We can connect to relational databases for analysing data using the pandas library as well as another additional library for implementing database connectivity.
This package is named as sqlalchemy which provides full SQL language functionality to be used in python.

Installing SQLAlchemy

The installation is very straight forward using Anaconda which we have discussed in the chapter Data Science Environment. Assuming you have installed Anaconda as described in this chapter,
run the following command in the Anaconda Prompt Window to install the SQLAlchemy package.

conda install sqlalchemy

Reading Relational Tables

We will use Sqlite3 as our relational database as it is very light weight and easy to use. Though the SQLAlchemy library can connect to a variety of relational sources including MySql, Oracle and Postgresql and Mssql.
We first create a database engine and then connect to the database engine using the to_sql function of the SQLAlchemy library.

In the below example we create the relational table by using the to_sql function from a dataframe already created by reading a csv file.
Then we use the read_sql_query function from pandas to execute and capture the results from various SQL queries.

from sqlalchemy import create_engine
import pandas as pd

data = pd.read_csv(''/path/input.csv'')

# Create the db engine
engine = create_engine(''sqlite:///:memory:'')

# Store the dataframe as a table
data.to_sql(''data_table'', engine)

# Query 1 on the relational table
res1 = pd.read_sql_query(''SELECT * FROM data_table'', engine)
print(''Result 1'')
print(res1)
print('''')

# Query 2 on the relational table
res2 = pd.read_sql_query(''SELECT dept,sum(salary) FROM data_table group by dept'', engine)
print(''Result 2'')
print(res2)

When we execute the above code, it produces the following result.

Result 1
   index  id    name  salary  start_date        dept
0      0   1    Rick  623.30  2012-01-01          IT
1      1   2     Dan  515.20  2013-09-23  Operations
2      2   3   Tusar  611.00  2014-11-15          IT
3      3   4    Ryan  729.00  2014-05-11          HR
4      4   5    Gary  843.25  2015-03-27     Finance
5      5   6   Rasmi  578.00  2013-05-21          IT
6      6   7  Pranab  632.80  2013-07-30  Operations
7      7   8    Guru  722.50  2014-06-17     Finance

Result 2
         dept  sum(salary)
0     Finance      1565.75
1          HR       729.00
2          IT      1812.30
3  Operations      1148.00

Inserting Data to Relational Tables

We can also insert data into relational tables using sql.execute function available in pandas. In the below code we previous csv file as input data set, store it in a relational table and then
insert another record using sql.execute.

from sqlalchemy import create_engine
from pandas.io import sql

import pandas as pd

data = pd.read_csv(''C:/Users/Rasmi/Documents/pydatasci/input.csv'')
engine = create_engine(''sqlite:///:memory:'')

# Store the Data in a relational table
data.to_sql(''data_table'', engine)

# Insert another row
sql.execute(''INSERT INTO data_table VALUES(?,?,?,?,?,?)'', engine, params=[(''id'',9,''Ruby'',711.20,''2015-03-27'',''IT'')])

# Read from the relational table
res = pd.read_sql_query(''SELECT ID,Dept,Name,Salary,start_date FROM data_table'', engine)
print(res)

When we execute the above code, it produces the following result.

   id        dept    name  salary  start_date
0   1          IT    Rick  623.30  2012-01-01
1   2  Operations     Dan  515.20  2013-09-23
2   3          IT   Tusar  611.00  2014-11-15
3   4          HR    Ryan  729.00  2014-05-11
4   5     Finance    Gary  843.25  2015-03-27
5   6          IT   Rasmi  578.00  2013-05-21
6   7  Operations  Pranab  632.80  2013-07-30
7   8     Finance    Guru  722.50  2014-06-17
8   9          IT    Ruby  711.20  2015-03-27

Deleting Data from Relational Tables

We can also delete data into relational tables using sql.execute function available in pandas. The below code deletes a row based on the input condition given.

from sqlalchemy import create_engine
from pandas.io import sql

import pandas as pd

data = pd.read_csv(''C:/Users/Rasmi/Documents/pydatasci/input.csv'')
engine = create_engine(''sqlite:///:memory:'')
data.to_sql(''data_table'', engine)

sql.execute(''Delete from data_table where name = (?) '', engine,  params=[(''Gary'')])

res = pd.read_sql_query(''SELECT ID,Dept,Name,Salary,start_date FROM data_table'', engine)
print(res)

When we execute the above code, it produces the following result.

   id        dept    name  salary  start_date
0   1          IT    Rick   623.3  2012-01-01
1   2  Operations     Dan   515.2  2013-09-23
2   3          IT   Tusar   611.0  2014-11-15
3   4          HR    Ryan   729.0  2014-05-11
4   6          IT   Rasmi   578.0  2013-05-21
5   7  Operations  Pranab   632.8  2013-07-30
6   8     Finance    Guru   722.5  2014-06-17