158 lines
3.9 KiB
ReStructuredText
158 lines
3.9 KiB
ReStructuredText
Plotting with Matplotlib
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------------------------
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Also creating a presentation with rst2pdf
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=========================================
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Data Structures
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---------------
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Favour simpler data structures if they do what you need. In order:
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#. Built-in Lists
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- 2xN data or simpler
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- Can't install system dependencies
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#. Numpy arrays
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- 2 (or higher) dimensional data
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- Lots of numerical calculations
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#. Pandas series/dataframes
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- 'Data Wrangling', reshaping, merging, sorting, querying
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- Importing from complex formats
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Shamelessly stolen from https://stackoverflow.com/a/45288000
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Loading Data from Disk
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----------------------
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Natively
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========
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.. code-block:: python
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>>> import csv
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>>> with open('eggs.csv', newline='') as csvfile:
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... spam = csv.reader(csvfile,
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... delimiter=' ',
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... quotechar='|')
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... for row in spam:
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... # Do things
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... pass
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Loading Data from Disk
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----------------------
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Numpy
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=====
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.. code-block:: python
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>>> import numpy
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>>> spam = numpy.genfromtxt('eggs.csv',
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... delimiter=' ',
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... dtype=None) # No error handling!
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>>> for row in spam:
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... # Do things
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... pass
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``numpy.genfromtxt`` will try to infer the datatype of each column if
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``dtype=None`` is set.
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``numpy.loadtxt`` is generally faster at runtime if your data is well formated
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(no missing values, only numerical data or constant length strings)
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Loading Data from Disk
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----------------------
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Numpy NB.
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=========
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**Remind me to look at some actual numpy usage at the end**
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- I think numpy does some type coercion when creating arrays.
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- Arrays created by ``numpy.genfromtxt`` can not in general be indexed like
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``data[xstart:xend, ystart:yend]``.
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- Data of unequal types are problematic! Pandas *may* be a better choice in
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that case.
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- Specifying some value for ``dtype`` is probably necessary in most cases in
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practice: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
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Loading Data from Disk
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----------------------
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Pandas
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======
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.. code-block:: python
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>>> import pandas
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>>> # dtype=None is def
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>>> spam = pandas.read_csv('eggs.csv',
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... delimiter=' ',
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... header=None)
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>>> for row in spam:
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... # Do things
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... pass
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``header=None`` is required if the flie does not have a header.
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Generating Data for Testing
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---------------------------
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Generating the data on the fly with numpy is convenient.
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.. code-block:: python
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>>> import numpy.random as ran
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>>> # For repeatability
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>>> ran.seed(7890234)
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>>> # Uniform [0, 1) floats
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>>> data = ran.rand(100, 2)
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>>> # Uniform [0, 1) floats
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>>> data = ran.rand(100, 100, 100)
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>>> # Std. normal floats
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>>> data = ran.randn(100)
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>>> # 3x14x15 array of binomial ints with n = 100, p = 0.1
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>>> data = ran.binomial(100, 0.1, (3, 14, 15))
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Plotting Time Series
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--------------------
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Plot data of the form:
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.. math:: y=f(t)
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Subplots
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--------
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Saving Plots
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------------
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So far I've just displayed plots with ``plt.show()``. You can actually save
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the plots from that interface manually, but when scripting, it's convenient
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to do so automatically:
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.. code-block:: python
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>>> # Some plotting has previously occured
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>>> plt.savefig('eggs.pdf', dpi=300, transparent=False)
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The output format is interpreted from the file extension.
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The keyword arguments are optional here. Other options exist.
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Error Bars
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----------
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Stacked Bar Graph
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-----------------
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Resources
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---------
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NumPy User Guide: https://docs.scipy.org/doc/numpy/user/index.html
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NumPy Reference: https://docs.scipy.org/doc/numpy/reference/index.html#reference
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Matplotlib example gallery: https://matplotlib.org/gallery/index.html
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Pandas: It probably exists. Good luck.
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This presentation: https://git.friedersdorff.com/max/plotting_with_matplotlib.git
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