<commit_before># Workshop: Python in Science and Research

Dive into the following topics

- Numeric Computation
- Data Visualization
    - basic plotting
- Text Analysis
- Social Network Analysis

## Front-Up Preparations

As this is a notebook class, you should try to set Python on your machine before we start with the actual workshop.

The easiest way to do so is by installing the [Anaconda Distribution](https://www.continuum.io/).
Just download and install the current distribution with Python 3.6 or higher as described [in the Anaconda Installation Instructions](https://docs.continuum.io/anaconda/install).

Then test the Anaconda installation by running your first notebook:
Therefore run in a terminal:
```bash
$ jupyter-notebook
```
, open in your browser [The Jupyter Notebook for the initial Installation Test](./nbs/0-a-Test%20of%20the%20Installation.ipynb) and run it ... 
If all runs through and you see as output in the last cell `All looks good!` you are done!

Further on in the workshop the following additional packages are required. We will install them together in the course, but if you want to play around upfront, please feel free to do so:
```bash
    conda install seaborn

    conda install -c scitools cartopy=0.15.0
    # or
    conda install -c conda-forge cartopy=0.15.1
    
    conda install networkx

    pip install graphviz
```
On OS-Level you also will need graphviz.
Please check the [Graphviz Homepage](http://www.graphviz.org/) about how to install on your Machine.

You can check, whether the additional components also work fine, with [The Jupyter Notebook for the Test of the additional Components](./nbs/0-b-Additional%20Component%20Installation%20Test.ipynb).

#### Refresher - Python(3) Basics
If you need a refresher of your Python knowledge the [Interactive Python Tutorial](https://www.learnpython.org/) is a good starting point.

The Lectures 
- `Learn the Basics`: All and
- `Advanced Tutorials` 
    - Generators
    - List Comprehensions
    - Multiple Function Arguments
    - Sets
    - Partial functions<commit_msg>second test nb<commit_after># Workshop: Python in Science and Research

Dive into the following topics

- Numeric Computation
- Data Visualization
    - basic plotting
- Text Analysis
- Social Network Analysis

## Up Front Preparations

As this is a notebook class, you should try to set Python on your machine before we start with the actual workshop.

The easiest way to do so is by installing the [Anaconda Distribution](https://www.continuum.io/).
Just download and install the current distribution with Python 3.6 or higher as described [in the Anaconda Installation Instructions](https://docs.continuum.io/anaconda/install).

Then test the Anaconda installation by running your first notebook:
Therefore run in a terminal:
```bash
$ jupyter-notebook
```
, open in your browser [The Jupyter Notebook for the initial Installation Test](./nbs/0-a-Test%20of%20the%20Installation.ipynb) and run it ... 
If all runs through and you see as output in the last cell `All looks good!` you are done!

### Optionals

Further on in the workshop the following additional packages are required. We will install them together in the course, but if you want to play around upfront, please feel free to do so:
```bash
    conda install seaborn

    conda install -c scitools cartopy=0.15.0
    # or
    conda install -c conda-forge cartopy=0.15.1
    
    conda install networkx

    pip install graphviz
```
On OS-Level you also will need graphviz.
Please check the [Graphviz Homepage](http://www.graphviz.org/) about how to install on your Machine.

You can check, whether the additional components also work fine, with [The Jupyter Notebook for the Test of the additional Components](./nbs/0-b-Additional%20Component%20Installation%20Test.ipynb).

#### Refresher - Python(3) Basics
If you need a refresher of your Python knowledge the [Interactive Python Tutorial](https://www.learnpython.org/) is a good starting point.

The Lectures 
- `Learn the Basics`: All and
- `Advanced Tutorials` 
    - Generators
    - List Comprehensions
    - Multiple Function Arguments
    - Sets
    - Partial functions
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{'dir': 'git-commits-cleaned', 'id': '582455', 'n_tokens_mistral': 1220, 'n_tokens_neox': 1153, 'n_words': 527}