{:.no_toc}
* TOC {:toc}Class has a very important job as a core container type in Python. It is really hard to find a good overview how to use them in a good practice manner.
Questions to David Rotermund
import numpy as np
a = np.ones((3, 3))
b = a.mean(axis=1).max()
print(b) # -> 1.0ndarray.fill(value)Fill the array with a scalar value.
import numpy as np
A = np.ones((3, 3))
A.fill(7)
print(A)Output:
[[7. 7. 7.]
[7. 7. 7.]
[7. 7. 7.]]ndarray.ndimNumber of array dimensions.
import numpy as np
A = np.ones((3, 3))
print(A.ndim) # -> 2ndarray.shapeTuple of array dimensions.
import numpy as np
A = np.ones((3, 3))
print(A.shape) # -> (3, 3)ndarray.sizeNumber of elements in the array.
import numpy as np
A = np.ones((3, 3))
print(A.size) # -> 9{: .topic-optional} This is an optional topic!
ndarray.nbytesTotal bytes consumed by the elements of the array.
{: .topic-optional} This is an optional topic!
ndarray.itemsizeLength of one array element in bytes.
ndarray.copy(order='C')Return a copy of the array.
ndarray.view([dtype][, type])New view of array with the same data.
ndarray.reshape(shape, order='C')Returns an array containing the same data with a new shape.
import numpy as np
A = np.arange(0, 6)
print(A.reshape((2, 3)))Output:
[[0 1 2]
[3 4 5]]WARNING!!! Don't confuse reshape with resize!
ndarray.squeeze(axis=None)Remove axes of length one from a.
import numpy as np
A = np.zeros((4, 1, 1))
print(A.shape) # -> (4, 1, 1)
A = A.squeeze()
print(A.shape) # -> (4,)
A = np.zeros((4, 1, 9, 1)) # -> (4, 1, 9, 1)
print(A.shape)
B = A.squeeze(axis=1) # -> (4, 9, 1)
print(B.shape)
print(np.may_share_memory(A, B)) # -> Truenumpy.moveaxis(a, source, destination)Move axes of an array to new positions.
Other axes remain in their original order.
import numpy as np
A = np.zeros((4, 1, 9, 1))
print(A.shape) # -> (4, 1, 9, 1)
B = np.moveaxis(A, 0, 1)
print(B.shape) # -> (1, 4, 9, 1)
print(np.may_share_memory(A, B)) # -> Truendarray.swapaxes(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.
import numpy as np
A = np.zeros((4, 1, 9, 1))
print(A.shape) # -> (4, 1, 9, 1)
B = A.swapaxes(0, 1)
print(B.shape) # -> (1, 4, 9, 1)
print(np.may_share_memory(A, B)) # -> Truenumpy.ndarray.T (Transposing a 2d matrix)
ndarray.TView of the transposed array.
Same as self.transpose().
import numpy as np
A = np.zeros((4, 9))
B = A.T
print(A.shape) # -> (4, 9)
print(B.shape) # -> (9, 4)
print(np.may_share_memory(A, B)) # -> True{: .topic-optional} This is an optional topic!
ndarray.transpose(*axes)Returns a view of the array with axes transposed.
ndarray.flatten(order='C')Return a copy of the array collapsed into one dimension.
import numpy as np
A = np.arange(0, 6)
A = A.reshape((2, 3))
print(A)
print()
B = A.flatten()
print(B)
print(np.may_share_memory(A, B)) # -> FalseOutput:
[[0 1 2]
[3 4 5]]
[0 1 2 3 4 5]ndarray.flatA 1-D iterator over the array.
This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.
import numpy as np
A = np.arange(0, 6)
A = A.reshape((2, 3))
print(A.flat[0]) # -> 0
print(A.flat[1]) # -> 1
print(A.flat[2]) # -> 2
print(A.flat[3]) # -> 3
print(A.flat[4]) # -> 4
print(A.flat[5]) # -> 5
for i in A:
print(i)
print("----")
for i in A.flat:
print(i)Output:
[0 1 2]
[3 4 5]
----
0
1
2
3
4
5ndarray.dtypeData-type of the array’s elements.
import numpy as np
A = np.zeros((0, 6), dtype=np.float32)
print(A.dtype) # -> float32
B = A.astype(dtype=np.int64)
print(B.dtype) # -> int64
print(np.may_share_memory(A, B)) # -> Falsendarray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)Copy of the array, cast to a specified type.
import numpy as np
A = np.zeros((0, 6), dtype=np.float32)
print(A.dtype) # -> float32
B = A.astype(dtype=np.int64)
print(B.dtype) # -> int64
print(np.may_share_memory(A, B)) # -> Falsendarray.realThe real part of the array.
import numpy as np
A = np.array(1 + 0.5j)
print(A.real) # -> 1.0ndarray.imagThe imaginary part of the array.
import numpy as np
A = np.array(1 + 0.5j)
print(A.imag) # -> 0.5ndarray.conj()Complex-conjugate all elements.
import numpy as np
A = np.array(1 + 0.5j)
print(A.conj()) # -> (1-0.5j)ndarray.sort(axis=-1, kind=None, order=None)Sort an array in-place. Refer to numpy.sort for full documentation.
import numpy as np
A = np.arange(0, 6)
A = np.concatenate((A, A))
print(A) # -> [0 1 2 3 4 5 0 1 2 3 4 5]
print()
A.sort()
print(A) # -> [0 0 1 1 2 2 3 3 4 4 5 5]ndarray.argsort(axis=-1, kind=None, order=None)Returns the indices that would sort this array.
import numpy as np
A = np.arange(0, 6)
A = np.concatenate((A, A))
print(A) # -> [0 1 2 3 4 5 0 1 2 3 4 5]
print()
idx = A.argsort()
print(idx) # -> [ 0 6 1 7 2 8 3 9 4 10 5 11]ndarray.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)Return the sum of the array elements over the given axis.
ndarray.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)Returns the average of the array elements along given axis.
import numpy as np
A = np.arange(0, 6).reshape((2, 3))
print(A.sum()) # -> 15
print(A.sum(axis=0)) # -> [3 5 7]
print(A.sum(axis=0).shape) # -> (3,)
print(A.sum(axis=1)) # -> [ 3 12]
print(A.sum(axis=1).shape) # -> (2,)
print(A.sum(axis=0, keepdims=True))
print(A.sum(axis=0, keepdims=True).shape) # -> (1, 3)
print()
print(A.sum(axis=1, keepdims=True))
print(A.sum(axis=0, keepdims=True).shape) # -> (1, 3)Output:
[[3 5 7]]
[[ 3]
[12]]ndarray.cumsum(axis=None, dtype=None, out=None)Return the cumulative sum of the elements along the given axis.
import numpy as np
A = np.arange(0, 6).reshape((2, 3))
print(A)
print()
print(A.cumsum()) # -> [ 0 1 3 6 10 15]
print(A.cumsum().shape) # -> (6,)
print(A.cumsum(axis=0))
print()
print(A.cumsum(axis=0).shape) # -> (2, 3)
print(A.cumsum(axis=1))
print(A.cumsum(axis=1).shape) # -> (2, 3)Output:
[[0 1 2]
[3 4 5]]
[[0 1 2]
[3 5 7]]
[[ 0 1 3]
[ 3 7 12]]ndarray.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)Return the product of the array elements over the given axis
import numpy as np
A = np.arange(1, 7).reshape((2, 3))
print(A)
print(A.prod()) # -> 720
print(A.prod().shape) # -> ()
print(A.prod(axis=0)) # -> [ 4 10 18]
print(A.prod(axis=0).shape) # -> (3,)
print(A.prod(axis=1)) # -> [ 6 120]
print(A.prod(axis=1).shape) # -> (2,)Output:
[[1 2 3]
[4 5 6]]ndarray.cumprod(axis=None, dtype=None, out=None)Return the cumulative product of the elements along the given axis.
import numpy as np
A = np.arange(1, 7).reshape((2, 3))
print(A)
print()
print(A.cumprod()) # -> [ 1 2 6 24 120 720]
print(A.cumprod().shape) # -> (6,)
print(A.cumprod(axis=0))
print()
print(A.cumprod(axis=0).shape) # -> (2, 3)
print(A.cumprod(axis=1))
print(A.cumprod(axis=1).shape) # -> (2, 3)Output:
[[1 2 3]
[4 5 6]]
[[ 1 2 3]
[ 4 10 18]]
[[ 1 2 6]
[ 4 20 120]]ndarray.clip(min=None, max=None, out=None, **kwargs)Return an array whose values are limited to [min, max]. One of max or min must be given.
import numpy as np
A = np.arange(0, 8).reshape((2, 4))
print(A)
print()
print(A.clip(min=1, max=6))Output:
[[0 1 2 3]
[4 5 6 7]]
[[1 1 2 3]
[4 5 6 6]]ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)Return the maximum along a given axis.
ndarray.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)Return the minimum along a given axis.
import numpy as np
A = np.arange(0, 6).reshape((2, 3))
print(A)
print()
print(A.max()) # -> 5
print(A.max(axis=0)) # -> [3 4 5]
print(A.max(axis=0).shape) # -> (3,)
print(A.max(axis=1)) # -> [2 5]
print(A.max(axis=1).shape) # -> (2,)
print(A.max(axis=0, keepdims=True)) # -> [[3 4 5]]
print(A.max(axis=0, keepdims=True).shape) # -> (1, 3)
print(A.max(axis=1, keepdims=True))
print(A.max(axis=0, keepdims=True).shape) # -> (1, 3)Output:
[[0 1 2]
[3 4 5]]
[[2]
[5]]ndarray.argmax(axis=None, out=None, *, keepdims=False)Return indices of the maximum values along the given axis.
ndarray.argmin(axis=None, out=None, *, keepdims=False)Return indices of the minimum values along the given axis.
import numpy as np
A = np.arange(0, 6).reshape((2, 3))
print(A)
print()
print(A.argmax()) # -> 5
print(A.argmax(axis=0)) # -> [1 1 1]
print(A.argmax(axis=0).shape) # -> (3,)
print(A.argmax(axis=1)) # -> [2 2]
print(A.argmax(axis=1).shape) # -> (2,)
print(A.argmax(axis=0, keepdims=True)) # -> [[1 1 1]]
print(A.argmax(axis=0, keepdims=True).shape) # -> (1, 3)
print(A.argmax(axis=1, keepdims=True))
print(A.argmax(axis=0, keepdims=True).shape) # -> (1, 3)Output:
[[0 1 2]
[3 4 5]]
[[2]
[2]]ndarray.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)Returns the standard deviation of the array elements along given axis.
ndarray.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)Returns the variance of the array elements, along given axis.
import numpy as np
rng = np.random.default_rng()
A = rng.random((2, 3))
print(A)
print()
print(A.var()) # -> 0.15743358550255018
print(A.var(axis=0)) # -> [0.19429192 0.15604444 0.00441136]
print(A.var(axis=0).shape) # -> (3,)
print(A.var(axis=1)) # -> [0.18135622 0.00196335]
print(A.var(axis=1).shape) # -> (2,)
print(A.var(axis=0, keepdims=True)) # -> [[0.19429192 0.15604444 0.00441136]]
print(A.var(axis=0, keepdims=True).shape) # -> (1, 3)
print(A.var(axis=1, keepdims=True))
print(A.var(axis=0, keepdims=True).shape) # -> (1, 3)Output:
[[0.9804056 0.82416017 0.00909 ]
[0.09883446 0.03411095 0.1419262 ]]
[[0.18135622]
[0.00196335]]ndarray.round(decimals=0, out=None)Return a with each element rounded to the given number of decimals.
import numpy as np
A = np.array(np.pi)
print(A) # -> 3.141592653589793
print(A.round(decimals=0)) # -> 3.0
print(A.round(decimals=1)) # -> 3.1
print(A.round(decimals=2)) # -> 3.14
print(A.round(decimals=3)) # -> 3.142WARNING!!! This might be unexpected behavior for you:
import numpy as np
print(np.round(1.5)) # -> 2.0
print(np.round(2.5)) # -> 2.0
print(np.round(2.5 + 1e-15)) # -> 3.0ndarray.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)Return the sum along diagonals of the array.
import numpy as np
A = np.eye(3)
print(A)
print(A.trace()) # -> 3.0[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]ndarray.diagonal(offset=0, axis1=0, axis2=1)Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.
import numpy as np
rng = np.random.default_rng()
A = rng.random((3, 3))
print(A)
print(A.diagonal()) # -> [0.7434178 0.11672896]
print(A.diagonal(offset=1)) # -> [0.7434178 0.11672896]
print(A.diagonal(offset=2)) # -> [0.84915636]
print(A.diagonal(offset=-1)) # -> [0.10826248 0.50223328]
print(A.diagonal(offset=-2)) # -> [0.43068892]Output
[[0.82574583 0.7434178 0.84915636]
[0.10826248 0.39898052 0.11672896]
[0.43068892 0.50223328 0.63444263]]| ndarray.item(*args) | Copy an element of an array to a standard Python scalar and return it. |
| ndarray.tolist() | Return the array as an a.ndim-levels deep nested list of Python scalars. |
| ndarray.itemset(*args) | Insert scalar into an array (scalar is cast to array's dtype, if possible) |
| ndarray.tostring([order]) | A compatibility alias for tobytes, with exactly the same behavior. |
| ndarray.tobytes([order]) | Construct Python bytes containing the raw data bytes in the array. |
| ndarray.tofile(fid[, sep, format]) | Write array to a file as text or binary (default). |
| ndarray.dump(file) | Dump a pickle of the array to the specified file. |
| ndarray.dumps() | Returns the pickle of the array as a string. |
| ndarray.astype(dtype[, order, casting, ...]) | Copy of the array, cast to a specified type. |
| ndarray.byteswap([inplace]) | Swap the bytes of the array elements |
| ndarray.copy([order]) | Return a copy of the array. |
| ndarray.view([dtype][, type]) | New view of array with the same data. |
| ndarray.getfield(dtype[, offset]) | Returns a field of the given array as a certain type. |
| ndarray.setflags([write, align, uic]) | Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively. |
| ndarray.fill(value) | Fill the array with a scalar value. |
| ndarray.reshape(shape[, order]) | Returns an array containing the same data with a new shape. |
| ndarray.resize(new_shape[, refcheck]) | Change shape and size of array in-place. |
| ndarray.transpose(*axes) | Returns a view of the array with axes transposed. |
| ndarray.swapaxes(axis1, axis2) | Return a view of the array with axis1 and axis2 interchanged. |
| ndarray.flatten([order]) | Return a copy of the array collapsed into one dimension. |
| ndarray.ravel([order]) | Return a flattened array. |
| ndarray.squeeze([axis]) | Remove axes of length one from a. |
| ndarray.take(indices[, axis, out, mode]) | Return an array formed from the elements of a at the given |
| ndarray.put(indices, values[, mode]) | Set a.flat[n] = values[n] for all n in indices. |
| ndarray.repeat(repeats[, axis]) | Repeat elements of an array. |
| ndarray.choose(choices[, out, mode]) | Use an index array to construct a new array from a set of choices. |
| ndarray.sort([axis, kind, order]) | Sort an array in-place. |
| ndarray.argsort([axis, kind, order]) | Returns the indices that would sort this array. |
| ndarray.partition(kth[, axis, kind, order]) | Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. |
| ndarray.argpartition(kth[, axis, kind, order]) | Returns the indices that would partition |
| ndarray.searchsorted(v[, side, sorter]) | Find indices where elements of v should be inserted in a to maintain order. |
| ndarray.nonzero() | Return the indices of the elements that are non-zero. |
| ndarray.compress(condition[, axis, out]) | Return selected slices of this array along given axis. |
| ndarray.diagonal([offset, axis1, axis2]) | Return specified diagonals. |
| ndarray.max([axis, out, keepdims, initial, ...]) | Return the maximum along a given axis. |
| ndarray.argmax([axis, out, keepdims]) | Return indices of the maximum values along the given axis. |
| ndarray.min([axis, out, keepdims, initial, ...]) | Return the minimum along a given axis. |
| ndarray.argmin([axis, out, keepdims]) | Return indices of the minimum values along the given axis. |
| ndarray.ptp([axis, out, keepdims]) | Peak to peak (maximum - minimum) value along a given axis. |
| ndarray.clip([min, max, out]) | Return an array whose values are limited to [min, max]. |
| ndarray.conj() | Complex-conjugate all elements. |
| ndarray.round([decimals, out]) | Return a with each element rounded to the given number of decimals. |
| ndarray.trace([offset, axis1, axis2, dtype, out]) | Return the sum along diagonals of the array. |
| ndarray.sum([axis, dtype, out, keepdims, ...]) | Return the sum of the array elements over the given axis. |
| ndarray.cumsum([axis, dtype, out]) | Return the cumulative sum of the elements along the given axis. |
| ndarray.mean([axis, dtype, out, keepdims, where]) | Returns the average of the array elements along given axis. |
| ndarray.var([axis, dtype, out, ddof, ...]) | Returns the variance of the array elements, along given axis. |
| ndarray.std([axis, dtype, out, ddof, ...]) | Returns the standard deviation of the array elements along given axis. |
| ndarray.prod([axis, dtype, out, keepdims, ...]) | Return the product of the array elements over the given axis |
| ndarray.cumprod([axis, dtype, out]) | Return the cumulative product of the elements along the given axis. |
| ndarray.all([axis, out, keepdims, where]) | Returns True if all elements evaluate to True. |
| ndarray.any([axis, out, keepdims, where]) | Returns True if any of the elements of a evaluate to True. |
Each of the arithmetic operations (+, -, *, /, //, %, divmod(), ** or pow(), <<, >>, &, ^, |, ~) and the comparisons (==, <, >, <=, >=, !=) is equivalent to the corresponding universal function in NumPy.
for in-place operations see here