Count of specific case per row in matrix

Question

I am fairly new to numpy and scientific computing and I struggle with a problem for several days, so I decided to post it here. I am trying to get a count for a specific occurence of a condition in a numpy array. ``` In [233]: import numpy as np In [234]: a= np.random.random([5,5]) In [235]: a >.7 Out[235]: array([[False, True, True, False, False], [ True, False, False, False, True], [ True, False, True, True, False], [False, False, False, False, False], [False, False, True, False, False]], dtype=bool) ``` What I would like to count the number of occurence of `True` in each row and keep the rows when this count reach a certain threshold: ex : ``` results=[] threshold = 2 for i,row in enumerate(a>.7): if len([value for value in row if value==True]) > threshold: results.append(i) # keep ids for each row that have more than 'threshold' times True ``` This is the non-optimized version of the code but I would love to achieve the same thing with numpy (I have a very large matrix to process)…

Accepted Answer

To make results reproducible, use some seed:

```
>>> np.random.seed(100)
```

Then for a sample matrix

```
>>> a = np.random.random([5,5])
```

Count number of occurences along axis with sum:

```
>>> (a >.7).sum(axis=1)
array([1, 0, 3, 1, 2])
```

You can get row numbers with `np.where`:

```
>>> np.where((a > .7).sum(axis=1) >= 2)
(array([2, 4]),)
```

To filter result, just use boolean indexing:

```
>>> a[(a > .7).sum(axis=1) >= 2]
array([[ 0.89041156,  0.98092086,  0.05994199,  0.89054594,  0.5769015 ],
       [ 0.54468488,  0.76911517,  0.25069523,  0.28589569,  0.85239509]])
```

Count of specific case per row in matrix

Solution

Problem