This article is the next in the series about the topic of Pandas and SQL similarity which we discussed earlier
Pandas and SQL similarity (2)
Check the dtypes
To check data types of the data frame, type ‘df.dtypes’ command
type sp_help table_name in the query editor. You may be wondering what is ‘nvarchar’
The key difference between varchar and nvarchar is the way they are stored, varchar is stored as regular 8-bit data(1 byte per character) and nvarchar stores data at 2 bytes per character. Due to this reason, nvarchar can hold upto 4000 characters and it takes double the space as SQL varchar.
You can visit for more information on varchar and nvarchar.
Check the data
Once you load the data in to the pandas you can view the data by running the ‘df.head()’ command. This command will give you output of first 5 rows and all columns by default. This output is not in the order.
You can specify the number of rows you want to see e.g. 10
To view last rows in the data frame, type ‘df.tail()’ command
We can sort the data frame by column name in the ascending order
by descending order
In SQL we have to use ‘ORDER BY’ Column to view the data.
Check the NULL Value
Every data set will have some kind of NULL or missing values either in one column or multiple column. Command to find out NULL Value is
The ‘Product Base Margin’ column has 72 missing values.
In SQL to get NULL Value we have to run below query for each column.
SELECT * from table where columnname IS NULL
Select the columns
What if we want to see the specific columns, how do we select them?
Note – Pandas will not display all rows in the output by default, to see all rows please run the command –> ‘pd.set_option(‘display.max_rows’, None)’
select [Row ID],[Order Priority] from table name.
Filter the data
Every data scientist or data analyst have to filter the data to get some insight. How do we do that in both pandas and SQL?
Lets say, we need details of all the orders shipped through ‘Regular Air’ only
and sent to California state.
SELECT * from table where [Ship Mode] = ‘Regular Air’
Hope you like the article about pandas and SQL command similarity part 2, Stay tuned for next update.
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 –
I am using Microsoft SQL Express edition for the demo purpose, which is available here for the download and installation procedure here.
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.
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