How to count occurence of true positives using pandas or numpy?

Question

I have two columns, Prediction and Ground Truth. I want to get a count of true positives as a series using either numpy or pandas.

For example, my data is:

Prediction GroundTruth
True       True
True       False
True       True
False      True
False      False
True       True

I want a list that should have the following output:

tp_list = [1,1,2,2,2,3]

Is there a one-liner way to do this in numpy or pandas?

Currently, this is my solution:

tp = 0
for p, g in zip(data.Prediction, data.GroundTruth):
  if p and g: # TP case
    tp = tp + 1
  tp_list.append(tp)

Answer 1

To get a running count (ie, cumulative sum) of true positives , ie, Prediction == True if and only if GroundTruth == True , the solution is a modification of @RafaelC's answer:

(df['Prediction'] & df['GroundTruth']).cumsum()
0    1
1    1
2    2
3    2
4    2
5    3

(df['Prediction'] & df['GroundTruth']).cumsum().tolist()
[1, 1, 2, 2, 2, 3]

Answer 2

If you want to know how many True you predicted that are actually True , use

(df['Prediction'] & df['GroundTruth']).cumsum()

0    1
1    1
2    2
3    2
4    2
5    3
dtype: int64

(thanks @Peter Leimbigiler for chiming in)

If you want to know how many you have predicted correctly just compare and use cumsum

(df['Prediction'] == df['GroundTruth']).cumsum()

which outputs

0    1
1    1
2    2
3    2
4    3
5    4
dtype: int64

Can always get a list by using .tolist()

(df4['Prediction'] == df4['GroundTruth']).cumsum().tolist()

[1, 1, 2, 2, 3, 4]

Answer 3

Maybe you can using all

df.all(1).cumsum().tolist()
Out[156]: [1, 1, 2, 2, 2, 3]

numpy solution

np.cumsum(np.all(df.values,1))
Out[159]: array([1, 1, 2, 2, 2, 3], dtype=int32)