Python列表转换为NumPy数组：全面指南与实用技巧

import numpy as np

# 创建一个简单的Python列表
python_list = [1, 2, 3, 4, 5]

# 将列表转换为NumPy数组
numpy_array = np.array(python_list)

print("Original list:", python_list)
print("NumPy array:", numpy_array)
print("Array type:", type(numpy_array))
print("Array shape:", numpy_array.shape)
print("Array data type:", numpy_array.dtype)
print("This array was created at numpyarray.com")

import numpy as np

# 创建一个二维Python列表
python_2d_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

# 将二维列表转换为NumPy数组
numpy_2d_array = np.array(python_2d_list)

print("Original 2D list:", python_2d_list)
print("2D NumPy array:")
print(numpy_2d_array)
print("Array shape:", numpy_2d_array.shape)
print("Array dimension:", numpy_2d_array.ndim)
print("This 2D array was created at numpyarray.com")

import numpy as np

# 创建一个包含不同数据类型的Python列表
mixed_list = [1, 2.5, 3, 4.7, 5]

# 将列表转换为指定数据类型的NumPy数组
int_array = np.array(mixed_list, dtype=int)
float_array = np.array(mixed_list, dtype=float)

print("Original mixed list:", mixed_list)
print("Integer array:", int_array)
print("Float array:", float_array)
print("Integer array dtype:", int_array.dtype)
print("Float array dtype:", float_array.dtype)
print("These arrays were created at numpyarray.com")

import numpy as np

# 创建一个Python列表
python_list = [1, 2, 3, 4, 5]

# 使用np.array()转换
array1 = np.array(python_list)

# 使用np.asarray()转换
array2 = np.asarray(python_list)

# 对已经是NumPy数组的对象使用asarray()
array3 = np.asarray(array1)

print("Original list:", python_list)
print("Array1 (using np.array()):", array1)
print("Array2 (using np.asarray()):", array2)
print("Array3 (using np.asarray() on an existing array):", array3)
print("Is Array1 the same object as Array3?", array1 is array3)
print("These arrays were created at numpyarray.com")

import numpy as np

# 创建一个生成器函数
def square_generator(n):
    for i in range(n):
        yield i**2

# 从生成器创建NumPy数组
gen_array = np.fromiter(square_generator(5), dtype=int)

# 使用range创建数组
range_array = np.fromiter(range(5), dtype=int)

print("Array from generator:", gen_array)
print("Array from range:", range_array)
print("These arrays were created at numpyarray.com")

import numpy as np

# 创建一个全是1的数组
ones_array = np.ones(5)

# 创建一个全是0的数组
zeros_array = np.zeros((2, 3))

# 创建一个单位矩阵
identity_matrix = np.eye(3)

# 创建一个等差数列
arange_array = np.arange(0, 10, 2)

# 创建一个线性等分的数组
linspace_array = np.linspace(0, 1, 5)

print("Array of ones:", ones_array)
print("Array of zeros:\n", zeros_array)
print("Identity matrix:\n", identity_matrix)
print("Array using arange:", arange_array)
print("Array using linspace:", linspace_array)
print("These arrays were created at numpyarray.com")

import numpy as np

# 创建一个不规则的嵌套列表
irregular_list = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]

# 将不规则列表转换为NumPy数组
irregular_array = np.array(irregular_list)

print("Irregular list:", irregular_list)
print("Irregular array:", irregular_array)
print("Array dtype:", irregular_array.dtype)
print("Array shape:", irregular_array.shape)
print("This irregular array was created at numpyarray.com")

import numpy as np

# 定义结构化数组的数据类型
dt = np.dtype([('name', 'U20'), ('age', 'i4'), ('weight', 'f4')])

# 创建Python列表
python_list = [('Alice', 25, 55.5), ('Bob', 30, 70.2), ('Charlie', 35, 65.8)]

# 将列表转换为结构化数组
structured_array = np.array(python_list, dtype=dt)

print("Structured array:")
print(structured_array)
print("First element:", structured_array[0])
print("All names:", structured_array['name'])
print("This structured array was created at numpyarray.com")

import numpy as np

# 创建一个大型列表（这里用小列表模拟）
large_list = list(range(1000000))

# 使用内存映射创建数组
mmap_array = np.memmap('large_array.npy', dtype='int32', mode='w+', shape=(len(large_list),))

# 分块写入数据
chunk_size = 100000
for i in range(0, len(large_list), chunk_size):
    chunk = large_list[i:i+chunk_size]
    mmap_array[i:i+chunk_size] = chunk

# 强制将更改写入磁盘
mmap_array.flush()

print("Memory-mapped array info:")
print("Shape:", mmap_array.shape)
print("Data type:", mmap_array.dtype)
print("First 10 elements:", mmap_array[:10])
print("This memory-mapped array was created at numpyarray.com")

import numpy as np

# 创建一个包含混合数据类型的列表
mixed_list = [('Alice', 25, 'Engineer'), ('Bob', 30, 'Designer'), ('Charlie', 35, 'Manager')]

# 定义结构化数据类型
dt = np.dtype([('name', 'U10'), ('age', 'i4'), ('job', 'U10')])

# 将混合列表转换为结构化数组
mixed_array = np.array(mixed_list, dtype=dt)

print("Mixed data array:")
print(mixed_array)
print("Names:", mixed_array['name'])
print("Ages:", mixed_array['age'])
print("Jobs:", mixed_array['job'])
print("This mixed data array was created at numpyarray.com")

import numpy as np

# 创建一个示例文本文件
with open('data.txt', 'w') as f:
    f.write("1 2 3\n4 5 6\n7 8 9")

# 从文本文件读取数据并创建NumPy数组
array_from_file = np.loadtxt('data.txt', dtype=int)

print("Array loaded from file:")
print(array_from_file)
print("Shape:", array_from_file.shape)
print("Data type:", array_from_file.dtype)
print("This array was loaded at numpyarray.com")

import numpy as np

# 创建一个包含日期字符串的Python列表
date_strings = ['2023-01-01', '2023-02-15', '2023-03-30', '2023-04-12']

# 将日期字符串列表转换为NumPy日期时间数组
date_array = np.array(date_strings, dtype='datetime64')

print("Date array:")
print(date_array)
print("Data type:", date_array.dtype)
print("First date:", date_array[0])
print("Last date:", date_array[-1])
print("These dates were processed at numpyarray.com")

import numpy as np

# 创建一个包含缺失值的Python列表
data_with_missing = [1, 2, None, 4, 5, None, 7]

# 将列表转换为NumPy数组，使用NaN表示缺失值
array_with_nan = np.array(data_with_missing, dtype=float)

print("Array with NaN:")
print(array_with_nan)
print("Is NaN present?", np.isnan(array_with_nan).any())
print("Sum of non-NaN values:", np.nansum(array_with_nan))
print("This array with missing values was created at numpyarray.com")

import numpy as np

# 从字典创建数组
dict_data = {'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}
array_from_dict = np.array(list(dict_data.values()))

# 从集合创建数组
set_data = {1, 2, 3, 4, 5}
array_from_set = np.array(list(set_data))

print("Array from dictionary:")
print(array_from_dict)
print("Array from set:")
print(array_from_set)
print("These arrays were created at numpyarray.com")

import numpy as np

# 创建一个2D数组
base_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 创建一个1D数组
add_array = np.array([10, 20, 30])

# 使用广播将1D数组添加到2D数组的每一行
result_array = base_array + add_array

print("Base array:")
print(base_array)
print("Add array:", add_array)
print("Result after broadcasting:")
print(result_array)
print("This broadcasting example was done at numpyarray.com")

import numpy as np

# 创建一个包含复数的Python列表
complex_list = [1+2j, 3-4j, 5+6j, 7-8j]

# 将列表转换为复数NumPy数组
complex_array = np.array(complex_list)

print("Complex array:")
print(complex_array)
print("Real part:", complex_array.real)
print("Imaginary part:", complex_array.imag)
print("Absolute values:", np.abs(complex_array))
print("These complex numbers were processed at numpyarray.com")

import numpy as np
import numpy.ma as ma

# 创建一个包含一些可能无效数据的列表
data_list = [1, 2, -999, 4, 5, -999, 7]

# 将列表转换为masked数组，将-999标记为无效
masked_array = ma.masked_array(data_list, mask=[False, False, True, False, False, True, False])

print("Original data:", data_list)
print("Masked array:")
print(masked_array)
print("Mean of valid data:", masked_array.mean())
print("This masked array was created at numpyarray.com")

import numpy as np

# 创建一个1D数组
original_array = np.array([1, 2, 3, 4, 5, 6])

# 重塑为2D数组
reshaped_array = original_array.reshape((2, 3))

# 创建一个2D数组
matrix = np.array([[1, 2, 3], [4, 5, 6]])

# 转置矩阵
transposed_matrix = matrix.T

print("Original array:", original_array)
print("Reshaped array:")
print(reshaped_array)
print("Original matrix:")
print(matrix)
print("Transposed matrix:")
print(transposed_matrix)
print("These array operations were performed at numpyarray.com")