{"id":1173384,"date":"2025-01-15T17:02:25","date_gmt":"2025-01-15T09:02:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1173384.html"},"modified":"2025-01-15T17:02:27","modified_gmt":"2025-01-15T09:02:27","slug":"python%e5%a6%82%e4%bd%95%e7%bb%9f%e8%ae%a1%e8%ae%a1%e7%ae%97%e9%a2%98","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1173384.html","title":{"rendered":"python\u5982\u4f55\u7edf\u8ba1\u8ba1\u7b97\u9898"},"content":{"rendered":"

\"python\u5982\u4f55\u7edf\u8ba1\u8ba1\u7b97\u9898\"<\/p>\n

Python\u7edf\u8ba1\u8ba1\u7b97\u9898\u7684\u65b9\u6cd5\u6709\uff1a\u5229\u7528\u5185\u7f6e\u51fd\u6570\u3001\u4f7f\u7528\u5217\u8868\u548c\u5b57\u5178\u3001\u501f\u52a9\u7b2c\u4e09\u65b9\u5e93\u5982pandas\u3001numpy\u3001scipy\u7b49\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528\u5185\u7f6e\u51fd\u6570\u6700\u4e3a\u57fa\u7840\uff0c\u9002\u5408\u7b80\u5355\u7684\u7edf\u8ba1\u8ba1\u7b97\uff1b\u4f7f\u7528\u5217\u8868\u548c\u5b57\u5178\u53ef\u4ee5\u7075\u6d3b\u5904\u7406\u4e0d\u540c\u7c7b\u578b\u7684\u6570\u636e\uff1b\u501f\u52a9\u7b2c\u4e09\u65b9\u5e93\u5219\u9002\u7528\u4e8e\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u8ba1\u7b97\u3002\u501f\u52a9pandas\u5e93\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u7edf\u8ba1\u5206\u6790\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u5229\u7528\u5185\u7f6e\u51fd\u6570<\/h3>\n<\/p>\n

Python\u63d0\u4f9b\u4e86\u8bb8\u591a\u5185\u7f6e\u51fd\u6570\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u8fdb\u884c\u57fa\u672c\u7684\u7edf\u8ba1\u8ba1\u7b97\u3002\u8fd9\u4e9b\u51fd\u6570\u5305\u62ecsum()<\/code>\u3001len()<\/code>\u3001min()<\/code>\u3001max()<\/code>\u7b49\u3002<\/p>\n<\/p>\n

1\u3001\u6c42\u548c\u4e0e\u5e73\u5747\u503c<\/h4>\n<\/p>\n

data = [1, 2, 3, 4, 5]<\/p>\n
total = sum(data)<\/p>\n
average = total \/ len(data)<\/p>\n
print(f"Sum: {total}, Average: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c<\/h4>\n<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
max_value = max(data)<\/p>\n
min_value = min(data)<\/p>\n
print(f"Max: {max_value}, Min: {min_value}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e9b\u5185\u7f6e\u51fd\u6570\u975e\u5e38\u4fbf\u6377\uff0c\u9002\u5408\u5904\u7406\u7b80\u5355\u7684\u7edf\u8ba1\u8ba1\u7b97\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u5217\u8868\u548c\u5b57\u5178<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u66f4\u590d\u6742\u7684\u6570\u636e\u7edf\u8ba1\u65f6\uff0c\u5217\u8868\u548c\u5b57\u5178\u662f\u975e\u5e38\u6709\u7528\u7684\u6570\u636e\u7ed3\u6784\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528\u5217\u8868<\/h4>\n<\/p>\n
\u5217\u8868\u53ef\u4ee5\u5b58\u50a8\u5927\u91cf\u7684\u6570\u636e\uff0c\u5e76\u4e14\u53ef\u4ee5\u901a\u8fc7\u7d22\u5f15\u8bbf\u95ee\u7279\u5b9a\u5143\u7d20\u3002<\/p>\n<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u6c42\u548c<\/strong><\/h2>\n
total = sum(data)<\/p>\n
\u5e73\u5747\u503c<\/strong><\/h2>\n
average = total \/ len(data)<\/p>\n
print(f"Sum: {total}, Average: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528\u5b57\u5178<\/h4>\n<\/p>\n
\u5b57\u5178\u53ef\u4ee5\u5b58\u50a8\u952e\u503c\u5bf9\u6570\u636e\uff0c\u9002\u5408\u5904\u7406\u5206\u7c7b\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']<\/p>\n
count_dict = {}<\/p>\n
for item in data:<\/p>\n
    if item in count_dict:<\/p>\n
        count_dict[item] += 1<\/p>\n
    else:<\/p>\n
        count_dict[item] = 1<\/p>\n
print(count_dict)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u501f\u52a9\u7b2c\u4e09\u65b9\u5e93<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u7edf\u8ba1\u8ba1\u7b97\uff0c\u53ef\u4ee5\u501f\u52a9\u7b2c\u4e09\u65b9\u5e93\u5982pandas\u3001numpy\u3001scipy\u7b49\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u80fd\u591f\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u4f7f\u7528pandas\u5e93<\/h4>\n<\/p>\n
pandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efaDataFrame<\/strong><\/h2>\n
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],<\/p>\n
        'Age': [24, 27, 22, 32],<\/p>\n
        'Score': [85, 90, 88, 79]}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u5e74\u9f84<\/strong><\/h2>\n
average_age = df['Age'].mean()<\/p>\n
print(f"Average Age: {average_age}")<\/p>\n
\u8ba1\u7b97\u6700\u9ad8\u5206\u6570<\/strong><\/h2>\n
max_score = df['Score'].max()<\/p>\n
print(f"Max Score: {max_score}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528numpy\u5e93<\/h4>\n<\/p>\n
numpy\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u9002\u5408\u8fdb\u884c\u5927\u89c4\u6a21\u7684\u6570\u503c\u8fd0\u7b97\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efa\u6570\u7ec4<\/strong><\/h2>\n
data = np.array([1, 2, 3, 4, 5])<\/p>\n
\u8ba1\u7b97\u548c<\/strong><\/h2>\n
total = np.sum(data)<\/p>\n
print(f"Sum: {total}")<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
average = np.mean(data)<\/p>\n
print(f"Average: {average}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528scipy\u5e93<\/h4>\n<\/p>\n
scipy\u662f\u4e00\u4e2a\u7528\u4e8e\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u7684\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u7edf\u8ba1\u51fd\u6570\u3002<\/p>\n<\/p>\n
from scipy import stats<\/p>\n
\u521b\u5efa\u6570\u636e<\/strong><\/h2>\n
data = [1, 2, 3, 4, 5]<\/p>\n
\u8ba1\u7b97\u4e2d\u4f4d\u6570<\/strong><\/h2>\n
median = stats.median(data)<\/p>\n
print(f"Median: {median}")<\/p>\n
\u8ba1\u7b97\u6807\u51c6\u5dee<\/strong><\/h2>\n
std_dev = stats.stdev(data)<\/p>\n
print(f"Standard Deviation: {std_dev}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b9e\u9645\u5e94\u7528\u6848\u4f8b<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5b9e\u9645\u5e94\u7528\u6848\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7edf\u8ba1\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
1\u3001\u6570\u636e\u9884\u5904\u7406<\/h4>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u8bfb\u53d6\u548c\u9884\u5904\u7406\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u5b66\u751f\u6210\u7ee9\u7684CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('students_scores.csv')<\/p>\n
\u67e5\u770b\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u6570\u636e\u6e05\u6d17<\/h4>\n<\/p>\n
\u5728\u8fdb\u884c\u7edf\u8ba1\u8ba1\u7b97\u4e4b\u524d\uff0c\u6211\u4eec\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u503c\u7b49\u3002<\/p>\n<\/p>\n
# \u53bb\u9664\u7f3a\u5931\u503c<\/p>\n
df.dropna(inplace=True)<\/p>\n
\u53bb\u9664\u91cd\u590d\u503c<\/strong><\/h2>\n
df.drop_duplicates(inplace=True)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u7edf\u8ba1\u5206\u6790<\/h4>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8fdb\u884c\u6570\u636e\u7684\u7edf\u8ba1\u5206\u6790\uff0c\u5305\u62ec\u8ba1\u7b97\u5e73\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u9ad8\u5206\u6570\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u5e73\u5747\u5206\u6570<\/p>\n
average_score = df['Score'].mean()<\/p>\n
print(f"Average Score: {average_score}")<\/p>\n
\u8ba1\u7b97\u6807\u51c6\u5dee<\/strong><\/h2>\n
std_dev = df['Score'].std()<\/p>\n
print(f"Standard Deviation: {std_dev}")<\/p>\n
\u8ba1\u7b97\u6700\u9ad8\u5206\u6570<\/strong><\/h2>\n
max_score = df['Score'].max()<\/p>\n
print(f"Max Score: {max_score}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h4>\n<\/p>\n
\u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u7edf\u8ba1\u7ed3\u679c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u5206\u6570\u5206\u5e03\u76f4\u65b9\u56fe<\/strong><\/h2>\n
plt.hist(df['Score'], bins=10, edgecolor='black')<\/p>\n
plt.xlabel('Score')<\/p>\n
plt.ylabel('Frequency')<\/p>\n
plt.title('Score Distribution')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u5185\u5bb9\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230Python\u5728\u7edf\u8ba1\u8ba1\u7b97\u65b9\u9762\u7684\u5f3a\u5927\u529f\u80fd\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u7edf\u8ba1\u8ba1\u7b97\uff0c\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0cPython\u90fd\u80fd\u63d0\u4f9b\u4fbf\u6377\u9ad8\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5229\u7528\u5185\u7f6e\u51fd\u6570\u3001\u4f7f\u7528\u5217\u8868\u548c\u5b57\u5178\u3001\u501f\u52a9\u7b2c\u4e09\u65b9\u5e93\u5982pandas\u3001numpy\u3001scipy\u7b49\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u8fdb\u884c\u7edf\u8ba1\u8ba1\u7b97\u3002<\/strong>\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u80fd\u591f\u4e8b\u534a\u529f\u500d\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u7b80\u5355\u7684\u7edf\u8ba1\u8ba1\u7b97\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\uff0c\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u7edf\u8ba1\u8ba1\u7b97\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u5904\u7406\u6570\u7ec4\u548c\u8fdb\u884c\u5404\u79cd\u6570\u5b66\u8fd0\u7b97\uff0c\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\u6846\u548c\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u751a\u81f3\u53ef\u4ee5\u901a\u8fc7Matplotlib\u6216Seaborn\u5e93\u6765\u53ef\u89c6\u5316\u6570\u636e\u3002\u7b80\u5355\u7684\u7edf\u8ba1\u8ba1\u7b97\u53ef\u4ee5\u901a\u8fc7\u8fd9\u4e9b\u5e93\u7684\u5185\u7f6e\u51fd\u6570\u5b9e\u73b0\uff0c\u6bd4\u5982\u8ba1\u7b97\u5747\u503c\u3001\u6807\u51c6\u5dee\u548c\u4e2d\u4f4d\u6570\u7b49\u3002<\/p>\n
\u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u4ee5\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff1f<\/strong>
\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\uff0c\u4f7f\u7528Pandas\u5e93\u662f\u4e00\u4e2a\u660e\u667a\u7684\u9009\u62e9\u3002\u5b83\u63d0\u4f9b\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6CSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\u3002\u901a\u8fc7\u5206\u7ec4\u3001\u6c47\u603b\u7b49\u64cd\u4f5c\uff0c\u4f60\u53ef\u4ee5\u5feb\u901f\u83b7\u5f97\u6240\u9700\u7684\u7edf\u8ba1\u4fe1\u606f\u3002\u6b64\u5916\uff0c\u4f7f\u7528Dask\u5e93\u53ef\u4ee5\u5904\u7406\u65e0\u6cd5\u5b8c\u5168\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\u7684\u6570\u636e\u96c6\uff0c\u5141\u8bb8\u8fdb\u884c\u5206\u5e03\u5f0f\u8ba1\u7b97\u3002<\/p>\n
Python\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u7edf\u8ba1\u5e93\uff1f<\/strong>
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