{"id":1184814,"date":"2025-01-15T19:31:11","date_gmt":"2025-01-15T11:31:11","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1184814.html"},"modified":"2025-01-15T19:31:16","modified_gmt":"2025-01-15T11:31:16","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e5%81%9a%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1184814.html","title":{"rendered":"\u5982\u4f55\u7528python\u505a\u65f6\u95f4\u5e8f\u5217"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u505a\u65f6\u95f4\u5e8f\u5217\"<\/p>\n

\u7528Python\u505a\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e\u3001Matplotlib\u548cSeaborn\u8fdb\u884c\u53ef\u89c6\u5316\u3001Statsmodels\u8fdb\u884c\u7edf\u8ba1\u5efa\u6a21\u3001ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3001Prophet\u8fdb\u884c\u590d\u6742\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/strong>\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u7684\u4e00\u4e2a\u65b9\u6cd5\uff1a\u4f7f\u7528ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong>\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/h3>\n<\/p>\n

ARIMA<\/strong>\u6a21\u578b\uff08\u81ea\u56de\u5f52\u79ef\u5206\u6ed1\u52a8\u5e73\u5747\u6a21\u578b\uff09\u662f\u4e00\u79cd\u5e38\u7528\u7684\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u65b9\u6cd5\u3002\u5b83\u7ed3\u5408\u4e86\u81ea\u56de\u5f52\uff08AR\uff09\u548c\u79fb\u52a8\u5e73\u5747\uff08MA\uff09\u4e24\u79cd\u6a21\u578b\u7684\u7279\u6027\uff0c\u5e76\u901a\u8fc7\u5dee\u5206\uff08I\uff09\u4f7f\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u53d8\u5f97\u5e73\u7a33\u3002ARIMA\u6a21\u578b\u7684\u4e3b\u8981\u53c2\u6570\u5305\u62ecp\uff08\u81ea\u56de\u5f52\u9636\u6570\uff09\u3001d\uff08\u5dee\u5206\u9636\u6570\uff09\u548cq\uff08\u79fb\u52a8\u5e73\u5747\u9636\u6570\uff09\u3002<\/p>\n<\/p>\n

1\u3001\u51c6\u5907\u548c\u5bfc\u5165\u6570\u636e<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\u548c\u6570\u636e\u3002Pandas\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u548c\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u5bfc\u5165\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('your_time_series_data.csv', index_col='Date', parse_dates=True)<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u68c0\u67e5\u548c\u9884\u5904\u7406\u6570\u636e<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u5efa\u6a21\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u68c0\u67e5\u548c\u9884\u5904\u7406\u6570\u636e\u3002\u9996\u5148\uff0c\u6211\u4eec\u53ef\u4ee5\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe\uff0c\u4ee5\u4fbf\u89c2\u5bdf\u6570\u636e\u7684\u8d8b\u52bf\u548c\u5b63\u8282\u6027\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/strong><\/h2>\n
data.plot()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u68c0\u67e5\u6570\u636e\u662f\u5426\u5e73\u7a33\u3002\u5982\u679c\u6570\u636e\u4e0d\u5e73\u7a33\uff0c\u5219\u9700\u8981\u8fdb\u884c\u5dee\u5206\u5904\u7406\uff0c\u4f7f\u5176\u53d8\u5f97\u5e73\u7a33\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.stattools import adfuller<\/p>\n
\u68c0\u67e5\u6570\u636e\u7684\u5e73\u7a33\u6027<\/strong><\/h2>\n
result = adfuller(data['value'])<\/p>\n
print('ADF Statistic:', result[0])<\/p>\n
print('p-value:', result[1])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5982\u679cp\u503c\u5c0f\u4e8e0.05\uff0c\u5219\u6570\u636e\u662f\u5e73\u7a33\u7684\uff1b\u5426\u5219\uff0c\u9700\u8981\u8fdb\u884c\u5dee\u5206\u5904\u7406\uff1a<\/p>\n<\/p>\n
data_diff = data.diff().dropna()<\/p>\n
data_diff.plot()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6784\u5efa\u548c\u62df\u5408ARIMA\u6a21\u578b<\/h4>\n<\/p>\n
\u5728\u786e\u5b9a\u4e86p\u3001d\u3001q\u53c2\u6570\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Statsmodels\u5e93\u4e2d\u7684ARIMA\u7c7b\u6765\u6784\u5efa\u548c\u62df\u5408\u6a21\u578b\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.arima_model import ARIMA<\/p>\n
\u6784\u5efaARIMA\u6a21\u578b<\/strong><\/h2>\n
model = ARIMA(data, order=(p, d, q))<\/p>\n
model_fit = model.fit(disp=0)<\/p>\n
\u8f93\u51fa\u6a21\u578b\u6458\u8981<\/strong><\/h2>\n
print(model_fit.summary())<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u8fdb\u884c\u9884\u6d4b<\/h4>\n<\/p>\n
\u62df\u5408\u6a21\u578b\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3002\u4ee5\u4e0b\u662f\u9884\u6d4b\u672a\u676510\u4e2a\u65f6\u95f4\u70b9\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u8fdb\u884c\u9884\u6d4b<\/p>\n
forecast, stderr, conf_int = model_fit.forecast(steps=10)<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(12, 6))<\/p>\n
plt.plot(data, label='Original')<\/p>\n
plt.plot(pd.Series(forecast, index=pd.date_range(start=data.index[-1], periods=10, freq='M')), label='Forecast')<\/p>\n
plt.fill_between(pd.Series(forecast, index=pd.date_range(start=data.index[-1], periods=10, freq='M')).index,<\/p>\n
                 conf_int[:, 0], conf_int[:, 1], color='k', alpha=.15)<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5c24\u5176\u5728\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\u975e\u5e38\u65b9\u4fbf\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u6765\u8bfb\u53d6\u3001\u6e05\u6d17\u548c\u64cd\u4f5c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
Pandas\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\uff0c\u5982CSV\u3001Excel\u7b49\u3002\u4ee5\u4e0b\u662f\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('your_time_series_data.csv', index_col='Date', parse_dates=True)<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u65f6\uff0c\u6211\u4eec\u901a\u5e38\u9700\u8981\u8fdb\u884c\u4e00\u4e9b\u5e38\u89c1\u7684\u64cd\u4f5c\uff0c\u5982\u91cd\u65b0\u91c7\u6837\u3001\u586b\u8865\u7f3a\u5931\u503c\u548c\u6eda\u52a8\u8ba1\u7b97\u7b49\u3002<\/p>\n<\/p>\n
\u91cd\u65b0\u91c7\u6837<\/strong>\uff1a<\/p>\n<\/p>\n
# \u6309\u6708\u91cd\u65b0\u91c7\u6837\u5e76\u8ba1\u7b97\u5e73\u5747\u503c<\/p>\n
data_resampled = data.resample('M').mean()<\/p>\n
print(data_resampled.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u586b\u8865\u7f3a\u5931\u503c<\/strong>\uff1a<\/p>\n<\/p>\n
# \u4f7f\u7528\u524d\u5411\u586b\u5145\u65b9\u6cd5\u586b\u8865\u7f3a\u5931\u503c<\/p>\n
data_filled = data.fillna(method='ffill')<\/p>\n
print(data_filled.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6eda\u52a8\u8ba1\u7b97<\/strong>\uff1a<\/p>\n<\/p>\n
# \u8ba1\u7b97\u6eda\u52a8\u5e73\u5747\u503c<\/p>\n
data_rolling = data.rolling(window=12).mean()<\/p>\n
print(data_rolling.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Matplotlib\u548cSeaborn\u8fdb\u884c\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
Matplotlib\u548cSeaborn\u662fPython\u4e2d\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u8d8b\u52bf\u548c\u5b63\u8282\u6027\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/h4>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/strong><\/h2>\n
data.plot()<\/p>\n
plt.title('Time Series Data')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7f8e\u89c2\u548c\u66f4\u590d\u6742\u7684\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Seaborn\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u7ed8\u5236\u65f6\u95f4\u5e8f\u5217\u56fe<\/strong><\/h2>\n
sns.lineplot(data=data)<\/p>\n
plt.title('Time Series Data')<\/p>\n
plt.xlabel('Date')<\/p>\n
plt.ylabel('Value')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528Statsmodels\u8fdb\u884c\u7edf\u8ba1\u5efa\u6a21<\/h3>\n<\/p>\n
Statsmodels\u662f\u4e00\u4e2a\u4e13\u95e8\u7528\u4e8e\u7edf\u8ba1\u5efa\u6a21\u7684Python\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u6a21\u578b\u548c\u5de5\u5177\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Statsmodels\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u548c\u5efa\u6a21\u3002<\/p>\n<\/p>\n
1\u3001\u6784\u5efa\u548c\u62df\u5408\u65f6\u95f4\u5e8f\u5217\u6a21\u578b<\/h4>\n<\/p>\n
Statsmodels\u63d0\u4f9b\u4e86\u591a\u79cd\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\uff0c\u5982ARIMA\u3001SARIMA\u3001\u5b63\u8282\u6027\u5206\u89e3\u7b49\u3002\u4ee5\u4e0b\u662f\u6784\u5efa\u548c\u62df\u5408ARIMA\u6a21\u578b\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.arima_model import ARIMA<\/p>\n
\u6784\u5efaARIMA\u6a21\u578b<\/strong><\/h2>\n
model = ARIMA(data, order=(p, d, q))<\/p>\n
model_fit = model.fit(disp=0)<\/p>\n
\u8f93\u51fa\u6a21\u578b\u6458\u8981<\/strong><\/h2>\n
print(model_fit.summary())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u8fdb\u884c\u5b63\u8282\u6027\u5206\u89e3<\/h4>\n<\/p>\n
\u5b63\u8282\u6027\u5206\u89e3\u662f\u4e00\u79cd\u5c06\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5206\u89e3\u4e3a\u8d8b\u52bf\u3001\u5b63\u8282\u6027\u548c\u6b8b\u5dee\u4e09\u90e8\u5206\u7684\u65b9\u6cd5\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Statsmodels\u8fdb\u884c\u5b63\u8282\u6027\u5206\u89e3\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from statsmodels.tsa.seasonal import seasonal_decompose<\/p>\n
\u8fdb\u884c\u5b63\u8282\u6027\u5206\u89e3<\/strong><\/h2>\n
decomposition = seasonal_decompose(data, model='additive')<\/p>\n
trend = decomposition.trend<\/p>\n
seasonal = decomposition.seasonal<\/p>\n
residual = decomposition.resid<\/p>\n
\u7ed8\u5236\u5206\u89e3\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(12, 8))<\/p>\n
plt.subplot(411)<\/p>\n
plt.plot(data, label='Original')<\/p>\n
plt.legend(loc='best')<\/p>\n
plt.subplot(412)<\/p>\n
plt.plot(trend, label='Trend')<\/p>\n
plt.legend(loc='best')<\/p>\n
plt.subplot(413)<\/p>\n
plt.plot(seasonal, label='Seasonality')<\/p>\n
plt.legend(loc='best')<\/p>\n
plt.subplot(414)<\/p>\n
plt.plot(residual, label='Residuals')<\/p>\n
plt.legend(loc='best')<\/p>\n
plt.tight_layout()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Prophet\u8fdb\u884c\u590d\u6742\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/h3>\n<\/p>\n
Prophet\u662fFacebook\u5f00\u6e90\u7684\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u5de5\u5177\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5177\u6709\u660e\u663e\u5b63\u8282\u6027\u548c\u8282\u5047\u65e5\u6548\u5e94\u7684\u6570\u636e\u3002\u5b83\u7684\u4f18\u70b9\u662f\u6613\u4e8e\u4f7f\u7528\uff0c\u5e76\u4e14\u80fd\u591f\u5904\u7406\u590d\u6742\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Prophet<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5Prophet\u5e93\uff1a<\/p>\n<\/p>\n
pip install prophet<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u5bfc\u5165Prophet\u5e93\uff1a<\/p>\n<\/p>\n
from prophet import Prophet<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u51c6\u5907\u6570\u636e<\/h4>\n<\/p>\n
Prophet\u8981\u6c42\u6570\u636e\u5177\u6709\u7279\u5b9a\u7684\u683c\u5f0f\uff0c\u5305\u542b\u4e24\u5217\uff1a\u65e5\u671f\uff08ds\uff09\u548c\u6570\u503c\uff08y\uff09\u3002\u4ee5\u4e0b\u662f\u51c6\u5907\u6570\u636e\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u51c6\u5907\u6570\u636e<\/p>\n
data = data.reset_index()<\/p>\n
data.columns = ['ds', 'y']<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6784\u5efa\u548c\u62df\u5408Prophet\u6a21\u578b<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6784\u5efa\u548c\u62df\u5408Prophet\u6a21\u578b\uff1a<\/p>\n<\/p>\n
# \u6784\u5efaProphet\u6a21\u578b<\/p>\n
model = Prophet()<\/p>\n
\u62df\u5408\u6a21\u578b<\/strong><\/h2>\n
model.fit(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u8fdb\u884c\u9884\u6d4b<\/h4>\n<\/p>\n
Prophet\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u672a\u6765\u65f6\u95f4\u70b9\u7684\u9884\u6d4b\u3002\u4ee5\u4e0b\u662f\u9884\u6d4b\u672a\u6765365\u5929\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u672a\u6765\u65f6\u95f4\u70b9\u7684\u6570\u636e\u6846<\/p>\n
future = model.make_future_dataframe(periods=365)<\/p>\n
\u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
forecast = model.predict(future)<\/p>\n
\u7ed8\u5236\u9884\u6d4b\u7ed3\u679c<\/strong><\/h2>\n
fig = model.plot(forecast)<\/p>\n
plt.show()<\/p>\n
\u7ed8\u5236\u6210\u5206\u56fe<\/strong><\/h2>\n
fig2 = model.plot_components(forecast)<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\uff0c\u4f7f\u5f97\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u53d8\u5f97\u7b80\u5355\u548c\u9ad8\u6548\u3002\u5728\u672c\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4f7f\u7528ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3001\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e\u3001\u4f7f\u7528Matplotlib\u548cSeaborn\u8fdb\u884c\u53ef\u89c6\u5316\u3001\u4f7f\u7528Statsmodels\u8fdb\u884c\u7edf\u8ba1\u5efa\u6a21\u4ee5\u53ca\u4f7f\u7528Prophet\u8fdb\u884c\u590d\u6742\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u65b9\u6cd5\u3002\u8fd9\u4e9b\u65b9\u6cd5\u4e0d\u4ec5\u9002\u7528\u4e8e\u521d\u5b66\u8005\uff0c\u4e5f\u9002\u7528\u4e8e\u6709\u7ecf\u9a8c\u7684\u6570\u636e\u79d1\u5b66\u5bb6\u3002\u901a\u8fc7\u5408\u7406\u9009\u62e9\u548c\u7ec4\u5408\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u5206\u6790\u548c\u9884\u6d4b\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u901a\u5e38\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bfb\u53d6CSV\u6587\u4ef6\u6216\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\uff0c\u5e76\u4f7f\u7528Pandas\u7684\u65f6\u95f4\u5e8f\u5217\u529f\u80fd\u8fdb\u884c\u7d22\u5f15\u548c\u5206\u6790\u3002\u4f8b\u5982\uff0c\u4f7f\u7528pd.to_datetime()<\/code>\u5c06\u65e5\u671f\u5217\u8f6c\u6362\u4e3aDatetimeIndex\uff0c\u4ece\u800c\u4fbf\u4e8e\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u64cd\u4f5c\u3002\u8fd8\u53ef\u4ee5\u5229\u7528Matplotlib\u5e93\u53ef\u89c6\u5316\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u7406\u89e3\u8d8b\u52bf\u548c\u5b63\u8282\u6027\u3002<\/p>\n
Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5e93\u53ef\u4ee5\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1f<\/strong>
\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u4e3b\u8981\u5e93\u5305\u62ecPandas\u3001NumPy\u548cStatsmodels\u3002Pandas\u63d0\u4f9b\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0cNumPy\u5219\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0c\u800cStatsmodels\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u6a21\u578b\uff0c\u80fd\u591f\u5e2e\u52a9\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u548c\u56de\u5f52\u5206\u6790\u3002\u6b64\u5916\uff0cMatplotlib\u548cSeaborn\u53ef\u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\uff0c\u5e2e\u52a9\u60a8\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u65f6\u95f4\u5e8f\u5217\u7279\u5f81\u3002<\/p>\n
\u5982\u4f55\u7528Python\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff1f<\/strong>
\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u6cd5\u5b9e\u73b0\uff0c\u5982ARIMA\u6a21\u578b\u3001\u5b63\u8282\u6027\u5206\u89e3\u548c\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u7b49\u3002\u4f7f\u7528Statsmodels\u5e93\u4e2d\u7684ARIMA<\/code>\u7c7b\uff0c\u53ef\u4ee5\u8f7b\u677e\u6784\u5efa\u548c\u8bc4\u4f30ARIMA\u6a21\u578b\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528Facebook\u7684Prophet\u5e93\uff0c\u5b83\u4e13\u4e3a\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u800c\u8bbe\u8ba1\uff0c\u80fd\u591f\u5904\u7406\u7f3a\u5931\u503c\u548c\u5b63\u8282\u6027\u53d8\u5316\u3002\u786e\u4fdd\u5728\u8fdb\u884c\u9884\u6d4b\u524d\u5bf9\u6570\u636e\u8fdb\u884c\u9002\u5f53\u7684\u9884\u5904\u7406\u548c\u7279\u5f81\u9009\u62e9\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u7528Python\u505a\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5904\u7406\u6570\u636e\u3001Matplotlib\u548cSeaborn\u8fdb\u884c\u53ef\u89c6\u5316\u3001 […]","protected":false},"author":3,"featured_media":1184824,"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\/1184814"}],"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=1184814"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184814\/revisions"}],"predecessor-version":[{"id":1184827,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1184814\/revisions\/1184827"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1184824"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1184814"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1184814"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1184814"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}