1.71 is small


## Conditions are tested once, in order.

• Python steps through the branches of the conditional in order, testing each in turn.
• So ordering matters.
grade = 85

grade is C

• Does not automatically go back and re-evaluate if values change.
velocity = 10.0
if velocity > 20.0:
print('moving too fast')
else:
velocity = 50.0

adjusting velocity

• Often use conditionals in a loop to “evolve” the values of variables.
velocity = 10.0
for i in range(5): # execute the loop 5 times
print(i, ':', velocity)
if velocity > 20.0:
print('moving too fast')
velocity = velocity - 5.0
else:
print('moving too slow')
velocity = velocity + 10.0
print('final velocity:', velocity)

0 : 10.0
moving too slow
1 : 20.0
moving too slow
2 : 30.0
moving too fast
3 : 25.0
moving too fast
4 : 20.0
moving too slow
final velocity: 30.0


## Create a table showing variables’ values to trace a program’s execution.

 i 0 . 1 . 2 . 3 . 4 . velocity 10 20.0 . 30.0 . 25.0 . 20.0 . 30.0
• The program must have a print statement outside the body of the loop to show the final value of velocity, since its value is updated by the last iteration of the loop.

## Compound Relations Using and, or, and Parentheses

Often, you want some combination of things to be true. You can combine relations within a conditional using and and or. Continuing the example above, suppose you have

mass     = [ 3.54,  2.07,  9.22,  1.86,  1.71]
velocity = [10.00, 20.00, 30.00, 25.00, 20.00]

i = 0
for i in range(5):
if mass[i] > 5 and velocity[i] > 20:
print("Fast heavy object.  Duck!")
elif mass[i] > 2 and mass[i] <= 5 and velocity[i] <= 20:
print("Normal traffic")
elif mass[i] <= 2 and velocity[i] <= 20:
print("Slow light object.  Ignore it")
else:
print("Whoa!  Something is up with the data.  Check it")


Just like with arithmetic, you can and should use parentheses whenever there is possible ambiguity. A good general rule is to always use parentheses when mixing and and or in the same condition. That is, instead of:

if mass[i] <= 2 or mass[i] >= 5 and velocity[i] > 20:


write one of these:

if (mass[i] <= 2 or mass[i] >= 5) and velocity[i] > 20:
if mass[i] <= 2 or (mass[i] >= 5 and velocity[i] > 20):


so it is perfectly clear to a reader (and to Python) what you really mean.

## Tracing Execution

What does this program print?

pressure = 71.9
if pressure > 50.0:
pressure = 25.0
elif pressure <= 50.0:
pressure = 0.0
print(pressure)


## Solution

25.0


## Trimming Values

Fill in the blanks so that this program creates a new list containing zeroes where the original list’s values were negative and ones where the original list’s values were positive.

original = [-1.5, 0.2, 0.4, 0.0, -1.3, 0.4]
result = ____
for value in original:
if ____:
result.append(0)
else:
____
print(result)

[0, 1, 1, 1, 0, 1]


## Solution

original = [-1.5, 0.2, 0.4, 0.0, -1.3, 0.4]
result = []
for value in original:
if value < 0.0:
result.append(0)
else:
result.append(1)
print(result)


## Processing Small Files

Modify this program so that it only processes files with fewer than 50 records.

import glob
import pandas as pd
for filename in glob.glob('data/*.csv'):
____:
print(filename, len(contents))


## Solution

import glob
import pandas as pd
for filename in glob.glob('data/*.csv'):
if len(contents) < 50:
print(filename, len(contents))


## Initializing

Modify this program so that it finds the largest and smallest values in the list no matter what the range of values originally is.

values = [...some test data...]
smallest, largest = None, None
for v in values:
if ____:
smallest, largest = v, v
____:
smallest = min(____, v)
largest = max(____, v)
print(smallest, largest)


What are the advantages and disadvantages of using this method to find the range of the data?

## Solution

values = [-2,1,65,78,-54,-24,100]
smallest, largest = None, None
for v in values:
if smallest is None and largest is None:
smallest, largest = v, v
else:
smallest = min(smallest, v)
largest = max(largest, v)
print(smallest, largest)


If you wrote == None instead of is None, that works too, but Python programmers always write is None because of the special way None works in the language.

It can be argued that an advantage of using this method would be to make the code more readable. However, a disadvantage is that this code is not efficient because within each iteration of the for loop statement, there are two more loops that run over two numbers each (the min and max functions). It would be more efficient to iterate over each number just once:

values = [-2,1,65,78,-54,-24,100]
smallest, largest = None, None
for v in values:
if smallest is None or v < smallest:
smallest = v
if largest is None or v > largest:
largest = v
print(smallest, largest)


Now we have one loop, but four comparison tests. There are two ways we could improve it further: either use fewer comparisons in each iteration, or use two loops that each contain only one comparison test. The simplest solution is often the best:

values = [-2,1,65,78,-54,-24,100]
smallest = min(values)
largest = max(values)
print(smallest, largest)


## Using Functions With Conditionals in Pandas

Functions will often contain conditionals. Here is a short example that will indicate which quartile the argument is in based on hand-coded values for the quartile cut points.

def calculate_life_quartile(exp):
if exp < 58.41:
# This observation is in the first quartile
return 1
elif exp >= 58.41 and exp < 67.05:
# This observation is in the second quartile
return 2
elif exp >= 67.05 and exp < 71.70:
# This observation is in the third quartile
return 3
elif exp >= 71.70:
# This observation is in the fourth quartile
return 4
else:
# This observation has bad data
return None

calculate_life_quartile(62.5)

2


That function would typically be used within a for loop, but Pandas has a different, more efficient way of doing the same thing, and that is by applying a function to a dataframe or a portion of a dataframe. Here is an example, using the definition above.

data = pd.read_csv('data/gapminder_all.csv')
data['life_qrtl'] = data['lifeExp_1952'].apply(calculate_life_quartile)


There is a lot in that second line, so let’s take it piece by piece. On the right side of the = we start with data['lifeExp'], which is the column in the dataframe called data labeled lifExp. We use the apply() to do what it says, apply the calculate_life_quartile to the value of this column for every row in the dataframe.

## Key Points

• Use if statements to control whether or not a block of code is executed.

• Conditionals are often used inside loops.

• Use else to execute a block of code when an if condition is not true.

• Use elif to specify additional tests.

• Conditions are tested once, in order.

• Create a table showing variables’ values to trace a program’s execution.

# Looping Over Data Sets

## Overview

Teaching: 5 min
Exercises: 10 min
Questions
• How can I process many data sets with a single command?

Objectives
• Be able to read and write globbing expressions that match sets of files.

• Use glob to create lists of files.

• Write for loops to perform operations on files given their names in a list.

## Use a for loop to process files given a list of their names.

• A filename is a character string.
• And lists can contain character strings.
import pandas as pd
for filename in ['data/gapminder_gdp_africa.csv', 'data/gapminder_gdp_asia.csv']:
print(filename, data.min())

data/gapminder_gdp_africa.csv gdpPercap_1952    298.846212
gdpPercap_1957    335.997115
gdpPercap_1962    355.203227
gdpPercap_1967    412.977514
⋮ ⋮ ⋮
gdpPercap_1997    312.188423
gdpPercap_2002    241.165877
gdpPercap_2007    277.551859
dtype: float64
data/gapminder_gdp_asia.csv gdpPercap_1952    331
gdpPercap_1957    350
gdpPercap_1962    388
gdpPercap_1967    349
⋮ ⋮ ⋮
gdpPercap_1997    415
gdpPercap_2002    611
gdpPercap_2007    944
dtype: float64


## Use glob.glob to find sets of files whose names match a pattern.

• In Unix, the term “globbing” means “matching a set of files with a pattern”.
• The most common patterns are:
• * meaning “match zero or more characters”
• ? meaning “match exactly one character”
• Python’s standard library contains the glob module to provide pattern matching functionality
• The glob module contains a function also called glob to match file patterns
• E.g., glob.glob('*.txt') matches all files in the current directory whose names end with .txt.
• Result is a (possibly empty) list of character strings.
import glob
print('all csv files in data directory:', glob.glob('data/*.csv'))

all csv files in data directory: ['data/gapminder_all.csv', 'data/gapminder_gdp_africa.csv', \
'data/gapminder_gdp_americas.csv', 'data/gapminder_gdp_asia.csv', 'data/gapminder_gdp_europe.csv', \
'data/gapminder_gdp_oceania.csv']

print('all PDB files:', glob.glob('*.pdb'))

all PDB files: []


## Use glob and for to process batches of files.

• Helps a lot if the files are named and stored systematically and consistently so that simple patterns will find the right data.
for filename in glob.glob('data/gapminder_*.csv'):
print(filename, data['gdpPercap_1952'].min())

data/gapminder_all.csv 298.8462121
data/gapminder_gdp_africa.csv 298.8462121
data/gapminder_gdp_americas.csv 1397.717137
data/gapminder_gdp_asia.csv 331.0
data/gapminder_gdp_europe.csv 973.5331948
data/gapminder_gdp_oceania.csv 10039.59564

• This includes all data, as well as per-region data.
• Use a more specific pattern in the exercises to exclude the whole data set.
• But note that the minimum of the entire data set is also the minimum of one of the data sets, which is a nice check on correctness.

## Determining Matches

Which of these files is not matched by the expression glob.glob('data/*as*.csv')?

1. data/gapminder_gdp_africa.csv
2. data/gapminder_gdp_americas.csv
3. data/gapminder_gdp_asia.csv

## Solution

1 is not matched by the glob.

## Minimum File Size

Modify this program so that it prints the number of records in the file that has the fewest records.

import glob
import pandas as pd
fewest = ____
for filename in glob.glob('data/*.csv'):
dataframe = pd.____(filename)
fewest = min(____, dataframe.shape[0])
print('smallest file has', fewest, 'records')


Note that the DataFrame.shape() method returns a tuple with the number of rows and columns of the data frame.

## Solution

import glob
import pandas as pd
fewest = float('Inf')
for filename in glob.glob('data/*.csv'):
fewest = min(fewest, dataframe.shape[0])
print('smallest file has', fewest, 'records')


You might have chosen to initialize the fewest variable with a number greater than the numbers you’re dealing with, but that could lead to trouble if you reuse the code with bigger numbers. Python lets you use positive infinity, which will work no matter how big your numbers are. What other special strings does the float function recognize?

## Comparing Data

Write a program that reads in the regional data sets and plots the average GDP per capita for each region over time in a single chart.

## Solution

This solution builds a useful legend by using the string split method to extract the region from the path ‘data/gapminder_gdp_a_specific_region.csv’.

import glob
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1,1)
for filename in glob.glob('data/gapminder_gdp*.csv'):
# extract <region> from the filename, expected to be in the format 'data/gapminder_gdp_<region>.csv'.
# we will split the string using the split method and _ as our separator,
# retrieve the last string in the list that split returns (<region>.csv),
# and then remove the .csv extension from that string.
region = filename.split('_')[-1][:-4]
dataframe.mean().plot(ax=ax, label=region)
plt.legend()
plt.show()


## Dealing with File Paths

The pathlib module provides useful abstractions for file and path manipulation like returning the name of a file without the file extension. This is very useful when looping over files and directories. In the example below, we create a Path object and inspect its attributes.

from pathlib import Path

p = Path("data/gapminder_gdp_africa.csv")
print(p.parent), print(p.stem), print(p.suffix)

data
gapminder_gdp_africa
.csv


Hint: It is possible to check all available attributes and methods on the Path object with the dir() function!

## Key Points

• Use a for loop to process files given a list of their names.

• Use glob.glob to find sets of files whose names match a pattern.

• Use glob and for to process batches of files.

Teaching: 0 min
Exercises: 0 min
Questions
Objectives

# Reflection exercise

Over break, reflect on and discuss the following:

• A common refrain in software engineering is “Don’t Repeat Yourself”. How do the techniques we’ve learned in the last lessons help us avoid repeating ourselves? Note that in practice there is some nuance to this and should be balanced with doing the simplest thing that could possibly work.
• What are the pros / cons of making a variable global or local to a function?
• When would you consider turning a block of code into a function definition?

# Writing Functions

## Overview

Teaching: 10 min
Exercises: 15 min
Questions
• How can I create my own functions?

Objectives
• Explain and identify the difference between function definition and function call.

• Write a function that takes a small, fixed number of arguments and produces a single result.

## Break programs down into functions to make them easier to understand.

• Human beings can only keep a few items in working memory at a time.
• Understand larger/more complicated ideas by understanding and combining pieces.
• Components in a machine.
• Lemmas when proving theorems.
• Functions serve the same purpose in programs.
• Encapsulate complexity so that we can treat it as a single “thing”.
• Also enables re-use.
• Write one time, use many times.

## Define a function using def with a name, parameters, and a block of code.

• Begin the definition of a new function with def.
• Followed by the name of the function.
• Must obey the same rules as variable names.
• Then parameters in parentheses.
• Empty parentheses if the function doesn’t take any inputs.
• We will discuss this in detail in a moment.
• Then a colon.
• Then an indented block of code.
def print_greeting():
print('Hello!')


## Defining a function does not run it.

• Defining a function does not run it.
• Like assigning a value to a variable.
• Must call the function to execute the code it contains.
print_greeting()

Hello!


## Arguments in call are matched to parameters in definition.

• Functions are most useful when they can operate on different data.
• Specify parameters when defining a function.
• These become variables when the function is executed.
• Are assigned the arguments in the call (i.e., the values passed to the function).
• If you don’t name the arguments when using them in the call, the arguments will be matched to parameters in the order the parameters are defined in the function.
def print_date(year, month, day):
joined = str(year) + '/' + str(month) + '/' + str(day)
print(joined)

print_date(1871, 3, 19)

1871/3/19


Or, we can name the arguments when we call the function, which allows us to specify them in any order:

print_date(month=3, day=19, year=1871)

1871/3/19

• Via Twitter: () contains the ingredients for the function while the body contains the recipe.

## Functions may return a result to their caller using return.

• Use return ... to give a value back to the caller.
• May occur anywhere in the function.
• But functions are easier to understand if return occurs:
• At the start to handle special cases.
• At the very end, with a final result.
def average(values):
if len(values) == 0:
return None
return sum(values) / len(values)

a = average([1, 3, 4])
print('average of actual values:', a)

average of actual values: 2.6666666666666665

print('average of empty list:', average([]))

average of empty list: None

result = print_date(1871, 3, 19)
print('result of call is:', result)

1871/3/19
result of call is: None


## Identifying Syntax Errors

1. Read the code below and try to identify what the errors are without running it.
2. Run the code and read the error message. Is it a SyntaxError or an IndentationError?
3. Fix the error.
4. Repeat steps 2 and 3 until you have fixed all the errors.
def another_function
print("Syntax errors are annoying.")
print("But at least python tells us about them!")
print("So they are usually not too hard to fix.")


## Solution

def another_function():
print("Syntax errors are annoying.")
print("But at least Python tells us about them!")
print("So they are usually not too hard to fix.")


## Definition and Use

What does the following program print?

def report(pressure):
print('pressure is', pressure)

print('calling', report, 22.5)


## Solution

calling <function report at 0x7fd128ff1bf8> 22.5


A function call always needs parenthesis, otherwise you get memory address of the function object. So, if we wanted to call the function named report, and give it the value 22.5 to report on, we could have our function call as follows

print("calling")
report(22.5)

calling
pressure is 22.5


## Order of Operations

1. What’s wrong in this example?

 result = print_time(11, 37, 59)

def print_time(hour, minute, second):
time_string = str(hour) + ':' + str(minute) + ':' + str(second)
print(time_string)

2. After fixing the problem above, explain why running this example code:

 result = print_time(11, 37, 59)
print('result of call is:', result)


gives this output:

 11:37:59
result of call is: None

3. Why is the result of the call None?

## Solution

1. The problem with the example is that the function print_time() is defined after the call to the function is made. Python doesn’t know how to resolve the name print_time since it hasn’t been defined yet and will raise a NameError e.g., NameError: name 'print_time' is not defined

2. The first line of output 11:37:59 is printed by the first line of code, result = print_time(11, 37, 59) that binds the value returned by invoking print_time to the variable result. The second line is from the second print call to print the contents of the result variable.

3. print_time() does not explicitly return a value, so it automatically returns None.

## Encapsulation

Fill in the blanks to create a function that takes a single filename as an argument, loads the data in the file named by the argument, and returns the minimum value in that data.

import pandas as pd

def min_in_data(____):
data = ____
return ____


## Solution

import pandas as pd

def min_in_data(filename):
return data.min()


## Find the First

Fill in the blanks to create a function that takes a list of numbers as an argument and returns the first negative value in the list. What does your function do if the list is empty? What if the list has no negative numbers?

def first_negative(values):
for v in ____:
if ____:
return ____


## Solution

def first_negative(values):
for v in values:
if v < 0:
return v


If an empty list or a list with all positive values is passed to this function, it returns None:

my_list = []
print(first_negative(my_list))

None


## Calling by Name

Earlier we saw this function:

def print_date(year, month, day):
joined = str(year) + '/' + str(month) + '/' + str(day)
print(joined)


We saw that we can call the function using named arguments, like this:

print_date(day=1, month=2, year=2003)

1. What does print_date(day=1, month=2, year=2003) print?
2. When have you seen a function call like this before?
3. When and why is it useful to call functions this way?

## Solution

1. 2003/2/1
2. We saw examples of using named arguments when working with the pandas library. For example, when reading in a dataset using data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country'), the last argument index_col is a named argument.
3. Using named arguments can make code more readable since one can see from the function call what name the different arguments have inside the function. It can also reduce the chances of passing arguments in the wrong order, since by using named arguments the order doesn’t matter.

## Encapsulation of an If/Print Block

The code below will run on a label-printer for chicken eggs. A digital scale will report a chicken egg mass (in grams) to the computer and then the computer will print a label.

import random
for i in range(10):

# simulating the mass of a chicken egg
# the (random) mass will be 70 +/- 20 grams
mass = 70 + 20.0 * (2.0 * random.random() - 1.0)

print(mass)

# egg sizing machinery prints a label
if mass >= 85:
print("jumbo")
elif mass >= 70:
print("large")
elif mass < 70 and mass >= 55:
print("medium")
else:
print("small")


The if-block that classifies the eggs might be useful in other situations, so to avoid repeating it, we could fold it into a function, get_egg_label(). Revising the program to use the function would give us this:

# revised version
import random
for i in range(10):

# simulating the mass of a chicken egg
# the (random) mass will be 70 +/- 20 grams
mass = 70 + 20.0 * (2.0 * random.random() - 1.0)

print(mass, get_egg_label(mass))


1. Create a function definition for get_egg_label() that will work with the revised program above. Note that the get_egg_label() function’s return value will be important. Sample output from the above program would be 71.23 large.
2. A dirty egg might have a mass of more than 90 grams, and a spoiled or broken egg will probably have a mass that’s less than 50 grams. Modify your get_egg_label() function to account for these error conditions. Sample output could be 25 too light, probably spoiled.

## Solution

def get_egg_label(mass):
# egg sizing machinery prints a label
egg_label = "Unlabelled"
if mass >= 90:
egg_label = "warning: egg might be dirty"
elif mass >= 85:
egg_label = "jumbo"
elif mass >= 70:
egg_label = "large"
elif mass < 70 and mass >= 55:
egg_label = "medium"
elif mass < 50:
egg_label = "too light, probably spoiled"
else:
egg_label = "small"
return egg_label


## Encapsulating Data Analysis

Assume that the following code has been executed:

import pandas as pd

japan = df.loc['Japan']

1. Complete the statements below to obtain the average GDP for Japan across the years reported for the 1980s.

year = 1983
gdp_decade = 'gdpPercap_' + str(year // ____)

2. Abstract the code above into a single function.

def avg_gdp_in_decade(country, continent, year):
____
____
____
return avg

3. How would you generalize this function if you did not know beforehand which specific years occurred as columns in the data? For instance, what if we also had data from years ending in 1 and 9 for each decade? (Hint: use the columns to filter out the ones that correspond to the decade, instead of enumerating them in the code.)

## Solution

1. The average GDP for Japan across the years reported for the 1980s is computed with:

year = 1983
gdp_decade = 'gdpPercap_' + str(year // 10)

2. That code as a function is:

def avg_gdp_in_decade(country, continent, year):
df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = df.loc[country]
gdp_decade = 'gdpPercap_' + str(year // 10)
return avg

3. To obtain the average for the relevant years, we need to loop over them:

def avg_gdp_in_decade(country, continent, year):
df = pd.read_csv('data/gapminder_gdp_' + continent + '.csv', index_col=0)
c = df.loc[country]
gdp_decade = 'gdpPercap_' + str(year // 10)
total = 0.0
num_years = 0
for yr_header in c.index: # c's index contains reported years
num_years = num_years + 1


The function can now be called by:

avg_gdp_in_decade('Japan','asia',1983)

20880.023800000003


## Simulating a dynamical system

In mathematics, a dynamical system is a system in which a function describes the time dependence of a point in a geometrical space. A canonical example of a dynamical system is the logistic map, a growth model that computes a new population density (between 0 and 1) based on the current density. In the model, time takes discrete values 0, 1, 2, …

1. Define a function called logistic_map that takes two inputs: x, representing the current population (at time t), and a parameter r = 1. This function should return a value representing the state of the system (population) at time t + 1, using the mapping function:

f(t+1) = r * f(t) * [1 - f(t)]

2. Using a for or while loop, iterate the logistic_map function defined in part 1, starting from an initial population of 0.5, for a period of time t_final = 10. Store the intermediate results in a list so that after the loop terminates you have accumulated a sequence of values representing the state of the logistic map at times t = [0,1,...,t_final] (11 values in total). Print this list to see the evolution of the population.

3. Encapsulate the logic of your loop into a function called iterate that takes the initial population as its first input, the parameter t_final as its second input and the parameter r as its third input. The function should return the list of values representing the state of the logistic map at times t = [0,1,...,t_final]. Run this function for periods t_final = 100 and 1000 and print some of the values. Is the population trending toward a steady state?

## Solution

1. def logistic_map(x, r):
return r * x * (1 - x)

2. initial_population = 0.5
t_final = 10
r = 1.0
population = [initial_population]
for t in range(t_final):
population.append( logistic_map(population[t], r) )

3. def iterate(initial_population, t_final, r):
population = [initial_population]
for t in range(t_final):
population.append( logistic_map(population[t], r) )
return population

for period in (10, 100, 1000):
population = iterate(0.5, period, 1)
print(population[-1])

0.06945089389714401
0.009395779870614648
0.0009913908614406382


The population seems to be approaching zero.

## Key Points

• Break programs down into functions to make them easier to understand.

• Define a function using def with a name, parameters, and a block of code.

• Defining a function does not run it.

• Arguments in call are matched to parameters in definition.

• Functions may return a result to their caller using return.

# Variable Scope

## Overview

Teaching: 10 min
Exercises: 10 min
Questions
• How do function calls actually work?

• How can I determine where errors occurred?

Objectives
• Identify local and global variables.

• Identify parameters as local variables.

• Read a traceback and determine the file, function, and line number on which the error occurred, the type of error, and the error message.

## The scope of a variable is the part of a program that can ‘see’ that variable.

• There are only so many sensible names for variables.
• People using functions shouldn’t have to worry about what variable names the author of the function used.
• People writing functions shouldn’t have to worry about what variable names the function’s caller uses.
• The part of a program in which a variable is visible is called its scope.
pressure = 103.9

temperature = t * 1.43 / pressure
return temperature

• pressure is a global variable.
• Defined outside any particular function.
• Visible everywhere.
• t and temperature are local variables in adjust.
• Defined in the function.
• Not visible in the main program.
• Remember: a function parameter is a variable that is automatically assigned a value when the function is called.
print('adjusted:', adjust(0.9))
print('temperature after call:', temperature)

adjusted: 0.01238691049085659

Traceback (most recent call last):
File "/Users/swcarpentry/foo.py", line 8, in <module>
print('temperature after call:', temperature)
NameError: name 'temperature' is not defined


## Local and Global Variable Use

Trace the values of all variables in this program as it is executed. (Use ‘—’ as the value of variables before and after they exist.)

limit = 100

def clip(value):
return min(max(0.0, value), limit)

value = -22.5
print(clip(value))


Read the traceback below, and identify the following:

1. How many levels does the traceback have?
2. What is the file name where the error occurred?
3. What is the function name where the error occurred?
4. On which line number in this function did the error occur?
5. What is the type of error?
6. What is the error message?
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-2-e4c4cbafeeb5> in <module>()
1 import errors_02
----> 2 errors_02.print_friday_message()

/Users/ghopper/thesis/code/errors_02.py in print_friday_message()
13
14 def print_friday_message():
---> 15     print_message("Friday")

/Users/ghopper/thesis/code/errors_02.py in print_message(day)
9         "sunday": "Aw, the weekend is almost over."
10     }
---> 11     print(messages[day])
12
13

KeyError: 'Friday'


## Solution

1. Three levels.
2. errors_02.py
3. print_message
4. Line 11
5. KeyError. These errors occur when we are trying to look up a key that does not exist (usually in a data structure such as a dictionary). We can find more information about the KeyError and other built-in exceptions in the Python docs.
6. KeyError: 'Friday'

## Key Points

• The scope of a variable is the part of a program that can ‘see’ that variable.

# Programming Style

## Overview

Teaching: 15 min
Exercises: 15 min
Questions
• How can I make my programs more readable?

• How do most programmers format their code?

• How can programs check their own operation?

Objectives
• Provide sound justifications for basic rules of coding style.

• Refactor one-page programs to make them more readable and justify the changes.

• Use Python community coding standards (PEP-8).

## Coding style

A consistent coding style helps others (including our future selves) read and understand code more easily. Code is read much more often than it is written, and as the Zen of Python states, “Readability counts”. Python proposed a standard style through one of its first Python Enhancement Proposals (PEP), PEP8.

Some points worth highlighting:

• document your code and ensure that assumptions, internal algorithms, expected inputs, expected outputs, etc., are clear
• use clear, semantically meaningful variable names
• use white-space, not tabs, to indent lines (tabs can cause problems across different text editors, operating systems, and version control systems)

• PEP8: a style guide for Python that discusses topics such as how to name variables, how to indent your code, how to structure your import statements, etc. Adhering to PEP8 makes it easier for other Python developers to read and understand your code, and to understand what their contributions should look like.
• To check your code for compliance with PEP8, you can use the pycodestyle application and tools like the black code formatter can automatically format your code to conform to PEP8 and pycodestyle (a Jupyter notebook formatter also exists nb_black).
• Some groups and organizations follow different style guidelines besides PEP8. For example, the Google style guide on Python makes slightly different recommendations. Google wrote an application that can help you format your code in either their style or PEP8 called yapf.
• With respect to coding style, the key is consistency. Choose a style for your project be it PEP8, the Google style, or something else and do your best to ensure that you and anyone else you are collaborating with sticks to it. Consistency within a project is often more impactful than the particular style used. A consistent style will make your software easier to read and understand for others and for your future self.

## Use assertions to check for internal errors.

Assertions are a simple but powerful method for making sure that the context in which your code is executing is as you expect.

def calc_bulk_density(mass, volume):
'''Return dry bulk density = powder mass / powder volume.'''
assert volume > 0
return mass / volume


If the assertion is False, the Python interpreter raises an AssertionError runtime exception. The source code for the expression that failed will be displayed as part of the error message. To ignore assertions in your code run the interpreter with the ‘-O’ (optimize) switch. Assertions should contain only simple checks and never change the state of the program. For example, an assertion should never contain an assignment.

## Use docstrings to provide builtin help.

If the first thing in a function is a character string that is not assigned directly to a variable, Python attaches it to the function, accessible via the builtin help function. This string that provides documentation is also known as a docstring.

def average(values):
"Return average of values, or None if no values are supplied."

if len(values) == 0:
return None
return sum(values) / len(values)

help(average)

Help on function average in module __main__:

average(values)
Return average of values, or None if no values are supplied.


## Multiline Strings

Often use multiline strings for documentation. These start and end with three quote characters (either single or double) and end with three matching characters.

"""This string spans
multiple lines.

Blank lines are allowed."""


## What Will Be Shown?

Highlight the lines in the code below that will be available as online help. Are there lines that should be made available, but won’t be? Will any lines produce a syntax error or a runtime error?

"Find maximum edit distance between multiple sequences."
# This finds the maximum distance between all sequences.

def overall_max(sequences):
'''Determine overall maximum edit distance.'''

highest = 0
for left in sequences:
for right in sequences:
'''Avoid checking sequence against itself.'''
if left != right:
this = edit_distance(left, right)
highest = max(highest, this)

# Report.
return highest


## Document This

Turn the comment in the following function into a docstring and check that help displays it properly.

def middle(a, b, c):
# Return the middle value of three.
# Assumes the values can actually be compared.
values = [a, b, c]
values.sort()
return values[1]


## Solution

def middle(a, b, c):
'''Return the middle value of three.
Assumes the values can actually be compared.'''
values = [a, b, c]
values.sort()
return values[1]


## Clean Up This Code

1. Read this short program and try to predict what it does.
2. Run it: how accurate was your prediction?
3. Refactor the program to make it more readable. Remember to run it after each change to ensure its behavior hasn’t changed.
4. Compare your rewrite with your neighbor’s. What did you do the same? What did you do differently, and why?
n = 10
s = 'et cetera'
print(s)
i = 0
while i < n:
# print('at', j)
new = ''
for j in range(len(s)):
left = j-1
right = (j+1)%len(s)
if s[left]==s[right]: new = new + '-'
else: new = new + '*'
s=''.join(new)
print(s)
i += 1


## Solution

Here’s one solution.

def string_machine(input_string, iterations):
"""
Takes input_string and generates a new string with -'s and *'s
corresponding to characters that have identical adjacent characters
or not, respectively.  Iterates through this procedure with the resultant
strings for the supplied number of iterations.
"""
print(input_string)
input_string_length = len(input_string)
old = input_string
for i in range(iterations):
new = ''
# iterate through characters in previous string
for j in range(input_string_length):
left = j-1
right = (j+1) % input_string_length  # ensure right index wraps around
if old[left] == old[right]:
new = new + '-'
else:
new = new + '*'
print(new)
# store new string as old
old = new

string_machine('et cetera', 10)

et cetera
*****-***
----*-*--
---*---*-
--*-*-*-*
**-------
***-----*
--**---**
*****-***
----*-*--
---*---*-
`

## Key Points

• Use docstrings to provide builtin help.

# Wrap-Up

## Overview

Teaching: 20 min
Exercises: 0 min
Questions
• What have we learned?

• What else is out there and where do I find it?

Objectives
• Name and locate scientific Python community sites for software, workshops, and help.

Leslie Lamport once said, “Writing is nature’s way of showing you how sloppy your thinking is.” The same is true of programming: many things that seem obvious when we’re thinking about them turn out to be anything but when we have to explain them precisely.

## Key Points

• Python supports a large and diverse community across academia and industry.

# Feedback

## Overview

Teaching: 0 min
Exercises: 15 min
Questions
• How did the class go?

Objectives
• Gather feedback on the class

Gather feedback from participants.

## Key Points

• We are constantly seeking to improve this course.