{"id":1013626,"date":"2024-12-27T11:49:32","date_gmt":"2024-12-27T03:49:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1013626.html"},"modified":"2024-12-27T11:49:34","modified_gmt":"2024-12-27T03:49:34","slug":"python%e5%a6%82%e4%bd%95%e7%bb%98%e5%88%b6%e6%97%a5%e5%8e%86%e5%9b%be","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1013626.html","title":{"rendered":"python\u5982\u4f55\u7ed8\u5236\u65e5\u5386\u56fe"},"content":{"rendered":"

\"python\u5982\u4f55\u7ed8\u5236\u65e5\u5386\u56fe\"<\/p>\n

\u7ed8\u5236\u65e5\u5386\u56fe\u662f\u6570\u636e\u53ef\u89c6\u5316\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u76f4\u89c2\u5730\u5c55\u793a\u65e5\u671f\u76f8\u5173\u7684\u6570\u636e\u53d8\u5316\u8d8b\u52bf\u3002\u5728Python\u4e2d\uff0c\u7ed8\u5236\u65e5\u5386\u56fe\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u3001seaborn<\/code>\u3001pandas<\/code>\u7b49\u5e93\uff0c\u521b\u5efa\u4e00\u4e2a\u70ed\u56fe\u6216\u65e5\u5386\u70ed\u56fe\u4ee5\u5c55\u793a\u6570\u636e\u7684\u65f6\u95f4\u53d8\u5316\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u7ed8\u5236\u65e5\u5386\u56fe\uff0c\u5e76\u63a2\u8ba8\u7ed8\u5236\u8fc7\u7a0b\u4e2d\u53ef\u80fd\u9047\u5230\u7684\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u51c6\u5907\u5de5\u4f5c<\/h3>\n<\/p>\n

\u5728\u5f00\u59cb\u7ed8\u5236\u65e5\u5386\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u786e\u4fdd\u5b89\u88c5\u4e86\u76f8\u5173\u7684Python\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecmatplotlib<\/code>\u3001seaborn<\/code>\u548cpandas<\/code>\u3002\u8fd9\u4e9b\u5e93\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install matplotlib seaborn pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b64\u5916\uff0c\u786e\u4fdd\u4f60\u7684Python\u73af\u5883\u4e2d\u5df2\u7ecf\u5b89\u88c5\u4e86numpy<\/code>\u548cdatetime<\/code>\u5e93\uff0c\u56e0\u4e3a\u5b83\u4eec\u5728\u6570\u636e\u5904\u7406\u548c\u65e5\u671f\u64cd\u4f5c\u4e2d\u4e5f\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n<\/p>\n
\u5728\u7ed8\u5236\u65e5\u5386\u56fe\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u51c6\u5907\u597d\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u6309\u65e5\u671f\u8bb0\u5f55\u7684\u6e29\u5ea6\u6570\u636e\uff0c\u6570\u636e\u683c\u5f0f\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {<\/p>\n
    'date': ['2023-01-01', '2023-01-02', '2023-01-03', ..., '2023-12-31'],<\/p>\n
    'temperature': [23, 25, 22, ..., 19]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u6709\u4e00\u5e74\u7684\u6e29\u5ea6\u6570\u636e\uff0c\u6bcf\u5929\u4e00\u4e2a\u8bb0\u5f55\u3002\u6570\u636e\u9700\u8981\u8f6c\u5316\u4e3a\u9002\u5408\u7ed8\u5236\u65e5\u5386\u56fe\u7684\u683c\u5f0f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u7ed8\u5236\u65e5\u5386\u56fe<\/h3>\n<\/p>\n
1\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u65e5\u5386\u56fe<\/h4>\n<\/p>\n
matplotlib<\/code>\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0c\u5c3d\u7ba1\u6ca1\u6709\u4e13\u95e8\u7684\u65e5\u5386\u56fe\u51fd\u6570\uff0c\u4f46\u901a\u8fc7\u4e00\u4e9b\u81ea\u5b9a\u4e49\u53ef\u4ee5\u5b9e\u73b0\u7c7b\u4f3c\u6548\u679c\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684\u65e5\u5386\u7f51\u683c<\/strong><\/h2>\n
fig, ax = plt.subplots(figsize=(12, 8))<\/p>\n
heatmap_data = np.random.rand(12, 31)  # \u5047\u8bbe\u670912\u4e2a\u6708\uff0c\u6bcf\u6708\u6700\u591a31\u5929<\/p>\n
\u4f7f\u7528imshow\u7ed8\u5236\u70ed\u56fe<\/strong><\/h2>\n
cax = ax.imshow(heatmap_data, cmap='coolwarm', aspect='auto')<\/p>\n
\u6dfb\u52a0\u989c\u8272\u6761<\/strong><\/h2>\n
cbar = fig.colorbar(cax)<\/p>\n
\u8bbe\u7f6e\u5750\u6807\u8f74\u6807\u7b7e<\/strong><\/h2>\n
ax.set_xticks(range(31))<\/p>\n
ax.set_yticks(range(12))<\/p>\n
ax.set_xticklabels(range(1, 32))<\/p>\n
ax.set_yticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])<\/p>\n
plt.title('Calendar Heatmap')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u91cc\uff0c\u6211\u4eec\u4f7f\u7528imshow<\/code>\u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u70ed\u56fe\uff0c\u8be5\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u7ed8\u5236\u4e8c\u7ef4\u6570\u7ec4\u6570\u636e\u3002\u6ce8\u610f\u5728\u8bbe\u7f6exticks<\/code>\u548cyticks<\/code>\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u4e0e\u6708\u4efd\u548c\u65e5\u671f\u5bf9\u5e94\u3002<\/p>\n<\/p>\n
2\u3001\u4f7f\u7528Seaborn\u7ed8\u5236\u65e5\u5386\u56fe<\/h4>\n<\/p>\n
seaborn<\/code>\u662f\u57fa\u4e8ematplotlib<\/code>\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u7684API\u3002<\/p>\n<\/p>\n
import seaborn as sns<\/p>\n
\u91cd\u65b0\u6784\u5efa\u6570\u636e\u4ee5\u9002\u5e94seaborn\u7684heatmap<\/strong><\/h2>\n
calendar_data = np.random.rand(12, 31)<\/p>\n
\u4f7f\u7528seaborn\u7684heatmap<\/strong><\/h2>\n
plt.figure(figsize=(12, 8))<\/p>\n
sns.heatmap(calendar_data, cmap='coolwarm', cbar_kws={'label': 'Temperature'})<\/p>\n
plt.xticks(ticks=np.arange(31) + 0.5, labels=range(1, 32))<\/p>\n
plt.yticks(ticks=np.arange(12) + 0.5, labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], rotation=0)<\/p>\n
plt.title('Calendar Heatmap using Seaborn')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
seaborn<\/code>\u7684heatmap<\/code>\u51fd\u6570\u4e0ematplotlib<\/code>\u7684imshow<\/code>\u7c7b\u4f3c\uff0c\u4f46\u63d0\u4f9b\u4e86\u66f4\u4e3a\u4e30\u5bcc\u7684\u5b9a\u5236\u9009\u9879\uff0c\u5982\u989c\u8272\u6761\u6807\u7b7e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u6570\u636e\u5904\u7406\u4e0e\u683c\u5f0f\u5316<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u51c6\u786e\u5730\u7ed8\u5236\u65e5\u5386\u56fe\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9002\u5f53\u5904\u7406\uff0c\u786e\u4fdd\u6bcf\u4e2a\u65e5\u671f\u90fd\u6709\u5bf9\u5e94\u7684\u6570\u636e\u70b9\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6570\u636e\u5904\u7406\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u586b\u5145<\/h4>\n<\/p>\n
\u5982\u679c\u67d0\u4e9b\u65e5\u671f\u7684\u6570\u636e\u7f3a\u5931\uff0c\u53ef\u4ee5\u4f7f\u7528\u63d2\u503c\u6216\u586b\u5145\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
df.set_index('date', inplace=True)<\/p>\n
df = df.asfreq('D')  # \u5c06\u65e5\u671f\u9891\u7387\u8bbe\u7f6e\u4e3a\u6bcf\u5929<\/p>\n
df['temperature'].fillna(method='ffill', inplace=True)  # \u4f7f\u7528\u524d\u5411\u586b\u5145<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u805a\u5408<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u591a\u5e74\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u6309\u6708\u6216\u5b63\u5ea6\u805a\u5408\uff1a<\/p>\n<\/p>\n
monthly_data = df.resample('M').mean()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u6807\u51c6\u5316<\/h4>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u5c55\u793a\u6570\u636e\u53d8\u5316\uff0c\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406\uff1a<\/p>\n<\/p>\n
df['normalized_temperature'] = (df['temperature'] - df['temperature'].mean()) \/ df['temperature'].std()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u65e5\u5386\u56fe\u7684\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u65e5\u5386\u56fe\u5728\u591a\u4e2a\u9886\u57df\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5305\u62ec\uff1a<\/p>\n<\/p>\n
1\u3001\u6c14\u5019\u4e0e\u73af\u5883\u7814\u7a76<\/h4>\n<\/p>\n
\u901a\u8fc7\u53ef\u89c6\u5316\u6bcf\u5929\u7684\u6c14\u6e29\u3001\u964d\u6c34\u91cf\u7b49\u6570\u636e\uff0c\u53ef\u4ee5\u8bc6\u522b\u51fa\u957f\u671f\u7684\u6c14\u5019\u53d8\u5316\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
2\u3001\u5065\u5eb7\u4e0e\u751f\u6d3b\u65b9\u5f0f<\/h4>\n<\/p>\n
\u5728\u5065\u5eb7\u76d1\u6d4b\u4e2d\uff0c\u65e5\u5386\u56fe\u53ef\u7528\u4e8e\u5c55\u793a\u65e5\u5e38\u6d3b\u52a8\u3001\u996e\u98df\u4e60\u60ef\u7b49\u6570\u636e\u7684\u53d8\u5316\u60c5\u51b5\u3002<\/p>\n<\/p>\n
3\u3001\u5546\u4e1a\u4e0e\u8425\u9500<\/h4>\n<\/p>\n
\u5728\u7535\u5546\u548c\u96f6\u552e\u9886\u57df\uff0c\u65e5\u5386\u56fe\u53ef\u4ee5\u663e\u793a\u9500\u552e\u6570\u636e\u7684\u5b63\u8282\u6027\u53d8\u5316\uff0c\u5e2e\u52a9\u5236\u5b9a\u8425\u9500\u7b56\u7565\u3002<\/p>\n<\/p>\n
\u516d\u3001\u8fdb\u9636\u6280\u5de7\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n
1\u3001\u989c\u8272\u6620\u5c04\u4f18\u5316<\/h4>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u989c\u8272\u6620\u5c04\u53ef\u4ee5\u4f7f\u6570\u636e\u66f4\u6613\u4e8e\u89e3\u8bfb\uff1a<\/p>\n<\/p>\n
cmap = sns.diverging_palette(220, 20, as_cmap=True)<\/p>\n
sns.heatmap(data, cmap=cmap)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6ce8\u91ca\u4e0e\u6807\u8bb0<\/h4>\n<\/p>\n
\u5728\u70ed\u56fe\u4e0a\u6dfb\u52a0\u6ce8\u91ca\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u7684\u53ef\u89e3\u91ca\u6027\uff1a<\/p>\n<\/p>\n
sns.heatmap(data, annot=True, fmt=".1f")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4ea4\u4e92\u5f0f\u56fe\u5f62<\/h4>\n<\/p>\n
\u901a\u8fc7plotly<\/code>\u6216bokeh<\/code>\u7b49\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u4ea4\u4e92\u5f0f\u65e5\u5386\u56fe\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u52a8\u6001\u67e5\u770b\u6570\u636e\u8be6\u60c5\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4f7f\u7528Python\u4e2d\u7684matplotlib<\/code>\u548cseaborn<\/code>\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u51fa\u5177\u6709\u5206\u6790\u610f\u4e49\u7684\u65e5\u5386\u56fe\u3002\u8fd9\u4e9b\u56fe\u5f62\u4e0d\u4ec5\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u7684\u521d\u6b65\u63a2\u7d22\uff0c\u8fd8\u80fd\u4e3a\u66f4\u6df1\u5165\u7684\u5206\u6790\u63d0\u4f9b\u89c6\u89c9\u652f\u6301\u3002\u4e3a\u4e86\u521b\u5efa\u4e00\u4e2a\u9ad8\u8d28\u91cf\u7684\u65e5\u5386\u56fe\uff0c\u6570\u636e\u7684\u51c6\u5907\u548c\u5904\u7406\u662f\u81f3\u5173\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u5728\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u8c03\u6574\u989c\u8272\u6620\u5c04\u3001\u6570\u636e\u683c\u5f0f\u4ee5\u53ca\u56fe\u5f62\u6837\u5f0f\uff0c\u4ee5\u83b7\u5f97\u6700\u4f18\u7684\u5c55\u793a\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u7b80\u5355\u7684\u65e5\u5386\u56fe\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Python\u7684matplotlib<\/code>\u5e93\u6765\u7ed8\u5236\u7b80\u5355\u7684\u65e5\u5386\u56fe\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86matplotlib<\/code>\u548cnumpy<\/code>\u5e93\u3002\u63a5\u4e0b\u6765\uff0c\u60a8\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u6708\u7684\u65e5\u5386\uff0c\u901a\u8fc7\u5faa\u73af\u751f\u6210\u6bcf\u4e00\u5929\uff0c\u5e76\u5c06\u5176\u7ed8\u5236\u5728\u56fe\u5f62\u4e0a\u3002\u4f7f\u7528calendar<\/code>\u6a21\u5757\u53ef\u4ee5\u5e2e\u52a9\u60a8\u83b7\u53d6\u6bcf\u4e2a\u6708\u7684\u5929\u6570\u548c\u661f\u671f\u51e0\u7684\u5f00\u59cb\u4f4d\u7f6e\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5e03\u5c40\u65e5\u5386\u3002<\/p>\n
\u5728Python\u4e2d\u7ed8\u5236\u65e5\u5386\u56fe\u9700\u8981\u54ea\u4e9b\u5e93\uff1f<\/strong>
\u7ed8\u5236\u65e5\u5386\u56fe\u901a\u5e38\u9700\u8981matplotlib<\/code>\u548cnumpy<\/code>\u5e93\u3002matplotlib<\/code>\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u800cnumpy<\/code>\u5219\u53ef\u4ee5\u5e2e\u52a9\u5904\u7406\u6570\u503c\u6570\u636e\u3002\u6b64\u5916\uff0c\u60a8\u53ef\u80fd\u8fd8\u4f1a\u4f7f\u7528calendar<\/code>\u6a21\u5757\u6765\u83b7\u53d6\u6709\u5173\u6708\u4efd\u548c\u65e5\u671f\u7684\u4fe1\u606f\uff0c\u786e\u4fdd\u60a8\u7684\u65e5\u5386\u56fe\u51c6\u786e\u4e14\u7f8e\u89c2\u3002<\/p>\n
\u5982\u4f55\u5b9a\u5236\u65e5\u5386\u56fe\u7684\u6837\u5f0f\u548c\u989c\u8272\uff1f<\/strong>
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