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Merge pull request #93 from staadecker/docs
Add documentation on Pandas
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‎README.md

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- [`Numerical Issues.md`](/docs/Numerical%20Issues.md): Information about detecting and resolving numerical issues.
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- [`Pandas.md`](/docs/Pandas.md): Crash course on the Pandas data manipulation library.
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Finally, you can generate documentation for the SWITCH modules by running `pydoc -w switch_model` after having installed
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SWITCH. This will build HTML documentation files from python doc strings which
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will include descriptions of each module, their intentions, model

‎docs/Pandas.md

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# Using Pandas
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[Pandas](https://pandas.pydata.org/) is a Python library that is used for data analysis and manipulation.
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In SWITCH, Pandas is mainly used to create graphs and also output files after solving.
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This document gives a brief overview of key concepts and commands
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to get started with Pandas. There are a lot better resources available
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online teaching Pandas, including entire online courses.
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Most importantly, the Pandas [documentation](https://pandas.pydata.org/docs/)
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and [API reference](https://pandas.pydata.org/docs/reference/index.html#api) should be your go-to
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when trying to learn something new about Pandas.
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## Key Concepts
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### DataFrame
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Dataframes is the main Pandas data structure and is responsible for
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storing tabular data.
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Dataframes have rows, columns and labelled axes (e.g. row or column names).
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When manipulating data,
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the common practice is to store your main dataframe in a variable called `df`.
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### Series
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A series can be thought of as a single column in a dataframe.
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It's a 1-dimensional array of values.
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### Indexes
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Pandas has two ways of working with dataframes: with or without custom indexes.
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Custom indexes are essentially labels for each row. For example, the following
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dataframe has 4 columns (A, B, C, D) and a custom index (the date).
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```
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A B C D
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2000-01-01 0.815944 -2.093889 0.677462 -0.982934
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2000-01-02 -1.688796 -0.771125 -0.119608 -0.308316
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2000-01-03 -0.527520 0.314343 0.852414 -1.348821
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2000-01-04 0.133422 3.016478 -0.443788 -1.514029
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2000-01-05 -1.451578 0.455796 0.559009 -0.247087
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```
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The same dataframe can be expressed without the custom index as follows.
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Here the date is a column just like the others and the index is the
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default index (just the row number).
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```
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date A B C D
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0 2000-01-01 0.815944 -2.093889 0.677462 -0.982934
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1 2000-01-02 -1.688796 -0.771125 -0.119608 -0.308316
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2 2000-01-03 -0.527520 0.314343 0.852414 -1.348821
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3 2000-01-04 0.133422 3.016478 -0.443788 -1.514029
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4 2000-01-05 -1.451578 0.455796 0.559009 -0.247087
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```
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Using custom indexes is quite powerful but more advanced. When starting
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out it's best to avoid custom indexes.
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### Chaining
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Every command you apply on a dataframe *returns* a new dataframe.
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That is commands *do not* modify the dataframe they're called on.
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For example, the following has no effect.
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`df.groupby("country")`
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Instead, you should always update your variable with the returned result.
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For example,
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`df = df.groupby("country")`
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This allows you to "chain" multiple operations together. E.g.
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`df = df.groupby("country").rename(...).some_other_command(...)`
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## Useful commands
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- `df = pandas.read_csv(filepath, index_col=False)`. This command
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reads a csv file from filepath and returns a dataframe that gets stored
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in `df`. `index_col=False` ensures that no custom index is automatically
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created.
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- `df.to_csv(filepath, index=False)`.
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This command will write a dataframe to `filepath`. `index=False` means
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that the index is not written to the file. This should
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be used if you're not using custom indexes since you probably don't
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want the default index (just the row numbers) to be outputted to your csv.
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- `df["column_name"]`: Returns a *Series* containing the values for that column.
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- `df[["column_1", "column_2"]]`: Returns a *DataFrame* containing only the specified columns.
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- `df[df["column_name"] == "some_value"]`: Returns a dataframe with only the rows
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where the condition in the square brackets is met. In this case we filter out
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all the rows where the value under `column_name` is not `"some_value"`.
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- `df.merge(other_df, on=["key_1", "key_2"])`: Merges `df` with `other_df`
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where the columns over which we are merging are `key_1` and `key_2`.
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- `df.info()`: Prints the columns in the dataframe and some info about each column.
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- `df.head()`: Prints the first few rows in the dataframe.
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- `df.drop_duplicates()`: Drops duplicate rows from the dataframe
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- `Series.unique()`: Returns a series where duplicate values are dropped.
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## Example
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This example shows how we can use Pandas to generate a more useful view
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of our generation plants from the SWITCH input files.
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```python
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import pandas as pd
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# READ
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kwargs = dict(
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index_col=False,
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dtype={"GENERATION_PROJECT": str}, # This ensures that the project id column is read as a string not an int
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)
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gen_projects = pd.read_csv("generation_projects_info.csv", *kwargs)
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costs = pd.read_csv("gen_build_costs.csv", *kwargs)
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predetermined = pd.read_csv("gen_build_predetermined.csv", *kwargs)
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# JOIN TABLES
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gen_projects = gen_projects.merge(
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costs,
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on="GENERATION_PROJECT",
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)
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gen_projects = gen_projects.merge(
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predetermined,
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on=["GENERATION_PROJECT", "build_year"],
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how="left" # Makes a left join
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)
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# FILTER
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# When uncommented will filter out all the projects that aren't wind.
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# gen_projects = gen_projects[gen_projects["gen_energy_source"] == "Wind"]
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# WRITE
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gen_projects.to_csv("projects.csv", index=False)
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```
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If you run the following code snippet in the `inputs folder` it will create a `projects.csv` file
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containing the project data, cost data and prebuild data all in one file.

‎docs/Performance.md

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- `--save-warm-start` and `--warm-start` both use an extension of the `gurobi_direct` solver interface which is
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generally slower than the `gurobi` solver interface (see section above).
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## Model formulation
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The way the model is formulated often has an impact on performance. Here are some rules of thumb.
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- For constraints, it is faster to use `<=` and `>=` rather than `==` when possible. If your constraint
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should be an equality, try to think about whether it is already being pushed against one of the bounds
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by the objective function.
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## Tools for improving performance
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- [Memory profiler](https://pypi.org/project/memory-profiler/) for generating plots of the memory

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