Pandas and SQL are the integral part of Data Science. It is used for data manipulation and analysis while SQL or Structured Query Language is usually used to fetch data, update the contents of the table, or operate on the structure of the database or tables, using any type of database tools, which will have a user interface to apply the operations on the database.
I believe Pandas and SQL are both similar in terms of analyzing the datasets, freshers like me find it difficult to remember the command syntax of both tool. A small effort from my end to show the command similarity.
Pandas Installation –
To work on both the tool, first we need to install them. I am using Anaconda distribution on my laptop which comes with all the required library pre-installed. To download Anaconda, please go to their site here
Launch the Jupyter Notebook
Load the pandas module,
Note – If you are not using Anaconda, then pandas installation procedure can be found here
SQL Installation –
Note – The only expectation here is that you should know pandas syntax.
Importing the data –
I am using ‘read_excel’ because my source file is Excel and added sheet_name parameter because i want to import only first sheet from the excel file.
You can import entire excel in to the SQL database with the help of SQL Import and Export wizard.
You have to choose source data source as ‘Microsoft Excel’ as shown in below screenshot
Choose the Excel file which you want to import and the correct Excel version
Select ‘SQL Server Native Client 11.0’ in the ‘Choose a Destination’ screen
Pick correct server name, authentication type and the database
You can select first option to copy entire data from the excel sheet or you can write custom SQL query to import only required data. I will go ahead and select first option.
Here please select the sheet you to import under first column, new table will be created in the database with the sheet name (second column).
It will import data and will create new table in to the database which we provided.
Lets work on the data now –
How to view entire data
‘df’ is nothing but a variable to store the data. Pandas can store or read different type of data which is called as DataFrame.
You can type ‘print(df)’ or ‘df’ to view the entire data.
Connect to the SQL Express using the SQL Management Studio
Click on the ‘New Query’
type below commands in the query field
‘select * from the dbo.Orders
Hope you enjoyed the article on pandas and SQL, please visit for next set of commands.