CODEWITHSUNDEEP
pythonmysqlpandassqlalchemy
MCM
MCM

Reputation: 1511

Write dataframe into mysql database

I wanted to write teh data into the mysql database. I am reading the current data first from the database and the calculate a new values. The new values should be written in the same order as the data in the databse as shown below. I don't want to overwrite existing data. I don't want to use to_sql.

I receive the following error message:

(mysql.connector.errors.DatabaseError) 1265 (01000): Data truncated for column 'log_return' at row 1 [SQL: 'INSERT INTO

The full code is below.

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)

The database is looks like the following.

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  
...

It should look like this:

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     0.000626
2016-11-23 00:00:00 AAPL    111.23    -0.005111
2016-11-25 00:00:00 AAPL    111.79    0.005022
2016-11-28 00:00:00 AAPL    111.57    -0.001970
2016-11-21 00:00:00 ACN     119,68   NULL
2016-11-22 00:00:00 ACN     119,48   -0,001672521
23.11.2016 00:00:00 ACN     119,82   0,002841623
2016-11-25 00:00:00 ACN     120,74   0,007648857
2016-11-28 00:00:00 ACN     120,76   0,000165631    
...

Upvotes: 0

Views: 1255

Answers (1)

Parfait
Parfait

Reputation: 107737

While shamefully, I don't know sqlalchemy only raw SQL, consider dumping pandas dataframe into a temp table then join it with final table:

# 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()

Alternatively, consider doing your log transformation directly in MySQL! From what I see, in your pandas/numpy code, you are log transforming the quotient of current row's adj_close with last row's adj_close. MySQL can run a self join to line up current and last row. And MySQL maintains natural log in its mathematical operators.

Below is the select statement that can be dumped to a temp table with CREATE AS ... or converted into a complex UPDATE query with nested SELECT statements:

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

Upvotes: 1

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