25

出于某种原因，我想以 csv 文件的形式从数据库（sqlite3）中转储一个表。我正在使用带有 elixir 的 python 脚本（基于 sqlalchemy）来修改数据库。我想知道是否有任何方法可以将我使用的表格转储到 csv。

我见过 sqlalchemy序列化程序，但它似乎不是我想要的。我做错了吗？我应该在关闭我的 sqlalchemy 会话后调用sqlite3 python 模块来转储到文件吗？还是我应该用自制的东西？

4

8 回答 8

38

稍微修改彼得汉森的答案，使用 SQLAlchemy 而不是原始数据库访问

import csv
outfile = open('mydump.csv', 'wb')
outcsv = csv.writer(outfile)
records = session.query(MyModel).all()
[outcsv.writerow([getattr(curr, column.name) for column in MyTable.__mapper__.columns]) for curr in records]
# or maybe use outcsv.writerows(records)

outfile.close()
于 2010-06-01T20:02:17.600 回答
28

有很多方法可以实现这一点，包括对实用程序的简单os.system()调用（sqlite3如果您安装了该实用程序），但大致是我从 Python 中所做的：

import sqlite3
import csv

con = sqlite3.connect('mydatabase.db')
outfile = open('mydump.csv', 'wb')
outcsv = csv.writer(outfile)

cursor = con.execute('select * from mytable')

# dump column titles (optional)
outcsv.writerow(x[0] for x in cursor.description)
# dump rows
outcsv.writerows(cursor.fetchall())

outfile.close()
于 2010-06-01T19:51:28.660 回答
19

我将上述示例改编为基于 sqlalchemy 的代码，如下所示：

import csv
import sqlalchemy as sqAl

metadata = sqAl.MetaData()
engine = sqAl.create_engine('sqlite:///%s' % 'data.db')
metadata.bind = engine

mytable = sqAl.Table('sometable', metadata, autoload=True)
db_connection = engine.connect()

select = sqAl.sql.select([mytable])
result = db_connection.execute(select)

fh = open('data.csv', 'wb')
outcsv = csv.writer(fh)

outcsv.writerow(result.keys())
outcsv.writerows(result)

fh.close

这适用于我的 sqlalchemy 0.7.9。我想这适用于所有 sqlalchemy 表和结果对象。

于 2012-11-07T09:50:39.890 回答
6 
with open('dump.csv', 'wb') as f:
    out = csv.writer(f)
    out.writerow(['id', 'description'])

    for item in session.query(Queue).all():
        out.writerow([item.id, item.description])

如果您不介意手工制作列标签，我发现这很有用。

于 2016-05-18T03:38:33.877 回答
5

我知道这很旧，但我只是遇到了这个问题，这就是我解决它的方法

from sqlalchemy import create_engine

basedir = os.path.abspath(os.path.dirname(__file__))
sql_engine = create_engine(os.path.join('sqlite:///' + os.path.join(basedir, 'single_file_app.db')), echo=False)
results = pd.read_sql_query('select * from users',sql_engine)
results.to_csv(os.path.join(basedir, 'mydump2.csv'),index=False,sep=";")
于 2019-07-23T10:46:04.543 回答
1

我花了很多时间寻找这个问题的解决方案，最后创造了这样的东西：

from sqlalchemy import inspect

with open(file_to_write, 'w') as file:
    out_csv = csv.writer(file, lineterminator='\n')

    columns = [column.name for column in inspect(Movies).columns][1:]
    out_csv.writerow(columns)

    session_3 = session_maker()

    extract_query = [getattr(Movies, col) for col in columns]
    for mov in session_3.query(*extract_query):
        out_csv.writerow(mov)

    session_3.close()

它创建一个 CSV 文件，其中包含列名和整个“movies”表的转储，没有“id”主列。

于 2020-05-15T13:20:48.820 回答
1 
import csv

f = open('ratings.csv', 'w')
out = csv.writer(f)
out.writerow(['id', 'user_id', 'movie_id', 'rating'])

for item in db.query.all():
    out.writerow([item.username, item.username, item.movie_name, item.rating])
f.close()
于 2018-01-13T01:44:38.323 回答
0

以模块化方式：使用带有 automap 和 mysql 的 slqalchemy 的示例。

数据库.py：

from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine

Base = automap_base()

engine = create_engine('mysql://user:pass@localhost:3306/database_name', echo=True)

Base.prepare(engine, reflect=True)

# Map the tables
State = Base.classes.states

session = Session(engine, autoflush=False)

export_to_csv.py：

from databases import *
import csv

def export():

    q = session.query(State)

    file = './data/states.csv'

    with open(file, 'w') as csvfile:
        outcsv = csv.writer(csvfile, delimiter=',',quotechar='"', quoting = csv.QUOTE_MINIMAL)

        header = State.__table__.columns.keys()

        outcsv.writerow(header)     

        for record in q.all():
            outcsv.writerow([getattr(record, c) for c in header ])

if __name__ == "__main__":
    export()

结果：

name,abv,country,is_state,is_lower48,slug,latitude,longitude,population,area Alaska,AK,US,y,n,alaska,61.370716,-152.404419,710231,571951.25 Alabama,AL,US,y,y,alabama ,32.806671,-86.79113,4779736,50744.0 Arkansas,AR,US,y,y,arkansas,34.969704,-92.373123,2915918,52068.17 Arizona,AZ,US,y,y,arizona,33.729759,-163271.431123617,加利福尼亚,CA,US,y,y,california,36.116203,-119.681564,37253956,155939.52 Colorado,CO,US,y,y,colorado,39.059811,-105.311104,5029196,103717.53 Connecticut,CT,US,y,y,connecticut ,41.597782,-72.755371,3574097,4844.8 District of Columbia,DC,US,n,n,district-of-columbia,38.897438,-77.026817,601723,68.34 Delaware,DE,US,y,y,delaware,39.318523,- 75.507141,897934,1953.56 Florida,FL,US,y,y,florida,27.766279,-81.686783,18801310,53926.82 Georgia,GA,US,y,y,georgia,33.040619,-83.643074,9687653,57906.14

于 2018-01-13T20:28:15.563 回答