Tyshawn  Braun

Tyshawn Braun

1600153260

SQL v. Pandas: Basic Syntax Comparison & Cheat Sheet

I’ve worked with SQL for several years before I started using Pandas in Python. I eventually get used to it but I struggled syntax difference between them when I started.

I want to gather syntax comparison in this article, also as cheat sheet. I’m going to mention to the basic syntax but if there is another example that doesn’t exist in this article, please let me know!

Dataset

I’m going to use this dataset.

import pandas as pd

df_list = [
  ["Lamar Jackson", 23, "Ravens", "QB"]
  , ["Russell Wilson", 31, "Seahawks", "QB"]
  , ["Aaron Donald", 29, "Rams", "DT"]
  , ["Patrick Mahomes", 24, "Chiefs", "QB"]
  , ["Michael Thomas", 27, "Saints", "WR"]
  , ["Christian McCaffrey", 24, "Panthers", "RB"]
  , ["George Kittle", 26, "49ers", "TE"]
  , ["DeAndre Hopkins", 28, "Cardinals", "WR"]
  , ["Stephon Gilmore", 29, "Patriots", "CB"]
  , ["Derrick Henry", 26, "Titans", "RB"]
]
df_columns = ["Name", "Age", "Team", "Position"]
players = pd.DataFrame(data=df_list, columns=df_columns)

#pandas #exploratory-data-analysis #data-science #sql #python

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SQL v. Pandas: Basic Syntax Comparison & Cheat Sheet
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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Chesley  Wehner

Chesley Wehner

1621497742

SQL Cheat Sheet — SQL Reference Guide for Data Analysis

Whether you’re learning SQL through one of our interactive SQL courses or by some other means, it can be really helpful to have a SQL cheat sheet.

Bookmark this article, or download and print the PDF, and keep it handy for quick reference the next time you’re writing an SQL query!

Need to brush up on your SQL before you’re ready for the cheat sheet? Check out our interactive online SQL Fundamentals course, read about why you should learn SQL, or do some research about SQL certifications and whether you’ll need one.

SQL Basics

SQL stands for Structured Query Language. It is a system for querying — requesting, filtering, and outputting — data from relational databases.

Developed in the 1970s, SQL was originally called SEQUEL. For this reason, today it is sometimes pronounced “Sequel” and sometimes pronounced “S.Q.L.” Either pronunciation is acceptable

Although there are many “flavors” of SQL, SQL in some form can be used for querying data from most relational database systems, including MySQL, SQLite, Oracle, Microsoft SQL Server, PostgreSQL, IBM DB2, Microsoft Azure SQL Database, Apache Hive, etc. databases.

#cheat sheets #sql #sql cheat sheet

SQL v. Pandas: Basic Syntax Comparison & Cheat Sheet

I’ve worked with SQL for several years before I started using Pandas in Python. I eventually get used to it but I struggled syntax difference between them when I started.

I want to gather syntax comparison in this article, also as cheat sheet. I’m going to mention to the basic syntax but if there is another example that doesn’t exist in this article, please let me know!

#pandas #data-science #sql #editors-pick #python

Tyshawn  Braun

Tyshawn Braun

1600153260

SQL v. Pandas: Basic Syntax Comparison & Cheat Sheet

I’ve worked with SQL for several years before I started using Pandas in Python. I eventually get used to it but I struggled syntax difference between them when I started.

I want to gather syntax comparison in this article, also as cheat sheet. I’m going to mention to the basic syntax but if there is another example that doesn’t exist in this article, please let me know!

Dataset

I’m going to use this dataset.

import pandas as pd

df_list = [
  ["Lamar Jackson", 23, "Ravens", "QB"]
  , ["Russell Wilson", 31, "Seahawks", "QB"]
  , ["Aaron Donald", 29, "Rams", "DT"]
  , ["Patrick Mahomes", 24, "Chiefs", "QB"]
  , ["Michael Thomas", 27, "Saints", "WR"]
  , ["Christian McCaffrey", 24, "Panthers", "RB"]
  , ["George Kittle", 26, "49ers", "TE"]
  , ["DeAndre Hopkins", 28, "Cardinals", "WR"]
  , ["Stephon Gilmore", 29, "Patriots", "CB"]
  , ["Derrick Henry", 26, "Titans", "RB"]
]
df_columns = ["Name", "Age", "Team", "Position"]
players = pd.DataFrame(data=df_list, columns=df_columns)

#pandas #exploratory-data-analysis #data-science #sql #python

Cayla  Erdman

Cayla Erdman

1596441660

Welcome Back the T-SQL Debugger with SQL Complete – SQL Debugger

When you develop large chunks of T-SQL code with the help of the SQL Server Management Studio tool, it is essential to test the “Live” behavior of your code by making sure that each small piece of code works fine and being able to allocate any error message that may cause a failure within that code.

The easiest way to perform that would be to use the T-SQL debugger feature, which used to be built-in over the SQL Server Management Studio tool. But since the T-SQL debugger feature was removed completely from SQL Server Management Studio 18 and later editions, we need a replacement for that feature. This is because we cannot keep using the old versions of SSMS just to support the T-SQL Debugger feature without “enjoying” the new features and bug fixes that are released in the new SSMS versions.

If you plan to wait for SSMS to bring back the T-SQL Debugger feature, vote in the Put Debugger back into SSMS 18 to ask Microsoft to reintroduce it.

As for me, I searched for an alternative tool for a T-SQL Debugger SSMS built-in feature and found that Devart company rolled out a new T-SQL Debugger feature to version 6.4 of SQL – Complete tool. SQL Complete is an add-in for Visual Studio and SSMS that offers scripts autocompletion capabilities, which help develop and debug your SQL database project.

The SQL Debugger feature of SQL Complete allows you to check the execution of your scripts, procedures, functions, and triggers step by step by adding breakpoints to the lines where you plan to start, suspend, evaluate, step through, and then to continue the execution of your script.

You can download SQL Complete from the dbForge Download page and install it on your machine using a straight-forward installation wizard. The wizard will ask you to specify the installation path for the SQL Complete tool and the versions of SSMS and Visual Studio that you plan to install the SQL Complete on, as an add-in, from the versions that are installed on your machine, as shown below:

Once SQL Complete is fully installed on your machine, the dbForge SQL Complete installation wizard will notify you of whether the installation was completed successfully or the wizard faced any specific issue that you can troubleshoot and fix easily. If there are no issues, the wizard will provide you with an option to open the SSMS tool and start using the SQL Complete tool, as displayed below:

When you open SSMS, you will see a new “Debug” tools menu, under which you can navigate the SQL Debugger feature options. Besides, you will see a list of icons that will be used to control the debug mode of the T-SQL query at the leftmost side of the SSMS tool. If you cannot see the list, you can go to View -> Toolbars -> Debugger to make these icons visible.

During the debugging session, the SQL Debugger icons will be as follows:

The functionality of these icons within the SQL Debugger can be summarized as:

  • Adding Breakpoints to control the execution pause of the T-SQL script at a specific statement allows you to check the debugging information of the T-SQL statements such as the values for the parameters and the variables.
  • Step Into is “navigate” through the script statements one by one, allowing you to check how each statement behaves.
  • Step Over is “execute” a specific stored procedure if you are sure that it contains no error.
  • Step Out is “return” from the stored procedure, function, or trigger to the main debugging window.
  • Continue executing the script until reaching the next breakpoint.
  • Stop Debugging is “terminate” the debugging session.
  • Restart “stop and start” the current debugging session.

#sql server #sql #sql debugger #sql server #sql server stored procedure #ssms #t-sql queries