Shulou(Shulou.com)05/31 Report--

This article mainly explains "the comparison of set operation between SQL and Python". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Next let the editor to take you to learn "SQL and Python set operation comparison" bar!

Set operation

Analytical problems usually require checking multiple different data sets. During the solution process, it is very effective to create a new set of data sets by comparing or combining different data sets.

For example, you might have two separate tables that contain transactional data to be transformed into a single transactional table, and you want to merge them into one table, or create a new transactional table that contains data from both tables.

In SQL, the collection operator can solve this problem. The set operator supports contrastive operations and can conditionally join the result sets of two SQL statements. The SQL collection operators include:

UNION: returns the result row from the two query statements to be compared. By default, UNION does not return duplicate rows if the same rows exist in the result set of two SQL statements. If you want to return duplicate rows, you can provide the optional ALL keyword to the collection operator UNION.

INTERSECT: returns rows in the result set of two query statements to be compared.

EXCEPT (or MINUS): returns a row in the result set of a query statement that is not to be compared.

Although there is no specific type of collection operator in Python, there are still a variety of functions in Python that can implement these functions.

To illustrate how to use these set operations in practice, assume that you are a bilateral market data scientist. There are buyers and sellers in your market, and the two are not mutually exclusive. Now, the buyer and seller exist in the database of the buyers and sellers tables, respectively. Let's take a look at several scenarios in which collection operations may play a role.

Merge and merge all

Suppose you want to merge all users that exist in the buyers and sellers tables into a new separate "users" table.

In SQL, you can do this using the UNION operator and the optional ALL keyword:

-- SQLselect user_id from modeanalytics.buyers union all select user_id from modeanalytics.sellers

In Python, you can use the pandas. Concat () function to copy the UNION ALL collection operations performed above.

The pandas .concat () method connects pandas objects (such as Dataframes,Series, and so on) along the selected axis. Assuming that you have stored the user_id fields in the buyers and sellers database tables in two pandas data box objects (buyers and sellers), you can copy the UNION ALL collection operations performed above using the following Python code:

# Python users = pd.concat ([buyers, sellers])

It is important to note that using the UNION and ALL keywords in SQL, or the pandas concat () method in Python, you will return users (both buyers and sellers) in both tables to duplicate rows. However, if you only want to return a single instance of one user in two tables:

In SQL, remove the ALL keyword from the UNION collection operator:

-- SQLselect user_id from modeanalytics.buyers union select user_id from modeanalytics.sellers

In Python, link the .drop _ duplicates () method to the connected pandas object:

# Python users = pd.concat ([buyers, sellers]) .drop_duplicates ()

Intersection

On the other hand, what if you want to create a separate user form that is both a buyer and a seller?

In SQL, you can use the INTERSECT collection operator:

-- SQLselect user_id from modeanalytics.buyers intersect select user_id from modeanalytics.sellers

In Python, you can use the pandas .merge () method in the data box:

Python buyers_and_sellers = buyers.merge (sellers)

By default, the pandas. Merge () method attempts to "internally" merge all columns in both data boxes. This default action basically replicates the steps of the SQL collection operator INTERSECT.

Difference set

Now, if you want to return to all buyer users, not seller users.

In SQL, you can use EXCEPT collection operations:

-- SQL select user_id from modeanalytics.buyers except select user_id from modeanalytics.sellers

It is worth noting that SQL's EXCEPT collection operator can only return to * tables, not to the second table. If you want to return to the seller's table, you need to reverse the order of the SELECT statements:

-- SQL select user_id from modeanalytics.sellers except select user_id from modeanalytics.buyers

In Python, use the pandas Boolean index technique and the pandas .isin () index to copy the functionality of the SQL EXCEPT collection operator:

# Python buyers_not_sellers = buyers [buyers.user _ id.isin (sellers.user_id) = = False] sellers_not_buyers = sellers [sellers.user _ id.isin (buyers.user_id) = = False] `

Fuzzy boundary

At this point, you have learned how to copy a large number of SQL collection operators in Python. In the end, the choice of programming mode is up to you, and you can even change it on a project-by-project basis. Only by mastering the overlap of analytical language functions can decisions be made effectively. This is the advantage of using multiple data analysis languages: you can customize a hybrid approach to meet your evolving needs.

At this point, I believe that everyone on the "SQL and Python set operation comparison" have a deeper understanding, might as well to the actual operation of it! Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.