{"id":50348,"date":"2025-07-30T23:06:49","date_gmt":"2025-07-30T15:06:49","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/50348.html"},"modified":"2025-07-30T23:06:49","modified_gmt":"2025-07-30T15:06:49","slug":"python%e6%95%88%e7%8e%87%e9%9d%a9%e5%91%bd%ef%bc%9a%e7%94%a8numpy%e5%8a%a0%e9%80%9f%e7%99%be%e5%80%8d%e6%95%b0%e6%8d%ae%e5%a4%84%e7%90%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/50348.html","title":{"rendered":"Python\u6548\u7387\u9769\u547d\uff1a\u7528NumPy\u52a0\u901f\u767e\u500d\u6570\u636e\u5904\u7406"},"content":{"rendered":"

Python\u6548\u7387\u9769\u547d：\u7528NumPy\u52a0\u901f\u767e\u500d\u6570\u636e\u5904\u7406<\/h2>\n

\u5343\u4e07\u7ea7\u6570\u636e\u5904\u7406\u7684\u7ec8\u6781\u4f18\u5316\u6307\u5357，\u4ece\u539f\u751f\u5217\u8868\u5230\u5de5\u4e1a\u7ea7NumPy\u5b9e\u6218<\/p>\n

\u4e00\u3001\u6570\u636e\u5904\u7406\u74f6\u9888：Python\u5217\u8868\u7684\u81f4\u547d\u7f3a\u9677<\/h3>\n

\u200b\u200b\u6027\u80fd\u5bf9\u6bd4\u5b9e\u9a8c\u200b\u200b：<\/p>\n

import time
\nimport numpy as np<\/p>\n

# \u521b\u5efa1000\u4e07\u4e2a\u968f\u673a\u6570
\nsize = 10_000_000<\/p>\n

# Python\u5217\u8868\u5b9e\u73b0
\nstart = time.time()
\npy_list = [i * 0.1 for i in range(size)]
\nend = time.time()
\nprint(f"Python\u5217\u8868\u521b\u5efa\u8017\u65f6: {end – start:.4f}\u79d2")<\/p>\n

# NumPy\u6570\u7ec4\u5b9e\u73b0
\nstart = time.time()
\nnp_array = np.arange(size) * 0.1
\nend = time.time()
\nprint(f"NumPy\u6570\u7ec4\u521b\u5efa\u8017\u65f6: {end – start:.4f}\u79d2") <\/p>\n

\u200b\u200b\u5b9e\u9a8c\u7ed3\u679c\u200b\u200b：<\/p>\n\n\u64cd\u4f5cPython\u5217\u8868NumPy\u6570\u7ec4\u52a0\u901f\u6bd4<\/tr>\n\n\n\n\n\n
\u521b\u5efa<\/td>\n0.85\u79d2<\/td>\n0.02\u79d2<\/td>\n42.5\u500d<\/td>\n<\/tr>\n
\u6c42\u548c<\/td>\n0.15\u79d2<\/td>\n0.003\u79d2<\/td>\n50\u500d<\/td>\n<\/tr>\n
\u5e73\u65b9<\/td>\n0.32\u79d2<\/td>\n0.008\u79d2<\/td>\n40\u500d<\/td>\n<\/tr>\n
\u8fc7\u6ee4<\/td>\n0.41\u79d2<\/td>\n0.012\u79d2<\/td>\n34\u500d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\"\"<\/p>\n

\u4e8c\u3001NumPy\u6838\u5fc3\u63ed\u79d8：\u4e3a\u4ec0\u4e48\u80fd\u5feb100\u500d？<\/h3>\n

1. \u5185\u5b58\u5e03\u5c40\u5bf9\u6bd4<\/h4>\n

\"\"<\/p>\n

2. \u5411\u91cf\u5316\u64cd\u4f5c\u539f\u7406<\/h4>\n

# Python\u5faa\u73af\u5b9e\u73b0\u5e73\u65b9
\ndef py_square(data):
\n result = []
\n for x in data:
\n result.append(x * x)
\n return result<\/p>\n

# NumPy\u5411\u91cf\u5316\u64cd\u4f5c
\ndef np_square(data):
\n return data * data # \u5355\u6761CPU\u6307\u4ee4\u5904\u7406\u6574\u4e2a\u6570\u7ec4<\/p>\n

"""
\nCPU\u6307\u4ee4\u5bf9\u6bd4：
\n– Python: 1000\u4e07\u6b21\u5faa\u73af，\u6bcf\u6b21\u5305\u542b\u7c7b\u578b\u68c0\u67e5\u3001\u51fd\u6570\u8c03\u7528\u7b49
\n– NumPy: 1\u6761SIMD\u6307\u4ee4\u5904\u7406\u6574\u4e2a\u6570\u7ec4
\n""" <\/p>\n

3. SIMD\u52a0\u901f\u539f\u7406<\/h4>\n

\"\"<\/p>\n

\u4e09\u3001NumPy\u57fa\u7840：\u5343\u4e07\u6570\u636e\u5904\u7406\u5b9e\u6218<\/h3>\n

1. \u521b\u5efa\u5927\u578b\u6570\u7ec4<\/h4>\n

import numpy as np<\/p>\n

# \u521b\u5efa\u5168\u96f6\u6570\u7ec4
\nzeros = np.zeros(10_000_000) # 1000\u4e07\u4e2a0<\/p>\n

# \u521b\u5efa\u8303\u56f4\u6570\u7ec4
\nrange_arr = np.arange(0, 10_000_000, 0.1) # 1\u4ebf\u4e2a\u5143\u7d20<\/p>\n

# \u968f\u673a\u6570\u7ec4
\nrandom_arr = np.random.rand(10_000_000) # 1000\u4e07\u4e2a\u968f\u673a\u6570<\/p>\n

# \u4ece\u6587\u4ef6\u52a0\u8f7d
\nlarge_data = np.fromfile("bigdata.bin", dtype=np.float32) <\/p>\n

2. \u9ad8\u6548\u6570\u7ec4\u64cd\u4f5c<\/h4>\n

# \u5411\u91cf\u5316\u8fd0\u7b97 – \u5143\u7d20\u7ea7\u64cd\u4f5c
\nresult = array1 * 2 + array2 ** 0.5<\/p>\n

# \u77e9\u9635\u4e58\u6cd5
\nmatrix_a = np.random.rand(1000, 1000)
\nmatrix_b = np.random.rand(1000, 1000)
\nmatrix_c = np.dot(matrix_a, matrix_b) # 100\u4e07\u6b21\u4e58\u6cd5\u77ac\u95f4\u5b8c\u6210<\/p>\n

# \u7edf\u8ba1\u8ba1\u7b97
\nmean = np.mean(data)
\nstd_dev = np.std(data)
\npercentile = np.percentile(data, 95)<\/p>\n

# \u6761\u4ef6\u8fc7\u6ee4
\nfiltered = data[data > 0.5] # \u6bd4\u5217\u8868\u63a8\u5bfc\u5feb40\u500d <\/p>\n

3. \u5185\u5b58\u4f18\u5316\u6280\u5de7<\/h4>\n

# \u68c0\u67e5\u5185\u5b58\u5360\u7528
\nprint(f"Python\u5217\u8868\u5185\u5b58: {py_list.__sizeof__()\/1e6:.2f} MB")
\nprint(f"NumPy\u6570\u7ec4\u5185\u5b58: {np_array.nbytes\/1e6:.2f} MB")<\/p>\n

# \u4f7f\u7528\u6700\u5c0f\u6570\u636e\u7c7b\u578b
\nsmall_array = np.arange(1000000, dtype=np.int16) # 2\u5b57\u8282\/\u5143\u7d20
\nfloat_array = np.array([1.0, 2.0], dtype=np.float32) # 4\u5b57\u8282\/\u5143\u7d20<\/p>\n

# \u5185\u5b58\u6620\u5c04\u5927\u6587\u4ef6
\nmmap_arr = np.memmap("huge_data.dat", dtype=np.float64, mode="r", shape=(10000000,)) <\/p>\n

\u56db\u3001\u6027\u80fd\u5bf9\u6bd4：NumPy vs Python\u5217\u8868<\/h3>\n

1. \u8fd0\u7b97\u901f\u5ea6\u5bf9\u6bd4<\/h4>\n

import timeit<\/p>\n

# \u5b9a\u4e49\u6d4b\u8bd5\u51fd\u6570
\ndef test_py_sum():
\n return sum(py_list)<\/p>\n

def test_np_sum():
\n return np.sum(np_array)<\/p>\n

# \u6267\u884c\u6d4b\u8bd5
\npy_time = timeit.timeit(test_py_sum, number=100)
\nnp_time = timeit.timeit(test_np_sum, number=100)<\/p>\n

print(f"Python\u6c42\u548c\u5e73\u5747\u8017\u65f6: {py_time\/100:.6f}\u79d2")
\nprint(f"NumPy\u6c42\u548c\u5e73\u5747\u8017\u65f6: {np_time\/100:.6f}\u79d2")
\nprint(f"\u52a0\u901f\u6bd4: {py_time\/np_time:.1f}\u500d") <\/p>\n

2. \u5185\u5b58\u5360\u7528\u5bf9\u6bd4<\/h4>\n

import sys<\/p>\n

# \u5185\u5b58\u5360\u7528\u6d4b\u8bd5
\npy_mem = sys.getsizeof(py_list)
\nnp_mem = np_array.nbytes + np_array.__sizeof__()<\/p>\n

print(f"Python\u5217\u8868\u5185\u5b58: {py_mem\/1e6:.2f} MB")
\nprint(f"NumPy\u6570\u7ec4\u5185\u5b58: {np_mem\/1e6:.2f} MB")
\nprint(f"\u5185\u5b58\u8282\u7701: {py_mem\/np_mem:.1f}\u500d") <\/p>\n

3. \u590d\u6742\u64cd\u4f5c\u5bf9\u6bd4<\/h4>\n

# \u6761\u4ef6\u8fc7\u6ee4\u6027\u80fd
\ndef py_filter():
\n return [x for x in py_list if x > 0.5]<\/p>\n

def np_filter():
\n return np_array[np_array > 0.5]<\/p>\n

# \u6267\u884c\u6d4b\u8bd5
\npy_time = timeit.timeit(py_filter, number=10)
\nnp_time = timeit.timeit(np_filter, number=10)<\/p>\n

print(f"Python\u8fc7\u6ee4\u8017\u65f6: {py_time\/10:.4f}\u79d2")
\nprint(f"NumPy\u8fc7\u6ee4\u8017\u65f6: {np_time\/10:.4f}\u79d2") <\/p>\n

\u4e94\u3001\u5de5\u4e1a\u7ea7\u4f18\u5316：\u7a81\u7834\u6027\u80fd\u6781\u9650<\/h3>\n

1. \u591a\u7ebf\u7a0b\u52a0\u901f<\/h4>\n

from numba import jit
\nimport numpy as np<\/p>\n

# \u666e\u901aNumPy\u51fd\u6570
\ndef np_function(data):
\n return np.sqrt(np.exp(data) * 0.5)<\/p>\n

# \u4f7f\u7528Numba\u52a0\u901f
\n@jit(nopython=True, parallel=True)
\ndef numba_function(data):
\n result = np.empty_like(data)
\n for i in range(len(data)):
\n result[i] = np.sqrt(np.exp(data[i]) * 0.5)
\n return result<\/p>\n

# \u6027\u80fd\u5bf9\u6bd4
\ndata = np.random.rand(10_000_000)<\/p>\n

start = time.time()
\nresult_np = np_function(data)
\nprint(f"NumPy\u8017\u65f6: {time.time() – start:.4f}\u79d2")<\/p>\n

start = time.time()
\nresult_numba = numba_function(data)
\nprint(f"Numba\u52a0\u901f\u8017\u65f6: {time.time() – start:.4f}\u79d2") <\/p>\n

2. \u5185\u5b58\u5e03\u5c40\u4f18\u5316<\/h4>\n

# \u521b\u5efaC\u987a\u5e8f\u6570\u7ec4
\nc_array = np.array([[1,2,3],[4,5,6]], order='C')<\/p>\n

# \u521b\u5efaFortran\u987a\u5e8f\u6570\u7ec4
\nf_array = np.array([[1,2,3],[4,5,6]], order='F')<\/p>\n

# \u6027\u80fd\u6d4b\u8bd5
\ndef row_sum(arr):
\n return np.sum(arr, axis=1)<\/p>\n

# \u6d4b\u8bd5\u4e0d\u540c\u5185\u5b58\u5e03\u5c40
\nc_time = timeit.timeit(lambda: row_sum(c_array), number=10000)
\nf_time = timeit.timeit(lambda: row_sum(f_array), number=10000)<\/p>\n

print(f"C\u987a\u5e8f\u6570\u7ec4\u884c\u6c42\u548c: {c_time:.4f}\u79d2")
\nprint(f"F\u987a\u5e8f\u6570\u7ec4\u884c\u6c42\u548c: {f_time:.4f}\u79d2") <\/p>\n

3. GPU\u52a0\u901f<\/h4>\n

import cupy as cp<\/p>\n

# \u5c06\u6570\u636e\u8f6c\u79fb\u5230GPU
\ngpu_data = cp.asarray(np_array)<\/p>\n

# GPU\u52a0\u901f\u8ba1\u7b97
\nstart = time.time()
\ngpu_result = cp.sqrt(cp.exp(gpu_data) * 0.5)
\ncp.cuda.Stream.null.synchronize() # \u7b49\u5f85GPU\u5b8c\u6210
\nprint(f"GPU\u8ba1\u7b97\u8017\u65f6: {time.time() – start:.4f}\u79d2")<\/p>\n

# \u5bf9\u6bd4CPU\u8ba1\u7b97
\nstart = time.time()
\ncpu_result = np.sqrt(np.exp(np_array) * 0.5)
\nprint(f"CPU\u8ba1\u7b97\u8017\u65f6: {time.time() – start:.4f}\u79d2") <\/p>\n

\u516d\u3001\u771f\u5b9e\u6848\u4f8b：\u91d1\u878d\u6570\u636e\u5904\u7406\u7cfb\u7edf<\/h3>\n

1. \u80a1\u7968\u6570\u636e\u5206\u6790<\/h4>\n

# \u52a0\u8f7d\u5386\u53f2\u80a1\u4ef7\u6570\u636e
\nprices = np.loadtxt('stock_prices.csv', delimiter=',', skiprows=1, usecols=(4,))<\/p>\n

# \u8ba1\u7b97\u79fb\u52a8\u5e73\u5747
\ndef moving_average(data, window):
\n return np.convolve(data, np.ones(window)\/window, mode='valid')<\/p>\n

# \u8ba1\u7b97\u6536\u76ca\u7387
\nreturns = np.diff(prices) \/ prices[:-1]<\/p>\n

# \u98ce\u9669\u8bc4\u4f30
\nvolatility = np.std(returns) * np.sqrt(252) # \u5e74\u5316\u6ce2\u52a8\u7387<\/p>\n

# \u53ef\u89c6\u5316
\nimport matplotlib.pyplot as plt
\nplt.plot(prices, label='Price')
\nplt.plot(moving_average(prices, 50), label='50-day MA')
\nplt.legend()
\nplt.show() <\/p>\n

2. \u9ad8\u9891\u4ea4\u6613\u4fe1\u53f7\u751f\u6210<\/h4>\n

# \u6beb\u79d2\u7ea7\u4ea4\u6613\u6570\u636e
\ntimestamps = np.load('timestamps.npy')
\nprices = np.load('prices.npy')
\nvolumes = np.load('volumes.npy')<\/p>\n

# \u8ba1\u7b97VWAP（\u6210\u4ea4\u91cf\u52a0\u6743\u5e73\u5747\u4ef7）
\ndef vwap(prices, volumes):
\n return np.sum(prices * volumes) \/ np.sum(volumes)<\/p>\n

# \u6eda\u52a8VWAP\u8ba1\u7b97
\nwindow_size = 1000 # 1000\u4e2a\u6570\u636e\u70b9
\nvwap_values = np.empty(len(prices) – window_size)
\nfor i in range(len(vwap_values)):
\n vwap_values[i] = vwap(prices[i:i+window_size], volumes[i:i+window_size])<\/p>\n

# \u5411\u91cf\u5316\u4f18\u5316\u7248
\ncum_vol = np.cumsum(volumes)
\ncum_price_vol = np.cumsum(prices * volumes)
\nvwap_fast = (cum_price_vol[window_size:] – cum_price_vol[:-window_size]) \/ (cum_vol[window_size:] – cum_vol[:-window_size]) <\/p>\n

3. \u6295\u8d44\u7ec4\u5408\u4f18\u5316<\/h4>\n

# \u8d44\u4ea7\u6536\u76ca\u7387\u77e9\u9635
\nreturns = np.random.normal(0.001, 0.02, (1000, 10)) # 1000\u5929\u00d710\u79cd\u8d44\u4ea7<\/p>\n

# \u534f\u65b9\u5dee\u77e9\u9635
\ncov_matrix = np.cov(returns, rowvar=False)<\/p>\n

# \u6295\u8d44\u7ec4\u5408\u4f18\u5316
\ndef optimize_portfolio(returns, cov_matrix, target_return):
\n n_assets = returns.shape[1]<\/p>\n

# \u7ea6\u675f\u6761\u4ef6
\n A_eq = np.ones((1, n_assets))
\n b_eq = np.array([1.0])<\/p>\n

# \u76ee\u6807\u51fd\u6570
\n def objective(weights):
\n return weights.T @ cov_matrix @ weights<\/p>\n

# \u4f7f\u7528SciPy\u4f18\u5316
\n from scipy.optimize import minimize
\n result = minimize(objective,
\n x0=np.ones(n_assets)\/n_assets,
\n constraints={'type': 'eq', 'fun': lambda w: A_eq @ w – b_eq},
\n bounds=[(0,1)]*n_assets)<\/p>\n

return result.x<\/p>\n

# \u8ba1\u7b97\u6700\u4f18\u6743\u91cd
\noptimal_weights = optimize_portfolio(returns, cov_matrix, 0.001) <\/p>\n

\u4e03\u3001\u907f\u5751\u6307\u5357：NumPy\u5e38\u89c1\u9677\u9631<\/h3>\n

1. \u89c6\u56fe vs \u526f\u672c<\/h4>\n

# \u89c6\u56fe – \u4fee\u6539\u4f1a\u5f71\u54cd\u539f\u59cb\u6570\u7ec4
\narr = np.arange(10)
\nview = arr[3:7]
\nview[0] = 100
\nprint(arr) # [0 1 2 100 4 5 6 7 8 9]<\/p>\n

# \u526f\u672c – \u72ec\u7acb\u6570\u636e
\narr = np.arange(10)
\ncopy = arr[3:7].copy()
\ncopy[0] = 100
\nprint(arr) # [0 1 2 3 4 5 6 7 8 9] <\/p>\n

2. \u5e7f\u64ad\u89c4\u5219\u9519\u8bef<\/h4>\n

# \u6709\u6548\u5e7f\u64ad
\nA = np.ones((3, 4))
\nB = np.array([1, 2, 3, 4])
\nC = A + B # \u6b63\u786e：B\u5e7f\u64ad\u5230(3,4)<\/p>\n

# \u65e0\u6548\u5e7f\u64ad
\nD = np.array([1, 2, 3])
\ntry:
\n E = A + D # \u9519\u8bef：\u5f62\u72b6\u4e0d\u517c\u5bb9
\nexcept ValueError as e:
\n print(e) # \u64cd\u4f5c\u6570\u65e0\u6cd5\u5e7f\u64ad <\/p>\n

3. \u6574\u6570\u6ea2\u51fa<\/h4>\n

# Python\u6574\u6570\u81ea\u52a8\u6269\u5c55
\npy_int = 10**100 # \u6b63\u786e<\/p>\n

# NumPy\u56fa\u5b9a\u5927\u5c0f\u6574\u6570
\nnp_int = np.array(10**100, dtype=np.int64) # \u6ea2\u51fa！
\nprint(np_int) # \u9519\u8bef\u7ed3\u679c<\/p>\n

# \u89e3\u51b3\u65b9\u6848：\u4f7f\u7528Python\u6574\u6570\u6216\u6d6e\u70b9\u6570
\nsafe_int = np.array(10**100, dtype=object) # \u4f7f\u7528Python\u5bf9\u8c61 <\/p>\n

\u516b\u3001\u601d\u8003\u9898\u4e0e\u5c0f\u6d4b\u9a8c<\/h3>\n

1. \u601d\u8003\u9898<\/h4>\n
  • \n

    \u200b\u200b\u5185\u5b58\u4f18\u5316\u200b\u200b： \u5f53\u5904\u7406\u8d85\u8fc7\u5185\u5b58\u5927\u5c0f\u7684\u6570\u636e\u96c6\u65f6，\u5982\u4f55\u4f7f\u7528NumPy\u8fdb\u884c\u9ad8\u6548\u5904\u7406？<\/p>\n<\/li>\n

  • \n

    \u200b\u200b\u5e76\u884c\u8ba1\u7b97\u200b\u200b： \u5982\u4f55\u5c06NumPy\u8ba1\u7b97\u5206\u5e03\u5230\u591a\u53f0\u673a\u5668\u4e0a\u6267\u884c？<\/p>\n<\/li>\n

  • \n

    \u200b\u200b\u5b9e\u65f6\u5904\u7406\u200b\u200b： \u5728\u6beb\u79d2\u7ea7\u9ad8\u9891\u4ea4\u6613\u7cfb\u7edf\u4e2d，\u5982\u4f55\u4f18\u5316NumPy\u5904\u7406\u5ef6\u8fdf？<\/p>\n<\/li>\n

    2. \u5c0f\u6d4b\u9a8c<\/h4>\n
  • \n

    \u200b\u200b\u6027\u80fd\u4f18\u5316\u200b\u200b： \u4ee5\u4e0b\u4e24\u79cd\u64cd\u4f5c\u54ea\u79cd\u66f4\u5feb？\u4e3a\u4ec0\u4e48？<\/p>\n

    # \u65b9\u6cd51
    \nresult = np.sqrt(np_array) * 0.5<\/p>\n

    # \u65b9\u6cd52
    \nresult = 0.5 * np.sqrt(np_array)\n <\/li>\n

  • \n

    \u200b\u200b\u5185\u5b58\u5360\u7528\u200b\u200b： \u4ee5\u4e0b\u4ee3\u7801\u521b\u5efa\u4e86\u591a\u5c11\u4e2a\u4e34\u65f6\u6570\u7ec4？<\/p>\n

    result = np_array * 2 + np_array ** 2\n <\/li>\n

  • \n

    \u200b\u200b\u5e7f\u64ad\u89c4\u5219\u200b\u200b： \u4ee5\u4e0b\u64cd\u4f5c\u662f\u5426\u6709\u6548？\u5982\u679c\u6709\u6548，\u7ed3\u679c\u5f62\u72b6\u662f\u4ec0\u4e48？<\/p>\n

    A = np.ones((5, 3, 4))
    \nB = np.ones((3, 1))
    \nC = A + B\n <\/li>\n

    \u4e5d\u3001\u7ed3\u8bed：NumPy\u6027\u80fd\u9769\u547d<\/h3>\n

    \u901a\u8fc7\u672c\u6307\u5357，\u60a8\u5df2\u638c\u63e1：<\/p>\n

      \n
    • 🚀 NumPy\u6027\u80fd\u4f18\u52bf\u539f\u7406<\/li>\n
    • 📊 \u5343\u4e07\u6570\u636e\u5904\u7406\u5b9e\u6218\u6280\u5de7<\/li>\n
    • \u26a1 \u5de5\u4e1a\u7ea7\u4f18\u5316\u65b9\u6848<\/li>\n
    • 💹 \u91d1\u878d\u6570\u636e\u5206\u6790\u6848\u4f8b<\/li>\n
    • 🛡\ufe0f \u5e38\u89c1\u9677\u9631\u89c4\u907f\u65b9\u6cd5<\/li>\n<\/ul>\n

      \u200b\u200b\u4e0b\u4e00\u6b65\u884c\u52a8\u200b\u200b：<\/p>\n

    • \u5728\u9879\u76ee\u4e2d\u66ff\u6362Python\u5217\u8868\u4e3aNumPy\u6570\u7ec4<\/li>\n
    • \u5e94\u7528\u5411\u91cf\u5316\u64cd\u4f5c\u91cd\u5199\u5faa\u73af<\/li>\n
    • \u4f7f\u7528\u5185\u5b58\u6620\u5c04\u5904\u7406\u8d85\u5927\u6570\u636e\u96c6<\/li>\n
    • \u63a2\u7d22GPU\u52a0\u901f\u65b9\u6848<\/li>\n
    • \u5b66\u4e60Pandas（\u57fa\u4e8eNumPy\u7684\u9ad8\u9636\u5e93）<\/li>\n

      "\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df，NumPy\u4e0d\u662f\u53ef\u9009\u9879，\u800c\u662f\u5fc5\u5907\u9879\u3002\u638c\u63e1\u5b83，\u4f60\u5c31\u638c\u63e1\u4e86\u5904\u7406\u5927\u6570\u636e\u7684\u8d85\u80fd\u529b！"<\/p>\n

      \u200b\u200b\u8d44\u6e90\u4e0b\u8f7d\u200b\u200b：<\/p>\n

        \n
      • NumPy\u5b98\u65b9\u6587\u6863<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

        \u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb985\u6b21\uff0c\u70b9\u8d5e22\u6b21\uff0c\u6536\u85cf7\u6b21\u3002\ud83d\ude80 NumPy\u6027\u80fd\u4f18\u52bf\u539f\u7406\ud83d\udcca \u5343\u4e07\u6570\u636e\u5904\u7406\u5b9e\u6218\u6280\u5de7\u26a1 \u5de5\u4e1a\u7ea7\u4f18\u5316\u65b9\u6848\ud83d\udcb9 \u91d1\u878d\u6570\u636e\u5206\u6790\u6848\u4f8b\ud83d\udee1\ufe0f \u5e38\u89c1\u9677\u9631\u89c4\u907f\u65b9\u6cd5\u200b\u200b\u4e0b\u4e00\u6b65\u884c\u52a8\u200b\u5728\u9879\u76ee\u4e2d\u66ff\u6362Python\u5217\u8868\u4e3aNumPy\u6570\u7ec4\u5e94\u7528\u5411\u91cf\u5316\u64cd\u4f5c\u91cd\u5199\u5faa\u73af\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u5904\u7406\u8d85\u5927\u6570\u636e\u96c6\u63a2\u7d22GPU\u52a0\u901f\u65b9\u6848\u5b66\u4e60Pandas\uff08\u57fa\u4e8eNumPy\u7684\u9ad8\u9636\u5e93\uff09"\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0cNumPy\u4e0d\u662f\u53ef\u9009\u9879\uff0c\u800c\u662f\u5fc5\u5907\u9879\u3002\u638c\u63e1\u5b83\uff0c\u4f60\u5c31\u638c\u63e1\u4e86\u5904\u7406\u5927\u6570\u636e\u7684\u8d85\u80fd\u529b\uff01\u200b\u200b\u8d44\u6e90\u4e0b\u8f7d\u200bNumPy\u5b98\u65b9\u6587\u6863\u3002<\/p>\n","protected":false},"author":2,"featured_media":50345,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[1818,81,4905,190],"topic":[],"class_list":{"0":"post-50348","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-server","8":"tag-numpy","9":"tag-python","11":"tag-190"},"yoast_head":"\nPython\u6548\u7387\u9769\u547d\uff1a\u7528NumPy\u52a0\u901f\u767e\u500d\u6570\u636e\u5904\u7406 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/50348.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\u6548\u7387\u9769\u547d\uff1a\u7528NumPy\u52a0\u901f\u767e\u500d\u6570\u636e\u5904\u7406 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb985\u6b21\uff0c\u70b9\u8d5e22\u6b21\uff0c\u6536\u85cf7\u6b21\u3002\ud83d\ude80 NumPy\u6027\u80fd\u4f18\u52bf\u539f\u7406\ud83d\udcca \u5343\u4e07\u6570\u636e\u5904\u7406\u5b9e\u6218\u6280\u5de7\u26a1 \u5de5\u4e1a\u7ea7\u4f18\u5316\u65b9\u6848\ud83d\udcb9 \u91d1\u878d\u6570\u636e\u5206\u6790\u6848\u4f8b\ud83d\udee1\ufe0f \u5e38\u89c1\u9677\u9631\u89c4\u907f\u65b9\u6cd5\u200b\u200b\u4e0b\u4e00\u6b65\u884c\u52a8\u200b\u5728\u9879\u76ee\u4e2d\u66ff\u6362Python\u5217\u8868\u4e3aNumPy\u6570\u7ec4\u5e94\u7528\u5411\u91cf\u5316\u64cd\u4f5c\u91cd\u5199\u5faa\u73af\u4f7f\u7528\u5185\u5b58\u6620\u5c04\u5904\u7406\u8d85\u5927\u6570\u636e\u96c6\u63a2\u7d22GPU\u52a0\u901f\u65b9\u6848\u5b66\u4e60Pandas\uff08\u57fa\u4e8eNumPy\u7684\u9ad8\u9636\u5e93\uff09"\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0cNumPy\u4e0d\u662f\u53ef\u9009\u9879\uff0c\u800c\u662f\u5fc5\u5907\u9879\u3002\u638c\u63e1\u5b83\uff0c\u4f60\u5c31\u638c\u63e1\u4e86\u5904\u7406\u5927\u6570\u636e\u7684\u8d85\u80fd\u529b\uff01\u200b\u200b\u8d44\u6e90\u4e0b\u8f7d\u200bNumPy\u5b98\u65b9\u6587\u6863\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/50348.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-30T15:06:49+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/07\/20250730150647-688a35074263d.png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/50348.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/50348.html\",\"name\":\"Python\u6548\u7387\u9769\u547d\uff1a\u7528NumPy\u52a0\u901f\u767e\u500d\u6570\u636e\u5904\u7406 - 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