Question

我想把数据写入mysql数据库。我首先从数据库中读取当前数据并计算新值。新值应按与数据库中数据相同的顺序写入，如下所示。我不想覆盖现有数据。我不想使用-proc:none。

我收到以下错误消息：

（mysql.connector.errors.DatabaseError）1265（01000）：数据被截断对于列＆＃39; log_return＆＃39;在第1行[SQL：＆＃39; INSERT INTO

完整的代码如下。

to_sql

数据库如下所示。

import sqlalchemy as sqlal
import pandas as pd
import numpy as np

mysql_engine = sqlal.create_engine(xxx)
mysql_engine.raw_connection()

metadata = sqlal.MetaData()

product  = sqlal.Table('product', metadata,
                       sqlal.Column('ticker', sqlal.String(10), primary_key=True, nullable=False, unique=True),                   
                       sqlal.Column('isin', sqlal.String(12), nullable=True),
                       sqlal.Column('product_name', sqlal.String(80), nullable=True),
                       sqlal.Column('currency', sqlal.String(3), nullable=True),
                       sqlal.Column('market_data_source', sqlal.String(20), nullable=True),
                       sqlal.Column('trading_location', sqlal.String(20), nullable=True),
                       sqlal.Column('country', sqlal.String(20), nullable=True),
                       sqlal.Column('sector', sqlal.String(80), nullable=True)
                       )

market_price_data = sqlal.Table('market_price_data', metadata,
                                sqlal.Column('Date', sqlal.DateTime, nullable=True),
                                sqlal.Column('ticker', sqlal.String(10), sqlal.ForeignKey('product.ticker'), nullable=True), 
                                sqlal.Column('adj_close', sqlal.Float, nullable=True),
                                sqlal.Column('log_return', sqlal.Float, nullable=True)
                                ) 

metadata.create_all(mysql_engine) 

GetTimeSeriesLevels = pd.read_sql_query('SELECT Date, ticker, adj_close FROM market_price_data Order BY ticker ASC', mysql_engine)
GetTimeSeriesLevels['log_return'] = np.log(GetTimeSeriesLevels.groupby('ticker')['adj_close'].apply(lambda x: x.div(x.shift(1)))).dropna()
GetTimeSeriesLevels['log_return'].fillna('NULL', inplace=True)
insert_yahoo_data = market_price_data.insert().values(GetTimeSeriesLevels [['log_return']].to_dict('records'))
mysql_engine.execute(insert_yahoo_data)

它应该是这样的：

Date                ticker  adj_close log_return
2016-11-21 00:00:00 AAPL    111.73    NULL  
2016-11-22 00:00:00 AAPL    111.8     NULL  
2016-11-23 00:00:00 AAPL    111.23    NULL      
2016-11-25 00:00:00 AAPL    111.79    NULL  
2016-11-28 00:00:00 AAPL    111.57    NULL  
2016-11-23 00:00:00 ACN     119.82    NULL  
2016-11-25 00:00:00 ACN     120.74    NULL  
2016-11-28 00:00:00 ACN     120.76    NULL  
2016-11-29 00:00:00 ACN     120.94    NULL  
2016-11-30 00:00:00 ACN     119.43    NULL  
...

Answer 1

虽然可耻，我不知道sqlalchemy只有原始SQL，考虑将pandas数据框转储到临时表中，然后将其与最终表连接：

# DUMP TO TEMP TABLE (REPLACING EACH TIME)
GetTimeSeriesLevels.to_sql(name='log_return_temp', con=mysql_engine, if_exists='replace', 
                           index=False)

# SQL UPDATE (USING TRANSACTION)
with engine.begin() as conn:     
    conn.execute("UPDATE market_price_data f" +
                 " INNER JOIN log_return_temp t" +
                 " ON f.Date = t.Date" +
                 " AND f.ticker = t.ticker" +
                 " SET f.log_return = t.log_return;")

engine.dispose()

或者，考虑直接在MySQL中进行日志转换！从我所看到的，在你的pandas / numpy代码中，你记录了当前行adj_close与最后一行adj_close的商的变换。 MySQL可以运行自联接来排列当前和最后一行。并且MySQL在其mathematical operators中维护自然日志。

下面是可以使用CREATE AS ...转储到临时表的select语句，或者转换为带有嵌套UPDATE语句的复杂SELECT查询：

SELECT t1.*, LOG(t1.adj_close / t2.adj_close) As log_return
FROM    
   (SELECT m.Date, m.ticker, m.adj_close, 
           (SELECT Count(*) FROM market_price_data sub 
            WHERE sub.Date <= m.Date AND sub.ticker = m.ticker) AS rank
    FROM market_price_data m) As t1

INNER JOIN 
   (SELECT m.Date, m.ticker, m.adj_close, 
           (SELECT Count(*) FROM market_price_data sub 
            WHERE sub.Date <= m.Date AND sub.ticker = m.ticker) AS rank
    FROM market_price_data m) As t1

ON t1.rank = (t2.rank - 1) AND t1.ticker = t2.ticker AND t1.Date = t2.Date

将数据帧写入mysql数据库

1 个答案: