{"id":1122469,"date":"2025-01-08T19:20:59","date_gmt":"2025-01-08T11:20:59","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1122469.html"},"modified":"2025-01-08T19:21:02","modified_gmt":"2025-01-08T11:21:02","slug":"python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%88%86%e7%bb%84%e5%90%8e%e5%8f%96%e6%9f%90%e5%88%97%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1122469.html","title":{"rendered":"python\u4e2d\u5982\u4f55\u5206\u7ec4\u540e\u53d6\u67d0\u5217\u503c"},"content":{"rendered":"

\"python\u4e2d\u5982\u4f55\u5206\u7ec4\u540e\u53d6\u67d0\u5217\u503c\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5206\u7ec4\u540e\u53d6\u67d0\u5217\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u7b49\u3002\u4e3b\u8981\u65b9\u5f0f\u6709\uff1a\u4f7f\u7528Pandas\u7684groupby\u51fd\u6570\u3001\u4f7f\u7528Pandas\u7684pivot_table\u51fd\u6570\u3001\u4f7f\u7528NumPy\u7684split\u51fd\u6570\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u5206\u7ec4\u548c\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\uff0c\u5e76\u63d0\u4f9b\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n

\u4f7f\u7528Pandas\u5e93\u7684groupby\u51fd\u6570\u662f\u6700\u5e38\u89c1\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u3001\u805a\u5408\u3001\u8fc7\u6ee4\u548c\u8f6c\u6362\u7b49\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u4f7f\u7528groupby\u51fd\u6570\u53ef\u4ee5\u6309\u6307\u5b9a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u4f7f\u7528get_group\u65b9\u6cd5\u63d0\u53d6\u7279\u5b9a\u7ec4\u7684\u6570\u636e\uff0c\u518d\u901a\u8fc7\u5217\u540d\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u7684groupby\u51fd\u6570<\/h3>\n<\/p>\n

Pandas\u5e93\u662f\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5c24\u5176\u5728\u5206\u7ec4\u64cd\u4f5c\u4e2d\u975e\u5e38\u65b9\u4fbf\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u4f7f\u7528Pandas\u7684groupby\u51fd\u6570\u8fdb\u884c\u5206\u7ec4\u5e76\u63d0\u53d6\u67d0\u5217\u503c\u7684\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u6846\uff08DataFrame\uff09\uff0c\u7528\u4e8e\u6f14\u793a\u5206\u7ec4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    'category': ['A', 'B', 'A', 'B', 'A', 'B'],<\/p>\n
    'value': [10, 15, 10, 15, 20, 25]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
  category  value<\/p>\n
0        A     10<\/p>\n
1        B     15<\/p>\n
2        A     10<\/p>\n
3        B     15<\/p>\n
4        A     20<\/p>\n
5        B     25<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528groupby\u51fd\u6570\u5206\u7ec4\u5e76\u63d0\u53d6\u5217\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528groupby\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\uff1a<\/p>\n<\/p>\n
grouped = df.groupby('category')<\/p>\n
for name, group in grouped:<\/p>\n
    print(f"Group: {name}")<\/p>\n
    print(group['value'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
Group: A<\/p>\n
0    10<\/p>\n
2    10<\/p>\n
4    20<\/p>\n
Name: value, dtype: int64<\/p>\n
Group: B<\/p>\n
1    15<\/p>\n
3    15<\/p>\n
5    25<\/p>\n
Name: value, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pandas\u7684pivot_table\u51fd\u6570<\/h3>\n<\/p>\n
pivot_table\u51fd\u6570\u662fPandas\u4e2d\u53e6\u4e00\u4e2a\u975e\u5e38\u6709\u7528\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\uff0c\u5e76\u5728\u5206\u7ec4\u540e\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n
\u5982\u679c\u5c1a\u672a\u5b89\u88c5Pandas\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u6846\uff08DataFrame\uff09\uff1a<\/p>\n<\/p>\n
data = {<\/p>\n
    'category': ['A', 'B', 'A', 'B', 'A', 'B'],<\/p>\n
    'value': [10, 15, 10, 15, 20, 25]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
  category  value<\/p>\n
0        A     10<\/p>\n
1        B     15<\/p>\n
2        A     10<\/p>\n
3        B     15<\/p>\n
4        A     20<\/p>\n
5        B     25<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528pivot_table\u51fd\u6570\u521b\u5efa\u900f\u89c6\u8868\u5e76\u63d0\u53d6\u5217\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528pivot_table\u51fd\u6570\u521b\u5efa\u6570\u636e\u900f\u89c6\u8868\uff0c\u7136\u540e\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\uff1a<\/p>\n<\/p>\n
pivot = df.pivot_table(index='category', values='value', aggfunc=list)<\/p>\n
print(pivot)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
          value<\/p>\n
category       <\/p>\n
A       [10, 10, 20]<\/p>\n
B       [15, 15, 25]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528NumPy\u7684split\u51fd\u6570<\/h3>\n<\/p>\n
\u867d\u7136Pandas\u662f\u5904\u7406\u6570\u636e\u7684\u9996\u9009\u5de5\u5177\uff0c\u4f46NumPy\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5206\u7ec4\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5\u548c\u5bfc\u5165NumPy\u5e93<\/h4>\n<\/p>\n
\u5982\u679c\u5c1a\u672a\u5b89\u88c5NumPy\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728Python\u4ee3\u7801\u4e2d\u5bfc\u5165NumPy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u521b\u5efa\u6570\u7ec4<\/h4>\n<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u7ec4\u7528\u4e8e\u6f14\u793a\u5206\u7ec4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
data = np.array([<\/p>\n
    ['A', 10],<\/p>\n
    ['B', 15],<\/p>\n
    ['A', 10],<\/p>\n
    ['B', 15],<\/p>\n
    ['A', 20],<\/p>\n
    ['B', 25]<\/p>\n
])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528split\u51fd\u6570\u5206\u7ec4\u5e76\u63d0\u53d6\u5217\u503c<\/h4>\n<\/p>\n
\u4f7f\u7528split\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\uff1a<\/p>\n<\/p>\n
categories, values = np.split(data, [1], axis=1)<\/p>\n
unique_categories = np.unique(categories)<\/p>\n
for category in unique_categories:<\/p>\n
    group_values = values[categories.flatten() == category]<\/p>\n
    print(f"Group: {category}")<\/p>\n
    print(group_values.flatten().astype(int))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
Group: A<\/p>\n
[10 10 20]<\/p>\n
Group: B<\/p>\n
[15 15 25]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5728Python\u4e2d\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\u3002<\/strong>\u6839\u636e\u6570\u636e\u7684\u89c4\u6a21\u548c\u5177\u4f53\u9700\u6c42\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u65b9\u6cd5\u3002Pandas\u5e93\u7684groupby\u548cpivot_table\u51fd\u6570\u662f\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u7684\u9996\u9009\u5de5\u5177\uff0c\u800cNumPy\u5e93\u7684split\u51fd\u6570\u5728\u5904\u7406\u5c0f\u89c4\u6a21\u6570\u636e\u65f6\u4e5f\u975e\u5e38\u6709\u6548\u3002\u65e0\u8bba\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u90fd\u53ef\u4ee5\u901a\u8fc7\u7b80\u6d01\u7684\u4ee3\u7801\u5b9e\u73b0\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\uff0c\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u63d0\u53d6\u67d0\u5217\u7684\u503c\u3002\u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165Pandas\u5e93\uff0c\u5e76\u521b\u5efa\u4e00\u4e2aDataFrame\u3002\u53ef\u4ee5\u4f7f\u7528groupby()<\/code>\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u4f7f\u7528agg()<\/code>\u6216apply()<\/code>\u6765\u63d0\u53d6\u7279\u5b9a\u5217\u7684\u503c\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5b9e\u73b0\u5206\u7ec4\u5e76\u53d6\u67d0\u5217\u7684\u5e73\u5747\u503c\uff1a<\/p>\n
import pandas as pd\n\ndata = {\n    'Category': ['A', 'B', 'A', 'B'],\n    'Value': [10, 20, 30, 40]\n}\n\ndf = pd.DataFrame(data)\ngrouped = df.groupby('Category')['Value'].mean()\nprint(grouped)\n<\/code><\/pre>\n
\u4f7f\u7528Pandas\u8fdb\u884c\u5206\u7ec4\u65f6\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
\u5728\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u65f6\uff0c\u7f3a\u5931\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u7ed3\u679c\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u6bd4\u5982\u4f7f\u7528dropna()<\/code>\u6765\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u6765\u586b\u5145\u7f3a\u5931\u503c\u3002\u786e\u4fdd\u5728\u5206\u7ec4\u4e4b\u524d\u5904\u7406\u8fd9\u4e9b\u7f3a\u5931\u503c\uff0c\u4ee5\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u5206\u6790\u7ed3\u679c\u3002<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u805a\u5408\u51fd\u6570\u5728\u5206\u7ec4\u540e\u63d0\u53d6\u6570\u636e\uff1f<\/strong>
\u5728\u4f7f\u7528groupby()<\/code>\u8fdb\u884c\u5206\u7ec4\u540e\uff0c\u53ef\u4ee5\u5e94\u7528\u591a\u79cd\u805a\u5408\u51fd\u6570\uff0c\u5982mean()<\/code>\u3001sum()<\/code>\u3001count()<\/code>\u3001min()<\/code>\u548cmax()<\/code>\u7b49\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u4f60\u83b7\u53d6\u4e0d\u540c\u7684\u7edf\u8ba1\u4fe1\u606f\u3002\u8fd8\u53ef\u4ee5\u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570\uff0c\u901a\u8fc7agg()<\/code>\u65b9\u6cd5\u4f20\u5165\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\u6765\u5b9e\u73b0\u66f4\u52a0\u7075\u6d3b\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5206\u7ec4\u540e\u53d6\u67d0\u5217\u503c\u7684\u65b9\u6cd5\u6709\u591a\u79cd\uff0c\u5e38\u89c1\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u7b49\u3002\u4e3b\u8981\u65b9\u5f0f\u6709\uff1a\u4f7f […]","protected":false},"author":3,"featured_media":1122472,"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\/1122469"}],"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=1122469"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1122469\/revisions"}],"predecessor-version":[{"id":1122474,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1122469\/revisions\/1122474"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1122472"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1122469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1122469"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1122469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}