Utility Functions (datascience.util
)¶
Utility functions
- datascience.util.is_non_string_iterable(value)[source]¶
Returns a boolean value representing whether a value is iterable.
- datascience.util.make_array(*elements)[source]¶
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)[source]¶
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:
the minimizing argument of a one-argument function
an array of minimizing arguments of a multi-argument function
- datascience.util.percentile(p, arr=None)[source]¶
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
andrbound
.- Args:
rbound
(numeric): right boundary of shaded regionlbound
(numeric): left boundary of shaded region; by default is negative infinitymean
(numeric): mean/expectation of normal distributionsd
(numeric): standard deviation of normal distribution
- datascience.util.plot_normal_cdf(rbound=None, lbound=None, mean=0, sd=1)[source]¶
Plots a normal curve with specified parameters and area below curve shaded between
lbound
andrbound
.- Args:
rbound
(numeric): right boundary of shaded regionlbound
(numeric): left boundary of shaded region; by default is negative infinitymean
(numeric): mean/expectation of normal distributionsd
(numeric): standard deviation of normal distribution
- datascience.util.proportions_from_distribution(table, label, sample_size, column_name='Random Sample')[source]¶
Adds a column named
column_name
containing the proportions of a random draw using the distribution inlabel
.This method uses
np.random.Generator.multinomial
to drawsample_size
samples from the distribution intable.column(label)
, then divides bysample_size
to create the resulting column of proportions.- Args:
table
: An instance ofTable
.label
: Label of column intable
. This column must contain adistribution (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 sampledproportions. Defaults to
'Random Sample'
.
- Returns:
A copy of
table
with a columncolumn_name
containing the sampled proportions. The proportions will sum to 1.- Throws:
ValueError
: If thelabel
is not in the table, or iftable.column(label)
does not sum to 1.
- datascience.util.sample_proportions(sample_size: int, probabilities)[source]¶
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)[source]¶
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])