Statistics and ML in python

Pandas-flavour

pandas plus statsmodels look a lot more like R. On the minus side, this combination lack some language features of R (e.g. regression formulae are not first class language features). On the plus side, they lack some language misfeatures of R (the object model being a box of turds, and copy-by-value semantics and all those other annoyances.)

I am not a huge fan of pandas, personally. The enginerring behind it is impressive, but it ends up not fitting my actual problems particularly well. This might be about my workflow being idiosyncratic, or it might be because the original author had an idiosyncratic workflow, and it is he who needed weird features that the rest of us do not.

In comparison to R, one crucial weakness is that R has a rich ecosystem of tools for dataframes. Python is a bit thinner.

Also (and I do not know if this was the true process or not) when pandas was designed, the made a bunch of design choices differently than R did, possibly thinking to himself “Why did R not do it this way which is clearly better”, only to discover that in practice R’s way was better, or that the cool hack ended up being a bit awkward when applied to python syntax. Chief among these is the obligatory indexing of rows in the table; I spend a lot of time fighting pandas insistence on wanting everything to be named, even if it is stuff that does not need to be named.

Anyway, this is still very usable and useful.

• pandas is more-or-less a dataframe class for python. Lots of nice things are built on this, such as …

• statsmodels, which is more-or-less R, but Python. Implements

  • Linear regression models
  • Generalized linear models
  • Discrete choice models
  • Robust linear models
  • Many models and functions for time series analysis
  • Nonparametric estimators
  • A wide range of statistical tests
  • etc

• patsy implements a formula language for pandas. This does lots of things, but most importantly, it

  • builds design matrices (i.e. it knows how to represent z~x^2+x+y^3 as a matrix, which only sounds trivial if you haven’t tried it)
  • statefully preconditions data (e.g. constructs data transforms that will correctly normalise the test set as well as the training data.)

• pandera implements type sanity and validation

The pandas API is popular; there are a few tools which aim to accelerate calculations by providing backends for it based on alternative data formats or parallelism.

• Rapids AI cuDF

  cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF also provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.

• Modin

  Scale your pandas workflow by changing a single line of code — The modin.pandas DataFrame is an extremely light-weight parallel DataFrame. Modin transparently distributes the data and computation so that all you need to do is continue using the pandas API as you were before installing Modin. Unlike other parallel DataFrame systems, Modin is an extremely light-weight, robust DataFrame. Because it is so light-weight, Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.

• Koalas: pandas API on Apache Spark seems to be Modin but for Spark.

• Python dataframe interchange protocol

  Python users today have a number of great choices for dataframe libraries. From Pandas and cuDF to Vaex, Koalas, Modin, Ibis, and more. Combining multiple types of dataframes in a larger application or analysis workflow, or developing a library which uses dataframes as a data structure, presents a challenge though. Those libraries all have different APIs, and there is no standard way of converting one type of dataframe into another.

Polars-flavour

• Polars (source)

Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as the memory model.

• Lazy | eager execution
• Multi-threaded
• SIMD
• Query optimization
• Powerful expression API
• Hybrid Streaming (larger than RAM datasets)
• Rust | Python | NodeJS | ...

Does not invent a python-specific data format but instead leverages Apache arrow. It looks like pandas in many ways, but is not 100% compatible, see Polars for pandas users. This means that the ecosystem is thinner again than the pandas ecosystem; on the other hand, some stuff looks easier than pandas, so maybe it is not too bad in practice.

R

%load_ext rpy2.ipython

%R library(robustbase)
%Rpush yy xx
%R mod <- lmrob(yy ~ xx);
%R params <- mod$coefficients;
%Rpull params

import pandas as pd
import feather

path = 'my_data.feather'
feather.write_dataframe(df, path)
df = feather.read_dataframe(path)

library(feather)

path <- "my_data.feather"
write_feather(df, path)
df <- read_feather(path)

Julia

See pycall from julia.

Use a known-good project structure

cookiecutter data science.

Local random number generator state

⚠️ this is out of date now; the new RNG API is much better.

Seeding your RNG can be a pain in the arse, especially if you are interfacing with an external library that doesn’t have RNG state passing in the API. So, use a context manager. Here’s one that works for numpy-based code:

from numpy.random import get_state, set_state, seed

class Seed(object):
  """
  context manager for reproducible seeding.

  >>> with Seed(5):
  >>>   print(np.random.rand())

  0.22199317108973948
  """
  def __init__(self, seed):
    self.seed = seed
    self.state = None

  def __enter__(self):
    self.state = get_state()
    seed(self.seed)

  def __exit__(self, exc_type, exc_value, traceback):
    set_state(self.state)

Exercise for the student: make it work with the default RNG also