{"id":1147675,"date":"2025-01-13T16:27:55","date_gmt":"2025-01-13T08:27:55","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1147675.html"},"modified":"2025-01-13T16:27:58","modified_gmt":"2025-01-13T08:27:58","slug":"python%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96%e5%a4%a7%e9%87%8f%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1147675.html","title":{"rendered":"python\u5982\u4f55\u8bfb\u53d6\u5927\u91cf\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u8bfb\u53d6\u5927\u91cf\u6570\u636e\"<\/p>\n

Python\u8bfb\u53d6\u5927\u91cf\u6570\u636e\u7684\u65b9\u5f0f\u6709\u591a\u79cd\uff0c\u5305\u62ec\u4f7f\u7528\u5185\u7f6e\u7684\u6587\u4ef6\u8bfb\u53d6\u65b9\u6cd5\u3001\u5229\u7528\u7b2c\u4e09\u65b9\u5e93\u5982Pandas\u3001Dask\u4ee5\u53ca\u4f7f\u7528\u6570\u636e\u5e93\u7ba1\u7406\u7cfb\u7edf\u8fdb\u884c\u5904\u7406\u3002\u6839\u636e\u6570\u636e\u7684\u89c4\u6a21\u548c\u7ed3\u6784\uff0c\u53ef\u4ee5\u9009\u62e9\u4e0d\u540c\u7684\u65b9\u6cd5\u6765\u4f18\u5316\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e\u7684\u6548\u7387<\/strong>\u3002\u5176\u4e2d\uff0c\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\u4e4b\u4e00\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\u3002\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u80fd\u591f\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u5904\u7406<\/strong>\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6\u5927\u91cf\u6570\u636e\u3002<\/p>\n<\/p>\n

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

Pandas\u662fPython\u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff0c\u5982CSV\u6587\u4ef6\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\uff0c\u4f7f\u5f97\u6570\u636e\u8bfb\u53d6\u548c\u64cd\u4f5c\u53d8\u5f97\u7b80\u5355\u9ad8\u6548\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5Pandas\u5e93<\/h4>\n<\/p>\n

\u5728\u4f7f\u7528Pandas\u5e93\u4e4b\u524d\uff0c\u9700\u8981\u5148\u5b89\u88c5\u5b83\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6\u662fPandas\u5e93\u7684\u57fa\u672c\u529f\u80fd\u4e4b\u4e00\u3002\u53ef\u4ee5\u4f7f\u7528pd.read_csv()<\/code>\u51fd\u6570\u8bfb\u53d6CSV\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('large_dataset.csv')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5206\u5757\u8bfb\u53d6<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u7279\u522b\u5927\u7684\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528chunksize<\/code>\u53c2\u6570\u8fdb\u884c\u5206\u5757\u8bfb\u53d6\u3002\u8fd9\u6837\u53ef\u4ee5\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6574\u4e2a\u6570\u636e\u96c6\u5230\u5185\u5b58\u4e2d\uff0c\u9632\u6b62\u5185\u5b58\u4e0d\u8db3\u7684\u95ee\u9898\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u5206\u5757\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
chunks = pd.read_csv('large_dataset.csv', chunksize=10000)<\/p>\n
for chunk in chunks:<\/p>\n
    # \u5904\u7406\u6bcf\u4e2a\u5757\u7684\u6570\u636e<\/p>\n
    print(chunk.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u4f7f\u7528\u6307\u5b9a\u5217<\/h4>\n<\/p>\n
\u5982\u679c\u53ea\u9700\u8981\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u67d0\u4e9b\u5217\uff0c\u53ef\u4ee5\u4f7f\u7528usecols<\/code>\u53c2\u6570\u6307\u5b9a\u8981\u8bfb\u53d6\u7684\u5217\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u53ea\u8bfb\u53d6\u6307\u5b9a\u5217<\/strong><\/h2>\n
data = pd.read_csv('large_dataset.csv', usecols=['column1', 'column2'])<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Dask\u5e93<\/h3>\n<\/p>\n
Dask\u662f\u53e6\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff0c\u9002\u7528\u4e8e\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002\u5b83\u63d0\u4f9b\u4e86\u4e0ePandas\u7c7b\u4f3c\u7684API\uff0c\u4f46\u652f\u6301\u5e76\u884c\u8ba1\u7b97\u548c\u5206\u5e03\u5f0f\u5904\u7406\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5Dask\u5e93<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5Dask\u5e93\uff1a<\/p>\n<\/p>\n
pip install dask<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6CSV\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528Dask\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5e76\u8fdb\u884c\u5e76\u884c\u5904\u7406\uff1a<\/p>\n<\/p>\n
import dask.dataframe as dd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = dd.read_csv('large_dataset.csv')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5206\u5757\u5904\u7406<\/h4>\n<\/p>\n
Dask\u81ea\u52a8\u5c06\u6570\u636e\u5206\u5757\u5904\u7406\uff0c\u5e76\u5728\u591a\u4e2a\u6838\u5fc3\u4e0a\u5e76\u884c\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
import dask.dataframe as dd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = dd.read_csv('large_dataset.csv')<\/p>\n
\u5206\u5757\u5904\u7406\u6570\u636e<\/strong><\/h2>\n
result = data.groupby('column1').sum().compute()<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u6570\u636e\u5e93\u7ba1\u7406\u7cfb\u7edf<\/h3>\n<\/p>\n
\u5bf9\u4e8e\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4f7f\u7528\u6570\u636e\u5e93\u7ba1\u7406\u7cfb\u7edf\uff08\u5982MySQL\u3001PostgreSQL\uff09\u8fdb\u884c\u6570\u636e\u5b58\u50a8\u548c\u8bfb\u53d6\u662f\u4e00\u4e2a\u9ad8\u6548\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5\u6570\u636e\u5e93\u9a71\u52a8<\/h4>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5b89\u88c5\u5bf9\u5e94\u7684\u6570\u636e\u5e93\u9a71\u52a8\uff0c\u4f8b\u5982MySQL\u7684mysql-connector<\/code>\uff1a<\/p>\n<\/p>\n
pip install mysql-connector-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8fde\u63a5\u6570\u636e\u5e93<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u5e93\u9a71\u52a8\u8fde\u63a5\u6570\u636e\u5e93\uff0c\u5e76\u8bfb\u53d6\u6570\u636e\uff1a<\/p>\n<\/p>\n
import mysql.connector<\/p>\n
\u8fde\u63a5\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = mysql.connector.connect(<\/p>\n
    host='localhost',<\/p>\n
    user='username',<\/p>\n
    password='password',<\/p>\n
    database='database_name'<\/p>\n
)<\/p>\n
\u521b\u5efa\u6e38\u6807<\/strong><\/h2>\n
cursor = conn.cursor()<\/p>\n
\u6267\u884c\u67e5\u8be2<\/strong><\/h2>\n
cursor.execute("SELECT * FROM large_table")<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
rows = cursor.fetchall()<\/p>\n
for row in rows:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528SQLAlchemy<\/h4>\n<\/p>\n
SQLAlchemy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684ORM\u5e93\uff0c\u652f\u6301\u591a\u79cd\u6570\u636e\u5e93\uff0c\u53ef\u4ee5\u7b80\u5316\u6570\u636e\u5e93\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
from sqlalchemy import create_engine<\/p>\n
import pandas as pd<\/p>\n
\u8fde\u63a5\u6570\u636e\u5e93<\/strong><\/h2>\n
engine = create_engine('mysql+mysqlconnector:\/\/username:password@localhost\/database_name')<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_sql('SELECT * FROM large_table', engine)<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528HDF5\u683c\u5f0f<\/h3>\n<\/p>\n
HDF5\u662f\u4e00\u79cd\u9ad8\u6548\u7684\u5b58\u50a8\u683c\u5f0f\uff0c\u9002\u5408\u5b58\u50a8\u5927\u91cf\u7684\u6570\u503c\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528h5py<\/code>\u5e93\u5904\u7406HDF5\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5h5py\u5e93<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5h5py\u5e93\uff1a<\/p>\n<\/p>\n
pip install h5py<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6HDF5\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528h5py\u5e93\u8bfb\u53d6HDF5\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
import h5py<\/p>\n
\u8bfb\u53d6HDF5\u6587\u4ef6<\/strong><\/h2>\n
with h5py.File('large_dataset.h5', 'r') as f:<\/p>\n
    data = f['dataset_name'][:]<\/p>\n
    print(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528PySpark<\/h3>\n<\/p>\n
PySpark\u662fApache Spark\u7684Python API\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5PySpark<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5PySpark\uff1a<\/p>\n<\/p>\n
pip install pyspark<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528PySpark\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u8fdb\u884c\u5206\u5e03\u5f0f\u5904\u7406\uff1a<\/p>\n<\/p>\n
from pyspark.sql import SparkSession<\/p>\n
\u521b\u5efaSparkSession<\/strong><\/h2>\n
spark = SparkSession.builder.appName('ReadLargeData').getOrCreate()<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = spark.read.csv('large_dataset.csv', header=True)<\/p>\n
data.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528PySpark\u8fdb\u884c\u6570\u636e\u5904\u7406\uff0c\u5e76\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\uff1a<\/p>\n<\/p>\n
from pyspark.sql import SparkSession<\/p>\n
\u521b\u5efaSparkSession<\/strong><\/h2>\n
spark = SparkSession.builder.appName('ReadLargeData').getOrCreate()<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = spark.read.csv('large_dataset.csv', header=True)<\/p>\n
\u6570\u636e\u5904\u7406<\/strong><\/h2>\n
result = data.groupBy('column1').sum('column2')<\/p>\n
result.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6570\u636e\u8bfb\u53d6\u4f18\u5316\u6280\u5de7<\/h3>\n<\/p>\n
1. \u4f7f\u7528\u5408\u9002\u7684\u6570\u636e\u683c\u5f0f<\/h4>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u683c\u5f0f\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u6570\u636e\u8bfb\u53d6\u7684\u6548\u7387\u3002\u4f8b\u5982\uff0cParquet\u548cORC\u683c\u5f0f\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5b58\u50a8\u548c\u8bfb\u53d6\uff0c\u5177\u6709\u826f\u597d\u7684\u538b\u7f29\u548c\u67e5\u8be2\u6027\u80fd\u3002<\/p>\n<\/p>\n
2. \u4f7f\u7528\u538b\u7f29\u6587\u4ef6<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528gzip\u3001bz2\u7b49\u538b\u7f29\u683c\u5f0f\u5b58\u50a8\u6570\u636e\uff0c\u8fd9\u6837\u53ef\u4ee5\u51cf\u5c11\u78c1\u76d8\u7a7a\u95f4\u5360\u7528\uff0c\u5e76\u5728\u8bfb\u53d6\u65f6\u81ea\u52a8\u89e3\u538b\u7f29\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
\u8bfb\u53d6\u538b\u7f29\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('large_dataset.csv.gz', compression='gzip')<\/p>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f18\u5316\u67e5\u8be2<\/h4>\n<\/p>\n
\u5728\u4f7f\u7528\u6570\u636e\u5e93\u7ba1\u7406\u7cfb\u7edf\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f18\u5316\u67e5\u8be2\u6765\u63d0\u9ad8\u6570\u636e\u8bfb\u53d6\u7684\u6548\u7387\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u7d22\u5f15\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u67e5\u8be2\u901f\u5ea6\uff1a<\/p>\n<\/p>\n
import mysql.connector<\/p>\n
\u8fde\u63a5\u6570\u636e\u5e93<\/strong><\/h2>\n
conn = mysql.connector.connect(<\/p>\n
    host='localhost',<\/p>\n
    user='username',<\/p>\n
    password='password',<\/p>\n
    database='database_name'<\/p>\n
)<\/p>\n
\u521b\u5efa\u6e38\u6807<\/strong><\/h2>\n
cursor = conn.cursor()<\/p>\n
\u521b\u5efa\u7d22\u5f15<\/strong><\/h2>\n
cursor.execute("CREATE INDEX idx_column1 ON large_table(column1)")<\/p>\n
\u6267\u884c\u67e5\u8be2<\/strong><\/h2>\n
cursor.execute("SELECT * FROM large_table WHERE column1 = 'value'")<\/p>\n
rows = cursor.fetchall()<\/p>\n
for row in rows:<\/p>\n
    print(row)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u63d0\u9ad8\u6570\u636e\u8bfb\u53d6\u7684\u6548\u7387\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
from multiprocessing import Pool<\/p>\n
\u5b9a\u4e49\u8bfb\u53d6\u51fd\u6570<\/strong><\/h2>\n
def read_csv_chunk(chunk):<\/p>\n
    return pd.read_csv(chunk)<\/p>\n
\u4f7f\u7528\u591a\u8fdb\u7a0b\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
chunks = ['large_dataset_part1.csv', 'large_dataset_part2.csv']<\/p>\n
with Pool() as pool:<\/p>\n
    data = pool.map(read_csv_chunk, chunks)<\/p>\n
    combined_data = pd.concat(data)<\/p>\n
    print(combined_data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
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\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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\u5728\u5904\u7406\u5927\u6587\u4ef6\u65f6\uff0c\u4f7f\u7528Python\u7684\u5185\u7f6e\u51fd\u6570\u5982open()<\/code>\u53ef\u80fd\u5bfc\u81f4\u5185\u5b58\u4e0d\u8db3\u3002\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u7684read_csv()<\/code>\u6216read_table()<\/code>\u51fd\u6570\uff0c\u5e76\u901a\u8fc7\u8bbe\u7f6echunksize<\/code>\u53c2\u6570\u6765\u5206\u5757\u8bfb\u53d6\u6570\u636e\u3002\u8fd9\u79cd\u65b9\u6cd5\u5141\u8bb8\u9010\u5757\u5904\u7406\u6587\u4ef6\uff0c\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6574\u4e2a\u6587\u4ef6\u5230\u5185\u5b58\u4e2d\u3002<\/p>\n
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\u9664\u4e86pandas<\/code>\uff0cdask<\/code>\u548cpyarrow<\/code>\u662f\u5904\u7406\u5927\u6570\u636e\u7684\u4f18\u79c0\u5e93\u3002dask<\/code>\u53ef\u4ee5\u5904\u7406\u6bd4\u5185\u5b58\u5927\u5f97\u591a\u7684\u6570\u636e\u96c6\uff0c\u5e76\u63d0\u4f9b\u7c7b\u4f3c\u4e8epandas<\/code>\u7684API\uff1b\u800cpyarrow<\/code>\u5219\u652f\u6301\u9ad8\u6548\u7684\u6570\u636e\u5e8f\u5217\u5316\u548c\u8bfb\u53d6\uff0c\u7279\u522b\u9002\u5408\u5904\u7406\u5217\u5f0f\u5b58\u50a8\u683c\u5f0f\uff08\u5982Parquet\u548cORC\uff09\u3002<\/p>\n
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