{"id":1017863,"date":"2024-12-27T12:35:01","date_gmt":"2024-12-27T04:35:01","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1017863.html"},"modified":"2024-12-27T12:35:05","modified_gmt":"2024-12-27T04:35:05","slug":"python%e5%a6%82%e4%bd%95%e8%be%93%e5%87%ba%e6%95%b0%e6%8d%ae%e9%9b%86","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1017863.html","title":{"rendered":"python\u5982\u4f55\u8f93\u51fa\u6570\u636e\u96c6"},"content":{"rendered":"

\"python\u5982\u4f55\u8f93\u51fa\u6570\u636e\u96c6\"<\/p>\n

\u8981\u5728Python\u4e2d\u8f93\u51fa\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5229\u7528Pandas\u5e93\u3001csv\u6a21\u5757\u3001\u4ee5\u53caNumPy\u5e93<\/strong>\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u5176\u4f18\u70b9\u548c\u9002\u7528\u573a\u666f\u3002\u4f8b\u5982\uff0cPandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\uff0c\u9002\u5408\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e<\/strong>\uff0c\u800ccsv\u6a21\u5757\u5219\u662fPython\u5185\u7f6e\u7684\u7528\u4e8e\u8bfb\u53d6\u548c\u5199\u5165CSV\u6587\u4ef6\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u5904\u7406\u7b80\u5355\u7684\u6587\u672c\u6570\u636e\u3002NumPy\u5e93\u5219\u4e3b\u8981\u7528\u4e8e\u5904\u7406\u5927\u578b\u7684\u6570\u503c\u6570\u636e\u96c6\uff0c\u7279\u522b\u662f\u5728\u79d1\u5b66\u8ba1\u7b97\u9886\u57df\u3002\u672c\u6587\u5c06\u91cd\u70b9\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u6765\u8f93\u51fa\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n

\u5229\u7528Pandas\u5e93\u8f93\u51fa\u6570\u636e\u96c6\u7684\u65b9\u6cd5\u4e3b\u8981\u5305\u62ec\u8bfb\u53d6\u6570\u636e\u3001\u5904\u7406\u6570\u636e\u3001\u4ee5\u53ca\u5c06\u6570\u636e\u8f93\u51fa\u5230\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\u4e2d\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u8bb8\u591a\u51fd\u6570\u6765\u8bfb\u53d6\u4e0d\u540c\u683c\u5f0f\u7684\u6570\u636e\uff0c\u4f8b\u5982CSV\u3001Excel\u3001SQL\u7b49\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aDataFrame\u5bf9\u8c61\u3002DataFrame\u662fPandas\u5e93\u4e2d\u6700\u91cd\u8981\u7684\u6570\u636e\u7ed3\u6784\u4e4b\u4e00\uff0c\u7c7b\u4f3c\u4e8e\u6570\u636e\u5e93\u4e2d\u7684\u8868\u683c\uff0c\u5305\u542b\u884c\u548c\u5217\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528Pandas\u5e93\u8f93\u51fa\u6570\u636e\u96c6<\/h3>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u8bfb\u53d6\u3001\u5904\u7406\u548c\u8f93\u51fa\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n

1. \u8bfb\u53d6\u6570\u636e\u96c6<\/h4>\n<\/p>\n

Pandas\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u8bfb\u53d6\u4e0d\u540c\u683c\u5f0f\u7684\u6570\u636e\u96c6\uff0c\u5982CSV\u3001Excel\u3001SQL\u7b49\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662fread_csv()<\/code>\u51fd\u6570\uff0c\u7528\u4e8e\u8bfb\u53d6CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u663e\u793a\u6570\u636e\u96c6\u7684\u524d\u4e94\u884c<\/strong><\/h2>\n
print(df.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9664\u4e86read_csv()<\/code>\uff0cPandas\u8fd8\u63d0\u4f9b\u4e86read_excel()<\/code>\u3001read_sql()<\/code>\u7b49\u51fd\u6570\uff0c\u7528\u4e8e\u8bfb\u53d6\u5176\u4ed6\u683c\u5f0f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
2. \u5904\u7406\u6570\u636e\u96c6<\/h4>\n<\/p>\n
\u5728\u8bfb\u53d6\u6570\u636e\u96c6\u540e\uff0c\u53ef\u4ee5\u5229\u7528Pandas\u63d0\u4f9b\u7684\u4e30\u5bcc\u529f\u80fd\u6765\u5904\u7406\u6570\u636e\u96c6\u3002\u4f8b\u5982\uff0c\u60a8\u53ef\u4ee5\u8fc7\u6ee4\u3001\u5408\u5e76\u3001\u5206\u7ec4\u6570\u636e\uff0c\u751a\u81f3\u8fdb\u884c\u6570\u636e\u7684\u6e05\u7406\u548c\u8f6c\u6362\u3002<\/p>\n<\/p>\n
# \u9009\u62e9\u7279\u5b9a\u7684\u5217<\/p>\n
df_filtered = df[['column1', 'column2']]<\/p>\n
\u6839\u636e\u6761\u4ef6\u8fc7\u6ee4\u6570\u636e<\/strong><\/h2>\n
df_filtered = df[df['column1'] > 10]<\/p>\n
\u5206\u7ec4\u5e76\u8ba1\u7b97\u805a\u5408\u503c<\/strong><\/h2>\n
df_grouped = df.groupby('column1').mean()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u4e9b\u64cd\u4f5c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u5728\u8f93\u51fa\u6570\u636e\u4e4b\u524d\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u4ee5\u4fbf\u8fdb\u4e00\u6b65\u5206\u6790\u6216\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
3. \u8f93\u51fa\u6570\u636e\u96c6<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u5b8c\u6570\u636e\u540e\uff0c\u60a8\u53ef\u80fd\u9700\u8981\u5c06\u6570\u636e\u96c6\u8f93\u51fa\u5230\u6587\u4ef6\u4e2d\u3002Pandas\u63d0\u4f9b\u4e86to_csv()<\/code>\u3001to_excel()<\/code>\u7b49\u65b9\u6cd5\u6765\u5c06DataFrame\u5bf9\u8c61\u8f93\u51fa\u5230\u4e0d\u540c\u683c\u5f0f\u7684\u6587\u4ef6\u4e2d\u3002<\/p>\n<\/p>\n
# \u8f93\u51fa\u6570\u636e\u96c6\u5230CSV\u6587\u4ef6<\/p>\n
df.to_csv('output.csv', index=False)<\/p>\n
\u8f93\u51fa\u6570\u636e\u96c6\u5230Excel\u6587\u4ef6<\/strong><\/h2>\n
df.to_excel('output.xlsx', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528csv\u6a21\u5757\u8f93\u51fa\u6570\u636e\u96c6<\/h3>\n<\/p>\n
Python\u7684\u5185\u7f6ecsv\u6a21\u5757\u63d0\u4f9b\u4e86\u8bfb\u53d6\u548c\u5199\u5165CSV\u6587\u4ef6\u7684\u57fa\u672c\u529f\u80fd\uff0c\u9002\u5408\u5904\u7406\u7b80\u5355\u7684\u6587\u672c\u6570\u636e\u3002<\/p>\n<\/p>\n
1. \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u4f7f\u7528csv\u6a21\u5757\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u9996\u5148\u9700\u8981\u6253\u5f00\u6587\u4ef6\uff0c\u7136\u540e\u4f7f\u7528csv.reader()<\/code>\u51fd\u6570\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\u3002<\/p>\n<\/p>\n
import csv<\/p>\n
\u6253\u5f00CSV\u6587\u4ef6<\/strong><\/h2>\n
with open('data.csv', mode='r') as file:<\/p>\n
    # \u521b\u5efaCSV\u9605\u8bfb\u5668<\/p>\n
    csv_reader = csv.reader(file)<\/p>\n
    # \u9010\u884c\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9<\/p>\n
    for row in csv_reader:<\/p>\n
        print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u5199\u5165CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u5199\u5165CSV\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528csv.writer()<\/code>\u51fd\u6570\u6765\u521b\u5efaCSV\u5199\u5165\u5668\uff0c\u5e76\u4f7f\u7528writerow()<\/code>\u6216writerows()<\/code>\u51fd\u6570\u6765\u5199\u5165\u6570\u636e\u3002<\/p>\n<\/p>\n
# \u6253\u5f00CSV\u6587\u4ef6<\/p>\n
with open('output.csv', mode='w', newline='') as file:<\/p>\n
    # \u521b\u5efaCSV\u5199\u5165\u5668<\/p>\n
    csv_writer = csv.writer(file)<\/p>\n
    # \u5199\u5165\u5355\u884c\u6570\u636e<\/p>\n
    csv_writer.writerow(['column1', 'column2'])<\/p>\n
    # \u5199\u5165\u591a\u884c\u6570\u636e<\/p>\n
    csv_writer.writerows([<\/p>\n
        [1, 'data1'],<\/p>\n
        [2, 'data2']<\/p>\n
    ])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528NumPy\u5e93\u8f93\u51fa\u6570\u636e\u96c6<\/h3>\n<\/p>\n
NumPy\u662fPython\u4e2d\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u7684\u6838\u5fc3\u5e93\uff0c\u5c24\u5176\u64c5\u957f\u5904\u7406\u5927\u578b\u6570\u503c\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
1. \u521b\u5efa\u548c\u5904\u7406\u6570\u7ec4<\/h4>\n<\/p>\n
NumPy\u7684\u6838\u5fc3\u662f\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61ndarray\uff0c\u53ef\u4ee5\u901a\u8fc7NumPy\u7684\u51fd\u6570\u6765\u521b\u5efa\u548c\u64cd\u4f5c\u6570\u7ec4\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efaNumPy\u6570\u7ec4<\/strong><\/h2>\n
array = np.array([[1, 2, 3], [4, 5, 6]])<\/p>\n
\u6570\u7ec4\u7684\u57fa\u672c\u64cd\u4f5c<\/strong><\/h2>\n
array_transposed = np.transpose(array)<\/p>\n
array_sum = np.sum(array, axis=0)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8f93\u51fa\u6570\u7ec4\u5230\u6587\u4ef6<\/h4>\n<\/p>\n
NumPy\u63d0\u4f9b\u4e86numpy.savetxt()<\/code>\u548cnumpy.save()<\/code>\u7b49\u51fd\u6570\u7528\u4e8e\u5c06\u6570\u7ec4\u8f93\u51fa\u5230\u6587\u672c\u6587\u4ef6\u6216\u4e8c\u8fdb\u5236\u6587\u4ef6\u3002<\/p>\n<\/p>\n
# \u8f93\u51fa\u6570\u7ec4\u5230\u6587\u672c\u6587\u4ef6<\/p>\n
np.savetxt('output.txt', array, delimiter=',')<\/p>\n
\u8f93\u51fa\u6570\u7ec4\u5230\u4e8c\u8fdb\u5236\u6587\u4ef6<\/strong><\/h2>\n
np.save('output.npy', array)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528\u5176\u4ed6\u6570\u636e\u683c\u5f0f\u8f93\u51fa\u6570\u636e\u96c6<\/h3>\n<\/p>\n
\u9664\u4e86\u5e38\u89c1\u7684CSV\u548cExcel\u683c\u5f0f\u5916\uff0cPython\u8fd8\u652f\u6301\u5176\u4ed6\u6570\u636e\u683c\u5f0f\uff0c\u5982JSON\u3001SQL\u6570\u636e\u5e93\u7b49\u3002<\/p>\n<\/p>\n
1. \u8f93\u51fa\u5230JSON\u683c\u5f0f<\/h4>\n<\/p>\n
JSON\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u4ea4\u6362\u683c\u5f0f\uff0cPython\u7684\u5185\u7f6ejson\u6a21\u5757\u63d0\u4f9b\u4e86\u5904\u7406JSON\u6570\u636e\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
import json<\/p>\n
\u6570\u636e\u96c6<\/strong><\/h2>\n
data = {'name': 'John', 'age': 30, 'city': 'New York'}<\/p>\n
\u8f93\u51fa\u6570\u636e\u5230JSON\u6587\u4ef6<\/strong><\/h2>\n
with open('output.json', 'w') as json_file:<\/p>\n
    json.dump(data, json_file)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8f93\u51fa\u5230SQL\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528SQLite\u7b49\u6570\u636e\u5e93\u6765\u5b58\u50a8\u548c\u8f93\u51fa\u6570\u636e\u96c6\u3002Python\u7684sqlite3\u6a21\u5757\u63d0\u4f9b\u4e86\u8bbf\u95eeSQLite\u6570\u636e\u5e93\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
import sqlite3<\/p>\n
\u521b\u5efa\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
conn = sqlite3.connect('example.db')<\/p>\n
\u521b\u5efa\u6e38\u6807\u5bf9\u8c61<\/strong><\/h2>\n
cursor = conn.cursor()<\/p>\n
\u521b\u5efa\u8868<\/strong><\/h2>\n
cursor.execute('''CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER)''')<\/p>\n
\u63d2\u5165\u6570\u636e<\/strong><\/h2>\n
cursor.execute('''INSERT INTO users (name, age) VALUES ('Alice', 25)''')<\/p>\n
\u63d0\u4ea4\u4e8b\u52a1<\/strong><\/h2>\n
conn.commit()<\/p>\n
\u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
cursor.execute('SELECT * FROM users')<\/p>\n
rows = cursor.fetchall()<\/p>\n
\u8f93\u51fa\u67e5\u8be2\u7ed3\u679c<\/strong><\/h2>\n
for row in rows:<\/p>\n
    print(row)<\/p>\n
\u5173\u95ed\u8fde\u63a5<\/strong><\/h2>\n
conn.close()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528\u9ad8\u7ea7\u5de5\u5177\u548c\u5e93\u8f93\u51fa\u6570\u636e\u96c6<\/h3>\n<\/p>\n
\u5728\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60<\/a>\u9886\u57df\uff0c\u5e38\u7528\u7684\u4e00\u4e9b\u9ad8\u7ea7\u5de5\u5177\u548c\u5e93\u4e5f\u63d0\u4f9b\u4e86\u8f93\u51fa\u6570\u636e\u96c6\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528HDF5\u683c\u5f0f<\/h4>\n<\/p>\n
HDF5\u662f\u4e00\u79cd\u7528\u4e8e\u5b58\u50a8\u548c\u7ba1\u7406\u5927\u578b\u6570\u636e\u96c6\u7684\u6587\u4ef6\u683c\u5f0f\u3002Python\u7684h5py\u5e93\u63d0\u4f9b\u4e86\u5bf9HDF5\u6587\u4ef6\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n
import h5py<\/p>\n
import numpy as np<\/p>\n
\u521b\u5efaHDF5\u6587\u4ef6<\/strong><\/h2>\n
with h5py.File('output.h5', 'w') as h5file:<\/p>\n
    # \u521b\u5efa\u6570\u636e\u96c6<\/p>\n
    h5file.create_dataset('dataset1', data=np.arange(100).reshape(10, 10))<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Parquet\u683c\u5f0f<\/h4>\n<\/p>\n
Parquet\u662f\u4e00\u79cd\u5217\u5f0f\u5b58\u50a8\u6587\u4ef6\u683c\u5f0f\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u5904\u7406\u3002Pandas\u63d0\u4f9b\u4e86\u5bf9Parquet\u683c\u5f0f\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u521b\u5efa\u6570\u636e\u96c6<\/strong><\/h2>\n
df = pd.DataFrame({'column1': range(10), 'column2': range(10, 20)})<\/p>\n
\u8f93\u51fa\u6570\u636e\u96c6\u5230Parquet\u6587\u4ef6<\/strong><\/h2>\n
df.to_parquet('output.parquet', index=False)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u5904\u7406\u548c\u8f93\u51fa\u5927\u89c4\u6a21\u6570\u636e\u96c6<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u548c\u8f93\u51fa\u5927\u89c4\u6a21\u6570\u636e\u96c6\u65f6\uff0c\u9700\u8981\u8003\u8651\u6548\u7387\u548c\u6027\u80fd\u95ee\u9898\u3002\u53ef\u4ee5\u901a\u8fc7\u5206\u5757\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\u3001\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6280\u672f\u6765\u63d0\u9ad8\u6027\u80fd\u3002<\/p>\n<\/p>\n
1. \u5206\u5757\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e<\/h4>\n<\/p>\n
Pandas\u7684read_csv()<\/code>\u548cto_csv()<\/code>\u51fd\u6570\u652f\u6301\u5206\u5757\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\uff0c\u8fd9\u5bf9\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5206\u5757\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
chunk_size = 1000<\/p>\n
for chunk in pd.read_csv('large_data.csv', chunksize=chunk_size):<\/p>\n
    # \u5904\u7406\u6bcf\u4e2a\u6570\u636e\u5757<\/p>\n
    print(chunk.head())<\/p>\n
\u5206\u5757\u5199\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.DataFrame({'column1': range(10000), 'column2': range(10000, 20000)})<\/p>\n
df.to_csv('large_output.csv', index=False, chunksize=chunk_size)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b<\/h4>\n<\/p>\n
Python\u7684concurrent.futures<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684\u63a5\u53e3\u6765\u5b9e\u73b0\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u5904\u7406\u3002<\/p>\n<\/p>\n
from concurrent.futures import ThreadPoolExecutor<\/p>\n
import pandas as pd<\/p>\n
\u5b9a\u4e49\u6570\u636e\u5904\u7406\u51fd\u6570<\/strong><\/h2>\n
def process_data(chunk):<\/p>\n
    # \u8fdb\u884c\u6570\u636e\u5904\u7406\u64cd\u4f5c<\/p>\n
    return chunk.sum()<\/p>\n
\u4f7f\u7528\u7ebf\u7a0b\u6c60\u5e76\u884c\u5904\u7406\u6570\u636e<\/strong><\/h2>\n
with ThreadPoolExecutor(max_workers=4) as executor:<\/p>\n
    results = executor.map(process_data, pd.read_csv('large_data.csv', chunksize=chunk_size))<\/p>\n
\u6c47\u603b\u7ed3\u679c<\/strong><\/h2>\n
total_sum = sum(results)<\/p>\n
print(total_sum)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u7684\u65b9\u6cd5\u548c\u6280\u5de7\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u6709\u6548\u5730\u8f93\u51fa\u548c\u5904\u7406\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u52a0\u8f7d\u548c\u8f93\u51fa\u6570\u636e\u96c6\uff1f<\/strong>
\u8981\u52a0\u8f7d\u548c\u8f93\u51fa\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7\u8fd0\u884cpip install pandas<\/code>\u6765\u5b89\u88c5\u3002\u7136\u540e\uff0c\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\u52a0\u8f7dCSV\u6587\u4ef6\uff0c\u5e76\u4f7f\u7528print()<\/code>\u6216DataFrame.head()<\/code>\u65b9\u6cd5\u8f93\u51fa\u6570\u636e\u96c6\u7684\u524d\u51e0\u884c\u3002\u4f8b\u5982\uff1a  <\/p>\n
import pandas as pd  \ndata = pd.read_csv('your_dataset.csv')  \nprint(data.head())  \n<\/code><\/pre>\n
\u5728Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u6570\u636e\u8f93\u51fa\u683c\u5f0f\uff1f<\/strong>
Python\u652f\u6301\u591a\u79cd\u6570\u636e\u8f93\u51fa\u683c\u5f0f\uff0c\u5305\u62ecCSV\u3001Excel\u3001JSON\u7b49\u3002\u901a\u8fc7Pandas\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5c06DataFrame\u8f93\u51fa\u4e3a\u8fd9\u4e9b\u683c\u5f0f\u3002\u4f7f\u7528DataFrame.to_csv('output.csv')<\/code>\u53ef\u4ee5\u5c06\u6570\u636e\u96c6\u4fdd\u5b58\u4e3aCSV\u6587\u4ef6\uff0cDataFrame.to_excel('output.xlsx')<\/code>\u53ef\u4ee5\u8f93\u51fa\u4e3aExcel\u6587\u4ef6\uff0c\u4f7f\u7528DataFrame.to_json('output.json')<\/code>\u53ef\u4ee5\u8f93\u51fa\u4e3aJSON\u683c\u5f0f\u3002<\/p>\n
\u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u548c\u8f93\u51fa\u5927\u578b\u6570\u636e\u96c6\uff1f<\/strong>
\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684chunksize<\/code>\u53c2\u6570\u6765\u5206\u5757\u8bfb\u53d6\u6570\u636e\u3002\u901a\u8fc7\u8bbe\u7f6epd.read_csv('large_dataset.csv', chunksize=1000)<\/code>\uff0c\u53ef\u4ee5\u9010\u5757\u5904\u7406\u6570\u636e\uff0c\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u8f93\u51fa\u65f6\uff0c\u53ef\u4ee5\u9010\u5757\u5199\u5165\u6587\u4ef6\uff0c\u786e\u4fdd\u4e0d\u4f1a\u56e0\u4e3a\u6570\u636e\u96c6\u8fc7\u5927\u800c\u5bfc\u81f4\u5185\u5b58\u6ea2\u51fa\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u8f93\u51fa\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5229\u7528Pandas\u5e93\u3001csv\u6a21\u5757\u3001\u4ee5\u53caNumPy\u5e93 […]","protected":false},"author":3,"featured_media":1017874,"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\/1017863"}],"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=1017863"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1017863\/revisions"}],"predecessor-version":[{"id":1017875,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1017863\/revisions\/1017875"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1017874"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1017863"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1017863"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1017863"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}