I have a 2d numpy array that contains some numbers like:
data =
[[1.1, 1.2, 1.3, 1.4],
[2.1, 2.2, 2.3, -1.0],
[-1.0, 3.2, 3.3, -1.0],
[-1.0, -1.0. -1.0, -1.0]]
I want to remove every row that contains the value -1.0 2 or more times, so I'm left with
data =
[[1.1, 1.2, 1.3, 1.4],
[2.1, 2.2, 2.3, -1.0]]
I found this question which looks like it's very close to what I'm trying to do, but I can't quite figure out how I can rewrite that to fit my use case.
You can easily do it with this piece of code:
new_data = data[(data == -1).sum(axis=1) < 2]
Result:
>>> new_data
array([[ 1.1, 1.2, 1.3, 1.4],
[ 2.1, 2.2, 2.3, -1. ]])
def remove_rows(data, threshold):
mask = np.array([np.sum(row == -1) < threshold for row in data])
return data[mask]
This function will return a new array with no rows having -1's more than or equal to the threshold
You need to pass in a Numpy array for it to work.
-1values. Then create an array of True and False values indicating whether each row satisfies the condition. Then mask the original array with the boolean array to remove the rows. And to help you get started, here's how you can do the first two steps...(data == -1).sum(axis=1) >= 2