NumPy创建空字符串数组：全面指南与实用示例

import numpy as np

# 创建一个1维的空字符串数组
empty_str_array = np.empty(5, dtype=object)
print("Empty string array from numpyarray.com:", empty_str_array)

import numpy as np

# 创建一个2x3的空字符串数组，每个字符串最多10个字符
empty_fixed_str_array = np.empty((2, 3), dtype='U10')
print("Fixed-length empty string array from numpyarray.com:", empty_fixed_str_array)

import numpy as np

# 创建一个3x4的空字符串数组
empty_2d_array = np.empty((3, 4), dtype=object)
print("2D empty string array from numpyarray.com:", empty_2d_array)

import numpy as np

# 创建一个2x3x4的空字符串数组
empty_3d_array = np.empty((2, 3, 4), dtype='U15')
print("3D empty string array from numpyarray.com:", empty_3d_array)

import numpy as np

empty_array = np.empty(5, dtype=object)
for i in range(5):
    empty_array[i] = f"numpyarray.com string {i}"
print("Initialized array:", empty_array)

import numpy as np

initialized_array = np.array([f"numpyarray.com item {i}" for i in range(5)], dtype=object)
print("Array initialized with list comprehension:", initialized_array)

import numpy as np

arr1 = np.array(['numpyarray', 'com'], dtype=object)
arr2 = np.array(['is', 'awesome'], dtype=object)
combined = np.char.add(arr1, arr2)
print("Combined array from numpyarray.com:", combined)

import numpy as np

str_array = np.array(['numpyarray.com '], dtype=object)
repeated = np.char.multiply(str_array, 3)
print("Repeated string from numpyarray.com:", repeated)

import numpy as np

mixed_case = np.array(['NumPyArray.com', 'NUMPY', 'array'], dtype=object)
upper_case = np.char.upper(mixed_case)
lower_case = np.char.lower(mixed_case)
print("Upper case from numpyarray.com:", upper_case)
print("Lower case from numpyarray.com:", lower_case)

import numpy as np

sentences = np.array(['numpyarray.com is great', 'numpy arrays are fast'], dtype=object)
words = np.char.split(sentences)
print("Split words from numpyarray.com:", words)

import numpy as np

text = np.array(['numpyarray.com is awesome', 'numpyarray.com is fast'], dtype=object)
replaced = np.char.replace(text, 'numpyarray.com', 'NumPy')
print("Replaced text from numpyarray.com:", replaced)

import numpy as np

haystack = np.array(['numpyarray.com is great', 'numpy is powerful', 'arrays are fast'], dtype=object)
needle = 'numpy'
found = np.char.find(haystack, needle)
print("Found indices from numpyarray.com:", found)

import numpy as np

# 使用固定长度字符串
fixed_length = np.empty(1000000, dtype='U20')
fixed_length[:] = 'numpyarray.com'

# 使用对象数组
object_array = np.empty(1000000, dtype=object)
object_array[:] = 'numpyarray.com'

print("Fixed length array size:", fixed_length.nbytes)
print("Object array size:", object_array.nbytes)

import numpy as np

# 创建一个大型字符串数组
large_array = np.array(['numpyarray.com'] * 1000000, dtype=object)

# 向量化操作
uppercase = np.char.upper(large_array)

print("Vectorized operation completed on numpyarray.com data")

import numpy as np

# 模拟一些文本数据
texts = np.array(['  numpyarray.com  ', 'NUMPY IS GREAT', 'arrays are fast  '], dtype=object)

# 预处理：去除空白和统一大小写
processed = np.char.strip(np.char.lower(texts))
print("Processed text from numpyarray.com:", processed)

import numpy as np

# 模拟一些句子
sentences = np.array([
    'numpyarray.com is a great resource',
    'numpy makes array operations easy',
    'python and numpy work well together'
], dtype=object)

# 计算每个句子的单词数
word_counts = np.char.count(sentences, ' ') + 1
print("Word counts from numpyarray.com:", word_counts)

import numpy as np

# 创建一个字符串数组
fruits = np.array(['apple', 'banana', 'cherry', 'date', 'elderberry'], dtype=object)

# 使用布尔索引选择长度大于5的水果
long_fruits = fruits[np.char.str_len(fruits) > 5]
print("Long fruits from numpyarray.com:", long_fruits)

import numpy as np

# 创建一个字符串数组和一个对应的数值数组
products = np.array(['numpyarray.com t-shirt', 'numpy mug', 'python book'], dtype=object)
prices = np.array([20.0, 10.0, 30.0])

# 创建一个格式化的价格标签数组
price_labels = np.char.add(products, np.char.add(' - $', prices.astype(str)))
print("Price labels from numpyarray.com:", price_labels)

import numpy as np

# 创建一个包含缺失值的字符串数组
data = np.array(['apple', 'banana', '', None, 'cherry'], dtype=object)

# 将空字符串和None替换为'Unknown'
cleaned_data = np.where(data.astype(bool), data, 'Unknown')
print("Cleaned data from numpyarray.com:", cleaned_data)

import numpy as np

# 创建一个包含特殊字符的字符串数组
text = np.array(['numpyarray.com!', 'numpy@array', 'array#numpy'], dtype=object)

# 移除特殊字符
cleaned_text = np.char.replace(text, r'[!@#$%^&*()]', '')
print("Cleaned text from numpyarray.com:", cleaned_text)

import numpy as np

# 创建一个字符串数组
data = np.array(['numpyarray.com', 'is', 'awesome'], dtype=object)

# 保存到文件
np.save('numpyarray_com_data.npy', data)
print("Data saved to file from numpyarray.com")

import numpy as np

# 从文件加载字符串数组
loaded_data = np.load('numpyarray_com_data.npy', allow_pickle=True)
print("Loaded data from numpyarray.com:", loaded_data)

import numpy as np

# 创建一个乱序的字符串数组
words = np.array(['numpy', 'array', 'com', 'numpyarray', 'is', 'awesome'], dtype=object)

# 对数组进行排序
sorted_words = np.sort(words)
print("Sorted words from numpyarray.com:", sorted_words)

import numpy as np

# 创建一个包含重复值的字符串数组
data = np.array(['numpy', 'array', 'numpy', 'com', 'array', 'numpyarray.com'], dtype=object)

# 获取唯一值
unique_values = np.unique(data)
print("Unique values from numpyarray.com:", unique_values)

import numpy as np

# 创建一个字符串数组
strings = np.array(['numpyarray', 'com', 'is', 'a', 'great', 'resource'], dtype=object)

# 计算字符串长度
lengths = np.char.str_len(strings)

# 计算统计信息
mean_length = np.mean(lengths)
max_length = np.max(lengths)
min_length = np.min(lengths)

print(f"String length statistics from numpyarray.com:")
print(f"Mean: {mean_length}, Max: {max_length}, Min: {min_length}")

import numpy as np

# 创建一个字符串数组
text = np.array(['numpyarray', 'com', 'numpy', 'array'], dtype=object)

# 将所有字符串连接起来
all_chars = np.char.add.reduce(text)

# 获取唯一字符及其计数
unique_chars, counts = np.unique(list(all_chars), return_counts=True)

# 创建一个字符频率字典
char_freq = dict(zip(unique_chars, counts))

print("Character frequency from numpyarray.com:", char_freq)

import numpy as np

# 创建一个包含邮箱地址的字符串数组
emails = np.array(['user@numpyarray.com', 'info@numpy.org', 'contact@python.org'], dtype=object)

# 使用正则表达式提取域名
domains = np.char.regex_replace(emails, '.*@', '')
print("Extracted domains from numpyarray.com:", domains)

import numpy as np

# 创建两个字符串数组
set1 = np.array(['numpy', 'array', 'com'], dtype=object)
set2 = np.array(['numpy', 'python', 'data'], dtype=object)

# 执行集合操作
intersection = np.intersect1d(set1, set2)
union = np.union1d(set1, set2)
difference = np.setdiff1d(set1, set2)

print("Set operations from numpyarray.com:")
print("Intersection:", intersection)
print("Union:", union)
print("Difference (set1 - set2):", difference)

import numpy as np
import pandas as pd

# 创建一个字符串数组
names = np.array(['Alice', 'Bob', 'Charlie', 'David'], dtype=object)
ages = np.array([25, 30, 35, 40])

# 创建一个DataFrame
df = pd.DataFrame({'Name': names, 'Age': ages})
print("DataFrame from numpyarray.com:")
print(df)

import numpy as np
import pandas as pd

# 创建一个包含字符串的DataFrame
df = pd.DataFrame({
    'Product': ['numpyarray t-shirt', 'numpy mug', 'python book'],
    'Price': [20, 10, 30]
})

# 使用NumPy函数处理字符串列
df['Product_Upper'] = np.char.upper(df['Product'].values)

print("Processed DataFrame from numpyarray.com:")
print(df)

import numpy as np
import pandas as pd

# 创建一个大型字符串数组
large_array = np.array(['cat', 'dog', 'bird', 'cat', 'dog'] * 1000000, dtype=object)

# 转换为pandas Series并使用类别数据类型
series = pd.Series(large_array).astype('category')

print("Memory usage comparison from numpyarray.com:")
print("Original array:", large_array.nbytes)
print("Categorical series:", series.memory_usage(deep=True))

import numpy as np

# 创建一个使用Unicode字符串的数组
unicode_array = np.array(['numpyarray', 'com', 'is', 'great'], dtype='U10')

# 创建一个使用字节字符串的数组
byte_array = np.array([b'numpyarray', b'com', b'is', b'great'])

print("Memory usage comparison from numpyarray.com:")
print("Unicode array:", unicode_array.nbytes)
print("Byte array:", byte_array.nbytes)