Theoretical Basics: Python

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.

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.

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:

print('Hello World!')
          

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)
          

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))
          

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 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:

x = 10
y = 5

print(x + y)
          

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

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

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:

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

import matplotlib.pyplot as plt

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

plt.plot(pills)  # This prepares a plot
plt.show()  # Show the plot that was created
            

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

import matplotlib.pyplot as plt
import numpy as np

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)

# 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()
            

import matplotlib.pyplot as plt
import numpy as np

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)

# 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)

# 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()
            

Knowing the right terms for the subject can help to better search for help, understand documentation and communicate issues and solutions. [Note: The original content included an image of Matplotlib Plot Anatomy here, which is not included in this document.]

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

Where to find help?