Python NumPy教程之数组的基本操作详解

[[ 1, 2, 3],
      [ 4, 2, 5]]

Here, rank = 2 (as it is 2-dimensional or it has 2 axes)
First dimension(axis) length = 2, second dimension has length = 3
overall shape can be expressed as: (2, 3)

# 演示基本数组特征的 Python 程序
import numpy as np
 
# 创建数组对象
arr = np.array( [[ 1, 2, 3],
                 [ 4, 2, 5]] )
 
# arr 对象的打印类型
print("Array is of type: ", type(arr))
 
# 打印数组维度（轴）
print("No. of dimensions: ", arr.ndim)
 
# 阵列的打印形状
print("Shape of array: ", arr.shape)
 
# 数组的打印大小（元素总数）
print("Size of array: ", arr.size)
 
# 打印数组中元素的类型
print("Array stores elements of type: ", arr.dtype)

# 演示数组创建技术的 Python 程序
import numpy as np
 
# 从浮点类型的列表创建数组
a = np.array([[1, 2, 4], [5, 8, 7]], dtype = 'float')
print ("Array created using passed list:\n", a)
 
# 从元组创建数组
b = np.array((1 , 3, 2))
print ("\nArray created using passed tuple:\n", b)
 
# 创建一个全为零的 3X4 数组
c = np.zeros((3, 4))
print ("\nAn array initialized with all zeros:\n", c)
 
# 创建一个复杂类型的常量值数组
d = np.full((3, 3), 6, dtype = 'complex')
print ("\nAn array initialized with all 6s."
            "Array type is complex:\n", d)

# 在 numpy 中演示索引的 Python 程序
import numpy as np
 
# 一个示例数组
arr = np.array([[-1, 2, 0, 4],
                [4, -0.5, 6, 0],
                [2.6, 0, 7, 8],
                [3, -7, 4, 2.0]])
 
# 切片数组
temp = arr[:2, ::2]
print ("Array with first 2 rows and alternate"
                    "columns(0 and 2):\n", temp)
 
# 整数数组索引示例
temp = arr[[0, 1, 2, 3], [3, 2, 1, 0]]
print ("\nElements at indices (0, 3), (1, 2), (2, 1),"
                                    "(3, 0):\n", temp)
 
# 布尔数组索引示例
cond = arr > 0 # cond is a boolean array
temp = arr[cond]
print ("\nElements greater than 0:\n", temp)

# 演示单个数组的基本操作的 Python 程序
import numpy as np
 
a = np.array([1, 2, 5, 3])
 
# 每个元素加 1
print ("Adding 1 to every element:", a+1)
 
# 从每个元素中减去 3
print ("Subtracting 3 from each element:", a-3)
 
# 将每个元素乘以 10
print ("Multiplying each element by 10:", a*10)
 
# 平方每个元素
print ("Squaring each element:", a**2)
 
# 修改现有数组
a *= 2
print ("Doubled each element of original array:", a)
 
# 数组转置
a = np.array([[1, 2, 3], [3, 4, 5], [9, 6, 0]])
 
print ("\nOriginal array:\n", a)
print ("Transpose of array:\n", a.T)

# 在 numpy 中演示一元运算符的 Python 程序
import numpy as np
 
arr = np.array([[1, 5, 6],
                [4, 7, 2],
                [3, 1, 9]])
 
# 数组的最大元素
print ("Largest element is:", arr.max())
print ("Row-wise maximum elements:",
                    arr.max(axis = 1))
 
# 数组的最小元素
print ("Column-wise minimum elements:",
                        arr.min(axis = 0))
 
# 数组元素之和
print ("Sum of all array elements:",
                            arr.sum())
 
# 每行的累积总和
print ("Cumulative sum along each row:\n",
                        arr.cumsum(axis = 1))

# 在 Numpy 中演示二元运算符的 Python 程序
import numpy as np
 
a = np.array([[1, 2],
            [3, 4]])
b = np.array([[4, 3],
            [2, 1]])
 
# 添加数组
print ("Array sum:\n", a + b)
 
# 乘法数组（元素乘法）
print ("Array multiplication:\n", a*b)
 
# 矩阵乘法
print ("Matrix multiplication:\n", a.dot(b))

# 在 numpy 中演示通用函数的 Python 程序
import numpy as np
 
# 创建一个正弦值数组
a = np.array([0, np.pi/2, np.pi])
print ("Sine values of array elements:", np.sin(a))
 
# exponential values
a = np.array([0, 1, 2, 3])
print ("Exponent of array elements:", np.exp(a))
 
# square root of array values
print ("Square root of array elements:", np.sqrt(a))

# Python Program to create a data type object
import numpy as np
 
# np.int16 is converted into a data type object.
print(np.dtype(np.int16))

# Python Program to create a data type object 
# containing a 32 bit big-endian integer
import numpy as np
 
# i4 represents integer of size 4 byte
# > represents big-endian byte ordering and
# < represents little-endian encoding.
# dt is a dtype object
dt = np.dtype('>i4')
 
print("Byte order is:",dt.byteorder)
 
print("Size is:",dt.itemsize)
 
print("Data type is:",dt.name)