使用sqlalchemy将csv文件加载到数据库中

Question

我正在尝试学习用Python编程.我想把csv文件放到数据库中.使用它是个好主意

Answer 1

由于SQLAlchemy的强大功能,我也在项目中使用它.它的强大功能来自面向对象的"与数据库交谈"的方式,而不是硬编码可能很难管理的SQL语句.更不用说,它也快得多.

直截了当地回答你的问题,是的!使用SQLAlchemy将CSV中的数据存储到数据库中是一件小事.这是一个完整的工作示例(我使用SQLAlchemy 1.0.6和Python 2.7.6):

from numpy import genfromtxt
from time import time
from datetime import datetime
from sqlalchemy import Column, Integer, Float, Date
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

def Load_Data(file_name):
    data = genfromtxt(file_name, delimiter=',', skip_header=1, converters={0: lambda s: str(s)})
    return data.tolist()

Base = declarative_base()

class Price_History(Base):
    #Tell SQLAlchemy what the table name is and if there's any table-specific arguments it should know about
    __tablename__ = 'Price_History'
    __table_args__ = {'sqlite_autoincrement': True}
    #tell SQLAlchemy the name of column and its attributes:
    id = Column(Integer, primary_key=True, nullable=False) 
    date = Column(Date)
    opn = Column(Float)
    hi = Column(Float)
    lo = Column(Float)
    close = Column(Float)
    vol = Column(Float)

if __name__ == "__main__":
    t = time()

    #Create the database
    engine = create_engine('sqlite:///csv_test.db')
    Base.metadata.create_all(engine)

    #Create the session
    session = sessionmaker()
    session.configure(bind=engine)
    s = session()

    try:
        file_name = "t.csv" #sample CSV file used:  http://www.google.com/finance/historical?q=NYSE%3AT&ei=W4ikVam8LYWjmAGjhoHACw&output=csv
        data = Load_Data(file_name) 

        for i in data:
            record = Price_History(**{
                'date' : datetime.strptime(i[0], '%d-%b-%y').date(),
                'opn' : i[1],
                'hi' : i[2],
                'lo' : i[3],
                'close' : i[4],
                'vol' : i[5]
            })
            s.add(record) #Add all the records

        s.commit() #Attempt to commit all the records
    except:
        s.rollback() #Rollback the changes on error
    finally:
        s.close() #Close the connection
    print "Time elapsed: " + str(time() - t) + " s." #0.091s

(注意:这不一定是执行此操作的"最佳"方式,但我认为这种格式对于初学者来说非常易读;它也非常快:插入251条记录时为0.091秒!)

我想如果你逐行浏览它,你会发现使用它是多么轻而易举.注意缺少SQL语句 - 万岁!我也冒昧地使用numpy将CSV内容加载到两行中,但如果你喜欢的话可以不用它来完成.

如果你想与传统的做法进行比较,这里有一个完整的例子供参考:

import sqlite3
import time
from numpy import genfromtxt

def dict_factory(cursor, row):
    d = {}
    for idx, col in enumerate(cursor.description):
        d[col[0]] = row[idx]
    return d


def Create_DB(db):      
    #Create DB and format it as needed
    with sqlite3.connect(db) as conn:
        conn.row_factory = dict_factory
        conn.text_factory = str

        cursor = conn.cursor()

        cursor.execute("CREATE TABLE [Price_History] ([id] INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE, [date] DATE, [opn] FLOAT, [hi] FLOAT, [lo] FLOAT, [close] FLOAT, [vol] INTEGER);")


def Add_Record(db, data):
    #Insert record into table
    with sqlite3.connect(db) as conn:
        conn.row_factory = dict_factory
        conn.text_factory = str

        cursor = conn.cursor()

        cursor.execute("INSERT INTO Price_History({cols}) VALUES({vals});".format(cols = str(data.keys()).strip('[]'), 
                    vals=str([data[i] for i in data]).strip('[]')
                    ))


def Load_Data(file_name):
    data = genfromtxt(file_name, delimiter=',', skiprows=1, converters={0: lambda s: str(s)})
    return data.tolist()


if __name__ == "__main__":
    t = time.time() 

    db = 'csv_test_sql.db' #Database filename 
    file_name = "t.csv" #sample CSV file used:  http://www.google.com/finance/historical?q=NYSE%3AT&ei=W4ikVam8LYWjmAGjhoHACw&output=csv

    data = Load_Data(file_name) #Get data from CSV

    Create_DB(db) #Create DB

    #For every record, format and insert to table
    for i in data:
        record = {
                'date' : i[0],
                'opn' : i[1],
                'hi' : i[2],
                'lo' : i[3],
                'close' : i[4],
                'vol' : i[5]
            }
        Add_Record(db, record)

    print "Time elapsed: " + str(time.time() - t) + " s." #3.604s

(注意:即使是以"旧"的方式,这绝不是最好的方法,但它是非常易读的,并且是SQLAlchemy方式与"旧"方式的"1对1"翻译.)

注意SQL语句:一个用于创建表,另一个用于插入记录.另外,请注意,维护长SQL字符串与简单的类属性添加相比,它更麻烦一些.喜欢SQLAlchemy到目前为止？

至于你的外键查询,当然.SQLAlchemy也有能力做到这一点.下面是一个类属性如何使用外键赋值的示例(假设ForeignKey该类也已从sqlalchemy模块导入):

class Asset_Analysis(Base):
    #Tell SQLAlchemy what the table name is and if there's any table-specific arguments it should know about
    __tablename__ = 'Asset_Analysis'
    __table_args__ = {'sqlite_autoincrement': True}
    #tell SQLAlchemy the name of column and its attributes:
    id = Column(Integer, primary_key=True, nullable=False) 
    fid = Column(Integer, ForeignKey('Price_History.id'))

它将"fid"列指向Price_History的id列的外键.

希望有所帮助!

Answer 2

如果您的CSV非常大,使用INSERTS非常无效.您应该使用不同基础的批量加载机制.例如在PostgreSQL中你应该使用"COPY FROM"方法:

with open(csv_file_path, 'r') as f:    
    conn = create_engine('postgresql+psycopg2://...').raw_connection()
    cursor = conn.cursor()
    cmd = 'COPY tbl_name(col1, col2, col3) FROM STDIN WITH (FORMAT CSV, HEADER FALSE)'
    cursor.copy_expert(cmd, f)
    conn.commit()

Answer 3

我遇到了完全相同的问题，但发现对熊猫使用两步过程反而更容易：

import pandas as pd
with open(csv_file_path, 'r') as file:
    data_df = pd.read_csv(file)
data_df.to_sql('tbl_name', con=engine, index=True, index_label='id', if_exists='replace')

请注意，我的方法与此类似，但是Google不知何故将我发送到了该线程，所以我想与大家分享。

Answer 4

要使用 sqlalchemy 将相对较小的 CSV 文件导入数据库，您可以使用engine.execute(my_table.insert(), list_of_row_dicts)，如sqlalchemy 教程的“执行多条语句”部分中所述。

这有时被称为“executemany”风格的调用，因为它会导致一个executemanyDBAPI 调用。DB 驱动程序可能会执行单个多值INSERT .. VALUES (..), (..), (..)语句，从而减少到 DB 的往返次数并加快执行速度：

• MySQL 连接器默认执行此操作
• Postgres的psycopg2确实 没有，除非你初始化它create_engine（...，executemany_mode = '值'） ）

根据sqlalchemy 的 FAQ，这是在不使用特定于数据库的批量加载方法（例如Postgres 中的COPY FROM 、MySQL 中的LOAD DATA LOCAL INFILE等）的情况下可以获得的最快速度。特别是它比使用普通 ORM（如在@Manuel J. Diaz 在这里回答), bulk_save_objects, 或bulk_insert_mappings.

import csv
from sqlalchemy import create_engine, Table, Column, Integer, MetaData

engine = create_engine('sqlite:///sqlalchemy.db', echo=True)

metadata = MetaData()
# Define the table with sqlalchemy:
my_table = Table('MyTable', metadata,
    Column('foo', Integer),
    Column('bar', Integer),
)
metadata.create_all(engine)
insert_query = my_table.insert()

# Or read the definition from the DB:
# metadata.reflect(engine, only=['MyTable'])
# my_table = Table('MyTable', metadata, autoload=True, autoload_with=engine)
# insert_query = my_table.insert()

# Or hardcode the SQL query:
# insert_query = "INSERT INTO MyTable (foo, bar) VALUES (:foo, :bar)"

with open('test.csv', 'r', encoding="utf-8") as csvfile:
    csv_reader = csv.reader(csvfile, delimiter=',')
    engine.execute(
        insert_query,
        [{"foo": row[0], "bar": row[1]} 
            for row in csv_reader]
    )