{"id":1177670,"date":"2025-01-15T18:00:50","date_gmt":"2025-01-15T10:00:50","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1177670.html"},"modified":"2025-01-15T18:00:53","modified_gmt":"2025-01-15T10:00:53","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8python%e7%bc%96%e7%a8%8b%e8%ae%a1%e7%ae%97%e6%af%94%e4%be%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1177670.html","title":{"rendered":"\u5982\u4f55\u4f7f\u7528Python\u7f16\u7a0b\u8ba1\u7b97\u6bd4\u4f8b"},"content":{"rendered":"

\"\u5982\u4f55\u4f7f\u7528Python\u7f16\u7a0b\u8ba1\u7b97\u6bd4\u4f8b\"<\/p>\n

\u4f7f\u7528Python\u7f16\u7a0b\u8ba1\u7b97\u6bd4\u4f8b\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u4f8b\u5982\u4f7f\u7528\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u7b49\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u662f\u6700\u76f4\u63a5\u548c\u7b80\u5355\u7684\u65b9\u5f0f\uff0c\u9002\u5408\u5904\u7406\u7b80\u5355\u7684\u6bd4\u4f8b\u8ba1\u7b97\u95ee\u9898\u3002NumPy\u548cPandas\u5e93\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u529f\u80fd\u548c\u4fbf\u5229\uff0c\u9002\u5408\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u548c\u8ba1\u7b97\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528\u57fa\u672c\u6570\u5b66\u8fd0\u7b97\u8ba1\u7b97\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n

\u4f7f\u7528\u57fa\u672c\u6570\u5b66\u8fd0\u7b97\u8ba1\u7b97\u6bd4\u4f8b<\/h3>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u8ba1\u7b97\u6bd4\u4f8b\u6700\u7b80\u5355\u7684\u65b9\u6cd5\u5c31\u662f\u4f7f\u7528\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u6570\u503cA\u548cB\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97A\u76f8\u5bf9\u4e8eB\u7684\u6bd4\u4f8b\uff0c\u90a3\u4e48\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\uff1a<\/p>\n<\/p>\n

ratio = A \/ B<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f8b\u5982\uff0c\u5982\u679cA\u662f50\uff0cB\u662f200\uff0c\u90a3\u4e48\u6bd4\u4f8b\u8ba1\u7b97\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
A = 50<\/p>\n
B = 200<\/p>\n
ratio = A \/ B<\/p>\n
print("Ratio:", ratio)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
Ratio: 0.25<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e00\u3001\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u6bd4\u4f8b<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u51fd\u6570\u548c\u65b9\u6cd5\u7528\u4e8e\u6570\u7ec4\u548c\u77e9\u9635\u64cd\u4f5c\u3002\u4f7f\u7528NumPy\u5e93\u8ba1\u7b97\u6bd4\u4f8b\u975e\u5e38\u65b9\u4fbf\uff0c\u7279\u522b\u662f\u5f53\u6211\u4eec\u9700\u8981\u5904\u7406\u5927\u91cf\u6570\u636e\u65f6\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5NumPy<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528NumPy\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8fd9\u4e2a\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5NumPy\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528NumPy\u8ba1\u7b97\u6bd4\u4f8b<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u6570\u7ec4A\u548cB\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e2a\u5143\u7d20\u7684\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
A = np.array([50, 100, 150])<\/p>\n
B = np.array([200, 400, 600])<\/p>\n
ratio = A \/ B<\/p>\n
print("Ratio:", ratio)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
Ratio: [0.25 0.25 0.25]<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e2a\u5143\u7d20\u7684\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u6bd4\u4f8b<\/h3>\n<\/p>\n
Pandas\u662fPython\u4e2d\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002\u4f7f\u7528Pandas\u5e93\u8ba1\u7b97\u6bd4\u4f8b\u975e\u5e38\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4f8b\u5982\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
1\u3001\u5b89\u88c5Pandas<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528Pandas\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u8fd9\u4e2a\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Pandas\uff1a<\/p>\n<\/p>\n
pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Pandas\u8ba1\u7b97\u6bd4\u4f8b<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aDataFrame\uff0c\u5b83\u5305\u542b\u4e86\u4e24\u5217\u6570\u636e\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e00\u884c\u7684\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'A': [50, 100, 150], 'B': [200, 400, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['Ratio'] = df['A'] \/ df['B']<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
     A    B  Ratio<\/p>\n
0   50  200   0.25<\/p>\n
1  100  400   0.25<\/p>\n
2  150  600   0.25<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e00\u884c\u7684\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\uff0c\u5e76\u6dfb\u52a0\u5230\u65b0\u7684\u5217\u4e2d\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97\u6bd4\u4f8b\u7684\u5b9e\u9645\u5e94\u7528<\/h3>\n<\/p>\n
\u8ba1\u7b97\u6bd4\u4f8b\u5728\u8bb8\u591a\u5b9e\u9645\u5e94\u7528\u4e2d\u975e\u5e38\u6709\u7528\uff0c\u4f8b\u5982\u5728\u6570\u636e\u5206\u6790\u3001\u7edf\u8ba1\u5b66\u3001\u8d22\u52a1\u5206\u6790\u7b49\u9886\u57df\u3002\u4e0b\u9762\u5c06\u4ecb\u7ecd\u4e00\u4e9b\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u4f8b\u5b50\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u5206\u6790\u4e2d\u7684\u6bd4\u4f8b\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0c\u8ba1\u7b97\u6bd4\u4f8b\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u548c\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u9500\u552e\u6570\u636e\u96c6\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e2a\u4ea7\u54c1\u7c7b\u522b\u7684\u9500\u552e\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Category': ['A', 'B', 'C', 'A', 'B', 'C'], 'Sales': [100, 200, 300, 400, 500, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
category_sales = df.groupby('Category')['Sales'].sum()<\/p>\n
total_sales = category_sales.sum()<\/p>\n
category_ratio = category_sales \/ total_sales<\/p>\n
print(category_ratio)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
Category<\/p>\n
A    0.333333<\/p>\n
B    0.333333<\/p>\n
C    0.333333<\/p>\n
Name: Sales, dtype: float64<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e2a\u4ea7\u54c1\u7c7b\u522b\u7684\u9500\u552e\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
2\u3001\u7edf\u8ba1\u5b66\u4e2d\u7684\u6bd4\u4f8b\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0c\u6bd4\u4f8b\u8ba1\u7b97\u53ef\u4ee5\u7528\u4e8e\u63cf\u8ff0\u6570\u636e\u7684\u7279\u5f81\u548c\u8d8b\u52bf\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u8003\u8bd5\u6210\u7ee9\u6570\u636e\u96c6\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e2a\u6210\u7ee9\u533a\u95f4\u7684\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Score': [50, 60, 70, 80, 90, 100]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
bins = [0, 60, 70, 80, 90, 100]<\/p>\n
labels = ['F', 'D', 'C', 'B', 'A']<\/p>\n
df['Grade'] = pd.cut(df['Score'], bins=bins, labels=labels)<\/p>\n
grade_counts = df['Grade'].value_counts()<\/p>\n
total_counts = grade_counts.sum()<\/p>\n
grade_ratio = grade_counts \/ total_counts<\/p>\n
print(grade_ratio)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
F    0.166667<\/p>\n
D    0.166667<\/p>\n
C    0.166667<\/p>\n
B    0.166667<\/p>\n
A    0.333333<\/p>\n
Name: Grade, dtype: float64<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e2a\u6210\u7ee9\u533a\u95f4\u7684\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
3\u3001\u8d22\u52a1\u5206\u6790\u4e2d\u7684\u6bd4\u4f8b\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5728\u8d22\u52a1\u5206\u6790\u4e2d\uff0c\u6bd4\u4f8b\u8ba1\u7b97\u53ef\u4ee5\u7528\u4e8e\u8bc4\u4f30\u516c\u53f8\u7684\u8d22\u52a1\u72b6\u51b5\u548c\u7ee9\u6548\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u516c\u53f8\u7684\u8d22\u52a1\u6570\u636e\u96c6\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e2a\u9879\u76ee\u7684\u6536\u5165\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Project': ['A', 'B', 'C'], 'Revenue': [100000, 200000, 300000]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
total_revenue = df['Revenue'].sum()<\/p>\n
project_ratio = df['Revenue'] \/ total_revenue<\/p>\n
df['Ratio'] = project_ratio<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
  Project  Revenue     Ratio<\/p>\n
0       A   100000  0.166667<\/p>\n
1       B   200000  0.333333<\/p>\n
2       C   300000  0.500000<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e2a\u9879\u76ee\u7684\u6536\u5165\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4f7f\u7528Python\u8fdb\u884c\u590d\u6742\u6bd4\u4f8b\u8ba1\u7b97<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6bd4\u4f8b\u8ba1\u7b97\u53ef\u80fd\u4f1a\u53d8\u5f97\u66f4\u52a0\u590d\u6742\uff0c\u4f8b\u5982\u9700\u8981\u8003\u8651\u6743\u91cd\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7b49\u3002\u4e0b\u9762\u5c06\u4ecb\u7ecd\u4e00\u4e9b\u590d\u6742\u6bd4\u4f8b\u8ba1\u7b97\u7684\u4f8b\u5b50\u3002<\/p>\n<\/p>\n
1\u3001\u8003\u8651\u6743\u91cd\u7684\u6bd4\u4f8b\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5b83\u5305\u542b\u4e86\u6bcf\u4e2a\u5b66\u751f\u7684\u6210\u7ee9\u548c\u6743\u91cd\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u52a0\u6743\u5e73\u5747\u6210\u7ee9\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Student': ['A', 'B', 'C'], 'Score': [85, 90, 95], 'Weight': [0.2, 0.3, 0.5]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
weighted_score = (df['Score'] * df['Weight']).sum()<\/p>\n
total_weight = df['Weight'].sum()<\/p>\n
weighted_average = weighted_score \/ total_weight<\/p>\n
print("Weighted Average Score:", weighted_average)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
Weighted Average Score: 91.5<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u52a0\u6743\u5e73\u5747\u6210\u7ee9\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
2\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u6bd4\u4f8b\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u96c6\uff0c\u5b83\u5305\u542b\u4e86\u6bcf\u4e2a\u6708\u7684\u9500\u552e\u6570\u636e\uff0c\u6211\u4eec\u60f3\u8981\u8ba1\u7b97\u6bcf\u4e2a\u6708\u7684\u9500\u552e\u589e\u957f\u6bd4\u4f8b\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr'], 'Sales': [1000, 1200, 1500, 1800]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['Previous_Sales'] = df['Sales'].shift(1)<\/p>\n
df['Growth_Ratio'] = (df['Sales'] - df['Previous_Sales']) \/ df['Previous_Sales']<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\u7ed3\u679c\u5c06\u662f\uff1a<\/p>\n<\/p>\n
  Month  Sales  Previous_Sales  Growth_Ratio<\/p>\n
0   Jan   1000             NaN           NaN<\/p>\n
1   Feb   1200          1000.0      0.200000<\/p>\n
2   Mar   1500          1200.0      0.250000<\/p>\n
3   Apr   1800          1500.0      0.200000<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u8868\u660e\u6bcf\u4e2a\u6708\u7684\u9500\u552e\u589e\u957f\u6bd4\u4f8b\u90fd\u8ba1\u7b97\u51fa\u6765\u4e86\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Python\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u7684\u6700\u4f73\u5b9e\u8df5<\/h3>\n<\/p>\n
\u5728\u4f7f\u7528Python\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u65f6\uff0c\u6709\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u63d0\u9ad8\u4ee3\u7801\u7684\u6548\u7387\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u5411\u91cf\u5316\u8fd0\u7b97<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u65f6\uff0c\u5c3d\u91cf\u4f7f\u7528\u5411\u91cf\u5316\u8fd0\u7b97\uff0c\u800c\u4e0d\u662f\u5faa\u73af\u3002\u5411\u91cf\u5316\u8fd0\u7b97\u53ef\u4ee5\u5927\u5927\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002\u4f8b\u5982\uff0c\u4f7f\u7528NumPy\u5e93\u7684\u5411\u91cf\u5316\u8fd0\u7b97\u53ef\u4ee5\u6bd4\u4f7f\u7528Python\u7684for\u5faa\u73af\u5feb\u5f97\u591a\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
A = np.array([50, 100, 150])<\/p>\n
B = np.array([200, 400, 600])<\/p>\n
ratio = A \/ B  # \u5411\u91cf\u5316\u8fd0\u7b97<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u6548\u7387\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u7684DataFrame\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'A': [50, 100, 150], 'B': [200, 400, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['Ratio'] = df['A'] \/ df['B']<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u65f6\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u3002\u5904\u7406\u7f3a\u5931\u503c\u53ef\u4ee5\u907f\u514d\u8ba1\u7b97\u9519\u8bef\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pandas\u7684fillna\u65b9\u6cd5\u53ef\u4ee5\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
data = {'A': [50, None, 150], 'B': [200, 400, 600]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['A'] = df['A'].fillna(0)<\/p>\n
df['Ratio'] = df['A'] \/ df['B']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u7528\u4e8e\u6bd4\u4f8b\u8ba1\u7b97\uff0c\u5305\u62ec\u4f7f\u7528\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u3001NumPy\u5e93\u3001Pandas\u5e93\u7b49\u3002\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\u3002\u4f7f\u7528\u57fa\u672c\u6570\u5b66\u8fd0\u7b97\u9002\u5408\u5904\u7406\u7b80\u5355\u7684\u6bd4\u4f8b\u8ba1\u7b97\u95ee\u9898\uff0cNumPy\u548cPandas\u5e93\u5219\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u529f\u80fd\u548c\u4fbf\u5229\uff0c\u9002\u5408\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u548c\u8ba1\u7b97\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8ba1\u7b97\u6bd4\u4f8b\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u7edf\u8ba1\u5b66\u3001\u8d22\u52a1\u5206\u6790\u7b49\u9886\u57df\u3002\u4f7f\u7528Python\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u65f6\uff0c\u9075\u5faa\u4e00\u4e9b\u6700\u4f73\u5b9e\u8df5\u53ef\u4ee5\u63d0\u9ad8\u4ee3\u7801\u7684\u6548\u7387\u548c\u53ef\u8bfb\u6027\uff0c\u4f8b\u5982\u4f7f\u7528\u5411\u91cf\u5316\u8fd0\u7b97\u3001\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u3001\u5904\u7406\u7f3a\u5931\u503c\u7b49\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u80fd\u591f\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u4f7f\u7528Python\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u4e24\u4e2a\u6570\u7684\u6bd4\u4f8b\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u9664\u6cd5\u8fd0\u7b97\u6765\u8ba1\u7b97\u4e24\u4e2a\u6570\u7684\u6bd4\u4f8b\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u4f60\u6709\u4e24\u4e2a\u6570a\u548cb\uff0c\u6bd4\u4f8b\u53ef\u4ee5\u901a\u8fc7a \/ b<\/code>\u6765\u8ba1\u7b97\u3002\u4e3a\u4e86\u907f\u514d\u9664\u96f6\u9519\u8bef\uff0c\u53ef\u4ee5\u5148\u68c0\u67e5b\u662f\u5426\u4e3a\u96f6\u3002\u6b64\u5916\uff0c\u4f7f\u7528Python\u7684\u5185\u7f6e\u51fd\u6570round()<\/code>\u53ef\u4ee5\u5e2e\u52a9\u4f60\u63a7\u5236\u5c0f\u6570\u70b9\u540e\u7684\u4f4d\u6570\uff0c\u4f7f\u8f93\u51fa\u66f4\u4e3a\u7f8e\u89c2\u3002<\/p>\n
\u5728Python\u4e2d\u6709\u6ca1\u6709\u73b0\u6210\u7684\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u8ba1\u7b97\u6bd4\u4f8b\uff1f<\/strong>
\u662f\u7684\uff0cPython\u4e2d\u6709\u591a\u4e2a\u5e93\u53ef\u4ee5\u5e2e\u52a9\u4f60\u8fdb\u884c\u6bd4\u4f8b\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u4f7f\u7528numpy<\/code>\u5e93\u4e2d\u7684np.divide()<\/code>\u51fd\u6570\u53ef\u4ee5\u5904\u7406\u6570\u7ec4\u4e2d\u7684\u6bd4\u4f8b\u8ba1\u7b97\u3002\u8fd9\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u975e\u5e38\u6709\u7528\u3002\u6b64\u5916\uff0cpandas<\/code>\u5e93\u4e5f\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u6570\u636e\u7ed3\u6784\u548c\u51fd\u6570\uff0c\u80fd\u591f\u8f7b\u677e\u8ba1\u7b97DataFrame\u4e2d\u7684\u5217\u6bd4\u4f8b\u3002<\/p>\n
\u5982\u4f55\u5c06\u8ba1\u7b97\u7684\u6bd4\u4f8b\u4ee5\u767e\u5206\u6bd4\u5f62\u5f0f\u8f93\u51fa\uff1f<\/strong>
\u5c06\u6bd4\u4f8b\u8f6c\u6362\u4e3a\u767e\u5206\u6bd4\u5f62\u5f0f\u76f8\u5f53\u7b80\u5355\u3002\u53ef\u4ee5\u5c06\u8ba1\u7b97\u51fa\u7684\u6bd4\u4f8b\u4e58\u4ee5100\uff0c\u5e76\u4f7f\u7528\u683c\u5f0f\u5316\u8f93\u51fa\u8fdb\u884c\u663e\u793a\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6bd4\u4f8b\u8ba1\u7b97\u7ed3\u679c\u4e3aratio<\/code>\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528print(f"The ratio is {ratio * 100:.2f}%")<\/code>\u6765\u8f93\u51fa\u767e\u5206\u6bd4\uff0c\u786e\u4fdd\u7ed3\u679c\u4fdd\u7559\u4e24\u4f4d\u5c0f\u6570\u3002\u8fd9\u79cd\u65b9\u5f0f\u4f7f\u5f97\u6570\u636e\u7684\u5448\u73b0\u66f4\u52a0\u76f4\u89c2\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u7f16\u7a0b\u8ba1\u7b97\u6bd4\u4f8b\u7684\u65b9\u6cd5\u6709\u5f88\u591a\uff0c\u4f8b\u5982\u4f7f\u7528\u57fa\u672c\u7684\u6570\u5b66\u8fd0\u7b97\u3001\u4f7f\u7528NumPy\u5e93\u3001\u4f7f\u7528Pandas\u5e93\u7b49\u3002 \u5176 […]","protected":false},"author":3,"featured_media":1177676,"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\/1177670"}],"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=1177670"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1177670\/revisions"}],"predecessor-version":[{"id":1177678,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1177670\/revisions\/1177678"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1177676"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1177670"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1177670"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1177670"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}