{"id":1143253,"date":"2025-01-08T22:48:26","date_gmt":"2025-01-08T14:48:26","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1143253.html"},"modified":"2025-01-08T22:48:55","modified_gmt":"2025-01-08T14:48:55","slug":"python%e5%a6%82%e4%bd%95%e5%9c%a8%e6%9f%90%e4%b8%80%e5%88%97%e6%b7%bb%e5%8a%a0%e6%95%b0%e6%8d%ae%e7%b1%bb%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1143253.html","title":{"rendered":"python\u5982\u4f55\u5728\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b"},"content":{"rendered":"

\"python\u5982\u4f55\u5728\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\"<\/p>\n

\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5728\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b<\/strong>\u3002\u5176\u4e2d\u4e3b\u8981\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u6216\u76f4\u63a5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c\u3002Pandas\u5e93\u662f\u5904\u7406\u6570\u636e\u6846\u6700\u5e38\u7528\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u5904\u7406\u548c\u64cd\u4f5c\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528NumPy\u5e93\u3001\u76f4\u63a5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c<\/strong>\uff0c\u662f\u5b9e\u73b0\u8fd9\u4e00\u4efb\u52a1\u7684\u4e09\u79cd\u4e3b\u8981\u65b9\u6cd5\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\u5728\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u5730\u5728\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n

1.1 \u521b\u5efa\u6570\u636e\u6846<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684DataFrame<\/code>\u65b9\u6cd5\u6765\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'Salary': [70000, 80000, 90000]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u6dfb\u52a0\u6570\u636e\u7c7b\u578b<\/h4>\n<\/p>\n
\u8981\u5728\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684astype<\/code>\u65b9\u6cd5\u3002\u8fd9\u4e2a\u65b9\u6cd5\u5141\u8bb8\u6211\u4eec\u5c06\u67d0\u4e00\u5217\u7684\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u6307\u5b9a\u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
df['Age'] = df['Age'].astype('float')<\/p>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u6dfb\u52a0\u65b0\u5217\u5e76\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b<\/h4>\n<\/p>\n
\u5982\u679c\u6211\u4eec\u60f3\u8981\u6dfb\u52a0\u4e00\u4e2a\u65b0\u5217\u5e76\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u5148\u521b\u5efa\u4e00\u4e2a\u65b0\u5217\uff0c\u7136\u540e\u4f7f\u7528astype<\/code>\u65b9\u6cd5\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
df['Bonus'] = [5000, 6000, 7000]<\/p>\n
df['Bonus'] = df['Bonus'].astype('float')<\/p>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528NumPy\u5e93<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u5904\u7406\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u7684\u6838\u5fc3\u5e93\u3002\u867d\u7136\u5b83\u4e0d\u662f\u4e13\u95e8\u4e3a\u6570\u636e\u6846\u8bbe\u8ba1\u7684\uff0c\u4f46\u6211\u4eec\u53ef\u4ee5\u7ed3\u5408Pandas\u5e93\u4f7f\u7528NumPy\u6765\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
2.1 \u521b\u5efa\u6570\u636e\u6846\u5e76\u5bfc\u5165NumPy\u5e93<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u6846\uff0c\u5e76\u5bfc\u5165NumPy\u5e93\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'Salary': [70000, 80000, 90000]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u4f7f\u7528NumPy\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528NumPy\u7684astype<\/code>\u65b9\u6cd5\u6765\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
df['Age'] = df['Age'].astype(np.float64)<\/p>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u76f4\u63a5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c<\/h3>\n<\/p>\n
\u5982\u679c\u6211\u4eec\u4e0d\u60f3\u4f7f\u7528Pandas\u6216NumPy\u5e93\uff0c\u8fd8\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c\u6765\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u3002\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u7b80\u5355\u7684\u6570\u636e\u7ed3\u6784\u548c\u5c0f\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
3.1 \u521b\u5efa\u5217\u8868\u6570\u636e\u6846<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u5217\u8868\u7684\u6570\u636e\u6846\u3002<\/p>\n<\/p>\n
data = {<\/p>\n
    'Name': ['Alice', 'Bob', 'Charlie'],<\/p>\n
    'Age': [25, 30, 35],<\/p>\n
    'Salary': [70000, 80000, 90000]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u6dfb\u52a0\u6570\u636e\u7c7b\u578b<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c\u6765\u8bbe\u7f6e\u67d0\u4e00\u5217\u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
df['Age'] = [float(i) for i in df['Age']]<\/p>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b9e\u9645\u5e94\u7528\u573a\u666f<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5904\u7406\u66f4\u4e3a\u590d\u6742\u7684\u6570\u636e\u7ed3\u6784\u548c\u66f4\u5927\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u4ecb\u7ecd\u4e00\u4e9b\u5b9e\u9645\u5e94\u7528\u573a\u666f\uff0c\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u8f6c\u6362\u548c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
4.1 \u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\u3002\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u548c\u5f02\u5e38\u503c\u3002\u5728\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b\u4e2d\uff0c\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u662f\u975e\u5e38\u91cd\u8981\u7684\u4e00\u6b65\u3002<\/p>\n<\/p>\n
# \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
df['Age'] = df['Age'].fillna(0)<\/p>\n
\u5904\u7406\u91cd\u590d\u503c<\/strong><\/h2>\n
df = df.drop_duplicates()<\/p>\n
\u5904\u7406\u5f02\u5e38\u503c<\/strong><\/h2>\n
df['Salary'] = df['Salary'].apply(lambda x: x if x > 0 else 0)<\/p>\n
\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b<\/strong><\/h2>\n
df['Age'] = df['Age'].astype('int')<\/p>\n
df['Salary'] = df['Salary'].astype('float')<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.2 \u6570\u636e\u8f6c\u6362<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5c06\u6570\u636e\u4ece\u4e00\u79cd\u5f62\u5f0f\u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u5f62\u5f0f\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5c06\u5b57\u7b26\u4e32\u65e5\u671f\u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61\uff0c\u6216\u8005\u5c06\u6574\u6570\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u3002<\/p>\n<\/p>\n
df['Date'] = ['2023-01-01', '2023-02-01', '2023-03-01']<\/p>\n
df['Date'] = pd.to_datetime(df['Date'])<\/p>\n
print(df.dtypes)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4.3 \u6570\u636e\u5206\u6790<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5904\u7406\u6570\u636e\u3002\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684\u5206\u7ec4\u548c\u805a\u5408\u529f\u80fd\u6765\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u6309\u5e74\u9f84\u5206\u7ec4\u5e76\u8ba1\u7b97\u5e73\u5747\u85aa\u6c34<\/p>\n
grouped = df.groupby('Age')['Salary'].mean()<\/p>\n
print(grouped)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u5728Python\u4e2d\u8f7b\u677e\u5730\u5728\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\u3002\u65e0\u8bba\u662f\u4f7f\u7528Pandas\u5e93\u3001NumPy\u5e93\u8fd8\u662f\u76f4\u63a5\u901a\u8fc7\u5217\u8868\u64cd\u4f5c\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u90fd\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u603b\u4e4b\uff0c\u5408\u7406\u8bbe\u7f6e\u6570\u636e\u7c7b\u578b<\/strong>\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\uff0c\u8fd8\u53ef\u4ee5\u907f\u514d\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u7684\u5404\u79cd\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u901a\u8fc7\u8fd9\u7bc7\u6587\u7ae0\uff0c\u60a8\u5bf9\u5982\u4f55\u5728Python\u4e2d\u5728\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\u6709\u4e86\u66f4\u6df1\u5165\u7684\u4e86\u89e3\u3002\u5982\u679c\u60a8\u6709\u4efb\u4f55\u95ee\u9898\u6216\u9700\u8981\u8fdb\u4e00\u6b65\u7684\u5e2e\u52a9\uff0c\u8bf7\u968f\u65f6\u8054\u7cfb\u6211\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4e3a\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6307\u5b9a\u6570\u636e\u7c7b\u578b\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u53ef\u4ee5\u8f7b\u677e\u4e3a\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6307\u5b9a\u6570\u636e\u7c7b\u578b\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u6765\u8f6c\u6362\u5217\u7684\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u60f3\u5c06\u540d\u4e3aage<\/code>\u7684\u5217\u8f6c\u6362\u4e3a\u6574\u6570\u7c7b\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
import pandas as pd\n\ndf = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': ['25', '30']})\ndf['age'] = df['age'].astype(int)\n<\/code><\/pre>\n
\u8fd9\u79cd\u65b9\u5f0f\u786e\u4fdd\u4e86\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n
\u5728Pandas\u6570\u636e\u6846\u4e2d\u5982\u4f55\u68c0\u67e5\u67d0\u4e00\u5217\u7684\u6570\u636e\u7c7b\u578b\uff1f<\/strong>
\u8981\u68c0\u67e5Pandas\u6570\u636e\u6846\u4e2d\u67d0\u4e00\u5217\u7684\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u4f7f\u7528dtypes<\/code>\u5c5e\u6027\u3002\u901a\u8fc7\u6307\u5b9a\u5217\u540d\uff0c\u53ef\u4ee5\u67e5\u770b\u8be5\u5217\u7684\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n
print(df['age'].dtypes)\n<\/code><\/pre>\n
\u8fd9\u5c06\u8fd4\u56deint64<\/code>\uff0c\u8868\u793a\u8be5\u5217\u7684\u6570\u636e\u7c7b\u578b\u4e3a\u6574\u6570\u3002<\/p>\n
\u5982\u4f55\u5728\u521b\u5efa\u6570\u636e\u6846\u65f6\u6307\u5b9a\u5217\u7684\u6570\u636e\u7c7b\u578b\uff1f<\/strong>
\u5728\u521b\u5efa\u6570\u636e\u6846\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7dtype<\/code>\u53c2\u6570\u6307\u5b9a\u6bcf\u4e00\u5217\u7684\u6570\u636e\u7c7b\u578b\u3002\u4f8b\u5982\uff1a<\/p>\n
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': ['25', '30']}, dtype='object')\n<\/code><\/pre>\n
\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6240\u6709\u5217\u90fd\u88ab\u8bbe\u7f6e\u4e3a\u5bf9\u8c61\u7c7b\u578b\u3002\u5982\u679c\u9700\u8981\u4e3a\u7279\u5b9a\u5217\u6307\u5b9a\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\uff0c\u53ef\u4ee5\u5148\u521b\u5efa\u6570\u636e\u6846\uff0c\u518d\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u8fdb\u884c\u8f6c\u6362\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5728\u6570\u636e\u6846\u7684\u67d0\u4e00\u5217\u6dfb\u52a0\u6570\u636e\u7c7b\u578b\u3002\u5176\u4e2d\u4e3b\u8981\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528Pandas\u5e93\u3001NumP […]","protected":false},"author":3,"featured_media":1143304,"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\/1143253"}],"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=1143253"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1143253\/revisions"}],"predecessor-version":[{"id":1143310,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1143253\/revisions\/1143310"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1143304"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1143253"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1143253"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1143253"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}