Contact: Dr. Awnesh Singh
Centre for Sustainable Futures, The University of the South Pacific, Suva, Fiji
- Python
- The Anaconda Python Distribution
- Installing the Anaconda Python Distribution
- Installation of Additional Libraries
- Some Exercises
- Remember...
What is Python? Python is an open-source high-level programming language that lets you work more quickly and integrate your systems more effectively, and let you do sophisticated data analysis. Python is realesed in two versions: Python 2 and Python 3 and is available from https://www.python.org/. We shall use Python 3 version for this training. Python uses several libraries to perform a range of tasks, which needs to be installed seperately.
Anaconda is a Python-based data processing and scientific computing platform. It has built in many very useful third-party libraries. Installing Anaconda is equivalent to automatically installing Python and some commonly used libraries so it makes the installation so much easier than regular Python installation. If you don't install Anaconda, but instead only install Python from https://www.python.org/, you also need to use pip to install various libraries one by one. It is painful and you need to consider compatibility, thus it is highly recommended to directly install Anaconda.
You will first need to to download (~662MB) and install the Anaconda Python Distribution. It is a completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing. It includes the python interpreter itself, the python standard library as well as a set of packages exposing data structures and methods for data manipulation and scientific computing and visualization. In particular, it provides Numpy, Scipy, Pandas, Matplotlib, scikit-learn, etc., i.e., all the main packages we will be using during the semester. The Anaconda Python distribution (select the version shipping with Python 3.7) should be downloaded for your platform (either 32-bit or 64-bit operating system) for the respective operating system (e.g., Windows) from https://www.anaconda.com/distribution/. Note the file has already be downloaded for the 64-bit version and shared with you in the FMS share-drive.
Execute the downloaded file and follow the instructions to install Anaconda For me. Once installed, the Anaconda Documentation webpage will open in your internet browser with instructions on Getting started with Anaconda. You will follow the instructions after you perform the tasks below. You can close the Anaconda Cloud webpage on the internet browser.
From the Start menu, right click on the Anaconda Prompt under Anaconda (64-bit) select More and click on Run as administrator (you may need administrator privileges).
Type the following at the terminal to update to the latest version of all pre-installed packages (the $ sign represents the prompt, e.g., (base) C:\Windows\system32>) and press Enter:
$ conda update conda
Then type the following:
$ conda update anaconda
You also might want to install pip to install packages from the Python Package Index by typing:
$ conda install pip
Now that you have installed these updates, return to the Anaconda Documentation webpage in your internet browser and follow the instructions on Getting started with Anaconda. You can use Python using several applications. We will be focussing on two applications for the training: Spyder and Jupyter Notebook. However, in the long-term, we will stick to Spyder.
Anaconda's conda environment is beneficial because it offers isolation, reproducibility, and streamlined package management. It allows you to create isolated environments for specific projects, ensuring that dependencies and package versions don't conflict. This also makes it easier to share your project's environment with others, ensuring they have the same configurations. Anaconda Environments.
Use the following command to create a new python environment:
# Replace <ENV_NAME> with a name for your environment
# Replace <PACKAGE> with your desired package
# Replace <VERSION> with your desired version (optional)
$ conda create --name <ENV_NAME> python=<VERSION> <PACKAGE>=<VERSION>
Example:
$ conda create --name myenv python=3.11.10 pandas numpy sckit-learn=1.6.1
Activate your new environment using the activate command:
$ activate myenv
Python uses libraries to perform specific tasks. Some of these librarires are alreay installed when you intall Anaconda using the procedure above. Type the following to see the list of libraries already installed:
$ conda list
When installing additional libraries below, select y when prompted by Proceed ([y]/n)?. Note that you may get a message that nothing needs to be installed # All requested packages already installed.
netcdf4 allows you to read and write netcdf files (version 3 and 4 supported), install it by typing:
$ conda install netcdf4
Basemap is a graphic library for plotting (static, publication quality) geographical maps (see http://matplotlib.org/basemap/). Basemap is available directly in Anaconda using the conda package manager, install by typing:
$ conda install basemap
seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. You should be able to install it with conda as well:
$ conda install seaborn
xarray is a library aimed at bringing the power of Pandas to multidimensional labelled arrays, such as the ones usually associated with geophysical quantities varying along time and space dimensions (e.g., [time, latitudes, longitudes], [time, level, latitudes, longitudes], etc.) and supports reading and writing netcdf files. It can be installed via conda by typing:
$ conda install xarray
Excel is a popular and powerful spreadsheet application for Windows. The openpyxl module allows your Python programs to read and modify Excel spreadsheet files. It can be installed via conda by typing:
$ conda install openpyxl
Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses. Cartopy makes use of the powerful PROJ.4, NumPy and Shapely libraries and includes a programmatic interface built on top of Matplotlib for the creation of publication quality maps. Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections. You will find cartopy especially useful for large area / small scale data, where Cartesian assumptions of spherical data traditionally break down. If you’ve ever experienced a singularity at the pole or a cut-off at the dateline, it is likely you will appreciate cartopy’s unique features! The module can be installed via conda by typing:
$ conda install -c conda-forge cartopy
eofs is a Python package for EOF analysis of spatial-temporal data. Using EOFs (empirical orthogonal functions) is a common technique to decompose a signal varying in time and space into a form that is easier to interpret in terms of spatial and temporal variance. eofs can be installed via conda by typing:
$ conda install -c conda-forge eofs
that you have all the necessary packages, type:
$ conda update --all
type
$ exit
There are numerous Python exercises to try out fron the internet. Some of the good ones are listed below:
Learning Python can be fun. But it is important to learn the very basics first before moving into more complex problems. The exercises above should help you learn the basics of Python and should prepare you well for data analysis later on. There are many Python forums that are helpful if you get stuck. Try to set up a Python user community at FMS and maybe meet once a week to share your progress. I am just an email away and can visit FMS anytime I am in Nadi. Lets start coding!