May 30, 2021 Article blog
2. First, in numpy N dimension array object is called ndarrey, some operations about it:
3. Second, in numpy random function library function summary:
Before you introduce numpy, let's talk about the basic concepts of Python.
Python is an advanced, dynamic, multi-form programming language. P
ython code often looks like pseudo-code, so you can implement a very powerful idea with a few lines of highly readable code.
Numpy is simple, a library of scientific calculations for Python that provides the functionality of matrix computing, which is typically used with Scipy and matplotlib. I
n fact, list already provides a matrix-like representation, but numpy gives us more functions.
The editor-in-chief here summarizes some of numpy's common operations.
First, introduce the numpy package:
import numpy as np
| attribute | illustrate |
|---|---|
| .ndim | Rank, that is, the number of axes or dimensions |
| .shape | The scale of the ndarray object, for the matrix, n row m column |
| .size | The number of elements of the ndarray object, equivalent to the value of n*m in .shape |
| .dtype | The type of element of the ndarray object |
| .itemsize | The size of each element in the ndarray object, in bytes |
| data type | illustrate |
|---|---|
| bool | Boolean type, True or False |
| intc | Consistent with the type of int in C, it is usually int32 or int64 |
| intp | An integer used for indexing, consistent with ssize_t in C, int32 or int64 |
| int8 | Integer of byte length, value: .128, 127 |
| int16 | Integer of 16-bit length, value: s32768, 32767 |
| int32 | Integer of 32-bit length, value: .231, 231-1 |
| int64 | Integer of 64-bit length, value: .263, 263-1 |
| data type | illustrate |
|---|---|
| bool | Boolean type, True or False |
| intc | Consistent with the type of int in C, it is usually int32 or int64 |
| intp | An integer used for indexing, consistent with ssize_t in C, int32 or int64 |
| int8 | Integer of byte length, value: .128, 127 |
| int16 | Integer of 16-bit length, value: s32768, 32767 |
| int32 | Integer of 32-bit length, value: .231, 231-1 |
| int64 | Integer of 64-bit length, value: .263, 263-1 |
3.ndarry element type (2):
| data type | illustrate |
|---|---|
| uint8 | 8-bit unsigned integer, value: s0, 255 |
| uint16 | 16-bit unsigned integer, value: s0, 65535 |
| uint32 | 32-bit unsigned integer, value: s0, 232-1 |
| uint64 | 32-bit unsigned integer, value: s0, 264-1 |
| float16 | 16-bit semi-precision floating points: 1-digit sign bit, 5-bit index, 10-bit tail |
| float32 | 32-bit semi-precision floating points: 1-digit sign bit, 8-bit index, 23-bit tail |
| float64 | 64-bit semi-precision floating points: 1-digit symbol bit, 11-bit index, 52-bit tail |
| complex64 | Plural types, real and imaginary are 32-bit floating points |
| complex128 | The number of complex numbers, real and imaginary parts are 64-bit floating point numbers. |
| function | illustrate |
|---|---|
| np.arange(n) | Similar to the range() function, which returns the ndarray type, the elements range from 0 to n-1 |
| np.ones(shape) | Based on the shape, a full 1 array is generated, shape is the tuple type np.zeros (shape) generates a full 0 array based on shape, and shape is the tuple type |
| np.full(shape,val) | An array is generated from the shape, and each element value is val |
| np.eye(n) | Create a square n-unit matrix with 1 diagonal and the rest 0 |
| np.ones_like(a) | An all-1 array is generated based on the shape of array a |
| np.zeros_like(a) | An all-0 array is generated based on the shape of array a |
| np.full_like(a,val) | An array is generated from the shape of array a, and each element value is val |
| np.linspace() | Fill the data with equal spacing based on the start and end data to form an array |
| np.concatenate() | Combine two or more numbers into a new array |
| .reshape(shape) | Without changing the array elements, an array of shape shapes is returned, and the original array remains unchanged |
| .resize(shape) | Consistent with the .reshape() function, but modifying the original array |
| .swapaxes(ax1,ax2) | Swap two dimensions in the array n dimensions |
| .flatten() | The array is degraded and returned to the collapsed one-dimensional array, which remains unchanged |
| np.abs(x) np.fabs(x) | Calculate the absolute value of each element of the array |
| np.sqrt(x) | Calculate the square root of each element of the array |
| np.square(x) | Calculate the squares of each element of the array |
| np.log(x) np.log10(x) np.log2(x) | Calculate the natural icings, 10-bottom icings, and 2-bottom icings of elements in an array |
| np.ceil(x) np.floor(x) | Calculate the ceiling or floor values for each element of the array |
| np.rint(x) | Calculates the rounding value of each element of the array |
| np.modf(x) | Returns the decimal and integer parts of each element of the array as two separate arrays |
| np.cos(x) np.cosh(x) np.sin(x) np.sinh(x) np.tan(x) np.tanh(x) | Calculate the normal and hyperbolic trigonometry functions of each element of the array |
| np.exp(x) | Calculates the exponential values of the elements of the array |
| np.sign(x) | Calculate the symbolic values of each element of the array, 1 (-), 0, -1 (-) |
| + ‐* / ** | The elements of the two arrays perform corresponding operations |
| np.maximum(x,y) np.fmax() np.minimum(x,y)np.fmin() | The maximum/minimum value at the element level is calculated |
| np.mod(x,y) | Element-level modal operations |
| np.copysign(x,y) | Assign the symbols of each element value in array y to the array x corresponding element |
| > < >= <= == != | Arithmetic comparisons produce boolean arrays |
| function | illustrate |
|---|---|
| rand(d0,d1,..,dn) | Create a random array based on d0-dn, floating points, s0,1), evenly distributed |
| randn(d0,d1,..,dn) | Create a random array based on d0-dn, with a standard normal distribution |
| randint(low[,high,shape]) | Create a random integer or array of integers based on shape, with a range of low, high |
| seed(s) | Random number of seeds, s is the given seed value |
| function | illustrate |
|---|---|
| shuffle(a) | Change array x according to the 1st axis of array a |
| permutation(a) | A new disordered array is generated according to the 1st axis of array a, without changing array x |
| choice(a[,size,replace,p]) | Extracting elements from one-dimensional array a with probability p to form a new array of size shapes indicates whether elements can be reused, defaulting to False |
| function | illustrate |
|---|---|
| uniform(low,high,size) | Produces an array with a uniform distribution, low start value, high end value, size shape |
| normal(loc,scale,size) | Produces an array with a normal distribution, loc mean, scale standard deviation, size shape |
| poisson(lam,size) | Produces an array with Poisson distribution, lam random event rate, size shape |
| function | illustrate |
|---|---|
| sum(a, axis=None) | Calculates the sum of array a-related elements, axis integers, or tuples, based on a given axis axis |
| mean(a, axis=None) | Calculates the expectations of array a-related elements, axis integers or tuples based on a given axis |
| average(a,axis=None,weights=None) | Calculate the weighted average of array a-related elements based on a given axis axis axis |
| std(a, axis=None) | Calculate the standard deviation of array a-related elements based on a given axis axis axis |
| var(a, axis=None) | Calculate the variance of array a-related elements based on the given axis axis axis axis |
| min(a) max(a) | Calculate the minimum and maximum values of the elements in array a |
| argmin(a) argmax(a) | Calculate the minimum and maximum values of the elements in array a and lower the scale by one dimension |
| unravel_index(index, shape) | Convert the one-dimensional subscript to a multidimensional subscript according to shape |
| ptp(a) | Calculates the difference between the maximum and minimum values of the elements in array a |
| median(a) | Calculate the median (median) of an element in array a |
| np.gradient(f) | Calculates the gradient of the elements in array f and returns each dimension gradient when f is multidimensional |