How to Code in Python using Spyder
What is Spyder?
- Spyder is an open-source cross-platform IDE. The Python Spyder IDE is written completely in Python. It is designed by scientists and is exclusively for scientists, data analysts, and engineers. It is also known as the Scientific Python Development IDE and has a huge set of remarkable features which are discussed below.
- As Mark said, you’re comparing apples with oranges. The Anaconda Python Distribution provides Python and a wide variety of libraries required for learning and for data science.
Features Spyder contains features like a text editor with syntax highlighting, code completion and variable exploring, which you can edit its values using a Graphical User Interface (GUI). Data science enthusiasts say “If you are switching from Matlab or Rstudio to Python; Spyder is the way to go, It very intuitive for scientific computing.”.
Spyder is an open-source Integrated Development Environment. It is written in python used for python, designed by scientists and exclusively made for scientists, data analysts, and engineers. It is also known as Scientific Python Development IDE.
It provides Editor to write code, a console to evaluate it, and view the results at any time, a variable explorer to examine the variables defined during evaluation and many other features for effectively developing the programs or application.
The easiest way to install spyder is by downloading the Anaconda python distribution. Spyder comes as default implementation with Anaconda python distribution. This is the recommended method to install spyder. You can download Anaconda from its website
By clicking on the download, you can download the version compatible with your system.
Once after you complete the installation process you can launch the spyder from the Anaconda Navigator or you can directly search into your system.
When you will start the spyder the first thing that you are going to get will look something like this
Create a file
- To create a new file go to File and click on New File
- The first component that we will be going to take is an editor which is on the left-hand side.
This is the place where you are going to write your python code every time. Now for a test, we will write a simple code.
And when you select it and hit shift + enter, it will execute the code in IPYTHON.
Ipython is on the bottom right-hand side. Any code you write in the editor, the output will be displayed in the Ipython console.
As you can see the output of the above code is displayed here.
You can see this just above the Ipython console or you can go to views, click on panes and select variable explorer. It shows the namespace content, functions, etc. If we declare a variable p = 7 in our code then you can see in the variable explorer its name, type, size, and its value.
It is helpful when you have multiple variables declared in your program, this content helps in figuring out the variables.
File Explorer is next to the variable explorer or you can go to views click on panes and select file explorer. It is a pane that is built-in file system and directory for browsing, to view files.
Help is next to the file explorer. You can use help pane to find, render, and to display documentation for any object, modules, functions, and methods.
The history log is next to the Ipython console. With the history log pane you can view a list of every command you entered in any connective Ipython console.
If you want to change the font size and use different functions of this IDE, you can go to tools > preferences and can change consoles, font size, shell sizes, and various other stuff.Tag: How to Code in Python using Spyder
- What is what: Python, Python packages, Spyder, Anaconda
- Test your installation
This is the most recent version of the installationinstructions. (Older versions from 2014/2013, where we have used Python 2 (!) are available here.)
These notes are provided primarily for students of graduate schools IMPRS and DASHH, staff and students at the Max Planck Institute for the Structure and Dynamics of Matter and others at DESY, as well as students at the University of Southampton (United Kingdom).
The objective of these introductory notes is to help readers install Python ontheir own computers, and to support their learning of programming, computationalscience and data science, and subsequently their studies, particular in naturalsciences, mathematics, engineering, and computer science.
In short, we suggest to use theAnaconda Python distribution.
By the nature of the information provided, the content is likelyto become partially outdated over time. For reference: thismini-introduction was written in September 2016, where Anaconda 4.1was available, and Python 3.5 is the default Python provided, andrevised in March 2021, where Anaconda 2020.11 and Python 3.8 werethe defaults.
What is what: Python, Python packages, Spyder, Anaconda
Python is a programming language in which we write computer programs. Theseprograms are stored in text files that have the ending .py, for examplehello.py which may contain:
Python is also a computer program (the technical term is 'interpreter') which executes Python programs, such as hello.py. On windows, the Python interpeter is called python.exe and from a command window we could execute the hello.py program by typing:
On Linux and OS X operating systems, the Python interpreter programis called Python, so we can run the program hello.py as:
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(This also works on Windows as the operating system does not needthe .exe extension.)
For scientific computing and computational modelling, we needadditional libraries (sometimes called packages) that are not part of thePython standard library. These allow us, for example, to create plots,operate on matricies, and use specialised numerical methods.
The packages we often need include are
- numpy (NUMeric Python): matrices and linear algebra
- pandas: Python data science tools (Series and Dataframes)
- scipy (SCIentific Python): many numerical routines
- matplotlib: (PLOTting LIBrary) creating plots of data
We also use in this training:
- sympy (SYMbolic Python): symbolic computation
- pytest (Python TESTing): a code testing framework
The packages numpy, scipy, pandas and matplotlib are essential components computational work with Python and widely used.
Sympy has a special role as it allows SYMbolic computationrather than numerical computation.
The pytest package and tool supports regression testing and testdriven development -- this is generally important, and particularly soin best practice software engineering for computational studies andresearch.
Spyder (home page) is s a powerfulinteractive development environment for the Python language withadvanced editing, interactive testing, debugging and introspectionfeatures. There is a separate blog entry providing asummary of key features of Spyder,which is also available as Spyder's tutorial from inside Spyder(Help -> Spyder tutorial).
The name SPYDER derives from 'Scientific PYthon Development EnviRonment' (SPYDER).
We will use it as the main environment to learn about Python,programming and computational science and engineering.
Useful features include
- provision of the IPython (Qt) console as an interactive prompt, which can display plots inline
- ability to execute snippets of code from the editor in the console
- continuous parsing of files in editor, and provision of visual warnings about potential errors
- step-by-step execution
- variable explorer
Anaconda is aPython distributions. Python distributions provide the Pythoninterpreter, together with a list of Python packages and sometimesother related tools, such as editors. To be more precise, Anaconda is notlimited to packaging Python packages, but initially emerged to caterfor Python-based applications and packages.
The packages provide by the Anaconda Python distribution include all of those that we need, and for that reason we suggest to use Anaconda here.
A key part of the Anaconda Python distribution is Spyder, aninteractive development environment for Python, including an editor.
In general, the installation of the Python interpreter (fromsource/binaries) is fairly straightforward, but installation ofadditional packages can be a bit tedious.
Instead of doing this manually, we suggest on this page to install the AnacondaPython distribution using these installation instructions, which provides thePython interpreter itself and all packages we need.
The Anaconda Python distribution is available for download forWindows, OS X and Linux operating systems (and free).
For Windows and OS X you are given a choice whether to download thegraphical installer or the next based installer. If you don't knowwhat the terminal (OS X) or command prompt (Windows) is, then you arebetter advised to choose the graphical version.
If you are using Linux, you probably want what is called 'Linux' and not what is'Linux POWER'. The 'Linux' target refers to the common x86 architecture.
Download the installer, start it, and follow instructions. Acceptdefault values as suggested.
During the installation, you may have the option to install additional editingenvironments. You don't need to install these for this course, but it shouldn'tdo any harm either.
If you are using Linux and you are happy to use the package managerof your distribution -- you will know who you are --, then you may bebetter advised to install the required packages indivdually ratherthan installing the whole Anaconda distribution.
Test your installation
Once you have installed Anaconda or the Python distribution of yourchoice, you can download a testing program andexecute it.
Running the tests with Spyder
This can be done either by typing spyder in a terminal orinside the Anaconda Prompt, or by starting Spyder through theAnaconda Navigator.
The current version of Spyder is 4.1.
Spyder may ask you if you want to install kite. This is not necessary forthe course.
Download thetesting file.
Open the file in Spyder via File -> Open.
The execute the file via Run -> Run.
If you get a pop up window, you can accept the default settings andclick on the run button.
You should see output similar to this in the lower right window ofspyder (you may also see a plot appearing):
If the test program produces these outputs, there is a very goodchance that Python and the six listed packages are installedcorrectly.
Running the tests from the console
Open a console:
- Windows: type cmd in the search box
- Mac OS X: Start the Terminal application that is located inthe Utilities folder in Applications
- Linux: start one of the shells you have available, or an xterm orso.
Download the testing fileto your machine.
Change directory into the folder you have downloaded the file to,and type:
If all the tests pass, you should see output similar to this:
If you install Python in other ways than through the Anacondadistribution and, for example, you have only installed the numpy,scipy and matplotlib package, the program's output would be:
Updating packages in the Anaconda installation
To update, for example, spyder and python, follow these steps:
Open a terminal (see step 1 in Running the tests from the console)
Update the conda program (this manages the updating) by typingthe following command into the console:
Confirm updates if asked to do so. More than one package may belisted to be updated.
Update individual packages, for example spyder:
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This introductory page from the Anaconda team may contain useful material to getstarted with the Anaconda.
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If you prefer a video run through of an anaconda installation, checkSteve Holden's post from June 2015
To lean more about Anaconda, try the documents and introductory tutorialsoffered at https://docs.anaconda.com/anaconda/ .