How to write Pandas dataframe to sqlite with Index

Question

I have a list of stockmarket data pulled from Yahoo in a pandas DataFrame (see format below). The date is serving as the index in the DataFrame. I want to write the data (including the index) out to a SQLite database.

             AAPL     GE
Date
2009-01-02  89.95  14.76
2009-01-05  93.75  14.38
2009-01-06  92.20  14.58
2009-01-07  90.21  13.93
2009-01-08  91.88  13.95

Based on my reading of the write_frame code for Pandas, it does not currently support writing the index. I've attempted to use to_records instead, but ran into the issue with Numpy 1.6.2 and datetimes. Now I'm trying to write tuples using .itertuples, but SQLite throws an error that the data type isn't supported (see code and result below). I'm relatively new to Python, Pandas and Numpy, so it is entirely possible I'm missing something obvious. I think I'm running into a problem trying to write a datetime to SQLite, but I think I might be overcomplicating this.

I think I may be able to fix the issue by upgrading to Numpy 1.7 or the development version of Pandas, which has a fix posted on GitHub. I'd prefer to develop using release versions of software - I'm new to this and I don't want stability issues confusing matters further.

Is there a way to accomplish this using Python 2.7.2, Pandas 0.10.0, and Numpy 1.6.2? Perhaps cleaning the datetimes somehow? I'm in a bit over my head, any help would be appreciated.

Code:

import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import sqlite3 as db

# download data from yahoo
all_data = {}

for ticker in ['AAPL', 'GE']:
    all_data[ticker] = pd.io.data.get_data_yahoo(ticker, '1/1/2009','12/31/2012')

# create a data frame
price = DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})

# get output ready for database export
output = price.itertuples()
data = tuple(output)

# connect to a test DB with one three-column table titled "Demo"
con = db.connect('c:/Python27/test.db')
wildcards = ','.join(['?'] * 3)
insert_sql = 'INSERT INTO Demo VALUES (%s)' % wildcards
con.executemany(insert_sql, data)

Result:

---------------------------------------------------------------------------
InterfaceError                            Traceback (most recent call last)
<ipython-input-15-680cc9889c56> in <module>()
----> 1 con.executemany(insert_sql, data)

InterfaceError: Error binding parameter 0 - probably unsupported type.

Answer 1

Unfortunately, pandas.io.write_frame no longer exists in more recent versions of Pandas in regards to the current accepted answer. For example I'm using pandas 0.19.2. You can do something like

from sqlalchemy import create_engine

disk_engine = create_engine('sqlite:///my_lite_store.db')
price.to_sql('stock_price', disk_engine, if_exists='append')

And then in turn preview your table with the following:

df = pd.read_sql_query('SELECT * FROM stock_price LIMIT 3',disk_engine)
df.head()

Answer 2

As you mention, at the moment you save the index, but what we can do is reset_index, saving the old index as a column ('Date').

price2 = price.reset_index()

In [11]: price2
Out[11]: 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1006 entries, 0 to 1005
Data columns:
Date    1006  non-null values
AAPL    1006  non-null values
GE      1006  non-null values
dtypes: datetime64[ns](1), float64(2)

Following the docs (setting a SQLite connection in memory):

import sqlite3
from pandas.io import sql
# Create your connection.
cnx = sqlite3.connect(':memory:')

We can save price2 to cnx:

sql.write_frame(price2, name='price2', con=cnx)

We can retrieve via read_frame:

p2 = sql.read_frame('select * from price2', cnx)

However, when stored (and retrieved) dates are unicode rather than Timestamp. To convert back to what we started with we could apply Timestamp to the column and set_index:

from pandas.lib import Timestamp
p2.Date = p2.Date.apply(Timestamp)
p = p2.set_index('Date')

We get back the same DataFrame as prices:

In [20]: p
Out[20]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1006 entries, 2009-01-02 00:00:00 to 2012-12-31 00:00:00
Data columns:
AAPL    1006  non-null values
GE      1006  non-null values
dtypes: float64(2)

Answer 3

Below is the code which worked for me. I was able to write it to SQLite DB.

import pandas as pd
import sqlite3 as sq
data = <This is going to be your pandas dataframe>
sql_data = 'D:\\SA.sqlite' #- Creates DB names SQLite
conn = sq.connect(sql_data)
cur = conn.cursor()
cur.execute('''DROP TABLE IF EXISTS SA''')
data.to_sql('SA', conn, if_exists='replace', index=False) # - writes the pd.df to SQLIte DB
pd.read_sql('select * from SentimentAnalysis', conn)
conn.commit()
conn.close()