Compute the precision
The precision is the ratio tp / (tp + fp)
where tp
is the number of true positives and fp
the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
The best value is 1 and the worst value is 0.
Read more in the User Guide.
Parameters: 

y_true : 1d arraylike, or label indicator array / sparse matrix 
Ground truth (correct) target values. 
y_pred : 1d arraylike, or label indicator array / sparse matrix 
Estimated targets as returned by a classifier. 
labels : list, optional 
The set of labels to include when average != 'binary' , and their order if average is None . Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Changed in version 0.17: parameter labels improved for multiclass problem. 
pos_label : str or int, 1 by default 
The class to report if average='binary' and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only. 
average : string, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’] 
This parameter is required for multiclass/multilabel targets. If None , the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data: 
'binary': 
Only report results for the class specified by pos_label . This is applicable only if targets (y_{true,pred} ) are binary. 
'micro': 
Calculate metrics globally by counting the total true positives, false negatives and false positives. 
'macro': 
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. 
'weighted': 
Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an Fscore that is not between precision and recall. 
'samples': 
Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from accuracy_score ). 
sample_weight : arraylike of shape = [n_samples], optional 
Sample weights. 
Returns: 

precision : float (if average is not None) or array of float, shape = [n_unique_labels] 
Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task. 
Examples
>>> from sklearn.metrics import precision_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> precision_score(y_true, y_pred, average='macro')
0.22...
>>> precision_score(y_true, y_pred, average='micro')
0.33...
>>> precision_score(y_true, y_pred, average='weighted')
...
0.22...
>>> precision_score(y_true, y_pred, average=None)
array([0.66..., 0. , 0. ])