Numpy核心語(yǔ)法和代碼整理匯總！

$?pip install numpy

$?conda install numpy

import numpy as np

#?1 dimensional
x = np.array([1,2,3])
#?2 dimensional
y = 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 order
y = 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?array
a?= 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?array
print(np.delete(b, 2))
>>> [1?2?4?5?6?7?8?9]

import?numpy as?np
a = np.array([1, 3, 5])
b = np.array([2, 4, 6])

# Stack two arrays row-wise
print(np.vstack((a,b)))
>>> [[1?3?5]
?????[2?4?6]]

# Stack two arrays column-wise
print(np.hstack((a,b)))
>>> [1?3?5?2?4?6]

# Split array into groups of ~3
a = 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 dimension
a = 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 arrays
z = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
c = z < 6
print(c)
>>> [ True??True??True??True??True?False?False?False?False?False]

#?Statistics of an array
a = np.array([1, 1, 2, 5, 8, 10, 11, 12])

#?Standard deviation
print(np.std(a))
>>> 4.2938910093294167

#?Median
print(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]]

import?numpy as?np
a1 = np.arange(0, 6)
a2 = np.arange(10, 16)
a3 = np.arange(20, 26)
a4 = np.arange(30, 36)
a5 = np.arange(40, 46)
a6 = np.arange(50, 56)
a = np.vstack((a1, a2, a3, a4, a5, a6))

# Index trick when working with two np-arrays
a?= 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 == 1
other_a?= a[b == 1]
#Saves every spot in a except at index where b != 1
other_other_a?= a[b != 1]

import?numpy as?np
x = np.array([4,6,8,1,2,6,9])
y = x > 5
print(x[y])
>>> [6?8?6?9]

# Even shorter
x = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])
print(x[x < 5])
>>> [1?2?3?4?4]