A complete guide to parsing NumPy functions

NumPy (Numerical Python) is an open source Python scientific computing library that provides multi-dimensional array objects and tools for operating on arrays. It is one of the core libraries of the Python data science ecosystem and is widely used in fields such as scientific computing, data analysis, and machine learning. This article will analyze the commonly used functions in the NumPy library one by one, including array creation, array operations, mathematical functions, statistical functions, linear algebra, etc., and provide specific code examples.

1. Array Creation
   NumPy provides a variety of methods to create arrays. Arrays can be created by specifying dimensions, data types, and initialization values. Commonly used functions are:

1.1 numpy.array(): Create an array from a list or tuple.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr)

# 输出：[1 2 3 4 5]

1.2 numpy.zeros(): Creates an all-zero array of specified dimensions.

import numpy as np

arr = np.zeros((3, 4))
print(arr)

"""
输出：
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
"""

1.3 numpy.ones(): Create an all-one array of specified dimensions.

import numpy as np

arr = np.ones((2, 3))
print(arr)

"""
输出：
[[1. 1. 1.]
 [1. 1. 1.]]
"""

1.4 numpy.arange(): Create an arithmetic array.

import numpy as np

arr = np.arange(0, 10, 2)
print(arr)

# 输出：[0 2 4 6 8]

2. Array operations
   NumPy provides many functions for array operations, including shape operations, indexing and slicing, expansion and stacking, and array transposition. Commonly used functions are:

2.1 reshape(): Change the shape of the array.

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
new_arr = arr.reshape((3, 2))
print(new_arr)

"""
输出：
[[1 2]
 [3 4]
 [5 6]]
"""

2.2 indexing and slicing: operate arrays through indexing and slicing.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr[2])       # 输出：3
print(arr[1:4])     # 输出：[2 3 4]
print(arr[:3])      # 输出：[1 2 3]
print(arr[-3:])     # 输出：[3 4 5]

2.3 concatenate(): Concatenate two or more arrays.

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.concatenate((arr1, arr2))
print(arr)

# 输出：[1 2 3 4 5 6]

2.4 transpose(): Transpose the array.

import numpy as np

arr = np.array([[1, 2], [3, 4]])
new_arr = np.transpose(arr)
print(new_arr)

"""
输出：
[[1 3]
 [2 4]]
"""

3. Mathematical functions
   NumPy provides a wealth of mathematical functions, such as numerical operations, trigonometric functions, logarithmic functions, exponential functions, etc. Commonly used functions are:

3.1 np.mean(): Calculate the average of an array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
mean = np.mean(arr)
print(mean)

# 输出：3.0

3.2 np.sin(): Calculate the sine value of the array element.

import numpy as np

arr = np.array([0, np.pi/2, np.pi])
sin = np.sin(arr)
print(sin)

# 输出：[0.         1.         1.2246468e-16]

3.3 np.exp(): Perform exponential operation on array elements.

import numpy as np

arr = np.array([1, 2, 3])
exp = np.exp(arr)
print(exp)

# 输出：[ 2.71828183  7.3890561  20.08553692]

4. Statistical functions
   NumPy provides commonly used statistical functions, including maximum, minimum, median, variance and standard deviation. Commonly used functions are:

4.1 np.max(): Calculate the maximum value of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
max_value = np.max(arr)
print(max_value)

# 输出：5

4.2 np.min(): Calculate the minimum value of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
min_value = np.min(arr)
print(min_value)

# 输出：1

4.3 np.median(): Calculate the median of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
median = np.median(arr)
print(median)

# 输出：3.0

4.4 np.var(): Calculate the variance of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
variance = np.var(arr)
print(variance)

# 输出：2.0

5. Linear Algebra
   NumPy provides basic linear algebra operation functions, such as matrix multiplication, matrix inversion, matrix determinant, etc. Commonly used functions are:

5.1 np.dot(): Calculate the dot product of two arrays.

import numpy as np

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])
dot_product = np.dot(arr1, arr2)
print(dot_product)

"""
输出：
[[19 22]
 [43 50]]
"""

5.2 np.linalg.inv(): Calculate the inverse of a matrix.

import numpy as np

arr = np.array([[1, 2], [3, 4]])
inverse = np.linalg.inv(arr)
print(inverse)

"""
输出：
[[-2.   1. ]
 [ 1.5 -0.5]]
"""

The above are only part of the functions in the NumPy library. By understanding how to use these common functions, we can use NumPy more efficiently to perform computing tasks such as array operations, mathematical operations, statistical analysis, and linear algebra. At the same time, by in-depth study of the relevant documents of the NumPy library, we can discover more powerful functions and functions to provide strong support for our scientific computing work.

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