Python Lesson 1

Python Lesson 1

Curro PB

Lesson outline

  1. Python programming language main features.
  2. Running Python: Programs, IPython, and Jupyter notebooks.

  3. Variable assignment and arithmetics
  4. Introduction to Numpy. First date…

Running Python

Apart from the three methods given below you can create an independent Python program and launch it from a terminal. Python programs have the .py suffix and to run a program you should use the command python We will go back to this at the end of the course.

The following three methods try to facilitate code development and make possible to explore results in an interactive way.

The Python interpreter

You can start a Python interpreter in a given terminal.

(py3) [pts/18][curro.modesto:~]$ python
Python 3.7.4 (default, Aug  9 2019, 18:51:30) 
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 2+2

To exit type CTRL-D.

The IPython console

The IPython project is a friendly alternative to the plain Python interpreter and it provides

  • tab completion
  • pretty-printed output
  • facilitates to execute arbitrary blocks of code (%run)
  • help ? and ??
  • additional context if exceptions are raised
  • magic commands: %run, %timeit, %%timeit

It is launched with the command

(py3) [pts/18][curro.modesto:~]$ jupyter qtconsole

Note that in some (older) environments the console should be launched as

(py3) [pts/18][curro.modesto:~]$hem/Python]$ ipython qtconsole
[TerminalIPythonApp] WARNING | Subcommand `ipython qtconsole` is deprecated and will be removed in future versions.
[TerminalIPythonApp] WARNING | You likely want to use `jupyter qtconsole` in the future

Using Jupyter notebooks

This is the method we will mostly use in the course. The notebooks are launched with the command

(py3) [pts/18][curro.modesto:~]$ jupyter notebook
[I 18:10:08.941 NotebookApp] Serving notebooks from local directory: /home/curro/ISP/Python
[I 18:10:08.941 NotebookApp] The Jupyter Notebook is running at:
[I 18:10:08.941 NotebookApp] http://localhost:8888/?token=d3225c10006bab47630b8013dfe3961e80e8a4f751536901
[I 18:10:08.941 NotebookApp]  or
[I 18:10:08.941 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 18:10:09.021 NotebookApp] 

    To access the notebook, open this file in a browser:
    Or copy and paste one of these URLs:

And the landing page will be displayed in a tab of a predefined browser. If the browser is not running it will also be launched. In the landing page click on the New button and select Python 3. A new notebook will open and you can run your first command in it.

Commands are run in cells, and one should distinguish between plain text cells, markdown cells, and command or code cells (which may have an accompanying output or not).

In markdown cells you can use this popular markup language (a superset of HTML) to add explanations and comentaries beyond the usual use of Python comment within code cells. Check in jupyter markdown support to learn more about this kind of cells. You can even use LaTeX commands to include equations and formulas into these cells.

Code cells, as the rest of the cells, have two modes: edit mode and command mode. This is similar to the way the text editor vi works, and sometimes could be a little exasperating. It is easy to get used to it. In command mode the left cell edge is light blue colored and your keystrokes are translated into commands or operations within the notebook. Jupyter provides a list of keybord shortcuts under the menu Help > Keyboard Shortcuts or by pressing H in command mode. Some of the most useful commands are

to insert a new cell above the current cell.
to insert a new cell below the current cell.
to change the current cell to Markdown.
to change the current cell to to code.
(press the key twice) to delete the current cell.
Shift Tab
to display the Docstring (documentation) for the the object you have just typed in a code cell (keep pressing this shortcut to cycle through modes of documentation).
Ctrl Shift –
to split the current cell into two from where your cursor is.
Esc F
to find and replace on your code but not the outputs.
Esc O
to toggle cell output.
Shift Up/Down
to select the next cell in an upwards/downwards direction.
Shift M
to merge multiple cells.

The edit mode is marked by a green left edge in the cell, your key typing is included as text into the cell.

You can swicth from command to edit mode (a) double clicking into the cell or (b) if the cell is highlighted, pressing return. To go back to command mode, click anywhere outside the cell or press ESC.

You are now ready for your first Python experience. Type the following statement in a cell and press SHIFT-RETURN to run the cell.

When you save the notebook (the system backups them from time to time too) the information will be saved (both inputs and outputs) into an .ipynb file.

Variable assignment and arithmetics

Variable assignation

Assiging one variable value

Assiging several variables simultaneously

You can check Tab-completion in the notebook. Another feature that works in the notebook is object introspection, using the ? character after any instance will open a pager where a basic information about the instance is displayed.

You can check what are the variables you’ve created and the modules you’ve loaded in the notebook with the %whos magic function.

Basic arithmetics

You can include comments in the preceding line or after your code using the hash mark (#).

Import the Numpy library

We start importing the Numpy library wich can be considered the foundational library for numerical calculations in Python.

We perform some simple array operations in order to test Numpy, and its most important contribution, the ndarray type. It stands for N-dimensional array object and it is possible the most salient feature of Numpy, providing fast array-oriented arithmetics and broadcasting possibilities. Apart from this, Numpy also provides a set of mathematical functions optimized to work with such arrays, tools for file I/O, and linear algebra, fast Fourier transforms and a pseudo-random number generator (and an API for communicating with C or Fortran libraries).

We can get a first example of this structure building an array of normally distributed random data

Every Numpy array has a shape and data type that can be obtained using the shape, size, and dtype methods.

Numpy first impressions

You have in Moodle a simple dataset consisting on the output of a climate model for average monthly temperatures between the years 1961 and 2096 for several cities in Cyprus. The files are of csv type, in the folder TData and can be downloaded as supplementary documentation for the course in Moodle. It will be our guinea pig in these first steps with Numpy.

Data reading

We first read the data using the numpy function loadtxt. You can get help on it using object introspection and later we will examine with more detail file input and output.

Read one of the provided files and store the data in a variable. You can check using tab completion if the data file path is correct.

Data handling

You can check that the variable you’ve created is of ndarry type with %whos or using the Python function type

As previously, you can use the numpy functions size and shape to characterize the data array

The function dtype has a different role, related to the building of derived types in Numpy, and it will not be addressed in this course.

In Python objects one can distinguish between attributes (data, objects are a named collection of attributes) and methods (functions). These functions provide information about the variable and they can be accessed using the syntax variable.method.

Numpy arrays have some particular features that are built to optimize code speed and efficiency, saving data in contiguous memory blocks and allowing to perform complex operations on such arrays.

You can access individual array elements (negative indexes imply counting from the end of the given dimension).

Or you can also work with full array slices or subarrays and perform operations with the arrays

In this case the default initial value is zero and the default last value is the last array element. Therefore, we can extract the temperature data and remove the information about the years as follows

We can compute, e.g. the maximum, minimum, and average monthly temperatures for the year 1961 as

As mentioned before you can use the attribute syntax, applying the max, min, and mean methods to the array


Exercise (1.1)

How would you compute minimum, maximum and average March temperatures for the given dataset?

Exercise (1.2)

Check the info on the numpy mean function and compute the average monthly temperatures and the average annual temperatures for the given dataset (Hint: axis option)

Exercise (1.3)

Get the maximum and minimum monthly temperatures for year 2000 and at what months did these temperatures occur.