add drafts for sections 2-4

_episodes/02-data.md

+---
+title: Working with Data
+teaching: 20
+exercises: 10
+questions:
+- "How should I work with numeric data in Python?"
+- "What's the recommended way to handle and analyse tabular data?"
+- "How can I import tabular data for analysis in Python and export the results?"
+objectives:
+- "handle and summarise numeric data with Numpy."
+- "filter values in their data based on a range of conditions."
+- "load tabular data into a Pandas dataframe object."
+- "describe what is meant by the data type of an array/series, and the impact this has on how the data is handled."
+- "add and remove columns from a dataframe."
+- "select, aggregate, and visualise data in a dataframe."
+keypoints:
+- "Specialised third-party libraries such as Numpy and Pandas provide powerful objects and functions that can help us analyse our data."
+- "Pandas dataframe objects allow us to efficiently load and handle large tabular data."
+- "Use the `pandas.read_csv` and `pandas.write_csv` functions to read and write tabular data."
+---
+
+## plan
+
+- Toby currently scheduled to lead this session
+- Numpy
+  - arrays
+  - masking
+  - aside about data types and potential hazards
+  - reading data from a file (with note that more will come later on this topic)
+  - link to existing image analysis material
+- Pandas
+  - when an array just isn't enough
+  - DataFrames - re-use material from [Software Carpentry][swc-python-gapminder]?
+    - ideally with a more relevant example dataset...
+    - include an aside about I/O - reading/writing files (pandas, numpy, `open()`, (?) bytes vs strings, (?) encoding)
+  - Finish with example of `df.plot()` to set the scene for plotting section
+
+{% include links.md %}

_episodes/03-plotting.md

+---
+title: Plotting Data
+teaching: 20
+exercises: 10
+questions:
+- "How can I create publication-ready figures with Python?"
+objectives:
+- "plot data in a Matplotlib figure."
+- "create multi-panelled figures."
+- "export figures in a variety of image formats."
+- "use interactive features of Jupyter to make it easier to fine-tune a plot."
+keypoints:
+- "Matplotlib is a powerful plotting library for Python."
+- "It can also be annoyingly fiddly. Jupyter can help with this."
+---
+
+## plan
+
+- Renato currently scheduled to lead this session
+- Matplotlib
+- plotnine?
+
+{% include links.md %}

_episodes/04-argparse.md

+---
+title: Parsing Command Line Arguments
+teaching: 20
+exercises: 10
+questions:
+- "How can I access arguments passed to my Python script at runtime?"
+- "How can I create a sophisticated command line interface for my script?"
+- "How can I provide the user with more information about how to run my code?"
+objectives:
+- "access command line arguments with `sys.argv`."
+- "parse and use arguments and options with `argparse`."
+- "create a comprehensive usage statement for their script."
+keypoints:
+- "Positional command line arguments can be accessed from inside a script through the `sys.argv` object."
+- "The `argparse` module allows us to create extensive and powerful command line interfaces for our scripts."
+- "`argparse` also constructs a standardised usage statement according to the parser's configuration."
+---
+
+## plan
+
+- Toby currently scheduled to lead this session
+- `sys.argv`
+- `argparse`
+  - positonal arguments
+  - options
+  - capturing multiple items in a single argument
+  - usage statements
+
+{% include links.md %}

_includes/links.md

@@ -46,6 +46,7 @@
 [rubygems]: https://rubygems.org/pages/download/
 [styles]: https://github.com/carpentries/styles/
 [swc-lessons]: https://software-carpentry.org/lessons/
+[swc-python-gapminder]: http://swcarpentry.github.io/python-novice-gapminder/
 [swc-releases]: https://github.com/swcarpentry/swc-releases
 [training]: https://carpentries.github.io/instructor-training/
 [workshop-repo]: {{ site.workshop_repo }}