{"id":1129014,"date":"2025-01-08T20:25:47","date_gmt":"2025-01-08T12:25:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1129014.html"},"modified":"2025-01-08T20:25:50","modified_gmt":"2025-01-08T12:25:50","slug":"python%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96csv%e6%96%87%e4%bb%b6%e7%9a%84%e6%9f%90%e4%b8%80%e5%88%97","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1129014.html","title":{"rendered":"python\u5982\u4f55\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217"},"content":{"rendered":"

\"python\u5982\u4f55\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\"<\/p>\n

Python\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u3001csv\u6a21\u5757\u3001numpy\u5e93\u7b49\u65b9\u6cd5\u3002<\/strong> \u5176\u4e2d\uff0cpandas\u5e93<\/strong>\u662f\u6700\u63a8\u8350\u7684\u65b9\u5f0f\uff0c\u56e0\u4e3a\u5b83\u529f\u80fd\u5f3a\u5927\u3001\u6613\u4e8e\u4f7f\u7528\uff0c\u53ef\u4ee5\u5904\u7406\u590d\u6742\u7684\u6570\u636e\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u4f7f\u7528pandas\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\u3002<\/p>\n<\/p>\n

\u4f7f\u7528pandas\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8981\u51e0\u884c\u4ee3\u7801\u3002\u9996\u5148\uff0c\u5b89\u88c5pandas\u5e93\uff08\u5982\u679c\u5c1a\u672a\u5b89\u88c5\uff09\uff1a<\/strong><\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\uff0c\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\uff1a<\/strong><\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('file.csv')<\/p>\n
\u9009\u62e9\u67d0\u4e00\u5217<\/strong><\/h2>\n
column_data = df['column_name']<\/p>\n
print(column_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u4efb\u610f\u5217\u5e76\u5bf9\u5176\u8fdb\u884c\u64cd\u4f5c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u5176\u4ed6\u65b9\u6cd5\u4ee5\u53ca\u4e00\u4e9b\u6df1\u5165\u7684\u64cd\u4f5c\u6280\u5de7\u3002<\/p>\n<\/p>\n
\u4e00\u3001\u4f7f\u7528pandas\u5e93<\/h2>\n<\/p>\n
1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f7f\u7528pandas\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ef\u4ee5\u901a\u8fc7pd.read_csv<\/code>\u51fd\u6570\u76f4\u63a5\u8bfb\u53d6CSV\u6587\u4ef6\u5e76\u8f6c\u6362\u4e3aDataFrame\u5bf9\u8c61\u3002DataFrame\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u8868\u683c\uff0c\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('file.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u9009\u62e9\u67d0\u4e00\u5217<\/h3>\n<\/p>\n
\u901a\u8fc7DataFrame\u5bf9\u8c61\u7684\u5217\u540d\uff0c\u53ef\u4ee5\u76f4\u63a5\u9009\u62e9\u67d0\u4e00\u5217\u7684\u6570\u636e\u3002\u6bd4\u5982\uff0c\u5982\u679cCSV\u6587\u4ef6\u4e2d\u6709\u4e00\u5217\u540d\u4e3a'column_name'\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u83b7\u53d6\u8be5\u5217\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
column_data = df['column_name']<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u6570\u636e\u5904\u7406\u4e2d\uff0c\u7ecf\u5e38\u4f1a\u9047\u5230\u7f3a\u5931\u503c\u3002\u53ef\u4ee5\u4f7f\u7528dropna<\/code>\u51fd\u6570\u5220\u9664\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528fillna<\/code>\u51fd\u6570\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
cleaned_data = column_data.dropna()<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
filled_data = column_data.fillna(0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h3>\n<\/p>\n
\u6709\u65f6\u5019\uff0c\u8bfb\u53d6\u7684\u5217\u6570\u636e\u7c7b\u578b\u53ef\u80fd\u4e0d\u662f\u6211\u4eec\u671f\u671b\u7684\u7c7b\u578b\u3002\u53ef\u4ee5\u4f7f\u7528astype<\/code>\u51fd\u6570\u8fdb\u884c\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3002<\/p>\n<\/p>\n
# \u5c06\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u6574\u6570<\/p>\n
int_data = column_data.astype(int)<\/p>\n
<\/code><\/pre>\n<\/p>\n
5\u3001\u6570\u636e\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u53ef\u4ee5\u5bf9\u5217\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u6bd4\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u3001\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u7b49\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u548c<\/p>\n
sum_value = column_data.sum()<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
mean_value = column_data.mean()<\/p>\n
\u8ba1\u7b97\u6700\u5927\u503c<\/strong><\/h2>\n
max_value = column_data.max()<\/p>\n
\u8ba1\u7b97\u6700\u5c0f\u503c<\/strong><\/h2>\n
min_value = column_data.min()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528csv\u6a21\u5757<\/h2>\n<\/p>\n
\u867d\u7136pandas\u529f\u80fd\u5f3a\u5927\uff0c\u4f46\u5728\u67d0\u4e9b\u7b80\u5355\u7684\u573a\u666f\u4e0b\uff0c\u4f7f\u7528Python\u5185\u7f6e\u7684csv\u6a21\u5757\u4e5f\u53ef\u4ee5\u6ee1\u8db3\u9700\u6c42\u3002csv\u6a21\u5757\u63d0\u4f9b\u4e86\u57fa\u672c\u7684CSV\u6587\u4ef6\u8bfb\u5199\u529f\u80fd\uff0c\u53ef\u4ee5\u7075\u6d3b\u5904\u7406\u5404\u79cdCSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\u9700\u8981\u5148\u521b\u5efa\u4e00\u4e2acsv.reader\u5bf9\u8c61\uff0c\u7136\u540e\u901a\u8fc7\u5faa\u73af\u904d\u5386\u6bcf\u4e00\u884c\u6570\u636e\u3002<\/p>\n<\/p>\n
import csv<\/p>\n
\u6253\u5f00CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('file.csv', mode='r') as file:<\/p>\n
    reader = csv.reader(file)<\/p>\n
    # \u8bfb\u53d6\u8868\u5934<\/p>\n
    header = next(reader)<\/p>\n
    # \u9009\u62e9\u67d0\u4e00\u5217<\/p>\n
    column_index = header.index('column_name')<\/p>\n
    column_data = [row[column_index] for row in reader]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
\u4e0epandas\u7c7b\u4f3c\uff0c\u53ef\u4ee5\u901a\u8fc7\u904d\u5386\u6570\u636e\u7684\u65b9\u5f0f\u5904\u7406\u7f3a\u5931\u503c\u3002\u8fd9\u91cc\u5047\u8bbe\u7f3a\u5931\u503c\u8868\u793a\u4e3a\u7a7a\u5b57\u7b26\u4e32\u3002<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
cleaned_data = [value for value in column_data if value]<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
filled_data = [value if value else '0' for value in column_data]<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u89e3\u6790\u548c\u7c7b\u578b\u8f6c\u6362\u51fd\u6570\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u6240\u9700\u7684\u7c7b\u578b\u3002<\/p>\n<\/p>\n
# \u5c06\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u6574\u6570<\/p>\n
int_data = [int(value) for value in column_data]<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
\u867d\u7136csv\u6a21\u5757\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u7edf\u8ba1\u5206\u6790\u529f\u80fd\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7Python\u5185\u7f6e\u51fd\u6570\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u548c<\/p>\n
sum_value = sum(int_data)<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
mean_value = sum(int_data) \/ len(int_data)<\/p>\n
\u8ba1\u7b97\u6700\u5927\u503c<\/strong><\/h2>\n
max_value = max(int_data)<\/p>\n
\u8ba1\u7b97\u6700\u5c0f\u503c<\/strong><\/h2>\n
min_value = min(int_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528numpy\u5e93<\/h2>\n<\/p>\n
numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u529f\u80fd\u3002\u867d\u7136numpy\u4e0d\u4e13\u95e8\u7528\u4e8e\u5904\u7406CSV\u6587\u4ef6\uff0c\u4f46\u53ef\u4ee5\u4e0ecsv\u6a21\u5757\u7ed3\u5408\u4f7f\u7528\uff0c\u8fdb\u884c\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
1\u3001\u8bfb\u53d6CSV\u6587\u4ef6<\/h3>\n<\/p>\n
\u4f7f\u7528numpy\u8bfb\u53d6CSV\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7numpy.loadtxt<\/code>\u51fd\u6570\u5b9e\u73b0\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = np.loadtxt('file.csv', delimiter=',', skiprows=1, usecols=(column_index,), dtype=str)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528numpy\u7684\u5e03\u5c14\u7d22\u5f15\u529f\u80fd\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
# \u5220\u9664\u7f3a\u5931\u503c<\/p>\n
cleaned_data = data[data != '']<\/p>\n
\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
filled_data = np.where(data == '', '0', data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528numpy\u7684\u7c7b\u578b\u8f6c\u6362\u51fd\u6570\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u6240\u9700\u7684\u7c7b\u578b\u3002<\/p>\n<\/p>\n
# \u5c06\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e3a\u6574\u6570<\/p>\n
int_data = data.astype(int)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u6570\u636e\u7edf\u8ba1\u5206\u6790<\/h3>\n<\/p>\n
numpy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u7ec4\u8fd0\u7b97\u529f\u80fd\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u548c<\/p>\n
sum_value = np.sum(int_data)<\/p>\n
\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/h2>\n
mean_value = np.mean(int_data)<\/p>\n
\u8ba1\u7b97\u6700\u5927\u503c<\/strong><\/h2>\n
max_value = np.max(int_data)<\/p>\n
\u8ba1\u7b97\u6700\u5c0f\u503c<\/strong><\/h2>\n
min_value = np.min(int_data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u603b\u7ed3<\/h2>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u4ecb\u7ecd\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4f7f\u7528pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e00\u5217\u662f\u6700\u7b80\u5355\u3001\u6700\u4fbf\u6377\u7684\u65b9\u5f0f<\/strong>\uff0c\u56e0\u4e3apandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u80fd\u591f\u8f7b\u677e\u5e94\u5bf9\u5404\u79cd\u590d\u6742\u7684\u9700\u6c42\u3002csv\u6a21\u5757\u548cnumpy\u5e93\u4e5f\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf<\/strong>\uff0c\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u573a\u666f\u3002\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\uff0c\u53d6\u51b3\u4e8e\u5b9e\u9645\u9700\u6c42\u548c\u6570\u636e\u5904\u7406\u7684\u590d\u6742\u5ea6\u3002<\/p>\n<\/p>\n
\u603b\u4e4b\uff0c\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u9ad8\u6548\u5730\u5904\u7406CSV\u6587\u4ef6\u7684\u6570\u636e<\/strong>\uff0c\u65e0\u8bba\u662f\u8fdb\u884c\u7b80\u5355\u7684\u6570\u636e\u8bfb\u53d6\uff0c\u8fd8\u662f\u590d\u6742\u7684\u6570\u636e\u5206\u6790\uff0c\u90fd\u80fd\u591f\u5f97\u5fc3\u5e94\u624b\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff01<\/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>
\u4f7f\u7528Python\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u7279\u5b9a\u5217\u53ef\u4ee5\u901a\u8fc7\u591a\u4e2a\u5e93\u5b9e\u73b0\uff0c\u6700\u5e38\u7528\u7684\u662fpandas<\/code>\u548ccsv<\/code>\u3002\u5982\u679c\u4f7f\u7528pandas<\/code>\uff0c\u53ef\u4ee5\u901a\u8fc7DataFrame<\/code>\u7684\u5217\u540d\u76f4\u63a5\u8bbf\u95ee\u7279\u5b9a\u5217\u3002\u4f8b\u5982\uff0c\u4f7f\u7528df['\u5217\u540d']<\/code>\u53ef\u4ee5\u8f7b\u677e\u83b7\u53d6\u6240\u9700\u7684\u5217\u3002\u5bf9\u4e8ecsv<\/code>\u5e93\uff0c\u60a8\u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6570\u636e\u5e76\u63d0\u53d6\u6240\u9700\u7684\u5217\u3002<\/p>\n
\u5728\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u5904\u7406\u7f3a\u5931\u503c\uff1f<\/strong>
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