签署:models.py 看起来如此:

class Representative(models.Model):
    parliament = models.CharField(max_length=128)
    name = models.CharField(max_length=128)
    country = models.CharField(max_length=128)
    party_group = models.CharField(max_length=128)
    national_party = models.CharField(max_length=128)
    position = models.CharField(max_length=128)

然后可以管理<代码>python。 ll/条码:

import csv
from your_app.models import Representative
# If you re using different field names, change this list accordingly.
# The order must also match the column order in the CSV file.
fields = [ parliament ,  name ,  country ,  party_group ,  national_party ,  position ]
for row in csv.reader(open( your_file.csv )):
    Representative.objects.create(**dict(zip(fields, row)))

你也做了。

<>Addendum (edit)

Per Thomas s request, here s an Interpretation of what **dict(zip(fields,row)):

最初,<代码>fields 载有我们界定的外地名称清单,row载有一份价值清单,该清单代表了CSV档案中的目前行文。

fields = [ parliament ,  name ,  country , ...]
row = [ 7 ,  Marta Andreasen ,  United Kingdom , ...]

>>> zip(fields, row)
[( parliament ,  7 ), ( name ,  Marta Andreasen ), ( country ,  United Kingdom ), ...]

<代码>字典>功能只是将奶制品清单转换成字典。

>>> dict(zip(fields, row))
{ parliament :  7 ,  name :  Marta Andreasen ,  country :  United Kingdom , ...}

<代码>**>是将字典转换成功能的关键词表的一种方式。 www.un.org/spanish/ga/president 例如,电话create (**dict(zip(field, row)))相当于:

create(parliament= 7 , name= Marta Andreasen , country= United Kingdom , ...)

希望这种明亮点。

As Siggy F 说,与Joschua相比,只有稍有不同:

1. 创建文本档案,备有您的图表,例如:

CREATE TABLE politicians (
    Parliament text, 
    Name text, 
    Country text, 
    Party_Group text, 
    National_Party text, 
    Position text
);

编制表格:

>>> import csv, sqlite3
>>> conn = sqlite3.connect( my.db )
>>> c = conn.cursor()
>>> with open( myschema.sql ) as f:            # read in schema file 
...   schema = f.read()
... 
>>> c.execute(schema)                          # create table per schema 
<sqlite3.Cursor object at 0x1392f50>
>>> conn.commit()                              # commit table creation

采用v模块,读取数据:

>>> csv_reader = csv.reader(open( myfile.txt ), skipinitialspace=True)
>>> csv_reader.next()                          # skip the first line in the file
[ Parliament ,  Name ,  Country , ...

# put all data in a tuple
# edit: decoding from utf-8 file to unicode
>>> to_db = tuple([i.decode( utf-8 ) for i in line] for line in csv_reader)
>>> to_db                                      # this will be inserted into table
[(u 7 , u Marta Andreasen , u United Kingdom , ...

插入数据:

>>> c.executemany("INSERT INTO politicians VALUES (?,?,?,?,?,?);", to_db)
<sqlite3.Cursor object at 0x1392f50>
>>> conn.commit()

2. 证明所有内容都符合预期:

>>> c.execute( SELECT * FROM politicians ).fetchall()
[(u 7 , u Marta Andreasen , u United Kingdom , ...

Edit:
And since you ve decoded (to unicode) on input, you need to be sure to encode on output.
For example:

with open( encoded_output.txt ,  w ) as f:
  for row in c.execute( SELECT * FROM politicians ).fetchall():
    for col in row:
      f.write(col.encode( utf-8 ))
      f.write( 
 )

您可使用单体模块阅读数据。 之后,您可添加“ql”声明,并使用http://docs.python.org/library/sqlite3.html#sqlite3.Cursor.executemany” rel=“nofollow noreferer”>executemany:

  cursor.executemany(sql, rows)

http://www.sqlalchemy.org/docs/fer/orm/sessions.html#sqlalchemy.orm.session.Session.add_all”rel=“nofollow noreferer”>add_all 如果你使用chem图。