Utility Functions (datascience.util)

Utility functions

datascience.util.is_non_string_iterable(value)

Returns a boolean value representing whether a value is iterable.

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.

Args:

elements (variadic): elements

Returns:

A NumPy array of same length as the provided varadic argument elements

>>> 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.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

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.proportions_from_distribution(table, label, sample_size, column_name='Random Sample')

Adds a column named column_name containing the proportions of a random draw using the distribution in label.

This method uses np.random.Generator.multinomial to draw sample_size samples from the distribution in table.column(label), then divides by sample_size to create the resulting column of proportions.

Args:

table: An instance of Table.

label: Label of column in table. This column must contain a

distribution (the values must sum to 1).

sample_size: The size of the sample to draw from the distribution.

column_name: The name of the new column that contains the sampled

proportions. Defaults to 'Random Sample'.

Returns:

A copy of table with a column column_name containing the sampled proportions. The proportions will sum to 1.

Throws:

ValueError: If the label is not in the table, or if

table.column(label) does not sum to 1.

datascience.util.sample_proportions(sample_size: int, probabilities)

Return the proportion of random draws for each outcome in a distribution.

This function is similar to np.random.Generator.multinomial, but returns proportions instead of counts.

Args:

sample_size: The size of the sample to draw from the distribution.

probabilities: An array of probabilities that forms a distribution.

Returns:

An array with the same length as probability that sums to 1.

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

Applies a function to each column and returns a Table.

Args:

table: The table to apply your function to.

func: The function to apply to each column.

subset: A list of columns to apply the function to; if None,

the function will be applied to all columns in table.

Returns:

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