{"id":1052379,"date":"2024-12-31T14:24:32","date_gmt":"2024-12-31T06:24:32","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1052379.html"},"modified":"2024-12-31T14:24:34","modified_gmt":"2024-12-31T06:24:34","slug":"python3%e8%bf%87%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1052379.html","title":{"rendered":"python3\u8fc7\u5927\u6570\u636e\u5982\u4f55\u8bfb\u53d6"},"content":{"rendered":"

\"python3\u8fc7\u5927\u6570\u636e\u5982\u4f55\u8bfb\u53d6\"<\/p>\n

Python3 \u8fc7\u5927\u6570\u636e\u5982\u4f55\u8bfb\u53d6<\/strong>\u53ef\u4ee5\u901a\u8fc7\u5206\u5757\u8bfb\u53d6\u3001\u4f7f\u7528\u5408\u9002\u7684\u5e93\u3001\u4f18\u5316\u5185\u5b58\u4f7f\u7528<\/strong>\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002\u5206\u5757\u8bfb\u53d6<\/strong>\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u6cd5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u65e0\u6cd5\u4e00\u6b21\u6027\u52a0\u8f7d\u5230\u5185\u5b58\u7684\u5927\u578b\u6587\u4ef6\u3002\u901a\u8fc7\u9010\u884c\u6216\u9010\u5757\u8bfb\u53d6\u6587\u4ef6\uff0c\u7a0b\u5e8f\u53ef\u4ee5\u5904\u7406\u66f4\u5927\u7684\u6570\u636e\u96c6\u800c\u4e0d\u4f1a\u8017\u5c3d\u5185\u5b58\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5206\u5757\u8bfb\u53d6<\/h3>\n<\/p>\n

\u5206\u5757\u8bfb\u53d6\u662f\u5904\u7406\u5927\u6570\u636e\u7684\u5e38\u89c1\u65b9\u6cd5\u4e4b\u4e00\u3002\u901a\u8fc7\u9010\u884c\u6216\u9010\u5757\u8bfb\u53d6\u6587\u4ef6\uff0c\u53ef\u4ee5\u6709\u6548\u964d\u4f4e\u5185\u5b58\u5360\u7528\u3002<\/p>\n<\/p>\n

\u9010\u884c\u8bfb\u53d6<\/h4>\n<\/p>\n

\u9010\u884c\u8bfb\u53d6\u662f\u6700\u7b80\u5355\u7684\u5206\u5757\u8bfb\u53d6\u65b9\u6cd5\u3002\u53ef\u4ee5\u901a\u8fc7 Python \u7684\u5185\u7f6e\u51fd\u6570 open()<\/code> \u548c readline()<\/code> \u5b9e\u73b0\u3002<\/p>\n<\/p>\n

def process_line(line):<\/p>\n
    # \u5904\u7406\u6bcf\u4e00\u884c\u6570\u636e\u7684\u51fd\u6570<\/p>\n
    pass<\/p>\n
with open('large_file.txt', 'r') as file:<\/p>\n
    for line in file:<\/p>\n
        process_line(line)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e\u6587\u672c\u6587\u4ef6\uff0c\u5f53\u6587\u4ef6\u975e\u5e38\u5927\u65f6\uff0c\u901a\u8fc7\u9010\u884c\u8bfb\u53d6\u53ef\u4ee5\u907f\u514d\u5185\u5b58\u6ea2\u51fa\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u5206\u5757\u8bfb\u53d6<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528 pandas<\/code> \u5e93\u7684 read_csv()<\/code> \u51fd\u6570\u8fdb\u884c\u5206\u5757\u8bfb\u53d6\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
chunk_size = 10000  # \u6bcf\u6b21\u8bfb\u53d6\u7684\u884c\u6570<\/p>\n
for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):<\/p>\n
    process_chunk(chunk)<\/p>\n
<\/code><\/pre>\n<\/p>\n
chunksize<\/code> \u53c2\u6570\u5b9a\u4e49\u4e86\u6bcf\u6b21\u8bfb\u53d6\u7684\u884c\u6570\uff0c\u8fd9\u79cd\u65b9\u6cd5\u9002\u7528\u4e8e CSV \u6587\u4ef6\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u60c5\u51b5\u8c03\u6574 chunksize<\/code> \u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u5408\u9002\u7684\u5e93<\/h3>\n<\/p>\n
\u4e0d\u540c\u7684\u6570\u636e\u683c\u5f0f\u53ef\u4ee5\u4f7f\u7528\u4e0d\u540c\u7684\u5e93\u8fdb\u884c\u8bfb\u53d6\uff0c\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n
Pandas<\/h4>\n<\/p>\n
pandas<\/code> \u662f\u5904\u7406\u8868\u683c\u6570\u636e\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u652f\u6301\u5206\u5757\u8bfb\u53d6\u548c\u591a\u79cd\u6570\u636e\u683c\u5f0f\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
df = pd.read_csv('large_file.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5bf9\u4e8e\u5927\u578b CSV \u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u5206\u5757\u8bfb\u53d6\u7684\u65b9\u5f0f\uff1a<\/p>\n<\/p>\n
chunk_size = 10000<\/p>\n
for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):<\/p>\n
    process_chunk(chunk)<\/p>\n
<\/code><\/pre>\n<\/p>\n
Dask<\/h4>\n<\/p>\n
Dask<\/code> \u662f\u4e00\u4e2a\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u4e13\u4e3a\u5904\u7406\u5927\u6570\u636e\u800c\u8bbe\u8ba1\u3002\u53ef\u4ee5\u7528\u6765\u5904\u7406\u6bd4\u5185\u5b58\u5927\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
import dask.dataframe as dd<\/p>\n
df = dd.read_csv('large_file.csv')<\/p>\n
df = df.compute()  # \u5c06 Dask dataframe \u8f6c\u6362\u4e3a Pandas dataframe<\/p>\n
<\/code><\/pre>\n<\/p>\n
Dask<\/code> \u652f\u6301\u5ef6\u8fdf\u8ba1\u7b97\u548c\u5e76\u884c\u5904\u7406\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u5904\u7406\u5927\u6570\u636e\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
PySpark<\/h4>\n<\/p>\n
PySpark<\/code> \u662f Apache Spark \u7684 Python API\uff0c\u9002\u7528\u4e8e\u5206\u5e03\u5f0f\u8ba1\u7b97\u548c\u5927\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n
from pyspark.sql import SparkSession<\/p>\n
spark = SparkSession.builder.appName('app_name').getOrCreate()<\/p>\n
df = spark.read.csv('large_file.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
PySpark<\/code> \u53ef\u4ee5\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u9002\u7528\u4e8e\u9700\u8981\u5206\u5e03\u5f0f\u8ba1\u7b97\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f18\u5316\u5185\u5b58\u4f7f\u7528<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u6570\u636e\u65f6\uff0c\u4f18\u5316\u5185\u5b58\u4f7f\u7528\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6548\u7387\u548c\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n
\u6570\u636e\u7c7b\u578b\u4f18\u5316<\/h4>\n<\/p>\n
\u5408\u7406\u9009\u62e9\u6570\u636e\u7c7b\u578b\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u4f8b\u5982\uff0c\u5c06\u6d6e\u70b9\u6570\u8f6c\u6362\u4e3a\u6574\u6570\u6216\u66f4\u5c0f\u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
import numpy as np<\/p>\n
df = pd.read_csv('large_file.csv', dtype={'column1': np.int32, 'column2': np.float32})<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5220\u9664\u4e0d\u5fc5\u8981\u7684\u6570\u636e<\/h4>\n<\/p>\n
\u5728\u5904\u7406\u6570\u636e\u65f6\uff0c\u5220\u9664\u4e0d\u5fc5\u8981\u7684\u5217\u548c\u884c\u53ef\u4ee5\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002<\/p>\n<\/p>\n
df = df.drop(columns=['unnecessary_column'])<\/p>\n
df = df.dropna()  # \u5220\u9664\u5305\u542b\u7a7a\u503c\u7684\u884c<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528\u751f\u6210\u5668<\/h4>\n<\/p>\n
\u751f\u6210\u5668\u662f\u4e00\u79cd\u60f0\u6027\u6c42\u503c\u7684\u65b9\u5f0f\uff0c\u53ef\u4ee5\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6240\u6709\u6570\u636e\u3002<\/p>\n<\/p>\n
def read_large_file(file_path):<\/p>\n
    with open(file_path, 'r') as file:<\/p>\n
        for line in file:<\/p>\n
            yield line<\/p>\n
for line in read_large_file('large_file.txt'):<\/p>\n
    process_line(line)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u751f\u6210\u5668\u5728\u5904\u7406\u5927\u6587\u4ef6\u65f6\u975e\u5e38\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ea\u5728\u9700\u8981\u65f6\u751f\u6210\u6570\u636e\uff0c\u800c\u4e0d\u662f\u4e00\u6b21\u6027\u52a0\u8f7d\u6240\u6709\u6570\u636e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5e76\u884c\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u5927\u6570\u636e\u65f6\uff0c\u5e76\u884c\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n
\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b<\/h4>\n<\/p>\n
Python \u7684 concurrent.futures<\/code> \u6a21\u5757\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\u63a5\u53e3\u3002<\/p>\n<\/p>\n
from concurrent.futures import ThreadPoolExecutor, as_completed<\/p>\n
def process_chunk(chunk):<\/p>\n
    # \u5904\u7406\u6bcf\u4e2a\u5757\u7684\u51fd\u6570<\/p>\n
    pass<\/p>\n
chunk_size = 10000<\/p>\n
chunks = [chunk for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size)]<\/p>\n
with ThreadPoolExecutor(max_workers=4) as executor:<\/p>\n
    futures = [executor.submit(process_chunk, chunk) for chunk in chunks]<\/p>\n
    for future in as_completed(futures):<\/p>\n
        result = future.result()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5206\u5e03\u5f0f\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5206\u5e03\u5f0f\u8ba1\u7b97\u53ef\u4ee5\u5c06\u4efb\u52a1\u5206\u6563\u5230\u591a\u4e2a\u8ba1\u7b97\u8282\u70b9\uff0c\u63d0\u9ad8\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n<\/p>\n
from dask.distributed import Client<\/p>\n
client = Client()<\/p>\n
df = dd.read_csv('large_file.csv')<\/p>\n
df = df.compute()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7 Dask<\/code> \u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u529f\u80fd\uff0c\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u6570\u636e\u5e93\u548c\u5927\u6570\u636e\u5e73\u53f0<\/h3>\n<\/p>\n
\u4f7f\u7528\u6570\u636e\u5e93\u548c\u5927\u6570\u636e\u5e73\u53f0\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u5904\u7406\u5927\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u5c06\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u53ef\u4ee5\u66f4\u65b9\u4fbf\u5730\u8fdb\u884c\u67e5\u8be2\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n
import sqlite3<\/p>\n
conn = sqlite3.connect('large_file.db')<\/p>\n
df = pd.read_sql_query('SELECT * FROM large_table', conn)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5927\u6570\u636e\u5e73\u53f0<\/h4>\n<\/p>\n
\u5927\u6570\u636e\u5e73\u53f0\u5982 Hadoop \u548c Spark \u53ef\u4ee5\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u9002\u7528\u4e8e\u5206\u5e03\u5f0f\u8ba1\u7b97\u573a\u666f\u3002<\/p>\n<\/p>\n
from pyspark.sql import SparkSession<\/p>\n
spark = SparkSession.builder.appName('app_name').getOrCreate()<\/p>\n
df = spark.read.csv('large_file.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u5904\u7406\u5927\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5206\u5757\u8bfb\u53d6\u3001\u4f7f\u7528\u5408\u9002\u7684\u5e93\u3001\u4f18\u5316\u5185\u5b58\u4f7f\u7528\u3001\u5e76\u884c\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u7b49\u65b9\u6cd5\u6765\u63d0\u9ad8\u6548\u7387\u548c\u7a33\u5b9a\u6027\u3002\u5408\u7406\u9009\u62e9\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u6839\u636e\u5177\u4f53\u60c5\u51b5\u8fdb\u884c\u4f18\u5316\uff0c\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u6570\u636e\u5904\u7406\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u5206\u5757\u8bfb\u53d6\uff0c\u53ef\u4ee5\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6240\u6709\u6570\u636e\uff0c\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u4f7f\u7528\u5408\u9002\u7684\u5e93\u5982 pandas<\/code>\u3001Dask<\/code> \u548c PySpark<\/code> \u53ef\u4ee5\u63d0\u9ad8\u5904\u7406\u6548\u7387\u3002\u4f18\u5316\u6570\u636e\u7c7b\u578b\u548c\u5220\u9664\u4e0d\u5fc5\u8981\u7684\u6570\u636e\uff0c\u53ef\u4ee5\u8fdb\u4e00\u6b65\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u4f7f\u7528\u751f\u6210\u5668\u548c\u591a\u7ebf\u7a0b\u3001\u591a\u8fdb\u7a0b\u5e76\u884c\u5904\u7406\uff0c\u53ef\u4ee5\u52a0\u5feb\u6570\u636e\u5904\u7406\u901f\u5ea6\u3002\u6700\u540e\uff0c\u901a\u8fc7\u6570\u636e\u5e93\u548c\u5927\u6570\u636e\u5e73\u53f0\uff0c\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u5b58\u50a8\u548c\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/p>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u6570\u636e\u89c4\u6a21\u548c\u5904\u7406\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u548c\u5de5\u5177\uff0c\u8fdb\u884c\u5408\u7406\u4f18\u5316\uff0c\u53ef\u4ee5\u6709\u6548\u89e3\u51b3 Python3 \u8fc7\u5927\u6570\u636e\u8bfb\u53d6\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python3\u4e2d\u8bfb\u53d6\u8d85\u5927\u6587\u4ef6\u800c\u4e0d\u5360\u7528\u8fc7\u591a\u5185\u5b58\uff1f<\/strong>
\u5bf9\u4e8e\u8d85\u5927\u6587\u4ef6\u7684\u5904\u7406\uff0c\u4f7f\u7528\u9010\u884c\u8bfb\u53d6\u7684\u65b9\u6cd5\u662f\u4e00\u79cd\u6709\u6548\u7684\u65b9\u5f0f\u3002\u53ef\u4ee5\u901a\u8fc7with open('filename', 'r') as file:<\/code>\u8bed\u53e5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\u5185\u5bb9\uff0c\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u5c06\u6574\u4e2a\u6587\u4ef6\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u4f7f\u7528\u751f\u6210\u5668\u6765\u5904\u7406\u6570\u636e\uff0c\u8fdb\u4e00\u6b65\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\u3002<\/p>\n
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