将 pyodbc.rows 列表转换为 pandas Dataframe 需要很长时间 [英] Convert list of pyodbc.rows to pandas Dataframe takes very long time

问题描述

是否有更快的方法将 pyodbc.rows 对象转换为 pandas Dataframe?将超过 1000 万个 pyodbc.rows 对象的列表转换为 pandas 数据框大约需要 30-40 分钟.

Is there a faster way to convert pyodbc.rows object to pandas Dataframe? It take about 30-40 minutes to convert a list of 10 million+ pyodbc.rows objects to pandas dataframe.

import pyodbc
import pandas

server = <server_ip> 
database = <db_name> 
username = <db_user> 
password = <password> 
port='1443'

conn = pyodbc.connect('DRIVER={SQL Server};SERVER='+server+';PORT='+port+';DATABASE='+database+';UID='+username+';PWD='+ password)

#takes upto 12 minutes
rows = cursor.execute("select top 10000000 * from [LSLTGT].[MBR_DIM] ").fetchall() 

#Read cursor data into Pandas dataframe.....Takes forever!
df = pandas.DataFrame([tuple(t) for t in rows]) 

推荐答案

使用生成器表达式而不是列表推导式可能会得到一些改进:

You might get some improvement by using a generator expression rather than a list comprehension:

df = pandas.DataFrame((tuple(t) for t in rows)) 

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