Python Cheat Sheet - Numpy
| Name | Description | Example |
|---|---|---|
a.shape |
The shape attribute of NumPy array a keeps a tuple of integers. Each integer describes the number of elements of the axis. | a = np.array([[1,2],[1,1],[0,0]])print(np.shape(a)) # (3, 2) |
a.ndim |
The ndim attribute is equal to the length of the shape tuple. | print(np.ndim(a)) # 2 |
* |
The asterisk (star) operator performs the Hadamard product, i.e., multiplies two matrices with equal shape element-wise. | a = np.array([[2, 0], [0, 2]])b = np.array([[1, 1], [1, 1]])print(a*b) # [[2 0] [0 2]] |
np.matmul(a,b) |
The standard matrix multiplication operator. Equivalent to the @ operator. | print(np.matmul(a,b))# [[2 2] [1 1]] |
np.arange([start, ]stop, [step, ]) |
Creates a new 1D numpy array with evenly spaced values | print(np.arange(0,10,2))# [0 2 4 6 8] |
np.linspace(start, stop, num=50) |
Creates a new 1D numpy array with evenly spread elements within the given interval | print(np.linspace(0,10,3))# [ 0. 5. 10.] |
np.average(a) |
Averages over all the values in the numpy array | a = np.array([[2, 0], [0, 2]])print(np.average(a)) # 1.0 |
<slice> = <val> |
Replace the <slice> as selected by the slicing operator with the value <val>. |
a = np.array([1, 0, 0, 0])a[:2] = 2print(a) # [2 2 0 0] |
np.var(a) |
Calculates the variance of a numpy array. | a = np.array([2, 6])print(np.var(a)) # 4.0 |
np.std(a) |
Calculates the standard deviation of a numpy array | print(np.std(a)) # 2.0 |
np.diff(a) |
Calculates the difference between subsequent values in NumPy array a | fibs = np.array([0, 1, 1, 2, 3, 5])print(np.diff(fibs, n=1)) |
np.cumsum(a) |
Calculates the cumulative sum of the elements in NumPy array a. | print(np.cumsum(np.arange(5)))# [ 0 1 3 6 10] |
np.sort(a) |
Creates a new NumPy array with the values from a (ascending). | a = np.array([10,3,7,1,0])print(np.sort(a))# [ 0 1 3 7 10] |
np.argsort(a) |
Returns the indices of a NumPy array so that the indexed values would be sorted. | print(np.argsort(a))# [4 3 1 2 0] |
np.max(a) |
Returns the maximal value of NumPy array a. | print(np.max(a)) # 10 |
np.argmax(a) |
Returns the index of the element with maximal value in the NumPy array a. | print(np.argmax(a)) # 0 |
np.nonzero(a) |
Returns the indices of the nonzero elements in NumPy array a. | print(np.nonzero(a))# [0 1 2 3] |
Reference Image: numpy
