{"id":1175590,"date":"2025-01-15T17:34:11","date_gmt":"2025-01-15T09:34:11","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1175590.html"},"modified":"2025-01-15T17:34:13","modified_gmt":"2025-01-15T09:34:13","slug":"python%e5%a6%82%e4%bd%95%e6%95%b0%e6%8d%ae%e8%bf%9b%e8%a1%8c%e8%bf%90%e7%ae%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1175590.html","title":{"rendered":"python\u5982\u4f55\u6570\u636e\u8fdb\u884c\u8fd0\u7b97"},"content":{"rendered":"

\"python\u5982\u4f55\u6570\u636e\u8fdb\u884c\u8fd0\u7b97\"<\/p>\n

Python\u8fdb\u884c\u6570\u636e\u8fd0\u7b97\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5305\u62ec\u57fa\u672c\u7b97\u672f\u8fd0\u7b97\u3001\u4f7f\u7528NumPy\u5e93\u8fdb\u884c\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3001\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\u3001\u4ee5\u53caSciPy\u5e93\u8fdb\u884c\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97\u3002<\/strong> \u5728\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\uff0cNumPy\u5e93\u56e0\u5176\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528NumPy\u8fdb\u884c\u6570\u636e\u8fd0\u7b97\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u57fa\u672c\u7b97\u672f\u8fd0\u7b97<\/h3>\n<\/p>\n

Python\u672c\u8eab\u652f\u6301\u57fa\u672c\u7684\u7b97\u672f\u8fd0\u7b97\uff0c\u5305\u62ec\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u3001\u9664\u6cd5\u3001\u53d6\u4f59\u548c\u5e42\u8fd0\u7b97\u7b49\u3002\u8fd9\u4e9b\u8fd0\u7b97\u53ef\u4ee5\u76f4\u63a5\u5728Python\u7684\u4ea4\u4e92\u5f0f\u89e3\u91ca\u5668\u4e2d\u8fdb\u884c\u3002<\/p>\n<\/p>\n

# \u52a0\u6cd5<\/p>\n
a = 5<\/p>\n
b = 3<\/p>\n
print(a + b)  # \u8f93\u51fa 8<\/p>\n
\u51cf\u6cd5<\/strong><\/h2>\n
print(a - b)  # \u8f93\u51fa 2<\/p>\n
\u4e58\u6cd5<\/strong><\/h2>\n
print(a * b)  # \u8f93\u51fa 15<\/p>\n
\u9664\u6cd5<\/strong><\/h2>\n
print(a \/ b)  # \u8f93\u51fa 1.6666666666666667<\/p>\n
\u53d6\u4f59<\/strong><\/h2>\n
print(a % b)  # \u8f93\u51fa 2<\/p>\n
\u5e42\u8fd0\u7b97<\/strong><\/h2>\n
print(a  b)  # \u8f93\u51fa 125<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u5e93\u8fdb\u884c\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u6700\u91cd\u8981\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u529f\u80fd\u3002\u8981\u4f7f\u7528NumPy\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u8fd9\u4e2a\u5e93\uff0c\u5e76\u5bfc\u5165\u5b83\u3002<\/p>\n<\/p>\n
# \u5b89\u88c5NumPy\u5e93<\/p>\n
pip install numpy<\/p>\n
\u5bfc\u5165NumPy\u5e93<\/strong><\/h2>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
1\u3001\u521b\u5efa\u6570\u7ec4<\/h4>\n<\/p>\n
NumPy\u7684\u6838\u5fc3\u5bf9\u8c61\u662fndarray\uff0c\u591a\u7ef4\u6570\u7ec4\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u521b\u5efa\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
# \u4ece\u5217\u8868\u521b\u5efa\u6570\u7ec4<\/p>\n
arr = np.array([1, 2, 3, 4, 5])<\/p>\n
print(arr)<\/p>\n
\u521b\u5efa\u5168\u96f6\u6570\u7ec4<\/strong><\/h2>\n
zeros = np.zeros((3, 3))<\/p>\n
print(zeros)<\/p>\n
\u521b\u5efa\u5168\u4e00\u6570\u7ec4<\/strong><\/h2>\n
ones = np.ones((2, 4))<\/p>\n
print(ones)<\/p>\n
\u521b\u5efa\u5355\u4f4d\u77e9\u9635<\/strong><\/h2>\n
identity = np.eye(3)<\/p>\n
print(identity)<\/p>\n
\u521b\u5efa\u968f\u673a\u6570\u7ec4<\/strong><\/h2>\n
random_arr = np.random.rand(2, 3)<\/p>\n
print(random_arr)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u7ec4\u8fd0\u7b97<\/h4>\n<\/p>\n
NumPy\u652f\u6301\u5404\u79cd\u6570\u7ec4\u8fd0\u7b97\uff0c\u64cd\u4f5c\u7b26\u4f1a\u6309\u5143\u7d20\u9010\u4e2a\u8fdb\u884c\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
# \u52a0\u6cd5<\/p>\n
arr1 = np.array([1, 2, 3])<\/p>\n
arr2 = np.array([4, 5, 6])<\/p>\n
print(arr1 + arr2)  # \u8f93\u51fa [5 7 9]<\/p>\n
\u51cf\u6cd5<\/strong><\/h2>\n
print(arr1 - arr2)  # \u8f93\u51fa [-3 -3 -3]<\/p>\n
\u4e58\u6cd5<\/strong><\/h2>\n
print(arr1 * arr2)  # \u8f93\u51fa [ 4 10 18]<\/p>\n
\u9664\u6cd5<\/strong><\/h2>\n
print(arr1 \/ arr2)  # \u8f93\u51fa [0.25 0.4  0.5 ]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u7528\u4e8e\u6570\u636e\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86DataFrame\u548cSeries\u4e24\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u521b\u5efaDataFrame<\/h4>\n<\/p>\n
import pandas as pd<\/p>\n
\u4ece\u5b57\u5178\u521b\u5efaDataFrame<\/strong><\/h2>\n
data = {<\/p>\n
    'A': [1, 2, 3],<\/p>\n
    'B': [4, 5, 6],<\/p>\n
    'C': [7, 8, 9]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u8fd0\u7b97<\/h4>\n<\/p>\n
Pandas\u652f\u6301\u5bf9DataFrame\u548cSeries\u8fdb\u884c\u5404\u79cd\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n
# \u52a0\u6cd5<\/p>\n
df['D'] = df['A'] + df['B']<\/p>\n
print(df)<\/p>\n
\u51cf\u6cd5<\/strong><\/h2>\n
df['E'] = df['B'] - df['C']<\/p>\n
print(df)<\/p>\n
\u4e58\u6cd5<\/strong><\/h2>\n
df['F'] = df['A'] * df['C']<\/p>\n
print(df)<\/p>\n
\u9664\u6cd5<\/strong><\/h2>\n
df['G'] = df['C'] \/ df['A']<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528SciPy\u5e93\u8fdb\u884c\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97<\/h3>\n<\/p>\n
SciPy\u662f\u5efa\u7acb\u5728NumPy\u57fa\u7840\u4e0a\u7684\u4e00\u4e2a\u9ad8\u7ea7\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u4f18\u5316\u3001\u7ebf\u6027\u4ee3\u6570\u3001\u79ef\u5206\u548c\u7edf\u8ba1\u7b49\u529f\u80fd\u3002<\/p>\n<\/p>\n
1\u3001\u4f18\u5316<\/h4>\n<\/p>\n
SciPy\u7684optimize\u6a21\u5757\u63d0\u4f9b\u4e86\u591a\u79cd\u4f18\u5316\u7b97\u6cd5\uff1a<\/p>\n<\/p>\n
from scipy import optimize<\/p>\n
\u5b9a\u4e49\u76ee\u6807\u51fd\u6570<\/strong><\/h2>\n
def f(x):<\/p>\n
    return x2 + 10*np.sin(x)<\/p>\n
\u4f7f\u7528BFGS\u7b97\u6cd5\u627e\u5230\u51fd\u6570\u7684\u6700\u5c0f\u503c<\/strong><\/h2>\n
result = optimize.minimize(f, x0=0)<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ebf\u6027\u4ee3\u6570<\/h4>\n<\/p>\n
SciPy\u7684linalg\u6a21\u5757\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97\uff1a<\/p>\n<\/p>\n
from scipy import linalg<\/p>\n
\u521b\u5efa\u77e9\u9635<\/strong><\/h2>\n
A = np.array([[1, 2], [3, 4]])<\/p>\n
\u8ba1\u7b97\u77e9\u9635\u7684\u9006<\/strong><\/h2>\n
A_inv = linalg.inv(A)<\/p>\n
print(A_inv)<\/p>\n
\u8ba1\u7b97\u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf<\/strong><\/h2>\n
eigenvalues, eigenvectors = linalg.eig(A)<\/p>\n
print(eigenvalues)<\/p>\n
print(eigenvectors)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Matplotlib\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
\u867d\u7136Matplotlib\u4e0d\u662f\u76f4\u63a5\u7528\u4e8e\u6570\u636e\u8fd0\u7b97\u7684\u5e93\uff0c\u4f46\u5b83\u662f\u79d1\u5b66\u8ba1\u7b97\u548c\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u56e0\u4e3a\u5b83\u80fd\u5e2e\u52a9\u6211\u4eec\u53ef\u89c6\u5316\u8fd0\u7b97\u7ed3\u679c\u3002<\/p>\n<\/p>\n
1\u3001\u57fa\u672c\u7ed8\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
x = np.linspace(0, 10, 100)<\/p>\n
y = np.sin(x)<\/p>\n
\u7ed8\u5236\u56fe\u5f62<\/strong><\/h2>\n
plt.plot(x, y)<\/p>\n
plt.xlabel('x')<\/p>\n
plt.ylabel('sin(x)')<\/p>\n
plt.title('Sine Wave')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7ed8\u5236\u591a\u4e2a\u5b50\u56fe<\/h4>\n<\/p>\n
# \u521b\u5efa\u6570\u636e<\/p>\n
x = np.linspace(0, 10, 100)<\/p>\n
y1 = np.sin(x)<\/p>\n
y2 = np.cos(x)<\/p>\n
\u521b\u5efa\u5b50\u56fe<\/strong><\/h2>\n
fig, axs = plt.subplots(2)<\/p>\n
\u7ed8\u5236\u7b2c\u4e00\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[0].plot(x, y1)<\/p>\n
axs[0].set_title('Sine Wave')<\/p>\n
\u7ed8\u5236\u7b2c\u4e8c\u4e2a\u5b50\u56fe<\/strong><\/h2>\n
axs[1].plot(x, y2)<\/p>\n
axs[1].set_title('Cosine Wave')<\/p>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u4f7f\u7528Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a><\/h3>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u5404\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u6570\u636e\u9884\u5904\u7406\u5de5\u5177\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
data = np.array([[1, 2], [3, 4], [5, 6]])<\/p>\n
\u6807\u51c6\u5316\u6570\u636e<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
data_scaled = scaler.fit_transform(data)<\/p>\n
print(data_scaled)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h4>\n<\/p>\n
from sklearn.linear_model import LinearRegression<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([1, 3, 3, 2, 5])<\/p>\n
\u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
model = LinearRegression()<\/p>\n
model.fit(X, y)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X)<\/p>\n
print(y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u4f7f\u7528TensorFlow\u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60<\/h3>\n<\/p>\n
TensorFlow\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u6df1\u5ea6\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u6784\u5efa\u548c\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n
1\u3001\u521b\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/h4>\n<\/p>\n
import tensorflow as tf<\/p>\n
from tensorflow.keras.models import Sequential<\/p>\n
from tensorflow.keras.layers import Dense<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([1, 3, 3, 2, 5])<\/p>\n
\u521b\u5efa\u6a21\u578b<\/strong><\/h2>\n
model = Sequential()<\/p>\n
model.add(Dense(10, input_dim=1, activation='relu'))<\/p>\n
model.add(Dense(1))<\/p>\n
\u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
model.compile(loss='mean_squared_error', optimizer='adam')<\/p>\n
\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
model.fit(X, y, epochs=100, verbose=0)<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
y_pred = model.predict(X)<\/p>\n
print(y_pred)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516b\u3001\u4f7f\u7528Statsmodels\u8fdb\u884c\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
Statsmodels\u662f\u4e00\u4e2aPython\u5e93\uff0c\u7528\u4e8e\u4f30\u8ba1\u548c\u63a8\u65ad\u7edf\u8ba1\u6a21\u578b\u3002<\/p>\n<\/p>\n
1\u3001\u7ebf\u6027\u56de\u5f52<\/h4>\n<\/p>\n
import statsmodels.api as sm<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
X = np.array([[1], [2], [3], [4], [5]])<\/p>\n
y = np.array([1, 3, 3, 2, 5])<\/p>\n
\u6dfb\u52a0\u5e38\u91cf\u9879<\/strong><\/h2>\n
X = sm.add_constant(X)<\/p>\n
\u521b\u5efa\u5e76\u62df\u5408\u6a21\u578b<\/strong><\/h2>\n
model = sm.OLS(y, X).fit()<\/p>\n
\u6253\u5370\u6a21\u578b\u6458\u8981<\/strong><\/h2>\n
print(model.summary())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/h4>\n<\/p>\n
# \u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/p>\n
data = sm.datasets.co2.load_pandas().data<\/p>\n
\u521b\u5efa\u5e76\u62df\u5408ARIMA\u6a21\u578b<\/strong><\/h2>\n
model = sm.tsa.ARIMA(data, order=(1, 1, 1)).fit()<\/p>\n
\u6253\u5370\u6a21\u578b\u6458\u8981<\/strong><\/h2>\n
print(model.summary())<\/p>\n
\u9884\u6d4b<\/strong><\/h2>\n
forecast = model.forecast(steps=10)<\/p>\n
print(forecast)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e5d\u3001\u4f7f\u7528SymPy\u8fdb\u884c\u7b26\u53f7\u8ba1\u7b97<\/h3>\n<\/p>\n
SymPy\u662f\u4e00\u4e2aPython\u5e93\uff0c\u7528\u4e8e\u7b26\u53f7\u6570\u5b66\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
1\u3001\u7b26\u53f7\u8fd0\u7b97<\/h4>\n<\/p>\n
import sympy as sp<\/p>\n
\u5b9a\u4e49\u7b26\u53f7\u53d8\u91cf<\/strong><\/h2>\n
x = sp.symbols('x')<\/p>\n
\u5b9a\u4e49\u51fd\u6570<\/strong><\/h2>\n
f = x2 + 2*x + 1<\/p>\n
\u6c42\u5bfc<\/strong><\/h2>\n
f_prime = sp.diff(f, x)<\/p>\n
print(f_prime)<\/p>\n
\u6c42\u79ef\u5206<\/strong><\/h2>\n
f_integral = sp.integrate(f, x)<\/p>\n
print(f_integral)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u89e3\u65b9\u7a0b<\/h4>\n<\/p>\n
# \u5b9a\u4e49\u65b9\u7a0b<\/p>\n
eq = sp.Eq(x2 + 2*x + 1, 0)<\/p>\n
\u89e3\u65b9\u7a0b<\/strong><\/h2>\n
solutions = sp.solve(eq, x)<\/p>\n
print(solutions)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\u7528\u4e8e\u6570\u636e\u8fd0\u7b97\u548c\u5206\u6790\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u7b97\u672f\u8fd0\u7b97\uff0c\u8fd8\u662f\u590d\u6742\u7684\u79d1\u5b66\u8ba1\u7b97\u548c\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0cPython\u90fd\u80fd\u9ad8\u6548\u5730\u5b8c\u6210\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u548c\u8fd0\u7b97\u6570\u503c\u6570\u636e\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684\u8fd0\u7b97\u7b26\u8fdb\u884c\u6570\u503c\u8fd0\u7b97\uff0c\u4f8b\u5982\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u9664\u6cd5\u3002\u901a\u8fc7\u7b80\u5355\u7684\u8868\u8fbe\u5f0f\uff0c\u6bd4\u5982 a + b<\/code> \u6216 a * b<\/code>\uff0c\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u8fd9\u4e9b\u64cd\u4f5c\u3002\u6b64\u5916\uff0cPython\u8fd8\u652f\u6301\u66f4\u590d\u6742\u7684\u6570\u5b66\u8fd0\u7b97\uff0c\u4f7f\u7528\u6807\u51c6\u5e93\u4e2d\u7684math<\/code>\u6a21\u5757\uff0c\u53ef\u4ee5\u8fdb\u884c\u5e73\u65b9\u6839\u3001\u4e09\u89d2\u51fd\u6570\u7b49\u9ad8\u7ea7\u8ba1\u7b97\u3002<\/p>\n
Python\u662f\u5426\u652f\u6301\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u6279\u91cf\u8fd0\u7b97\uff1f<\/strong>
\u662f\u7684\uff0cPython\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u5982NumPy\u548cPandas\uff0c\u7528\u4e8e\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u6279\u91cf\u8fd0\u7b97\u3002NumPy\u5141\u8bb8\u5bf9\u6570\u7ec4\u8fdb\u884c\u5143\u7d20\u7ea7\u8fd0\u7b97\uff0c\u800cPandas\u5219\u63d0\u4f9b\u4e86\u5bf9\u8868\u683c\u6570\u636e\u7684\u9ad8\u6548\u5904\u7406\u80fd\u529b\u3002\u5229\u7528\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\u3001\u6570\u636e\u6e05\u6d17\u548c\u8f6c\u6362\u7b49\u64cd\u4f5c\uff0c\u4ece\u800c\u63d0\u9ad8\u6570\u636e\u5904\u7406\u6548\u7387\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u5e76\u8fdb\u884c\u8ba1\u7b97\uff1f<\/strong>
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u7f3a\u5931\u503c\u662f\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\u3002\u4f7f\u7528Pandas\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7fillna()<\/code>\u65b9\u6cd5\u6765\u586b\u8865\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002\u5904\u7406\u5b8c\u7f3a\u5931\u503c\u540e\uff0c\u53ef\u4ee5\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u8ba1\u7b97\u548c\u5206\u6790\uff0c\u786e\u4fdd\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u8fdb\u884c\u6570\u636e\u8fd0\u7b97\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5305\u62ec\u57fa\u672c\u7b97\u672f\u8fd0\u7b97\u3001\u4f7f\u7528NumPy\u5e93\u8fdb\u884c\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u3001\u4f7f\u7528Pandas […]","protected":false},"author":3,"featured_media":1175595,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175590"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1175590"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175590\/revisions"}],"predecessor-version":[{"id":1175597,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1175590\/revisions\/1175597"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1175595"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1175590"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1175590"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1175590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}