There are many Integrated Development Environments [IDE] for Python, and every programmer has their own preference. An IDE is a software application that provides facilities for software development.

* [Jupyter notebooks](http://jupyter.org/) integrates visualization with code and is suitable to make tutorials, simple dashboards, quick visualizations, and do prototype testing. Jupyter Notebooks run in your browser on a localhost server or on a web server. They allow for various programming languages, e.g. Python, R, Julia, Spark or PySpark.

Spyder is a IDE for developing python mainly for scientific purposes. Fun fact, it is [completely written in python](https://github.com/spyder-ide/spyder)! Spyder is a very complete IDE that looks a bit like Rstudio. It shows the variables present in the current session, it has a code editor, a console and a figures pain in the main view.

Jupyter Notebooks is actually not a IDE but it is very useful for writing code. Jupyter stands for the languages that once can use (*JU*lia, *PY*thon and *R*) and notebooks means that they are actually files instead of an IDE (such as Rstudio or Spyder). The notebooks can be interpreted and run by varying interpreters of which we will cover 2 later on. Jupyter Notebooks integrate code and visualization, and are therefore very helpful for demonstration purposes and to be run by online interperters (such as google colab). first we will show how to run Jupyter Notebooks locally. To do thi install `jupyter` and the module `folium` in an existing or new environment that includes Python and start Jupyter:

Make a new folder: *New* → *Folder*, rename the folder (check the box next to the new 'Untitled Folder' and click **'Rename'** in the top) and, in this folder, create a new Python3 Jupyter Notebook *New* → *Python 3*. Give your notebook a name by clicking on *untitled*. Note that this creates a file with the extension *.ipynb*, which stands for Jupyter Notebook.

Your Jupyter Notebook is automatically saved as an `.ipynb` file on your computer (the file extension comes from the historic name "IPython Notebook"). The notebook can be downloaded as a Python script, pdf or html. You can also save it manually.

As said before, jupyter is locally opened in your browser. It does not connect to the internet, but it does show the possibilities, one could create something online that can run your notebooks for you on the cloud. This is exactly what Google does with Google Colab. Google Colab is a cloud service that allows you to run your jupyter notebooks on the Google cloud for free. Let's see what this looks like:

* Go to https://colab.research.google.com/notebooks/empty.ipynb (note the similaritie and differences between jupyter locally and on google colab)

* type `!pip install folium` and press ctrl+enter to run and install folium

* In a new cell run the same python code as locally to create and show a new folium map

For this course we will rarely use jupyter notebooks and or google colab, but it is good to know they exist. Especially google colab is being used more and more in the scientific community and you are likely to come across these during other courses.

Now that we know how to set up an environment and run code, lets use this new knowledge and run some python code. Again, During this course advise you to code in Spyder, as this IDE is the recommended IDE for the python part of this course. To practice you might also want to try out jupyter locally and google colab to run the same code.

In the tutorial about R and Python we have gone over the differences and similarities of python and R. This tutorial also contains some basic python syntax, in this tutorial we assume you know this content, but we will go over a few basics here as well. The example below are mostly meant for reference purposes, we assume you understand most of this refresher already.

In python we assign variable using the equals sign (`=`):

Printing in python is done using the `print` function. We can print variables directly:

We can use string formatting to use flexible strings, for example for printing. to start a formatted string, we put a `f` before the string. We can use curly brackets `{}` in this formatted string. The text between these curly brackets is executed as regular python code.

In python we import a package using the `import` statement (instead of th the `library` function in R) . For example importing the pandas package goes as follows