Theoretical Basics

📋 Content

• 📋 Content
• Why should we learn a scientific programming language?
• Why Python?
• The print()-Function
• Variables
• Data types
• Converting data types
• Operators
• Arithmetic operators
• Assignment operators
• Getting started with pyplot
  • Creating simple plots
  • Adding Details
• Anatomy of a Plot
  • Artists and Figures
  • Where to find help?

Why should we learn a scientific programming language?

Statistical tools like Excel already do a very good job regarding statistical analysis and visualization. And: They are easy- and ready-to-use. Scientific programming languages perform the same job, the mathematical background behind them is the same (with some exceptions in the numerical implementation). But: There is no Graphical User Interface (GUI), we have to write each analysis step by our own.

So, why should we learn Python …? The answer is:

• Automatization
  • At the beginning it takes a little longer to get where you want to go.
  • But when you’re done, you have a powerful tool to process all of your current (and future) data, without manually repeating your analysis steps!
• Standardization and Reproducibility
  • By pipelining your data through an automatized analysis, you ensure, that each of your data sets is processed and analyzed the same way (if it makes sense!).
  • This standardization ensures reproducibility of your results for you and for others and enables inter- dataset/-group comparisons.
• Perform more complex tasks
  • You can go beyond the scope of any black-boxed suite-solution.

Why Python?

Python is one of the most popular programming languages in the world. It is used for a wide variety of tasks, including data analysis. It has also become the language of choice for machine learning. Here are some more points:

• Open-source: Free and continuously improved by a worldwide community.
• Portable: You can install Python wherever you want to (PC, Mac, …), your code will work everywhere.
• “Interpreted“: Your code is directly executed without previously being compiled into the actual machine-language instructions (i.e. machine-code, the language, your computer is operating with).
• “High-level“ programming language: Use natural language elements to “tell“ the computer what you want.
• Extensive support of libraries: Install as much additional modules as and when you need to, e.g. fancy plotting toolboxes, statistics toolboxes … they are all free (in other programming solutions you have to pay for such add-ons).
• Collaboration: collaborate and exchange with your local or worldwide colleagues, who also use Python.

The print()-Function

One of the first things you will probably do is output (print). To write information, you can use the print() function and implement it as a core function. As it is a core function, you will automatically have access to it if Python is installed. To use print() in your program, use an argument:

Variables

As one of the basics of programming, you need to know that you are processing data. Since your program works with data, you may need to remember a certain value while running the program. You use variables to do this.

In the following example, a variable is declared (saved) and then output. Try:

x = 5

print(x)

Data types

A variable requires a data type. In the previous program, x assumes the type int. However, there are many other data types. Here are some basic examples that you are likely to come across:

You can use the type() function to determine the data type, try:

a = 'Yeah, my name is ...'

print(type(a))

Converting data types

Sometimes it makes sense to change the data type of a variable. This can be done as follows, try:

a = '5'
b = int(a)

print(type(b))

Operators

Operators allow you to perform various operations on variables and their values. In general, you have a left-hand side and a right-hand side and an operator in the middle:

Python uses two types of operators: arithmetic operators and assignments.

Arithmetic operators

Arithmetic operators are used to perform calculations such as addition, subtraction, division and multiplication. Here are some of the arithmetic operators you can use:

Assignment operators

You can use assignment operators to assign values to a variable throughout its life cycle. Below you will find some assignment operators that you are likely to use when creating programs:

Getting started with pyplot

The matplotlib framework offers the module pyplot as a set of convenient shortcuts for common plotting tasks. Pyplot also is the way through which users will mostly interact with the features of matplotlib.

Creating simple plots

Imagine we are keeping track of the amount of medication a patient takes over a year:

Since we have not given any explicit x- axis values, pyplot has used the index of our list for that purpose.

Note: Pyplot internally keeps track of state of the plot. You as the user only get to see it once you call the show() function. This way of programming is called imperative. There is also a different approach to things, which is called object oriented which is used in the underlying workings of matplotlib. For clarity reasons we will show pyplot first and then expand upon that a bit later. To help with understanding, imagine you want to have a cake. Imperative style is like calling a baker and describing how you want your cake to look like. Object oriented style is like ordering the ingredients and then modifying the soon-to-be-cake yourself until it looks like you want it.

A simple way to plot something with specific x-values is to have separate lists of x and y values. The x-values do not need to be numbers, category names work equally as well. Try:

import matplotlib.pyplot as plt

months = [
    "Jan", "Feb", "Mar", "Apr", "May", "Jun",
    "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
]

pills = [10, 12, 13, 15, 12, 11,
         9, 8, 8, 10, 11, 12
        ]

plt.plot(months, pills)
plt.show()

Adding Details

We now have a very simple plot, but it is lacking a few things that we expect. Let us add some basic things like labels, a title and maybe a better scale for the y-axis.

# ... add this to the code above

# First let's put in the overall decorations
plt.xlabel("Month")
plt.ylabel("#")
plt.title("Amount of pills per month")

# We want our y-axis to start at 7 and go up to 16 with a resolution of 1
amt_pills_marks = np.arange(7,17,1)
plt.yticks(amt_pills_marks)

plt.show()

For the viewers convenience we also want to add horizontal lines to indicate the minimum and maximum values of the pills amount.

# Add the lines for the lowest and highest pills amount
marker_lines = [min(pills), max(pills)]
plt.hlines(
    y=marker_lines,
    xmin=months[0],
    xmax=months[-1],
    linestyles="dotted",
    colors="lightgray"
)

plt.show()

Anatomy of a Plot

Knowing the right terms for the subject can help to better search for help, understand documentation and communicate issues and solutions. In the following image you can find the naming of the elements in a plot as used by matplotlib:

There are further terms that you may want to keep in mind when working with the framework.

Warning: In the nomenclature of the framework there is a difference between - Axis: the number line that gets printed on the side of the plot - Axes: the collection of all plotted elements that represent data (roughly: the plotting area)

Artists and Figures

An artist determines the rendering style of plot elements. The figure is the collection of all plot elements. Figures can be nested into other figures. The nested element is called a subfigure. Implementation-wise, the figure is an artist (i.e. it controls its own plotting style). The rectangle for the figure’s background is contained in the attribute patch.

Where to find help?

The official developers’ documentation websites act as a glossary paired with very good examples and often tutorials:

• https://www.python.org/doc/, https://docs.python.org/3/
• https://matplotlib.org
• https://numpy.org/doc/
• https://pandas.pydata.org/docs/