mlpack.preprocess_describe

preprocess_describe(...)
Descriptive Statistics

>>> from mlpack import preprocess_describe

This utility takes a dataset and prints out the descriptive statistics of the data. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of information, or the quantitative description itself. The program does not modify the original file, but instead prints out the statistics to the console. The printed result will look like a table.

Optionally, width and precision of the output can be adjusted by a user using the 'width' and 'precision' parameters. A user can also select a specific dimension to analyze if there are too many dimensions. The 'population' parameter can be specified when the dataset should be considered as a population. Otherwise, the dataset will be considered as a sample.

So, a simple example where we want to print out statistical facts about the dataset 'X' using the default settings, we could run

>>> preprocess_describe(input=X, verbose=True)

If we want to customize the width to 10 and precision to 5 and consider the dataset as a population, we could run

>>> preprocess_describe(input=X, width=10, precision=5, verbose=True)

input options

output options

The return value from the binding is a dict containing the following elements: