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# Data manipulation, analysis and visualisation in Python
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Specialist course Doctoral schools of Ghent University,<br>
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31 May 1, 3 June 2021
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20, 21 and 22 December 2021
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Joris Van den Bossche, Stijn Van Hoey
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# Who are you?
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Go to https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?both
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Go to https://hackmd.io/d6wqmazERIyz__dlfwKq1w?both
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<small>(You do not need to sign in, just start editing.)</small>
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<iframe src="https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?view" height="400px" width="800px"></iframe>
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<iframe src="https://hackmd.io/d6wqmazERIyz__dlfwKq1w?view" height="400px" width="800px"></iframe>
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---
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<a href="https://twitter.com/svanhoey"><i class="fa fa-twitter" aria-hidden="true"></i> SVanHoey</a>,
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<a href="https://github.com/stijnvanhoey"><i class="fa fa-github" aria-hidden="true"></i> stijnvanhoey</a>
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* Research software engineer
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* Freelance developer and teacher
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* Research software engineer at Fluves
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.center[
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![:scale 90%](./img/work_stijn_1.png)]
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---
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class: middle, section_background
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# Introduction
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---
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class: center, middle
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index | date
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:-----:|:----:
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2 | 19930000
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8 | 1992-930
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27 | 20050500
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34 | 201405.01
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162 | 7/9/2287
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1400 | 0.0
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2800 | start of the year 2015
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3777 | Summer
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8733 | 2013-2016
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26766 | 26/09/2002 and later 1/1/2016
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40788 | Nan
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41277 | /
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51002 | -999
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51007 | -9999
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--
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.center[Never underestimate the creativity of humans!]
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---
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class: center, middle
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![:scale 100%](./img/datacleaning1.jpg)
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---
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class: center, middle
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![:scale 100%](./img/datacleaning2.jpg)
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---
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class: left, middle
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> In this course, we focus on the Python *scientific ecosystem* to tackle data manipulation and visualisation tasks.
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---
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class: middle, section_background
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# Setting up a working environment
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1. Make sure to (re)download ALL the course material, see [2-getting-the-course-materials](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html#2-getting-the-course-materials) also if you already did this before.
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1. Next, also do section 3 and 4 of the [setup](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html) instructions.
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> If you succesfully done 1, 2 and 3, give a 👍 `thumbs up` to the _are-you-ready_ message in the discord `#status` channel.
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> If you succesfully done 1, 2 and 3, put up your `green sticky note` on your computer screen.
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Next:
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- Surf to and fill in [the questionnaire](https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?both)
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- Surf to and fill in [the questionnaire](https://hackmd.io/d6wqmazERIyz__dlfwKq1w?both)
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- In JupyterLab, start with the `notebooks/00-jupyter_introduction.ipynb` individually.
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Installation or setup issues? Post them on the discord `#installation-issues` channel
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Installation or setup issues? Put up your `orange/red/yellow sticky note` on your computer screen.
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### Time is divided between
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- group sessions: we explain new concepts (aka 'theory')
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- break-out sessions: you work on exercises or case studies
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- practise sessions: you work on exercises
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In case of questions, remarks, suggestions, you can always post messages in the discord `#daily-channel`
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In case of questions, remarks, suggestions, you can always interrupt us and just ask.
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During practise sessions, use the `orange/red/yellow sticky note` on top of your screen to let us know you have a question.
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### Status check
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We will regularly ask for a check (ready with exercise, installation succesfull...). Use the discord `#status` channel to put 👍 (`thumbs up`).
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We will regularly ask for a check (ready with exercise, installation succesfull...). Use the `green sticky note` on top of your laptop screen to say 👍.
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### Feel lost?
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Feel free to send a discord direct message to one of us, we are stand by.
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Just ask either one of us, we are here to help you.
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or see the [contributing guidelines](https://github.com/jorisvandenbossche/DS-python-data-analysis/blob/master/CONTRIBUTING.md)
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---
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class: middle, section_background
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# Introduction
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---
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class: center, middle
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index | date
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:-----:|:----:
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2 | 19930000
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8 | 1992-930
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27 | 20050500
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34 | 201405.01
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162 | 7/9/2287
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1400 | 0.0
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2800 | start of the year 2015
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3777 | Summer
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8733 | 2013-2016
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26766 | 26/09/2002 and later 1/1/2016
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40788 | Nan
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41277 | /
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51002 | -999
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51007 | -9999
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--
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.center[Never underestimate the creativity of humans!]
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---
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class: center, middle
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![:scale 100%](./img/datacleaning1.jpg)
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---
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class: center, middle
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![:scale 100%](./img/datacleaning2.jpg)
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---
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class: left, middle
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> In this course, we focus on the Python *scientific ecosystem* to tackle data manipulation and visualisation tasks.
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---
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class: middle, section_background
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# Working with Python
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# Conda
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- You can install different version of Python and other packages alongside on your computer
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- Easily share environments with other
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## Small overview of conda commands
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```
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# Working with Python
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# Writing Python code
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## IPython console
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* When working with array data (e.g. images, numerical algorithms): just stick with numpy
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* When working with multidimensional labeled data (e.g. climate data): have a look at [xarray](http://xarray.pydata.org/en/stable/)
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## Key features
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![:scale 100%](http://esq.h-cdn.co/assets/15/51/980x490/landscape-1450137389-john-cleese.JPG)
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### https://forms.gle/4yAv8dZxbBBpNsCK6
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### https://forms.gle/Fgi6CfSNPZDDWTUu9
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Please fill in the questionnaire!

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