data-transport

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Writing to SQLite3+¶

The requirements to get started are minimal (actually none). The cell below creates a dataframe that will be stored within SQLite 3+

In [1]:

#
# Writing to PostgreSQL database
#
import transport
from transport import providers
import pandas as pd
_data = pd.DataFrame({"name":['James Bond','Steve Rogers','Steve Nyemba'],'age':[55,150,44]})
sqw = transport.factory.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends',context='write')
sqw.write(_data,if_exists='replace') #-- default is append
print (transport.__version__)

2.0.0

Reading from SQLite3+¶

The cell below reads the data that has been written by the cell above and computes the average age within a PostreSQL (simple query).

Basic read of the designated table (friends) created above
Execute an aggregate SQL against the table

NOTE

It is possible to use transport.factory.instance or transport.instance they are the same. It allows the maintainers to know that we used a factory design pattern.

In [2]:

import transport
from transport import providers
pgr = transport.instance(provider=providers.SQLITE,database='/home/steve/demo.db3',table='friends')
_df = pgr.read()
_query = 'SELECT COUNT(*) _counts, AVG(age) from friends'
_sdf = pgr.read(sql=_query)
print (_df)
print ('--------- STATISTICS ------------')
print (_sdf)

           name  age
0    James Bond   55
1  Steve Rogers  150
2  Steve Nyemba   44
--------- STATISTICS ------------
   _counts  AVG(age)
0        3      83.0

The cell bellow show the content of an auth_file, in this case if the dataset/table in question is not to be shared then you can use auth_file with information associated with the parameters.

NOTE:

The auth_file is intended to be JSON formatted. This is an overkill for SQLite ;-)

In [5]:

{
    "provider":"sqlite",
    "database":"/home/steve/demo.db3","table":"friends"
}

In [ ]:

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3.6 KiB Raw Blame History

Writing to SQLite3+¶

Reading from SQLite3+¶

3.6 KiB

Raw Blame History