Utility Functions (datascience.util)

Utility functions

datascience.util.make_array(*elements)

Returns an array containing all the arguments passed to this function. A simple way to make an array with a few elements.

As with any array, all arguments should have the same type.

>>> make_array(0)
array([0])
>>> make_array(2, 3, 4)
array([2, 3, 4])
>>> make_array("foo", "bar")
array(['foo', 'bar'], 
      dtype='<U3')
>>> make_array()
array([], dtype=float64)

datascience.util.percentile(p, arr=None)

Returns the pth percentile of the input array (the value that is at least as great as p% of the values in the array).

If arr is not provided, percentile returns itself curried with p

>>> percentile(74.9, [1, 3, 5, 9])
5
>>> percentile(75, [1, 3, 5, 9])
5
>>> percentile(75.1, [1, 3, 5, 9])
9
>>> f = percentile(75)
>>> f([1, 3, 5, 9])
5

datascience.util.plot_cdf_area(rbound=None, lbound=None, mean=0, sd=1)

Plots a normal curve with specified parameters and area below curve shaded between lbound and rbound.

Args:

rbound (numeric): right boundary of shaded region

lbound (numeric): left boundary of shaded region; by default is negative infinity

mean (numeric): mean/expectation of normal distribution

sd (numeric): standard deviation of normal distribution

datascience.util.plot_normal_cdf(rbound=None, lbound=None, mean=0, sd=1)

Plots a normal curve with specified parameters and area below curve shaded between lbound and rbound.

Args:

rbound (numeric): right boundary of shaded region

lbound (numeric): left boundary of shaded region; by default is negative infinity

mean (numeric): mean/expectation of normal distribution

sd (numeric): standard deviation of normal distribution

datascience.util.table_apply(table, func, subset=None)

Applies a function to each column and returns a Table.

Uses pandas apply under the hood, then converts back to a Table

Args:

table : instance of Table

The table to apply your function to

func : function

Any function that will work with DataFrame.apply

subset : list | None

A list of columns to apply the function to. If None, function will be applied to all columns in table

tab : instance of Table

A table with the given function applied. It will either be the shape == shape(table), or shape (1, table.shape[1])

datascience.util.minimize(f, start=None, smooth=False, log=None, array=False, **vargs)

Minimize a function f of one or more arguments.

Args:

f: A function that takes numbers and returns a number

start: A starting value or list of starting values

smooth: Whether to assume that f is smooth and use first-order info

log: Logging function called on the result of optimization (e.g. print)

vargs: Other named arguments passed to scipy.optimize.minimize

Returns either:

1. the minimizing argument of a one-argument function
2. an array of minimizing arguments of a multi-argument function