# Single-variable Distributions (datascience.tables.Table)¶

## Constucting¶

In [1]: from prob140 import *

In [2]: dist1 = Table().values(np.array([2, 3, 5])).probability(np.array([0.25, 0.5, 0.25]))

In [3]: print(dist1)
Value | Probability
2     | 0.25
3     | 0.5
5     | 0.25

In [4]: dist2 = Table().values(np.arange(1, 8, 2)).probability_function(lambda x: 1/4)

In [5]: print(dist2)
Value | Probability
1     | 0.25
3     | 0.25
5     | 0.25
7     | 0.25

 Table.values(*args) Table.probability(values) Assigns probabilities to domain values. Table.probability_function(pfunc) Assigns probabilities to a Distribution via a probability function.

## Utitilies¶

 Table.prob_event(x) Finds the probability of an event x. Table.event(x) Shows the probability that distribution takes on value x or list of values x. Table.cdf(x) Finds the cdf of the distribution Table.ev() Finds expected value of distribution Table.sd() Finds standard deviation of Distribution. Table.var() Finds variance of distribution Table.normalized() Returns the distribution by making the proabilities sum to 1 Table.sample_from_dist([n]) Randomly samples from the distribution. emp_dist(values) Takes an array of values and returns an empirical distribution

## Plotting¶

 Plot(dist[, width, event, edges, show_ev, …]) Plots the histogram for a single distribution. Plots(*labels_and_dists[, width, edges]) Overlays histograms for multiple probability distributions together.