{"id":1127564,"date":"2025-01-08T20:11:25","date_gmt":"2025-01-08T12:11:25","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1127564.html"},"modified":"2025-01-08T20:11:28","modified_gmt":"2025-01-08T12:11:28","slug":"python%e5%a6%82%e4%bd%95%e9%80%89%e5%8f%96csv%e4%b8%ad%e7%9a%84%e4%b8%80%e5%88%97%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1127564.html","title":{"rendered":"python\u5982\u4f55\u9009\u53d6csv\u4e2d\u7684\u4e00\u5217\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u9009\u53d6csv\u4e2d\u7684\u4e00\u5217\u6570\u636e\"<\/p>\n

Python\u9009\u53d6CSV\u4e2d\u7684\u4e00\u5217\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528csv\u6a21\u5757\u3001\u4f7f\u7528Numpy\u5e93\u3002<\/strong> \u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93<\/strong>\u662f\u6700\u7b80\u5355\u548c\u5e38\u7528\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u548c\u9ad8\u6548\u7684\u6027\u80fd\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e09\u79cd\u65b9\u6cd5\uff0c\u5e76\u89e3\u91ca\u5982\u4f55\u5728\u4e0d\u540c\u7684\u60c5\u5883\u4e0b\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

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

1.1 \u5b89\u88c5\u548c\u5bfc\u5165Pandas\u5e93<\/h4>\n<\/p>\n

\u8981\u4f7f\u7528Pandas\u5e93\uff0c\u4f60\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\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\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
1.2 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
df = pd.read_csv('your_file.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5176\u4e2d\uff0cyour_file.csv<\/code>\u662f\u4f60\u7684CSV\u6587\u4ef6\u7684\u8def\u5f84\u3002<\/p>\n<\/p>\n
1.3 \u9009\u53d6\u4e00\u5217\u6570\u636e<\/h4>\n<\/p>\n
\u8981\u9009\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4e00\u5217\u6570\u636e\uff0c\u4f60\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u5217\u540d\u8fdb\u884c\u8bbf\u95ee\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u60f3\u9009\u53d6\u540d\u4e3acolumn_name<\/code>\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
column_data = df['column_name']<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u5c06\u8fd4\u56de\u4e00\u4e2aPandas Series\u5bf9\u8c61\uff0c\u5305\u542b\u8be5\u5217\u7684\u6240\u6709\u6570\u636e\u3002<\/p>\n<\/p>\n
1.4 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Pandas\u9009\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('your_file.csv')<\/p>\n
\u9009\u53d6\u4e00\u5217\u6570\u636e<\/strong><\/h2>\n
column_data = df['column_name']<\/p>\n
\u6253\u5370\u9009\u53d6\u7684\u6570\u636e<\/strong><\/h2>\n
print(column_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528csv\u6a21\u5757<\/h3>\n<\/p>\n
2.1 \u5bfc\u5165csv\u6a21\u5757<\/h4>\n<\/p>\n
Python\u5185\u7f6e\u7684csv\u6a21\u5757\u53ef\u4ee5\u7528\u4e8e\u5904\u7406CSV\u6587\u4ef6\u3002\u9996\u5148\u5bfc\u5165csv\u6a21\u5757\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.2 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\u9700\u8981\u6253\u5f00\u6587\u4ef6\uff0c\u7136\u540e\u521b\u5efa\u4e00\u4e2acsv.reader\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
with open('your_file.csv', mode='r') as file:<\/p>\n
    csv_reader = csv.reader(file)<\/p>\n
    header = next(csv_reader)  # \u8bfb\u53d6\u8868\u5934<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.3 \u9009\u53d6\u4e00\u5217\u6570\u636e<\/h4>\n<\/p>\n
\u4f60\u9700\u8981\u627e\u5230\u5217\u7684\u7d22\u5f15\uff0c\u7136\u540e\u63d0\u53d6\u8be5\u5217\u7684\u6570\u636e\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u60f3\u9009\u53d6\u540d\u4e3acolumn_name<\/code>\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
column_index = header.index('column_name')<\/p>\n
column_data = [row[column_index] for row in csv_reader]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2.4 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528csv\u6a21\u5757\u9009\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
import csv<\/p>\n
with open('your_file.csv', mode='r') as file:<\/p>\n
    csv_reader = csv.reader(file)<\/p>\n
    header = next(csv_reader)  # \u8bfb\u53d6\u8868\u5934<\/p>\n
    column_index = header.index('column_name')<\/p>\n
    column_data = [row[column_index] for row in csv_reader]<\/p>\n
print(column_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528Numpy\u5e93<\/h3>\n<\/p>\n
3.1 \u5b89\u88c5\u548c\u5bfc\u5165Numpy\u5e93<\/h4>\n<\/p>\n
\u8981\u4f7f\u7528Numpy\u5e93\uff0c\u4f60\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165Numpy\u5e93\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.2 \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528Numpy\u8bfb\u53d6CSV\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7np.genfromtxt()<\/code>\u51fd\u6570\uff1a<\/p>\n<\/p>\n
data = np.genfromtxt('your_file.csv', delimiter=',', dtype=None, names=True, encoding='utf-8')<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.3 \u9009\u53d6\u4e00\u5217\u6570\u636e<\/h4>\n<\/p>\n
Numpy\u8bfb\u53d6\u7684CSV\u6570\u636e\u901a\u5e38\u662f\u4e00\u4e2a\u7ed3\u6784\u5316\u6570\u7ec4\uff0c\u4f60\u53ef\u4ee5\u901a\u8fc7\u5217\u540d\u8fdb\u884c\u8bbf\u95ee\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u60f3\u9009\u53d6\u540d\u4e3acolumn_name<\/code>\u7684\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
column_data = data['column_name']<\/p>\n
<\/code><\/pre>\n<\/p>\n
3.4 \u793a\u4f8b\u4ee3\u7801<\/h4>\n<\/p>\n
\u4ee5\u4e0b\u662f\u4e00\u4e2a\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528Numpy\u9009\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4e00\u5217\u6570\u636e\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = np.genfromtxt('your_file.csv', delimiter=',', dtype=None, names=True, encoding='utf-8')<\/p>\n
\u9009\u53d6\u4e00\u5217\u6570\u636e<\/strong><\/h2>\n
column_data = data['column_name']<\/p>\n
\u6253\u5370\u9009\u53d6\u7684\u6570\u636e<\/strong><\/h2>\n
print(column_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4e0d\u540c\u65b9\u6cd5\u7684\u6bd4\u8f83<\/h3>\n<\/p>\n
4.1 \u6027\u80fd\u6bd4\u8f83<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u5927\u578bCSV\u6587\u4ef6\u65f6\uff0cPandas\u901a\u5e38\u5177\u6709\u66f4\u597d\u7684\u6027\u80fd\uff0c\u56e0\u4e3a\u5b83\u662f\u4e13\u95e8\u4e3a\u6570\u636e\u5206\u6790\u8bbe\u8ba1\u7684\uff0c\u5177\u6709\u9ad8\u6548\u7684\u5e95\u5c42\u5b9e\u73b0\u3002Numpy\u5728\u5904\u7406\u6570\u503c\u6570\u636e\u65f6\u6027\u80fd\u4e5f\u975e\u5e38\u4f18\u79c0\uff0c\u4f46\u5728\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u65f6\u53ef\u80fd\u4e0d\u5982Pandas\u65b9\u4fbf\u3002csv\u6a21\u5757\u7531\u4e8e\u662fPython\u5185\u7f6e\u6a21\u5757\uff0c\u9002\u7528\u4e8e\u5c0f\u578b\u6587\u4ef6\u7684\u7b80\u5355\u5904\u7406\uff0c\u4f46\u5728\u6027\u80fd\u548c\u529f\u80fd\u4e0a\u4e0d\u5982Pandas\u548cNumpy\u3002<\/p>\n<\/p>\n
4.2 \u4f7f\u7528\u573a\u666f<\/h4>\n<\/p>\n
Pandas\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u590d\u6742\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u7684\u573a\u666f\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u5982\u6570\u636e\u6e05\u6d17\u3001\u5408\u5e76\u3001\u5206\u7ec4\u7b49\uff0c\u975e\u5e38\u9002\u5408\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\u3002<\/p>\n<\/p>\n
csv\u6a21\u5757<\/strong>\uff1a\u9002\u7528\u4e8e\u7b80\u5355\u7684CSV\u6587\u4ef6\u8bfb\u53d6\u548c\u5199\u5165\u64cd\u4f5c\uff0c\u7279\u522b\u662f\u5728\u4e0d\u9700\u8981\u8fdb\u884c\u590d\u6742\u6570\u636e\u5904\u7406\u7684\u60c5\u51b5\u4e0b\u3002csv\u6a21\u5757\u662fPython\u5185\u7f6e\u6a21\u5757\uff0c\u65e0\u9700\u989d\u5916\u5b89\u88c5\uff0c\u975e\u5e38\u65b9\u4fbf\u3002<\/p>\n<\/p>\n
Numpy\u5e93<\/strong>\uff1a\u9002\u7528\u4e8e\u9700\u8981\u8fdb\u884c\u9ad8\u6548\u6570\u503c\u8ba1\u7b97\u7684\u573a\u666f\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u5927\u91cf\u6570\u503c\u6570\u636e\u65f6\u3002Numpy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u5b66\u51fd\u6570\u548c\u9ad8\u6548\u7684\u6570\u7ec4\u64cd\u4f5c\uff0c\u975e\u5e38\u9002\u5408\u79d1\u5b66\u8ba1\u7b97\u548c\u5de5\u7a0b\u9886\u57df\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86Python\u4e2d\u5982\u4f55\u9009\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4e00\u5217\u6570\u636e\uff0c\u5206\u522b\u4f7f\u7528\u4e86Pandas\u5e93\u3001csv\u6a21\u5757\u548cNumpy\u5e93\u4e09\u79cd\u65b9\u6cd5\u3002Pandas\u5e93<\/strong>\u662f\u6700\u5e38\u7528\u548c\u63a8\u8350\u7684\u65b9\u6cd5\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u548c\u9ad8\u6548\u7684\u6027\u80fd\u3002csv\u6a21\u5757<\/strong>\u9002\u7528\u4e8e\u7b80\u5355\u7684CSV\u6587\u4ef6\u64cd\u4f5c\uff0c\u800cNumpy\u5e93<\/strong>\u9002\u7528\u4e8e\u9ad8\u6548\u6570\u503c\u8ba1\u7b97\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5e2e\u52a9\u4f60\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u6765\u5904\u7406CSV\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u7279\u5b9a\u5217\uff1f<\/strong>
\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u9009\u62e9\u7279\u5b9a\u5217\u3002\u9996\u5148\uff0c\u901a\u8fc7import pandas as pd<\/code>\u5bfc\u5165Pandas\u3002\u7136\u540e\uff0c\u4f7f\u7528pd.read_csv('\u6587\u4ef6\u8def\u5f84.csv')<\/code>\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u6700\u540e\u901a\u8fc7dataframe['\u5217\u540d']<\/code>\u6765\u83b7\u53d6\u6240\u9700\u5217\u7684\u6570\u636e\u3002<\/p>\n
\u5728\u9009\u53d6CSV\u4e2d\u7684\u4e00\u5217\u6570\u636e\u65f6\uff0c\u662f\u5426\u53ef\u4ee5\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
\u662f\u7684\uff0cPandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002\u5728\u9009\u62e9\u7279\u5b9a\u5217\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528dataframe['\u5217\u540d'].dropna()<\/code>\u6765\u53bb\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528dataframe['\u5217\u540d'].fillna(\u586b\u5145\u503c)<\/code>\u6765\u586b\u8865\u7f3a\u5931\u503c\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u5b8c\u6574\u6027\u548c\u51c6\u786e\u6027\u3002<\/p>\n
\u5982\u4f55\u5c06\u9009\u53d6\u7684\u5217\u6570\u636e\u4fdd\u5b58\u5230\u65b0\u7684CSV\u6587\u4ef6\u4e2d\uff1f<\/strong>
\u53ef\u4ee5\u901a\u8fc7Pandas\u7684to_csv<\/code>\u65b9\u6cd5\u5c06\u9009\u53d6\u7684\u5217\u6570\u636e\u4fdd\u5b58\u5230\u65b0\u7684CSV\u6587\u4ef6\u4e2d\u3002\u9996\u5148\uff0c\u9009\u53d6\u6240\u9700\u5217\u5e76\u8d4b\u503c\u7ed9\u4e00\u4e2a\u65b0\u7684DataFrame\uff0c\u4f8b\u5982new_df = dataframe[['\u5217\u540d']]<\/code>\u3002\u7136\u540e\uff0c\u4f7f\u7528new_df.to_csv('\u65b0\u6587\u4ef6\u540d.csv', index=False)<\/code>\u5c06\u5176\u5bfc\u51fa\u4e3a\u65b0\u7684CSV\u6587\u4ef6\uff0cindex=False<\/code>\u53c2\u6570\u53ef\u4ee5\u907f\u514d\u5728\u6587\u4ef6\u4e2d\u5199\u5165\u884c\u7d22\u5f15\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u9009\u53d6CSV\u4e2d\u7684\u4e00\u5217\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u6b65\u9aa4\u5b9e\u73b0\uff1a\u4f7f\u7528Pandas\u5e93\u3001\u4f7f\u7528csv\u6a21\u5757\u3001\u4f7f\u7528Numpy\u5e93\u3002 […]","protected":false},"author":3,"featured_media":1127572,"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\/1127564"}],"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=1127564"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1127564\/revisions"}],"predecessor-version":[{"id":1127573,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1127564\/revisions\/1127573"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1127572"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1127564"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1127564"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1127564"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}