If you use numpy, this is easy: or if you want the 0’s, You’ll get the same result. *note that slice is actually the name of a builtin-type. Generally, I would advise giving your object a different “name”. Another way, if you’re working with lists of lists*: (Note that the 0’s here are unnecessary: [arr[i][:2] for i in range(2)] would … Read more
You need to use np.transpose to rearrange dimensions. Now, n x m x 3 is to be converted to 3 x (n*m), so send the last axis to the front and shift right the order of the remaining axes (0,1). Finally , reshape to have 3 rows. Thus, the implementation would be – Sample run –
Just catch the exception and ignore it:
You’re not saying how exactly putdata() is not behaving. I’m assuming you’re doing This is because putdata expects a sequence of tuples and you’re giving it a numpy array. This will work but it is very slow. As of PIL 1.1.6, the “proper” way to convert between images and numpy arrays is simply although the resulting array is in a different … Read more
numpy.linspace() gives you a one-dimensional NumPy array. For example: Therefore: cannot work. You would need some kind of two-dimensional array with two elements in the second dimension: Now you can do this:
First of all, index is a list method. Here v is a numpy array and you need to do the following: Get the elements using these indices: Verify using assert:
The I attribute only exists on matrix objects, not ndarrays. You can use numpy.linalg.inv to invert arrays: Note that the way you’re generating matrices, not all of them will be invertible. You will either need to change the way you’re generating matrices, or skip the ones that aren’t invertible. Also, if you want to go through all 3×3 matrices with elements drawn … Read more
There are 4 dimensions of the tensor you want to convert. : means that the first dimension should be copied as it is and converted, same goes for the third and fourth dimension. ::-1 means that for the second axes it reverses the the axes
2017 Answer – pandas 0.20: .ix is deprecated. Use .loc See the deprecation in the docs .loc uses label based indexing to select both rows and columns. The labels being the values of the index or the columns. Slicing with .loc includes the last element. Let’s assume we have a DataFrame with the following columns:foo, bar, quz, ant, cat, sat, dat. .loc accepts the same slice … Read more
It specifies the axis along which the means are computed. By default axis=0. This is consistent with the numpy.mean usage when axis is specified explicitly (in numpy.mean, axis==None by default, which computes the mean value over the flattened array) , in which axis=0 along the rows (namely, index in pandas), and axis=1 along the columns. For added clarity, one may choose to specify axis=’index’ (instead of axis=0) or axis=’columns’ (instead of axis=1).