使用 Python 将 CSV 文件导入 sqlite3 数据库表
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【中文标题】使用 Python 将 CSV 文件导入 sqlite3 数据库表【英文标题】:Importing a CSV file into a sqlite3 database table using Python 【发布时间】:2011-02-22 16:36:26 【问题描述】:我有一个 CSV 文件,我想使用 Python 将此文件批量导入我的 sqlite3 数据库。命令是“.import .....”。但它似乎不能像这样工作。谁能给我一个如何在 sqlite3 中做到这一点的例子?我正在使用 Windows 以防万一。 谢谢
【问题讨论】:
请提供不起作用的 actual 命令和 actual 错误消息。 “进口......”可以是任何东西。 “不能工作”太模糊了,我们无法猜测。没有细节,我们无能为力。 我所说的实际命令是“.import”,它说语法错误新“.import” 请在问题中实际发布实际命令。请在问题中实际发布实际的错误消息。请不要添加简单重复的 cmets。请使用实际操作的实际复制和粘贴来更新问题。 【参考方案1】:.import
命令是 sqlite3 命令行工具的一个功能。要在 Python 中执行此操作,您应该使用 Python 拥有的任何工具(例如 csv module)简单地加载数据,然后像往常一样插入数据。
这样,您还可以控制插入的类型,而不是依赖于 sqlite3 看似未记录的行为。
【讨论】:
无需准备插页。 SQL 语句的源和编译结果保存在缓存中。 @John Machin:是否有链接指向 SQLite 如何做到这一点? @Marcelo:如果您对它是如何完成的(为什么?)感兴趣,请查看 sqlite 源代码或在 sqlite 邮件列表中询问。 @John Machin:我很感兴趣,因为在我遇到的所有 SQLite 文档中,没有一个关于自动缓存未准备好的语句的词。我认为阅读源代码或调查邮件列表来发现诸如是否应该准备我的 SQL 语句这样基本的东西是不合理的。您在这方面的信息来源是什么? @Marcelo:实际上它是在 Python sqlite3 包装器模块中完成的。 docs.python.org/library/… 说“”“sqlite3 模块内部使用语句缓存来避免 SQL 解析开销。如果要显式设置为连接缓存的语句数,可以设置 cached_statements 参数。当前实现的默认值为缓存 100 条语句。"""【参考方案2】:import csv, sqlite3
con = sqlite3.connect(":memory:") # change to 'sqlite:///your_filename.db'
cur = con.cursor()
cur.execute("CREATE TABLE t (col1, col2);") # use your column names here
with open('data.csv','r') as fin: # `with` statement available in 2.5+
# csv.DictReader uses first line in file for column headings by default
dr = csv.DictReader(fin) # comma is default delimiter
to_db = [(i['col1'], i['col2']) for i in dr]
cur.executemany("INSERT INTO t (col1, col2) VALUES (?, ?);", to_db)
con.commit()
con.close()
【讨论】:
如果您遇到与我相同的问题:确保将 col1 和 col2 更改为 csv 文件中的列标题。最后通过调用 con.close() 关闭与数据库的连接。 谢谢,@乔纳斯。更新帖子。 当我尝试这种方法时,我不断收到not all arguments converted during string formatting
。
我试过这个方法,但它对我不起作用。您能否在这里查看我的数据集(它们很正常,除了某些列有空值)并尝试使用您的代码导入它们? ***.com/questions/46042623/…
此代码未针对非常大的 csv 文件(GB 顺序)进行优化【参考方案3】:
非常感谢伯尼的answer!不得不稍微调整一下——这对我有用:
import csv, sqlite3
conn = sqlite3.connect("pcfc.sl3")
curs = conn.cursor()
curs.execute("CREATE TABLE PCFC (id INTEGER PRIMARY KEY, type INTEGER, term TEXT, definition TEXT);")
reader = csv.reader(open('PC.txt', 'r'), delimiter='|')
for row in reader:
to_db = [unicode(row[0], "utf8"), unicode(row[1], "utf8"), unicode(row[2], "utf8")]
curs.execute("INSERT INTO PCFC (type, term, definition) VALUES (?, ?, ?);", to_db)
conn.commit()
我的文本文件 (PC.txt) 如下所示:
1 | Term 1 | Definition 1
2 | Term 2 | Definition 2
3 | Term 3 | Definition 3
【讨论】:
【参考方案4】:#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys, csv, sqlite3
def main():
con = sqlite3.connect(sys.argv[1]) # database file input
cur = con.cursor()
cur.executescript("""
DROP TABLE IF EXISTS t;
CREATE TABLE t (COL1 TEXT, COL2 TEXT);
""") # checks to see if table exists and makes a fresh table.
with open(sys.argv[2], "rb") as f: # CSV file input
reader = csv.reader(f, delimiter=',') # no header information with delimiter
for row in reader:
to_db = [unicode(row[0], "utf8"), unicode(row[1], "utf8")] # Appends data from CSV file representing and handling of text
cur.execute("INSERT INTO neto (COL1, COL2) VALUES(?, ?);", to_db)
con.commit()
con.close() # closes connection to database
if __name__=='__main__':
main()
【讨论】:
【参考方案5】:创建一个到磁盘上文件的 sqlite 连接留给读者作为练习......但是现在 pandas 库可以实现两行
df = pandas.read_csv(csvfile)
df.to_sql(table_name, conn, if_exists='append', index=False)
【讨论】:
使用 sep=';'。 pandas 文档清楚地概述了如何处理这个问题。 有没有办法在不使用 RAM 的情况下使用 pandas?,我有一个巨大的 .csv (7gb) 我无法作为数据框导入然后附加到数据库。 是的,pandas 中有一种方法可以分块读取,而不是一次全部读取。恐怕我无法完全回忆起我的头顶。我想你添加 chunksize=df
,所以我将您的示例缩短为:pandas.read_csv(csvfile).to_sql(table_name, conn, if_exists='append', index=False)
我就像“来吧....继续滚动....这里必须是熊猫答案........很好!”【参考方案6】:
我的 2 美分(更通用):
import csv, sqlite3
import logging
def _get_col_datatypes(fin):
dr = csv.DictReader(fin) # comma is default delimiter
fieldTypes =
for entry in dr:
feildslLeft = [f for f in dr.fieldnames if f not in fieldTypes.keys()]
if not feildslLeft: break # We're done
for field in feildslLeft:
data = entry[field]
# Need data to decide
if len(data) == 0:
continue
if data.isdigit():
fieldTypes[field] = "INTEGER"
else:
fieldTypes[field] = "TEXT"
# TODO: Currently there's no support for DATE in sqllite
if len(feildslLeft) > 0:
raise Exception("Failed to find all the columns data types - Maybe some are empty?")
return fieldTypes
def escapingGenerator(f):
for line in f:
yield line.encode("ascii", "xmlcharrefreplace").decode("ascii")
def csvToDb(csvFile, outputToFile = False):
# TODO: implement output to file
with open(csvFile,mode='r', encoding="ISO-8859-1") as fin:
dt = _get_col_datatypes(fin)
fin.seek(0)
reader = csv.DictReader(fin)
# Keep the order of the columns name just as in the CSV
fields = reader.fieldnames
cols = []
# Set field and type
for f in fields:
cols.append("%s %s" % (f, dt[f]))
# Generate create table statement:
stmt = "CREATE TABLE ads (%s)" % ",".join(cols)
con = sqlite3.connect(":memory:")
cur = con.cursor()
cur.execute(stmt)
fin.seek(0)
reader = csv.reader(escapingGenerator(fin))
# Generate insert statement:
stmt = "INSERT INTO ads VALUES(%s);" % ','.join('?' * len(cols))
cur.executemany(stmt, reader)
con.commit()
return con
【讨论】:
if len(feildslLeft) > 0: always true ,因此引发异常。请检查并更正此问题。 有什么方法可以做到这一点而不必 fseek(),以便可以在流上使用它? @mwag 您可以跳过列类型检查并将所有列作为文本导入。【参考方案7】:您可以使用blaze
和odo
有效地做到这一点
import blaze as bz
csv_path = 'data.csv'
bz.odo(csv_path, 'sqlite:///data.db::data')
Odo 会将 csv 文件存储到架构 data
下的 data.db
(sqlite 数据库)
或者你直接使用odo
,不使用blaze
。无论哪种方式都很好。阅读此documentation
【讨论】:
bz 未定义:P 它可能是非常旧的包,因为他的内部错误:AttributeError: 'SubDiGraph' object has no attribute 'edge' 也得到相同的属性错误:虽然 GitHub 上似乎有 cmets,但【参考方案8】:基于 Guy L 解决方案(喜欢它),但可以处理转义字段。
import csv, sqlite3
def _get_col_datatypes(fin):
dr = csv.DictReader(fin) # comma is default delimiter
fieldTypes =
for entry in dr:
feildslLeft = [f for f in dr.fieldnames if f not in fieldTypes.keys()]
if not feildslLeft: break # We're done
for field in feildslLeft:
data = entry[field]
# Need data to decide
if len(data) == 0:
continue
if data.isdigit():
fieldTypes[field] = "INTEGER"
else:
fieldTypes[field] = "TEXT"
# TODO: Currently there's no support for DATE in sqllite
if len(feildslLeft) > 0:
raise Exception("Failed to find all the columns data types - Maybe some are empty?")
return fieldTypes
def escapingGenerator(f):
for line in f:
yield line.encode("ascii", "xmlcharrefreplace").decode("ascii")
def csvToDb(csvFile,dbFile,tablename, outputToFile = False):
# TODO: implement output to file
with open(csvFile,mode='r', encoding="ISO-8859-1") as fin:
dt = _get_col_datatypes(fin)
fin.seek(0)
reader = csv.DictReader(fin)
# Keep the order of the columns name just as in the CSV
fields = reader.fieldnames
cols = []
# Set field and type
for f in fields:
cols.append("\"%s\" %s" % (f, dt[f]))
# Generate create table statement:
stmt = "create table if not exists \"" + tablename + "\" (%s)" % ",".join(cols)
print(stmt)
con = sqlite3.connect(dbFile)
cur = con.cursor()
cur.execute(stmt)
fin.seek(0)
reader = csv.reader(escapingGenerator(fin))
# Generate insert statement:
stmt = "INSERT INTO \"" + tablename + "\" VALUES(%s);" % ','.join('?' * len(cols))
cur.executemany(stmt, reader)
con.commit()
con.close()
【讨论】:
【参考方案9】:import csv, sqlite3
def _get_col_datatypes(fin):
dr = csv.DictReader(fin) # comma is default delimiter
fieldTypes =
for entry in dr:
feildslLeft = [f for f in dr.fieldnames if f not in fieldTypes.keys()]
if not feildslLeft: break # We're done
for field in feildslLeft:
data = entry[field]
# Need data to decide
if len(data) == 0:
continue
if data.isdigit():
fieldTypes[field] = "INTEGER"
else:
fieldTypes[field] = "TEXT"
# TODO: Currently there's no support for DATE in sqllite
if len(feildslLeft) > 0:
raise Exception("Failed to find all the columns data types - Maybe some are empty?")
return fieldTypes
def escapingGenerator(f):
for line in f:
yield line.encode("ascii", "xmlcharrefreplace").decode("ascii")
def csvToDb(csvFile,dbFile,tablename, outputToFile = False):
# TODO: implement output to file
with open(csvFile,mode='r', encoding="ISO-8859-1") as fin:
dt = _get_col_datatypes(fin)
fin.seek(0)
reader = csv.DictReader(fin)
# Keep the order of the columns name just as in the CSV
fields = reader.fieldnames
cols = []
# Set field and type
for f in fields:
cols.append("\"%s\" %s" % (f, dt[f]))
# Generate create table statement:
stmt = "create table if not exists \"" + tablename + "\" (%s)" % ",".join(cols)
print(stmt)
con = sqlite3.connect(dbFile)
cur = con.cursor()
cur.execute(stmt)
fin.seek(0)
reader = csv.reader(escapingGenerator(fin))
# Generate insert statement:
stmt = "INSERT INTO \"" + tablename + "\" VALUES(%s);" % ','.join('?' * len(cols))
cur.executemany(stmt, reader)
con.commit()
con.close()
【讨论】:
请正确格式化您的代码并添加一些说明【参考方案10】:为了简单起见,您可以使用项目 Makefile 中的 sqlite3 命令行工具。
%.sql3: %.csv
rm -f $@
sqlite3 $@ -echo -cmd ".mode csv" ".import $< $*"
%.dump: %.sql3
sqlite3 $< "select * from $*"
make test.sql3
然后从现有的 test.csv 文件创建 sqlite 数据库,其中包含单个表“test”。然后你可以make test.dump
来验证内容。
【讨论】:
【参考方案11】:如果 CSV 文件必须作为 python 程序的一部分导入,那么为了简单和高效,您可以按照以下建议的方式使用os.system
:
import os
cmd = """sqlite3 database.db <<< ".import input.csv mytable" """
rc = os.system(cmd)
print(rc)
重点是通过指定数据库的文件名,数据会自动保存,假设读取没有错误。
【讨论】:
***.com/questions/6466711/… @PatrickT - 这不正是最后一段所说的吗?【参考方案12】:.import
是正确的方法,但这是来自 SQLite3 命令行程序的命令。这个问题的许多最佳答案都涉及本机 python 循环,但如果您的文件很大(我的文件是 10^6 到 10^7 条记录),您希望避免将所有内容读入 pandas 或使用本机 python 列表理解/循环(虽然我没有计时比较)。
对于大文件,我相信最好的选择是使用subprocess.run()
来执行sqlite的导入命令。在下面的示例中,我假设表已经存在,但 csv 文件在第一行有标题。请参阅.import
docs 了解更多信息。
subprocess.run()
from pathlib import Path
db_name = Path('my.db').resolve()
csv_file = Path('file.csv').resolve()
result = subprocess.run(['sqlite3',
str(db_name),
'-cmd',
'.mode csv',
'.import --skip 1 ' + str(csv_file).replace('\\','\\\\')
+' <table_name>'],
capture_output=True)
编辑说明:sqlite3 的 .import
命令已改进,因此它可以将第一行视为标题名称,甚至可以跳过前 x 行(需要版本 >=3.32,如前所述在this answer中。如果您有旧版本的sqlite3,您可能需要先创建表,然后在导入前剥离csv的第一行。--skip 1
参数在3.32之前会出错
说明
在命令行中,您要查找的命令是 sqlite3 my.db -cmd ".mode csv" ".import file.csv table"
。 subprocess.run()
运行命令行进程。 subprocess.run()
的参数是一个字符串序列,它被解释为一个命令,后面跟着它的所有参数。
sqlite3 my.db
打开数据库
数据库后的-cmd
标志允许您将多个后续命令传递给 sqlite 程序。在 shell 中,每个命令都必须用引号引起来,但在这里,它们只需要成为序列中自己的元素
'.mode csv'
符合您的预期
'.import --skip 1'+str(csv_file).replace('\\','\\\\')+' <table_name>'
是导入命令。
不幸的是,由于 subprocess 将所有后续内容作为带引号的字符串传递给 -cmd
,因此如果您有 Windows 目录路径,则需要将反斜杠加倍。
剥离标题
这不是问题的重点,但这是我使用的。同样,我不想在任何时候将整个文件读入内存:
with open(csv, "r") as source:
source.readline()
with open(str(csv)+"_nohead", "w") as target:
shutil.copyfileobj(source, target)
【讨论】:
无法使--skip 1
与 3.32.3 和 3.36.0 一起使用
命令行中的@roman 或subprocess.run()
?
我同意这是处理大文件的唯一方法。【参考方案13】:
我发现可能有必要将数据从 csv 传输到数据库中分块进行拆分,以免内存不足。可以这样做:
import csv
import sqlite3
from operator import itemgetter
# Establish connection
conn = sqlite3.connect("mydb.db")
# Create the table
conn.execute(
"""
CREATE TABLE persons(
person_id INTEGER,
last_name TEXT,
first_name TEXT,
address TEXT
)
"""
)
# These are the columns from the csv that we want
cols = ["person_id", "last_name", "first_name", "address"]
# If the csv file is huge, we instead add the data in chunks
chunksize = 10000
# Parse csv file and populate db in chunks
with conn, open("persons.csv") as f:
reader = csv.DictReader(f)
chunk = []
for i, row in reader:
if i % chunksize == 0 and i > 0:
conn.executemany(
"""
INSERT INTO persons
VALUES(?, ?, ?, ?)
""", chunk
)
chunk = []
items = itemgetter(*cols)(row)
chunk.append(items)
【讨论】:
【参考方案14】:如果您的 CSV 文件非常大,这里有一些可行的解决方案。按照另一个答案的建议使用 to_sql
,但设置 chunksize 以便它不会尝试一次处理整个文件。
import sqlite3
import pandas as pd
conn = sqlite3.connect('my_data.db')
c = conn.cursor()
users = pd.read_csv('users.csv')
users.to_sql('users', conn, if_exists='append', index = False, chunksize = 10000)
您也可以使用 Dask,如 here 所述,并行编写大量 Pandas DataFrame:
dto_sql = dask.delayed(pd.DataFrame.to_sql)
out = [dto_sql(d, 'table_name', db_url, if_exists='append', index=True)
for d in ddf.to_delayed()]
dask.compute(*out)
更多详情请见here。
【讨论】:
【参考方案15】:下面也可以根据CSV头添加字段名:
import sqlite3
def csv_sql(file_dir,table_name,database_name):
con = sqlite3.connect(database_name)
cur = con.cursor()
# Drop the current table by:
# cur.execute("DROP TABLE IF EXISTS %s;" % table_name)
with open(file_dir, 'r') as fl:
hd = fl.readline()[:-1].split(',')
ro = fl.readlines()
db = [tuple(ro[i][:-1].split(',')) for i in range(len(ro))]
header = ','.join(hd)
cur.execute("CREATE TABLE IF NOT EXISTS %s (%s);" % (table_name,header))
cur.executemany("INSERT INTO %s (%s) VALUES (%s);" % (table_name,header,('?,'*len(hd))[:-1]), db)
con.commit()
con.close()
# Example:
csv_sql('./surveys.csv','survey','eco.db')
【讨论】:
【参考方案16】:这样您也可以在 CSV 上进行连接:
import sqlite3
import os
import pandas as pd
from typing import List
class CSVDriver:
def __init__(self, table_dir_path: str):
self.table_dir_path = table_dir_path # where tables (ie. csv files) are located
self._con = None
@property
def con(self) -> sqlite3.Connection:
"""Make a singleton connection to an in-memory SQLite database"""
if not self._con:
self._con = sqlite3.connect(":memory:")
return self._con
def _exists(self, table: str) -> bool:
query = """
SELECT name
FROM sqlite_master
WHERE type ='table'
AND name NOT LIKE 'sqlite_%';
"""
tables = self.con.execute(query).fetchall()
return table in tables
def _load_table_to_mem(self, table: str, sep: str = None) -> None:
"""
Load a CSV into an in-memory SQLite database
sep is set to None in order to force pandas to auto-detect the delimiter
"""
if self._exists(table):
return
file_name = table + ".csv"
path = os.path.join(self.table_dir_path, file_name)
if not os.path.exists(path):
raise ValueError(f"CSV table table does not exist in self.table_dir_path")
df = pd.read_csv(path, sep=sep, engine="python") # set engine to python to skip pandas' warning
df.to_sql(table, self.con, if_exists='replace', index=False, chunksize=10000)
def query(self, query: str) -> List[tuple]:
"""
Run an SQL query on CSV file(s).
Tables are loaded from table_dir_path
"""
tables = extract_tables(query)
for table in tables:
self._load_table_to_mem(table)
cursor = self.con.cursor()
cursor.execute(query)
records = cursor.fetchall()
return records
extract_tables():
import sqlparse
from sqlparse.sql import IdentifierList, Identifier, Function
from sqlparse.tokens import Keyword, DML
from collections import namedtuple
import itertools
class Reference(namedtuple('Reference', ['schema', 'name', 'alias', 'is_function'])):
__slots__ = ()
def has_alias(self):
return self.alias is not None
@property
def is_query_alias(self):
return self.name is None and self.alias is not None
@property
def is_table_alias(self):
return self.name is not None and self.alias is not None and not self.is_function
@property
def full_name(self):
if self.schema is None:
return self.name
else:
return self.schema + '.' + self.name
def _is_subselect(parsed):
if not parsed.is_group:
return False
for item in parsed.tokens:
if item.ttype is DML and item.value.upper() in ('SELECT', 'INSERT',
'UPDATE', 'CREATE', 'DELETE'):
return True
return False
def _identifier_is_function(identifier):
return any(isinstance(t, Function) for t in identifier.tokens)
def _extract_from_part(parsed):
tbl_prefix_seen = False
for item in parsed.tokens:
if item.is_group:
for x in _extract_from_part(item):
yield x
if tbl_prefix_seen:
if _is_subselect(item):
for x in _extract_from_part(item):
yield x
# An incomplete nested select won't be recognized correctly as a
# sub-select. eg: 'SELECT * FROM (SELECT id FROM user'. This causes
# the second FROM to trigger this elif condition resulting in a
# StopIteration. So we need to ignore the keyword if the keyword
# FROM.
# Also 'SELECT * FROM abc JOIN def' will trigger this elif
# condition. So we need to ignore the keyword JOIN and its variants
# INNER JOIN, FULL OUTER JOIN, etc.
elif item.ttype is Keyword and (
not item.value.upper() == 'FROM') and (
not item.value.upper().endswith('JOIN')):
tbl_prefix_seen = False
else:
yield item
elif item.ttype is Keyword or item.ttype is Keyword.DML:
item_val = item.value.upper()
if (item_val in ('COPY', 'FROM', 'INTO', 'UPDATE', 'TABLE') or
item_val.endswith('JOIN')):
tbl_prefix_seen = True
# 'SELECT a, FROM abc' will detect FROM as part of the column list.
# So this check here is necessary.
elif isinstance(item, IdentifierList):
for identifier in item.get_identifiers():
if (identifier.ttype is Keyword and
identifier.value.upper() == 'FROM'):
tbl_prefix_seen = True
break
def _extract_table_identifiers(token_stream):
for item in token_stream:
if isinstance(item, IdentifierList):
for ident in item.get_identifiers():
try:
alias = ident.get_alias()
schema_name = ident.get_parent_name()
real_name = ident.get_real_name()
except AttributeError:
continue
if real_name:
yield Reference(schema_name, real_name,
alias, _identifier_is_function(ident))
elif isinstance(item, Identifier):
yield Reference(item.get_parent_name(), item.get_real_name(),
item.get_alias(), _identifier_is_function(item))
elif isinstance(item, Function):
yield Reference(item.get_parent_name(), item.get_real_name(),
item.get_alias(), _identifier_is_function(item))
def extract_tables(sql):
# let's handle multiple statements in one sql string
extracted_tables = []
statements = list(sqlparse.parse(sql))
for statement in statements:
stream = _extract_from_part(statement)
extracted_tables.append([ref.name for ref in _extract_table_identifiers(stream)])
return list(itertools.chain(*extracted_tables))
示例(假设account.csv
和tojoin.csv
存在于/path/to/files
):
db_path = r"/path/to/files"
driver = CSVDriver(db_path)
query = """
SELECT tojoin.col_to_join
FROM account
LEFT JOIN tojoin
ON account.a = tojoin.a
"""
driver.query(query)
【讨论】:
【参考方案17】:"""
cd Final_Codes
python csv_to_db.py
CSV to SQL DB
"""
import csv
import sqlite3
import os
import fnmatch
UP_FOLDER = os.path.dirname(os.getcwd())
DATABASE_FOLDER = os.path.join(UP_FOLDER, "Databases")
DBNAME = "allCompanies_database.db"
def getBaseNameNoExt(givenPath):
"""Returns the basename of the file without the extension"""
filename = os.path.splitext(os.path.basename(givenPath))[0]
return filename
def find(pattern, path):
"""Utility to find files wrt a regex search"""
result = []
for root, dirs, files in os.walk(path):
for name in files:
if fnmatch.fnmatch(name, pattern):
result.append(os.path.join(root, name))
return result
if __name__ == "__main__":
Database_Path = os.path.join(DATABASE_FOLDER, DBNAME)
# change to 'sqlite:///your_filename.db'
csv_files = find('*.csv', DATABASE_FOLDER)
con = sqlite3.connect(Database_Path)
cur = con.cursor()
for each in csv_files:
with open(each, 'r') as fin: # `with` statement available in 2.5+
# csv.DictReader uses first line in file for column headings by default
dr = csv.DictReader(fin) # comma is default delimiter
TABLE_NAME = getBaseNameNoExt(each)
Cols = dr.fieldnames
numCols = len(Cols)
"""
for i in dr:
print(i.values())
"""
to_db = [tuple(i.values()) for i in dr]
print(TABLE_NAME)
# use your column names here
ColString = ','.join(Cols)
QuestionMarks = ["?"] * numCols
ToAdd = ','.join(QuestionMarks)
cur.execute(f"CREATE TABLE TABLE_NAME (ColString);")
cur.executemany(
f"INSERT INTO TABLE_NAME (ColString) VALUES (ToAdd);", to_db)
con.commit()
con.close()
print("Execution Complete!")
当您在文件夹中有大量 csv 文件并希望一次转换为单个 .db 文件时,这应该会派上用场!
请注意,您不必事先知道文件名、表名或字段名(列名)!
酷啊?!
【讨论】:
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