prob140.Table.to_joint¶

Table.to_joint(X_column_label=None, Y_column_label=None, probability_column_label=None, reverse=True)¶

Converts a table of probabilities associated with two variables into a JointDistribution object

Parameters:

Parameters:	table : Table You can either call pass in a Table directly or call the toJoint() method of that Table. See examples. X_column_label (optional) : str Label for the first variable. Defaults to the same label as that of first variable of Table. Y_column_label (optional) : str Label for the second variable. Defaults to the same label as that of second variable of Table. probability_column_label (optional) : str Label for probabilities. reverse (optional) : bool If True, the vertical values will be reversed.
Returns:	JointDistribution A JointDistribution object.

table : Table: You can either call pass in a Table directly or call the toJoint() method of that Table. See examples.
X_column_label (optional) : str: Label for the first variable. Defaults to the same label as that of first variable of Table.
Y_column_label (optional) : str: Label for the second variable. Defaults to the same label as that of second variable of Table.
probability_column_label (optional) : str: Label for probabilities.
reverse (optional) : bool: If True, the vertical values will be reversed.

Returns:

JointDistribution: A JointDistribution object.

Examples

>>> dist1 = Table().values([0,1],[2,3])
>>> dist1['Probability'] = make_array(0.1, 0.2, 0.3, 0.4)
>>> dist1.to_joint()
     X=0  X=1
Y=3  0.2  0.4
Y=2  0.1  0.3
>>> dist2 = Table().values('Coin1',['H','T'], 'Coin2', ['H','T'])
>>> dist2['Probability'] = np.array([0.4*0.6, 0.6*0.6, 0.4*0.4, 0.6*0.4])
>>> dist2.toJoint()
         Coin1=H  Coin1=T
Coin2=T     0.36     0.24
Coin2=H     0.24     0.16