Tag: data

Handling and Converting Data Types in Python Pandas

Handling and Converting Data Types in Python Pandas

Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis.

Chris Moffit

In this short tutorial, Chris shows how to the pandas dtypes map to the numpy and base Python data types.

A screenshot of the data type mapping.

Moreover, Chris demonstrates how to handle and convert data types so you can speed up your data analysis. Both using custom functions and anonymous lambda functions.

A snapshot from the original blog.

A very handy guide indeed, after which you will be able to read in your datasets into Python in the right format from the get-go!

Using data type casting, lambda functions, and functional programming to read in data in Python. Via pbpython.com/pandas_dtypes.html

10 Guidelines to Better Table Design

10 Guidelines to Better Table Design

Jon Schwabisch recently proposed ten guidelines for better table design.

Next to the academic paper, Jon shared his recommendations in a Twitter thread.

Let me summarize them for you:

  • Right-align your numbers
  • Left-align your texts
  • Use decimals appropriately (one or two is often enough)
  • Display units (e.g., $, %) sparsely (e.g., only on first row)
  • Highlight outliers
  • Highlight column headers
  • Use subtle highlights and dividers
  • Use white space between rows and columns
  • Use white space (or dividers) to highlight groups
  • Use visualizations for large tables
Highlights in a table. Via twitter.com/jschwabish/status/1290324966190338049/photo/2
Visualizations in a table. Via twitter.com/jschwabish/status/1290325409570197509/photo/3
Example of a well-organized table. Via twitter.com/jschwabish/status/1290325663543627784/photo/2
Data Science vs. Data Alchemy – by Lucas Vermeer

Data Science vs. Data Alchemy – by Lucas Vermeer

How do scurvy, astronomy, alchemy and data science relate to each other?

In this goto conference presentation, Lucas Vermeer — Director of Experimentation at Booking.com — uses some amazing storytelling to demonstrate how the value of data (science) is largely by organizations capability to gather the right data — the data they actually need.

It’s a definite recommendation to watch for data scientists and data science leaders out there.

Here are the slides, and they contain some great oneliners:

An ABC of Artificial Intelligence Concepts

An ABC of Artificial Intelligence Concepts

Yet another great resource by one of the teams at Google in collaboration with Oxford:

An ABC of Artificial Intelligence-related concepts!

The G is for GANs: Generative Adverserial Networks.

Want to know what GANs are all about?

Just read along with Google’s laymen explanation! Here’s an excerpt:

The P is for Predictions.

Currently the ABC is only available in English, but other language translations come available soon.

Check it out yourself!

Probability Distributions mapped and explained by their relationships

Probability Distributions mapped and explained by their relationships

Sean Owen created this handy cheat sheet that shows the most common probability distributions mapped by their underlying relationships.

Probability distributions are fundamental to statistics, just like data structures are to computer science. They’re the place to start studying if you mean to talk like a data scientist. 

Sean Owen (via)

Owen argues that the probability distributions relate to each other in intuitive and interesting ways that makes it easier for you to recall them. For instance, several follow naturally from the Bernoulli distribution. Having this map by hand should thus help you really understand what these distributions imply.

On top of that, it’s just a nice geeky network poster!

Sean’s map of the relationships between probability distributions (via)

Now, Sean didn’t just make a fancy map. In the original blog he also explains each of the distributions and how it relates to the others. Having this knowledge is vital to being a good data scientist / analyst.

You can sometimes get away with simple analysis using R or scikit-learn without quite understanding distributions, just like you can manage a Java program without understanding hash functions. But it would soon end in tears, bugs, bogus results, or worse: sighs and eye-rolling from stats majors.

Sean Owen (via)

For instance, here’s Sean explaining the Binomial distribution:

The binomial distribution may be thought of as the sum of outcomes of things that follow a Bernoulli distribution. Toss a fair coin 20 times; how many times does it come up heads? This count is an outcome that follows the binomial distribution. Its parameters are n, the number of trials, and p, the probability of a “success” (here: heads, or 1). Each flip is a Bernoulli-distributed outcome, or trial. Reach for the binomial distribution when counting the number of successes in things that act like a coin flip, where each flip is independent and has the same probability of success.

Sean Owen (via)

Header image via Alison-Static

Simulating data with Bayesian networks, by Daniel Oehm

Simulating data with Bayesian networks, by Daniel Oehm

Daniel Oehm wrote this interesting blog about how to simulate realistic data using a Bayesian network.

Bayesian networks are a type of probabilistic graphical model that uses Bayesian inference for probability computations. Bayesian networks aim to model conditional dependence, and therefore causation, by representing conditional dependence by edges in a directed graph. Through these relationships, one can efficiently conduct inference on the random variables in the graph through the use of factors.

Devin Soni via Medium

As Bayes nets represent data as a probabilistic graph, it is very easy to use that structure to simulate new data that demonstrate the realistic patterns of the underlying causal system. Daniel’s post shows how to do this with bnlearn.

Daniel’s example Bayes net

New data is simulated from a Bayes net (see above) by first sampling from each of the root nodes, in this case sex. Then followed by the children conditional on their parent(s) (e.g. sport | sex and hg | sex) until data for all nodes has been drawn. The numbers on the nodes below indicate the sequence in which the data is simulated, noting that rcc is the terminal node.

Daniel Oehms in his blog

The original and simulated datasets are compared in a couple of ways 1) observing the distributions of the variables 2) comparing the output from various models and 3) comparing conditional probability queries. The third test is more of a sanity check. If the data is generated from the original Bayes net then a new one fit on the simulated data should be approximately the same. The more rows we generate the closer the parameters will be to the original values.

The original data alongside the generated data in Daniel’s example

As you can see, a Bayesian network allows you to generate data that looks, feels, and behaves a lot like the data on which you based your network on in the first place.

This can be super useful if you want to generate a synthetic / fake / artificial dataset without sharing personal or sensitive data.

Moreover, the underlying Bayesian net can be very useful to compute missing values. In Daniel’s example, he left out some values on purpose (pretending they were missing) and imputed them with the Bayes net. He found that the imputed values for the missing data points were quite close to the original ones:

For two variables, the original values plotted against the imputed replacements.

In the original blog, Daniel goes on to show how to further check the integrity of the simulated data using statistical models and shares all his code so you can try this out yourself. Please do give his website a visit as Daniel has many more interesting statistics blogs!