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class: center, middle
# Data manipulation, analysis and visualisation in Python
Specialist course Doctoral schools of Ghent University,<br>
31 May 1, 3 June 2021
Joris Van den Bossche, Stijn Van Hoey
https://github.com/jorisvandenbossche/DS-python-data-analysis
---
class: center, middle
# Who are you?
Go to https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?both
<small>(You do not need to sign in, just start editing.)</small>
<iframe src="https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?view" height="400px" width="800px"></iframe>
---
### Joris Van den Bossche
<a href="https://twitter.com/jorisvdbossche"><i class="fa fa-twitter" aria-hidden="true"></i> jorisvdbossche</a>,
<a href="https://github.com/jorisvandenbossche"><i class="fa fa-github" aria-hidden="true"></i> jorisvandenbossche</a><br>
* Open source software developer and teacher
* Pandas, GeoPandas, scikit-learn, Apache Arrow
---
### Stijn Van Hoey
<a href="https://twitter.com/svanhoey"><i class="fa fa-twitter" aria-hidden="true"></i> SVanHoey</a>,
<a href="https://github.com/stijnvanhoey"><i class="fa fa-github" aria-hidden="true"></i> stijnvanhoey</a>
* Research software engineer
.center[
]
---
class: middle, section_background
# Introduction
---
class: center, middle
index | date
:-----:|:----:
2 | 19930000
8 | 1992-930
27 | 20050500
34 | 201405.01
162 | 7/9/2287
1400 | 0.0
2800 | start of the year 2015
3777 | Summer
8733 | 2013-2016
26766 | 26/09/2002 and later 1/1/2016
40788 | Nan
41277 | /
51002 | -999
51007 | -9999
--
.center[Never underestimate the creativity of humans!]
---
class: center, middle
---
class: center, middle
---
class: left, middle
> In this course, we focus on the Python *scientific ecosystem* to tackle data manipulation and visualisation tasks.
---
class: middle, section_background
# Setting up a working environment
---
## Setting up a working environment
For the setup instructions, see the [setup page](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html).
---
class: left, middle
1. Everyone has conda installed and the environment setup? If not, see [1-install-python-and-the-required-python-packages](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html#1-install-python-and-the-required-python-packages)
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.
1. Next, also do section 3 and 4 of the [setup](https://jorisvandenbossche.github.io/DS-python-data-analysis/setup.html) instructions.
> If you succesfully done 1, 2 and 3, give a 👍 `thumbs up` to the _are-you-ready_ message in the discord `#status` channel.
Next:
- Surf to and fill in [the questionnaire](https://hackmd.io/EY59dn0TQoqsMv5k_A9xKg?both)
- In JupyterLab, start with the `notebooks/00-jupyter_introduction.ipynb` individually.
Installation or setup issues? Post them on the discord `#installation-issues` channel
---
class: center, middle
When you see something like this...
...relax, you're ready to start!
---
class: middle, left
### Time is divided between
- group sessions: we explain new concepts (aka 'theory')
- break-out sessions: you work on exercises or case studies
In case of questions, remarks, suggestions, you can always post messages in the discord `#daily-channel`
### Status check
We will regularly ask for a check (ready with exercise, installation succesfull...). Use the discord `#status` channel to put 👍 (`thumbs up`).
### Feel lost?
Feel free to send a discord direct message to one of us, we are stand by.
---
class: middle, center
Report bugs, typo's, suggestions... as issues ([New issue](https://github.com/jorisvandenbossche/DS-python-data-analysis/issues/new)) <br>or just by mail.
or see the [contributing guidelines](https://github.com/jorisvandenbossche/DS-python-data-analysis/blob/master/CONTRIBUTING.md)
---
# Conda
### Why using conda?
- Consistent package manager across Windows, Mac and Linux
- Many precompiled packages available
- Less problems with installation!
--
### Why different environments?
- Manage the dependencies of a specific project/paper/group/...
- You can install different version of Python and other packages alongside on your computer
- Easily share environments with other
---
## Small overview of conda commands
Creating a new environment:
```
conda create -n my_env python=3.7 pandas
# or from environment file
conda env create -f environment.yml
```
Activating an environment:
```
conda activate my_env
```
Install a new package:
```
conda install matplotlib # if not working, try: pip install ...
```
List all installed packages: `conda list`
List all your environments: `conda info -e`
See the docs: http://conda.pydata.org/docs/using/index.html
---
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### Keep track of your python ecosystem <br>with an `environment.yml`
<br><br>
```
conda env export > environment.yml
```
---
class: middle, section_background
# Working with Python
---
## IPython console
<br>
.center[]
---
## Interactive Development Environment (IDE)
* [**Spyder**](https://pythonhosted.org/spyder/) is shipped with Anaconda. The familiar environment for Matlab/Rstudio-users...
* [**PyCharm**](https://www.jetbrains.com/pycharm/): Popular for web-development and Django applications, powerful when doing 'real' development (packages, libraries, software)
* [Eclipse + **pyDev plugin**](http://www.pydev.org/): If you like working in Eclipse, just add the python environment
* [**VS Code**](https://code.visualstudio.com/), [**Atom**](https://atom.io/), ...
---
## Jupyter Notebook
<small>(*previously called IPython notebook*)</small>
**Jupyter notebook** provides an **interactive** scripting environment,<br> ideal for exploration, prototyping,...
.center[]
--
...the stuff we're dealing with in this course!
---
class: middle, day_background
# Overview of the first day
* Introduction to Jupyter notebook/lab
* Introduction to Pandas fundamentals
---
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# Pandas
---
class: center, middle
---
## Pandas: data analysis in python
For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.
What is `pandas`?
* Pandas can be thought of as *NumPy arrays with labels* for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.
* Pandas can also be thought of as `R`'s `data.frame` in Python.
* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...
It's documentation: http://pandas.pydata.org/pandas-docs/stable/
---
## When do you need pandas?
When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):
- Import data
- Clean up messy data
- Explore data, gain insight into data
- Process and prepare your data for analysis
- Analyse your data (together with scikit-learn, statsmodels, ...)
---
## Attention!
Pandas is great for working with heterogeneous and tabular 1D/2D data, but not all types of data fit in such structures!
* When working with array data (e.g. images, numerical algorithms): just stick with numpy
* When working with multidimensional labeled data (e.g. climate data): have a look at [xarray](http://xarray.pydata.org/en/stable/)
---
## Key features
* Fast, easy and flexible input/output for a lot of different data formats
* Working with missing data (`.dropna()`, `pd.isnull()`)
* Merging and joining (`concat`, `join`)
* Grouping: `groupby` functionality
* Reshaping (`stack`, `pivot`)
* Powerful time series manipulation (resampling, timezones, ..)
* Easy plotting
---
# Let's get to it!
### Pandas fundamentals...
1. Data structures `pandas_01_data_structures.ipynb`
2. Basic Operations `pandas_02_basic_operations.ipynb`
3. Indexing and selecting data `pandas_03a_selecting_data.ipynb`
4. Time series data `pandas_04_time_series_data.ipynb`
5. (Combining data `pandas_05_combining_datasets.ipynb`)
---
class: middle, day_background
# Overview of the second day
1. *Case study:* Bike count
2. Pandas: indexing part II
3. Pandas: groupby operations
4. *Case study:* Processing observations field study data
5. Visualization: Introduction to matplotlib and seaborn
6. Pandas: reshaping data
7. *Case study:* Bacterial resistance lab study
---
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# Overview of the third day
1. *Case study:* Bacterial resistance study - finish -
2. *Case study:* Analyse observations field study data
3. *Case study:* Analyse air quality sensor data
4. *Case study:* Process air quality sensor data
5. Visualization: overview visualization landscape
6. Closing remarks
---
class: center, middle
# How are you feeling?
### https://forms.gle/4yAv8dZxbBBpNsCK6
Please fill in the questionnaire!
---
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# Closing notes
---
class: center, middle
# Python's scientific ecosystem
#### ## Adjusted from figure by Jake VanderPlas
---
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background-image: url(./img/JakeVdP-ecosystem1.svg)
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background-image: url(./img/JakeVdP-ecosystem5.svg)
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---
# A rich ecosystem of packages:
<br>
**Machine learning**: scikit-learn, tensorflow, pytorch, keras, chainer, ...
**Performance**: Numba, Cython, Numexpr, Pythran, C/Fortran wrappers, ...
**Visualisation**: Bokeh, Seaborn, Plotnine, Altair, Plotly, Mayavi, HoloViews, datashader, vaex ...
**Data structures and parallel/distributed computation**: Xarray, Dask, Distributed, Cupy, ...
Specialized packages in many **scientific fields**: astronomy, natural language processing, image processing, geospatial, ...
**Packaging and distribution**: pip/wheels, conda, Anaconda, Canopy, ...
---
class: center, middle
### Reading advice
[Good Enough Practices in Scientific Computing](https://arxiv.org/pdf/1609.00037v1.pdf)
> "*However, while most scientists are careful to validate their laboratory and field equipment, most do not know how reliable their software is*"
---
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## Good luck!
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