{"id":1146394,"date":"2025-01-08T23:16:34","date_gmt":"2025-01-08T15:16:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1146394.html"},"modified":"2025-01-08T23:16:37","modified_gmt":"2025-01-08T15:16:37","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8python%e4%b8%80%e5%88%97%e4%b8%ad%e6%9f%90%e4%b8%aa%e5%ad%97%e6%ae%b5%e7%9a%84%e4%b8%aa%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1146394.html","title":{"rendered":"\u5982\u4f55\u5728python\u4e00\u5217\u4e2d\u67d0\u4e2a\u5b57\u6bb5\u7684\u4e2a\u6570\u636e"},"content":{"rendered":"

\"\u5982\u4f55\u5728python\u4e00\u5217\u4e2d\u67d0\u4e2a\u5b57\u6bb5\u7684\u4e2a\u6570\u636e\"<\/p>\n

\u8981\u5728Python\u4e2d\u7edf\u8ba1\u4e00\u5217\u4e2d\u67d0\u4e2a\u5b57\u6bb5\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u7528\u57fa\u672c\u7684Python\u6570\u636e\u7ed3\u6784\u5982\u5217\u8868\u548c\u5b57\u5178\u3001\u5229\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u7b49\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u9610\u8ff0\u5982\u4f55\u4f7f\u7528\u8fd9\u51e0\u79cd\u65b9\u6cd5\u6765\u7edf\u8ba1\u6570\u636e\uff0c\u5e76\u6df1\u5165\u89e3\u6790\u6bcf\u79cd\u65b9\u6cd5\u7684\u4f18\u7f3a\u70b9\u3002<\/strong><\/p>\n<\/p>\n

Pandas\u5e93\u662f\u5904\u7406\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\u3001\u57fa\u672cPython\u6570\u636e\u7ed3\u6784\u5177\u6709\u7075\u6d3b\u6027\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u9002\u7528\u4e8e\u590d\u6742\u7684\u5b57\u7b26\u4e32\u5339\u914d\u4efb\u52a1\u3002<\/strong><\/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\u64cd\u4f5c\u529f\u80fd\u3002\u5728Pandas\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528DataFrame\u5bf9\u8c61\u6765\u5b58\u50a8\u548c\u64cd\u4f5c\u6570\u636e\u3002<\/p>\n<\/p>\n

1.1 \u5b89\u88c5\u548c\u5bfc\u5165Pandas<\/h4>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Pandas\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u521b\u5efaDataFrame<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2aCSV\u6587\u4ef6data.csv<\/code>\uff0c\u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
name,age,gender<\/p>\n
Alice,25,F<\/p>\n
Bob,30,M<\/p>\n
Charlie,35,M<\/p>\n
David,40,M<\/p>\n
Eve,45,F<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5c06CSV\u6587\u4ef6\u8bfb\u53d6\u5230DataFrame\u4e2d\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.3 \u7edf\u8ba1\u5b57\u6bb5\u51fa\u73b0\u6b21\u6570<\/h4>\n<\/p>\n
\u5047\u8bbe\u8981\u7edf\u8ba1gender<\/code>\u5217\u4e2d\u6bcf\u4e2a\u6027\u522b\u51fa\u73b0\u7684\u6b21\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528value_counts<\/code>\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n
gender_counts = df['gender'].value_counts()<\/p>\n
print(gender_counts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
M    3<\/p>\n
F    2<\/p>\n
Name: gender, dtype: int64<\/p>\n
<\/code><\/pre>\n<\/p>\n
Pandas\u5e93\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u80fd\u529b\u548c\u7b80\u6d01\u7684\u8bed\u6cd5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002<\/strong><\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u57fa\u672cPython\u6570\u636e\u7ed3\u6784<\/h3>\n<\/p>\n
\u5982\u679c\u4e0d\u60f3\u4f9d\u8d56\u5916\u90e8\u5e93\uff0c\u53ef\u4ee5\u4f7f\u7528\u57fa\u672c\u7684Python\u6570\u636e\u7ed3\u6784\u5982\u5217\u8868\u548c\u5b57\u5178\u6765\u7edf\u8ba1\u5b57\u6bb5\u6570\u636e\u3002<\/p>\n<\/p>\n
2.1 \u4f7f\u7528\u5217\u8868\u548c\u5b57\u5178<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u5217\u8868\u8868\u793a\u6570\u636e\uff1a<\/p>\n<\/p>\n
data = [<\/p>\n
    {'name': 'Alice', 'age': 25, 'gender': 'F'},<\/p>\n
    {'name': 'Bob', 'age': 30, 'gender': 'M'},<\/p>\n
    {'name': 'Charlie', 'age': 35, 'gender': 'M'},<\/p>\n
    {'name': 'David', 'age': 40, 'gender': 'M'},<\/p>\n
    {'name': 'Eve', 'age': 45, 'gender': 'F'}<\/p>\n
]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\u6765\u7edf\u8ba1gender<\/code>\u5b57\u6bb5\u7684\u51fa\u73b0\u6b21\u6570\uff1a<\/p>\n<\/p>\n
gender_counts = {}<\/p>\n
for record in data:<\/p>\n
    gender = record['gender']<\/p>\n
    if gender in gender_counts:<\/p>\n
        gender_counts[gender] += 1<\/p>\n
    else:<\/p>\n
        gender_counts[gender] = 1<\/p>\n
print(gender_counts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
{'F': 2, 'M': 3}<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528\u57fa\u672cPython\u6570\u636e\u7ed3\u6784\u7684\u4f18\u70b9\u5728\u4e8e\u7075\u6d3b\u6027\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u5b9a\u5236\u3002<\/strong><\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f<\/h3>\n<\/p>\n
\u6b63\u5219\u8868\u8fbe\u5f0f\u9002\u7528\u4e8e\u590d\u6742\u7684\u5b57\u7b26\u4e32\u5339\u914d\u4efb\u52a1\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u975e\u7ed3\u6784\u5316\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n
3.1 \u5bfc\u5165\u6b63\u5219\u8868\u8fbe\u5f0f\u6a21\u5757<\/h4>\n<\/p>\n
Python\u5185\u7f6e\u4e86re<\/code>\u6a21\u5757\uff0c\u7528\u4e8e\u5904\u7406\u6b63\u5219\u8868\u8fbe\u5f0f\uff1a<\/p>\n<\/p>\n
import re<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u5339\u914d\u5b57\u6bb5<\/h4>\n<\/p>\n
\u5047\u8bbe\u6709\u4e00\u4e2a\u5b57\u7b26\u4e32\u8868\u793a\u6570\u636e\uff1a<\/p>\n<\/p>\n
name: Alice, age: 25, gender: F<\/p>\n
name: Bob, age: 30, gender: M<\/p>\n
name: Charlie, age: 35, gender: M<\/p>\n
name: David, age: 40, gender: M<\/p>\n
name: Eve, age: 45, gender: F<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u63d0\u53d6gender<\/code>\u5b57\u6bb5\u5e76\u7edf\u8ba1\u51fa\u73b0\u6b21\u6570\uff1a<\/p>\n<\/p>\n
data = '''<\/p>\n
name: Alice, age: 25, gender: F<\/p>\n
name: Bob, age: 30, gender: M<\/p>\n
name: Charlie, age: 35, gender: M<\/p>\n
name: David, age: 40, gender: M<\/p>\n
name: Eve, age: 45, gender: F<\/p>\n
'''<\/p>\n
pattern = re.compile(r'gender: (\\w)')<\/p>\n
matches = pattern.findall(data)<\/p>\n
gender_counts = {}<\/p>\n
for gender in matches:<\/p>\n
    if gender in gender_counts:<\/p>\n
        gender_counts[gender] += 1<\/p>\n
    else:<\/p>\n
        gender_counts[gender] = 1<\/p>\n
print(gender_counts)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n<\/p>\n
{'F': 2, 'M': 3}<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u6b63\u5219\u8868\u8fbe\u5f0f\u7684\u4f18\u70b9\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u5b57\u7b26\u4e32\u5339\u914d\u80fd\u529b\uff0c\u9002\u7528\u4e8e\u5904\u7406\u590d\u6742\u7684\u6587\u672c\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n
\u56db\u3001\u6bd4\u8f83\u548c\u603b\u7ed3<\/h3>\n<\/p>\n
4.1 \u6bd4\u8f83<\/h4>\n<\/p>\n
    \n
  • Pandas\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff0c\u5c24\u5176\u662f\u5927\u578b\u6570\u636e\u96c6\u3002\u5176\u4f18\u70b9\u5728\u4e8e\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u548c\u7b80\u6d01\u7684\u8bed\u6cd5\u3002<\/li>\n
  • \u57fa\u672cPython\u6570\u636e\u7ed3\u6784<\/strong>\uff1a\u9002\u7528\u4e8e\u5c0f\u578b\u6570\u636e\u96c6\u548c\u7b80\u5355\u4efb\u52a1\uff0c\u5177\u6709\u8f83\u9ad8\u7684\u7075\u6d3b\u6027\u3002<\/li>\n
  • \u6b63\u5219\u8868\u8fbe\u5f0f<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u975e\u7ed3\u6784\u5316\u6570\u636e\u548c\u590d\u6742\u7684\u5b57\u7b26\u4e32\u5339\u914d\u4efb\u52a1\uff0c\u5f3a\u5927\u4f46\u8bed\u6cd5\u8f83\u4e3a\u590d\u6742\u3002<\/li>\n<\/ul>\n
    4.2 \u603b\u7ed3<\/h4>\n<\/p>\n
    \u5728Python\u4e2d\u7edf\u8ba1\u4e00\u5217\u4e2d\u67d0\u4e2a\u5b57\u6bb5\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002\u5bf9\u4e8e\u5904\u7406\u5927\u578b\u7ed3\u6784\u5316\u6570\u636e\uff0c\u63a8\u8350\u4f7f\u7528Pandas\u5e93\uff1b\u5bf9\u4e8e\u5c0f\u578b\u6570\u636e\u96c6\u548c\u7b80\u5355\u4efb\u52a1\uff0c\u53ef\u4ee5\u4f7f\u7528\u57fa\u672cPython\u6570\u636e\u7ed3\u6784\uff1b\u5bf9\u4e8e\u590d\u6742\u7684\u5b57\u7b26\u4e32\u5339\u914d\u4efb\u52a1\uff0c\u53ef\u4ee5\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u3002\u6839\u636e\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u7684\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
    \u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u7edf\u8ba1\u4e00\u5217\u4e2d\u67d0\u4e2a\u5b57\u6bb5\u7684\u6570\u636e\uff0c\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u6570\u636e\u5904\u7406\u9700\u6c42\u3002<\/strong><\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u7edf\u8ba1\u4e00\u5217\u4e2d\u7279\u5b9a\u5b57\u6bb5\u7684\u6570\u91cf\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8f7b\u677e\u7edf\u8ba1\u6570\u636e\u6846\u4e2d\u67d0\u4e00\u5217\u7279\u5b9a\u5b57\u6bb5\u7684\u6570\u91cf\u3002\u9996\u5148\uff0c\u786e\u4fdd\u60a8\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u901a\u8fc7\u8c03\u7528df['\u5217\u540d'].value_counts()<\/code>\uff0c\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u8be5\u5217\u4e2d\u6bcf\u4e2a\u5b57\u6bb5\u7684\u9891\u6570\u3002\u5982\u679c\u60f3\u8981\u83b7\u53d6\u67d0\u4e2a\u7279\u5b9a\u5b57\u6bb5\u7684\u4e2a\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528df['\u5217\u540d'].value_counts().get('\u5b57\u6bb5\u540d', 0)<\/code>\uff0c\u8fd9\u6837\u5373\u4f7f\u5b57\u6bb5\u4e0d\u5b58\u5728\u4e5f\u4e0d\u4f1a\u62a5\u9519\uff0c\u8fd4\u56de0\u3002<\/p>\n
    \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\u4ee5\u786e\u4fdd\u5b57\u6bb5\u7edf\u8ba1\u7684\u51c6\u786e\u6027\uff1f<\/strong>
    \u5728\u8fdb\u884c\u5b57\u6bb5\u7edf\u8ba1\u4e4b\u524d\uff0c\u5904\u7406\u7f3a\u5931\u503c\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u60a8\u53ef\u4ee5\u4f7f\u7528df.dropna(subset=['\u5217\u540d'])<\/code>\u6765\u5220\u9664\u8be5\u5217\u4e2d\u7684\u7f3a\u5931\u503c\uff0c\u786e\u4fdd\u7edf\u8ba1\u7ed3\u679c\u7684\u51c6\u786e\u6027\u3002\u4e5f\u53ef\u4ee5\u4f7f\u7528df['\u5217\u540d'].fillna('\u9ed8\u8ba4\u503c')<\/code>\u586b\u5145\u7f3a\u5931\u503c\uff0c\u4ee5\u4fbf\u7edf\u8ba1\u65f6\u4e0d\u53d7\u5f71\u54cd\u3002\u786e\u4fdd\u9009\u62e9\u9002\u5408\u60a8\u6570\u636e\u7684\u5904\u7406\u65b9\u5f0f\uff0c\u4ee5\u63d0\u9ad8\u6570\u636e\u5206\u6790\u7684\u8d28\u91cf\u3002<\/p>\n
    \u5982\u4f55\u4f7f\u7528Python\u7684\u6761\u4ef6\u7b5b\u9009\u6765\u7edf\u8ba1\u7279\u5b9a\u5b57\u6bb5\u7684\u6570\u91cf\uff1f<\/strong>
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