CODEWITHSUNDEEP
pythonpandasnumpy
Raashid
Raashid

Reputation: 149

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

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)

Upvotes: 3

Views: 1599

Answers (3)

BENY
BENY

Reputation: 323306

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)

Upvotes: 2

rafaelc
rafaelc

Reputation: 59274

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]

Upvotes: 4

Peter Leimbigler
Peter Leimbigler

Reputation: 11105

To get a running count (i.e., cumulative sum) of true positives, i.e., 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]

Upvotes: 4

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