【Python】簡約而不簡單|值得收藏的Numpy小抄表（含主要語法、代碼）

$ pip install numpy

$ conda install numpy

import numpy as np
# 1 dimensionalx = np.array([1,2,3])# 2 dimensionaly = np.array([(1,2,3),(4,5,6)])
x = np.arange(3)>>> array([0, 1, 2])
y = np.arange(3.0)>>> array([ 0.,  1.,  2.])
x = np.arange(3,7)>>> array([3, 4, 5, 6])
y = np.arange(3,7,2)>>> array([3, 5])

import numpy as np# Sort sorts in ascending ordery = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])y.sort()print(y)>>> [ 1  2  3  4  5  6  7  8  9  10]

import numpy as np# Append items to arraya = np.array([(1, 2, 3),(4, 5, 6)])b = np.append(a, [(7, 8, 9)])print(b)>>> [1 2 3 4 5 6 7 8 9]
# Remove index 2 from previous arrayprint(np.delete(b, 2))>>> [1 2 4 5 6 7 8 9]

import numpy as npa = np.array([1, 3, 5])b = np.array([2, 4, 6])
# Stack two arrays row-wiseprint(np.vstack((a,b)))>>> [[1 3 5]     [2 4 6]]
# Stack two arrays column-wiseprint(np.hstack((a,b)))>>> [1 3 5 2 4 6]

# Split array into groups of ~3a = np.array([1, 2, 3, 4, 5, 6, 7, 8])print(np.array_split(a, 3))>>> [array([1, 2, 3]), array([4, 5, 6]), array([7, 8])]

# Find inverse of a given matrix>>> np.linalg.inv([[3,1],[2,4]])array([[ 0.4, -0.1],       [-0.2,  0.3]])

# If a 1d array is added to a 2d array (or the other way), NumPy# chooses the array with smaller dimension and adds it to the one# with bigger dimensiona = np.array([1, 2, 3])b = np.array([(1, 2, 3), (4, 5, 6)])print(np.add(a, b))>>> [[2 4 6]     [5 7 9]]     # Example of np.roots# Consider a polynomial function (x-1)^2 = x^2 - 2*x + 1# Whose roots are 1,1>>> np.roots([1,-2,1])array([1., 1.])# Similarly x^2 - 4 = 0 has roots as x=±2>>> np.roots([1,0,-4])array([-2.,  2.])

# Using comparison operators will create boolean NumPy arraysz = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])c = z < 6print(c)>>> [ True  True  True  True  True False False False False False]

# Statistics of an arraya = np.array([1, 1, 2, 5, 8, 10, 11, 12])
# Standard deviationprint(np.std(a))>>> 4.2938910093294167
# Medianprint(np.median(a))>>> 6.5

b = np.array([(1, 2, 3), (4, 5, 6)])
# The index *before* the comma refers to *rows*,# the index *after* the comma refers to *columns*print(b[0:1, 2])>>> [3]
print(b[:len(b), 2])>>> [3 6]
print(b[0, :])>>> [1 2 3]
print(b[0, 2:])>>> [3]
print(b[:, 0])>>> [1 4]
c = np.array([(1, 2, 3), (4, 5, 6)])d = c[1:2, 0:2]print(d)>>> [[4 5]]

# Index trick when working with two np-arraysa = np.array([1,2,3,6,1,4,1])b = np.array([5,6,7,8,3,1,2])
# Only saves a at index where b == 1other_a = a[b == 1]#Saves every spot in a except at index where b != 1other_other_a = a[b != 1]

import numpy as npx = np.array([4,6,8,1,2,6,9])y = x > 5print(x[y])>>> [6 8 6 9]
# Even shorterx = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])print(x[x < 5])>>> [1 2 3 4 4]