有多种方法可以连接到Teradata并将表导出到Pandas。这是四个+：
# You can install teradata via PIP: pip install teradata
# to get a list of your odbc drivers names, you Could do: teradata.tdodbc.drivers
# You don’t need to install teradata odbc driver if using method='rest'.
# See sending data from df to teradata for connection example
import teradata
import pandas as pd
host,username,password = 'HOST','UID', 'PWD'
#Make a connection
udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False)
with udaExec.connect(method="odbc",system=host, username=username,
password=password, driver="DRIVERNAME") as connect:
query = "SELECT * FROM DATABASEX.TABLENAMEX;"
#Reading query to df
df = pd.read_sql(query,connect)
# do something with df,e.g.
print(df.head()) #to see the first 5 rows
from @ymzkala：此软件包不需要您安装Teradata驱动程序（此软件包除外）。
# Installing python -m pip install teradatasql
import teradatasql
with teradatasql.connect(host='host', user='username', password='password') as connect:
df = pd.read_sql(query, connect)
import pyodbc
#You can install teradata via PIP: pip install pyodbc
#to get a list of your odbc drivers names, you Could do: pyodbc.drivers()
#Make a connection
link = 'DRIVER={DRIVERNAME};DBCNAME={hostname};UID={uid};PWD={pwd}'.format(
DRIVERNAME=DRIVERNAME,hostname=hostname,
uid=username, pwd=password)
with pyodbc.connect(link,autocommit=True) as connect:
#Reading query to df
df = pd.read_sql(query,connect)
#You can install sqlalchemy via PIP: pip install sqlalchemy-teradata
#Note: It is not pip install sqlalchemy. If you already have sqlalchemy, you still need sqlalchemy-teradata to get teradata dialects
from sqlalchemy import create_engine
#Make a connection
link = 'teradata://{username}:{password}@{hostname}/?driver={DRIVERNAME}'.format(
username=username,hostname=hostname,DRIVERNAME=DRIVERNAME)
with create_engine(link) as connect:
#Reading query to df
df = pd.read_sql(query,connect)
还有第五种方法，使用giraffez模块。我喜欢使用此模块，因为它随MLOAD，FASTLOAD，BULKEXPORT等一起提供。对于初学者来说，唯一的问题是其要求（例如C / C ++编译器，Teradata CLIv2和TPT API标头/ lib文件）。
注意：已于2018年7月13日更新，使用上下文管理器确保会话关闭
我们可以将数据从df发送到Teradata。避免’odbc‘1 MB限制和odbc驱动程序依赖性，我们可以使用’rest’方法。我们需要主机ip_address，而不是驱动程序参数。 df中的列顺序应与Teradata表中的列顺序匹配。
import teradata
import pandas as pd
# HOST_IP can be found by executing *>>nslookup viewpoint* or *ping viewpoint*
udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False)
with udaExec.connect(method="rest",system="DBName", username="UserName",
password="Password", host="HOST_IP_ADDRESS") as connect:
data = [tuple(x) for x in df.to_records(index=False)]
connect.executemany("INSERT INTO DATABASE.TABLEWITH5COL values(?,?,?,?,?)",data,batch=True)
使用“ odbc”，您必须将数据分块为少于1MB的块，以避免出现“ [HY001] [Teradata] [ODBC Teradata驱动程序]内存分配错误”错误：例如
import teradata
import pandas as pd
import numpy as np
udaExec = teradata.UdaExec (appName="test", version="1.0", logConsole=False)
with udaExec.connect(method="odbc",system="DBName", username="UserName",
password="Password", driver="DriverName") as connect:
#We can divide our huge_df to small chuncks. E.g. 100 churchs
chunks_df = np.array_split(huge_df, 100)
#Import chuncks to Teradata
for i,_ in enumerate(chunks_df):
data = [tuple(x) for x in chuncks_df[i].to_records(index=False)]
connect.executemany("INSERT INTO DATABASE.TABLEWITH5COL values(?,?,?,?,?)",data,batch=True)
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