{"id":979262,"date":"2024-12-27T06:45:00","date_gmt":"2024-12-26T22:45:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/979262.html"},"modified":"2024-12-27T06:45:02","modified_gmt":"2024-12-26T22:45:02","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%88%86%e7%bb%84%e8%81%9a%e5%90%88","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/979262.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u5206\u7ec4\u805a\u5408"},"content":{"rendered":"

\"python\u5982\u4f55\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\"<\/p>\n

\u5728Python\u4e2d\uff0c\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Pandas\u5e93\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0c\u4f7f\u5f97\u5206\u7ec4\u805a\u5408\u64cd\u4f5c\u53d8\u5f97\u975e\u5e38\u7b80\u5355\u548c\u76f4\u89c2\u3002\u8981\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684groupby<\/code>\u65b9\u6cd5\u3001\u5b9a\u4e49\u805a\u5408\u51fd\u6570\u3001\u7075\u6d3b\u8fd0\u7528\u591a\u79cd\u805a\u5408\u64cd\u4f5c<\/strong>\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u65b9\u5f0f\uff1a\u4f7f\u7528Pandas\u5e93\u4e2d\u7684groupby<\/code>\u65b9\u6cd5\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u3002<\/p>\n<\/p>\n

Pandas\u5e93\u7684groupby<\/code>\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u6839\u636e\u4e00\u4e2a\u6216\u591a\u4e2a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u7ec4\u5e94\u7528\u4e00\u79cd\u6216\u591a\u79cd\u805a\u5408\u51fd\u6570\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u7ec4\u5305\u542b\u9500\u552e\u6570\u636e\u7684DataFrame\uff0c\u5176\u4e2d\u5305\u62ec\u5217\uff1a\u65e5\u671f\u3001\u9500\u552e\u4eba\u5458\u3001\u4ea7\u54c1\u548c\u9500\u552e\u989d\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528groupby<\/code>\u6765\u6309\u9500\u552e\u4eba\u5458\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u9500\u552e\u4eba\u5458\u7684\u603b\u9500\u552e\u989d\u548c\u9500\u552e\u6b21\u6570\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
data = {'\u65e5\u671f': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-01'],<\/p>\n
        '\u9500\u552e\u4eba\u5458': ['Alice', 'Bob', 'Alice', 'Bob'],<\/p>\n
        '\u4ea7\u54c1': ['\u4ea7\u54c1A', '\u4ea7\u54c1B', '\u4ea7\u54c1A', '\u4ea7\u54c1C'],<\/p>\n
        '\u9500\u552e\u989d': [200, 150, 250, 300]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u6309\u9500\u552e\u4eba\u5458\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u603b\u9500\u552e\u989d\u548c\u9500\u552e\u6b21\u6570<\/strong><\/h2>\n
result = df.groupby('\u9500\u552e\u4eba\u5458').agg({'\u9500\u552e\u989d': 'sum', '\u4ea7\u54c1': 'count'}).reset_index()<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528groupby<\/code>\u65b9\u6cd5\u7ed3\u5408agg<\/code>\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u548c\u805a\u5408\u3002\u5728\u4e0b\u9762\u7684\u6b63\u6587\u4e2d\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5728\u4e0d\u540c\u573a\u666f\u4e2d\u5e94\u7528\u5206\u7ec4\u805a\u5408\u3001Pandas\u5e93\u7684\u5176\u4ed6\u9ad8\u7ea7\u529f\u80fd\u4ee5\u53ca\u5982\u4f55\u4f18\u5316\u4ee3\u7801\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u4e00\u3001PANDAS\u5e93\u7684\u57fa\u672c\u4f7f\u7528<\/strong><\/h2>\n
Pandas\u5e93\u662fPython\u4e2d\u5904\u7406\u6570\u636e\u7684\u5229\u5668\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u8981\u8fdb\u884c\u5206\u7ec4\u805a\u5408\uff0c\u9996\u5148\u9700\u8981\u7406\u89e3Pandas\u5e93\u7684\u57fa\u672c\u4f7f\u7528\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u7684\u8bfb\u53d6\u4e0e\u521b\u5efa<\/h2>\n<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u524d\uff0c\u9996\u5148\u9700\u8981\u8bfb\u53d6\u6216\u521b\u5efa\u6570\u636e\u3002Pandas\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u6709read_csv<\/code>\u3001read_excel<\/code>\u7b49\u3002\u521b\u5efa\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame<\/code>\u548cSeries<\/code>\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u4eceCSV\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
df = pd.read_csv('sales_data.csv')<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
data = {'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
        'Sales': [200, 150, 300]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u7684\u57fa\u7840\u64cd\u4f5c<\/h2>\n<\/p>\n
\u5728\u638c\u63e1\u6570\u636e\u8bfb\u53d6\u4e0e\u521b\u5efa\u540e\uff0c\u8fd8\u9700\u8981\u4e86\u89e3\u6570\u636e\u7684\u57fa\u7840\u64cd\u4f5c\uff0c\u5982\u9009\u62e9\u884c\u5217\u3001\u8fc7\u6ee4\u3001\u6392\u5e8f\u7b49\u3002<\/p>\n<\/p>\n
# \u9009\u62e9\u5217<\/p>\n
df['Name']<\/p>\n
\u9009\u62e9\u591a\u5217<\/strong><\/h2>\n
df[['Name', 'Sales']]<\/p>\n
\u8fc7\u6ee4\u6570\u636e<\/strong><\/h2>\n
df[df['Sales'] > 150]<\/p>\n
\u6392\u5e8f<\/strong><\/h2>\n
df.sort_values(by='Sales', ascending=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001GROUPBY\u65b9\u6cd5\u7684\u5e94\u7528<\/strong><\/h2>\n
groupby<\/code>\u65b9\u6cd5\u662f\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u7684\u5173\u952e\uff0c\u5b83\u80fd\u591f\u6839\u636e\u6307\u5b9a\u7684\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u7ec4\u5e94\u7528\u7279\u5b9a\u7684\u805a\u5408\u51fd\u6570\u3002<\/p>\n<\/p>\n
1\u3001\u5355\u5217\u5206\u7ec4\u805a\u5408<\/h2>\n<\/p>\n
groupby<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5bf9\u5355\u5217\u8fdb\u884c\u5206\u7ec4\uff0c\u7ed3\u5408agg<\/code>\u6216apply<\/code>\u65b9\u6cd5\u5bf9\u7ec4\u5185\u6570\u636e\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
# \u6309\u9500\u552e\u4eba\u5458\u5206\u7ec4\uff0c\u8ba1\u7b97\u9500\u552e\u603b\u989d<\/p>\n
grouped = df.groupby('Name').agg({'Sales': 'sum'}).reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u591a\u5217\u5206\u7ec4\u805a\u5408<\/h2>\n<\/p>\n
\u5bf9\u4e8e\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0c\u53ef\u80fd\u9700\u8981\u5bf9\u591a\u5217\u8fdb\u884c\u5206\u7ec4\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u4f20\u9012\u591a\u4e2a\u5217\u540d\u7ed9groupby<\/code>\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
# \u6309\u9500\u552e\u4eba\u5458\u548c\u4ea7\u54c1\u5206\u7ec4\uff0c\u8ba1\u7b97\u9500\u552e\u603b\u989d<\/p>\n
grouped = df.groupby(['Name', 'Product']).agg({'Sales': 'sum'}).reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570<\/h2>\n<\/p>\n
\u9664\u4e86\u4f7f\u7528\u5185\u7f6e\u7684\u805a\u5408\u51fd\u6570\uff0c\u8fd8\u53ef\u4ee5\u5b9a\u4e49\u81ea\u5b9a\u4e49\u7684\u805a\u5408\u51fd\u6570\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n
# \u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570\uff0c\u8ba1\u7b97\u9500\u552e\u7684\u5e73\u5747\u503c<\/p>\n
def custom_agg(x):<\/p>\n
    return x.mean()<\/p>\n
grouped = df.groupby('Name').agg({'Sales': custom_agg}).reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u591a\u79cd\u805a\u5408\u64cd\u4f5c\u7684\u7ec4\u5408<\/strong><\/h2>\n
\u5728\u5b9e\u8df5\u4e2d\uff0c\u5e38\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u591a\u79cd\u805a\u5408\u64cd\u4f5c\uff0cPandas\u7684agg<\/code>\u65b9\u6cd5\u652f\u6301\u540c\u65f6\u5e94\u7528\u591a\u79cd\u805a\u5408\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
1\u3001\u540c\u65f6\u5e94\u7528\u591a\u79cd\u805a\u5408\u64cd\u4f5c<\/h2>\n<\/p>\n
\u53ef\u4ee5\u5728agg<\/code>\u65b9\u6cd5\u4e2d\u4f20\u9012\u591a\u4e2a\u805a\u5408\u64cd\u4f5c\uff0c\u4ee5\u4fbf\u540c\u65f6\u8ba1\u7b97\u591a\u4e2a\u6307\u6807\u3002<\/p>\n<\/p>\n
# \u540c\u65f6\u8ba1\u7b97\u603b\u548c\u548c\u5e73\u5747\u503c<\/p>\n
grouped = df.groupby('Name').agg({'Sales': ['sum', 'mean']}).reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4e0d\u540c\u5217\u5e94\u7528\u4e0d\u540c\u805a\u5408\u64cd\u4f5c<\/h2>\n<\/p>\n
\u5bf9\u4e8e\u4e0d\u540c\u7684\u5217\uff0c\u53ef\u4ee5\u6307\u5b9a\u4e0d\u540c\u7684\u805a\u5408\u64cd\u4f5c\uff0c\u5b9e\u73b0\u66f4\u7075\u6d3b\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u9500\u552e\u603b\u989d\u8ba1\u7b97\u548c\u9500\u552e\u6b21\u6570\u8ba1\u7b97<\/p>\n
grouped = df.groupby('Name').agg({'Sales': 'sum', 'Product': 'count'}).reset_index()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u7ec4\u540e\u8fdb\u884c\u5176\u4ed6\u64cd\u4f5c<\/h2>\n<\/p>\n
\u5728\u5206\u7ec4\u805a\u5408\u540e\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u7ed3\u679c\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\uff0c\u5982\u6392\u5e8f\u3001\u8fc7\u6ee4\u7b49\u3002<\/p>\n<\/p>\n
# \u5206\u7ec4\u540e\u6392\u5e8f<\/p>\n
grouped = grouped.sort_values(by=('Sales', 'sum'), ascending=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001PANDAS\u5e93\u7684\u9ad8\u7ea7\u529f\u80fd<\/strong><\/h2>\n
Pandas\u5e93\u4e0d\u4ec5\u652f\u6301\u57fa\u672c\u7684\u5206\u7ec4\u805a\u5408\u64cd\u4f5c\uff0c\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u6570\u636e\u5206\u6790\u9700\u6c42\u3002<\/p>\n<\/p>\n
1\u3001\u900f\u89c6\u8868\uff08Pivot Table\uff09<\/h2>\n<\/p>\n
\u900f\u89c6\u8868\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u4e00\u79cd\u91cd\u8981\u5de5\u5177\uff0c\u53ef\u4ee5\u5feb\u901f\u8f6c\u6362\u6570\u636e\u683c\u5f0f\u5e76\u8fdb\u884c\u805a\u5408\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u900f\u89c6\u8868\u8fdb\u884c\u5206\u7ec4\u805a\u5408<\/p>\n
pivot_table = df.pivot_table(values='Sales', index='Name', columns='Product', aggfunc='sum')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4ea4\u53c9\u8868\uff08Crosstab\uff09<\/h2>\n<\/p>\n
\u4ea4\u53c9\u8868\u7528\u4e8e\u8ba1\u7b97\u4e24\u4e2a\u6216\u591a\u4e2a\u56e0\u7d20\u7684\u9891\u7387\u5206\u5e03\uff0c\u662f\u53e6\u4e00\u79cd\u6570\u636e\u5206\u6790\u5e38\u7528\u5de5\u5177\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u9500\u552e\u4eba\u5458\u548c\u4ea7\u54c1\u4e4b\u95f4\u7684\u4ea4\u53c9\u9891\u7387<\/p>\n
crosstab = pd.crosstab(df['Name'], df['Product'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u7a97\u53e3\u51fd\u6570<\/h2>\n<\/p>\n
\u7a97\u53e3\u51fd\u6570\u7528\u4e8e\u5728\u7279\u5b9a\u7684\u7a97\u53e3\u5185\u5bf9\u6570\u636e\u8fdb\u884c\u8fd0\u7b97\uff0c\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u6eda\u52a8\u5e73\u5747\u503c\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u9500\u552e\u989d\u7684\u6eda\u52a8\u5e73\u5747\u503c<\/p>\n
df['Rolling Mean'] = df['Sales'].rolling(window=2).mean()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f18\u5316\u4ee3\u7801\u6027\u80fd<\/strong><\/h2>\n
\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u65f6\uff0c\u4f18\u5316\u4ee3\u7801\u6027\u80fd\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u63d0\u9ad8Pandas\u6027\u80fd\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c<\/h2>\n<\/p>\n
Pandas\u4e2d\u7684\u8bb8\u591a\u64cd\u4f5c\u90fd\u662f\u77e2\u91cf\u5316\u7684\uff0c\u5c3d\u91cf\u907f\u514d\u4f7f\u7528Python\u7684\u5faa\u73af\uff0c\u800c\u662f\u4f7f\u7528Pandas\u7684\u5185\u7f6e\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c\u8ba1\u7b97\u9500\u552e\u989d\u7684\u5bf9\u6570<\/p>\n
df['Log Sales'] = np.log(df['Sales'])<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u51cf\u5c11\u5185\u5b58\u5360\u7528<\/h2>\n<\/p>\n
\u5728\u52a0\u8f7d\u5927\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u6570\u636e\u7c7b\u578b\u6765\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002<\/p>\n<\/p>\n
# \u6307\u5b9a\u6570\u636e\u7c7b\u578b<\/p>\n
df = pd.read_csv('sales_data.csv', dtype={'Sales': 'float32'})<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5206\u5757\u5904\u7406\u5927\u6570\u636e<\/h2>\n<\/p>\n
\u5bf9\u4e8e\u8d85\u5927\u89c4\u6a21\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u5206\u5757\u5904\u7406\u7684\u65b9\u5f0f\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u5206\u5757\u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
chunksize = 10000<\/p>\n
for chunk in pd.read_csv('sales_data.csv', chunksize=chunksize):<\/p>\n
    # \u5bf9\u6bcf\u4e2a\u5757\u8fdb\u884c\u5904\u7406<\/p>\n
    process(chunk)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u5185\u5bb9\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u4e2d\u7684Pandas\u5e93\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u64cd\u4f5c\uff0c\u4ece\u57fa\u7840\u4f7f\u7528\u5230\u9ad8\u7ea7\u529f\u80fd\u4ee5\u53ca\u6027\u80fd\u4f18\u5316\uff0c\u4e3a\u60a8\u63d0\u4f9b\u4e86\u5168\u9762\u7684\u6307\u5bfc\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u5206\u7ec4\u805a\u5408\u6280\u672f\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u7ed3\u5408\u5177\u4f53\u95ee\u9898\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u5c06\u66f4\u5177\u6210\u6548\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528Pandas\u8fdb\u884c\u5206\u7ec4\u805a\u5408\uff1f<\/strong>
Pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u4f7f\u7528groupby()\u51fd\u6570\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5bfc\u5165Pandas\u5e93\u5e76\u52a0\u8f7d\u6570\u636e\u3002\u63a5\u7740\uff0c\u4f7f\u7528groupby()\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u7ed3\u5408agg()\u51fd\u6570\u5e94\u7528\u805a\u5408\u64cd\u4f5c\uff0c\u4f8b\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u7b49\u3002\u8fd9\u6837\u53ef\u4ee5\u8f7b\u677e\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u6790\u548c\u7edf\u8ba1\u3002<\/p>\n
\u5728Python\u4e2d\u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u805a\u5408\u51fd\u6570\uff1f<\/strong>
\u5728\u4f7f\u7528Pandas\u8fdb\u884c\u5206\u7ec4\u805a\u5408\u65f6\uff0c\u53ef\u4ee5\u9009\u62e9\u591a\u79cd\u805a\u5408\u51fd\u6570\uff0c\u5982sum()\u3001mean()\u3001count()\u3001min()\u548cmax()\u7b49\u3002\u6b64\u5916\uff0c\u60a8\u8fd8\u53ef\u4ee5\u81ea\u5b9a\u4e49\u805a\u5408\u51fd\u6570\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u7684\u5206\u6790\u9700\u6c42\u3002\u901a\u8fc7\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u51fd\u6570\uff0c\u80fd\u591f\u6df1\u5165\u6316\u6398\u6570\u636e\u4e2d\u7684\u4fe1\u606f\u3002<\/p>\n
\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u4ee5\u786e\u4fdd\u805a\u5408\u7ed3\u679c\u7684\u51c6\u786e\u6027\uff1f<\/strong>
\u5728\u8fdb\u884c\u5206\u7ec4\u805a\u5408\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\uff0c\u4f8b\u5982\u4f7f\u7528fillna()\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528dropna()\u65b9\u6cd5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u60a8\u53ef\u4ee5\u786e\u4fdd\u805a\u5408\u64cd\u4f5c\u7684\u7ed3\u679c\u66f4\u52a0\u51c6\u786e\u548c\u53ef\u9760\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u5b9e\u73b0\u5206\u7ec4\u805a\u5408\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\uff0c\u5176\u4e2d\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u4f7f\u7528Pandas\u5e93\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a […]","protected":false},"author":3,"featured_media":979276,"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\/979262"}],"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=979262"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/979262\/revisions"}],"predecessor-version":[{"id":979280,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/979262\/revisions\/979280"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/979276"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=979262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=979262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=979262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}