Column and row axes are the opposite to Pandas/numpy

Why does axis=0 in this course mean columns, and axis=1 means rows?
I’ve been coding using pandas and numpy for about 2 years and this is the first confusion i got during this course
for some unknown reason X matrix contains training examples as columns, not rows (according to a test in week3), probably for the same reason i cannot answer correctly to other questions due to reversed axes in the course
also it’s not stated that X is transposed either, this could make sense

this is how i used to imagining the table data, on the 0-th row we have first training example, not on the 0-th column, because columns represent features of an instance, and on the rows we fill it with values

same here, usually to answer this i would without doubt write m = X.shape[0], but here it equals 2, to it’s transposed from the beginning for some reason, why?

There is no universal rule for this. It is individual preference. In this course, we use columns to represent number of samples and rows to represent number of features. So, if a shape is (3, 150), it means we have 3 features and 150 samples.

Moreover, axis = 0 means row and axis = 1 is column and this has nothing to do with this course. This is a Python thing.