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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Apache Impala and SQL magic for IPython/Jupyter notebooks
with Kerberos authentication
1. Load SQL magic extension (uses ipython-sql by Catherine Devlin)
Step1:  2. Connect to the target database
requires Cloudera impyla package and thrift_sasl
edit the value of config_args as relevant for your environment
Step2:  3. Run SQL on the target using the %%sql cell magic or %sql line magic
Step3:  Bind variables
Step4:  Additional example of the integration with the IPython environment
Step5:  The integration with Pandas opens many additional possibilities for data analysis
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Python Code:
%load_ext sql
Explanation: Apache Impala and SQL magic for IPython/Jupyter notebooks
with Kerberos authentication
1. Load SQL magic extension (uses ipython-sql by Catherine Devlin)
End of explanation
%config SqlMagic.connect_args="{'kerberos_service_name':'impala', 'auth_mechanism':'GSSAPI'}"
%sql impala://impalasrv-prod:21050/test2
Explanation: 2. Connect to the target database
requires Cloudera impyla package and thrift_sasl
edit the value of config_args as relevant for your environment
End of explanation
%%sql 
select * from emp
Explanation: 3. Run SQL on the target using the %%sql cell magic or %sql line magic
End of explanation
Employee_name="SCOTT"
%sql select * from emp where ename=:Employee_name
Explanation: Bind variables
End of explanation
myResultSet=%sql select ename "Employee Name", sal "Salary" from emp
%matplotlib inline
import matplotlib
matplotlib.style.use('ggplot')
myResultSet.bar()
Explanation: Additional example of the integration with the IPython environment
End of explanation
my_DataFrame=myResultSet.DataFrame()
my_DataFrame.head()
Explanation: The integration with Pandas opens many additional possibilities for data analysis
End of explanation
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