How-To Tutorials - Data Science

DataPro is a weekly, expert-curated newsletter trusted by 120k+ global data professionals. Built by data practitioners, it blends first-hand industry experience with practical insights and peer-driven learning.Make sure to subscribe here so you never miss a key update in the data world. Introduction Med-PaLM 2 is a large language model (LLM) from Google Research, designed for the medical domain. It is trained on a massive dataset of text and code, including medical journals, textbooks, and clinical trials. Med-PaLM 2 can answer questions about a wide range of medical topics, including diseases, treatments, and procedures. It can also generate text, translate languages, and write different kinds of creative content. Use Cases Med-PaLM 2 can be used for a variety of purposes in the healthcare industry, including: Medical research: Med-PaLM 2 can be used to help researchers find and analyze medical data. It can also be used to generate hypotheses and test new ideas. Clinical decision support: Med-PaLM 2 can be used to help doctors diagnose diseases and make treatment decisions. It can also be used to provide patients with information about their condition and treatment options. Health education: Med-PaLM 2 can be used to create educational materials for patients and healthcare professionals. It can also be used to answer patients' questions about their health. Drug discovery: Med-PaLM 2 can be used to help researchers identify new drug targets and develop new drugs. Personalized medicine: Med-PaLM 2 can be used to help doctors personalize treatment for individual patients. It can do this by taking into account the patient's medical history, genetic makeup, and other factors. How to Get Started Med-PaLM 2 is currently available to a limited number of Google Cloud customers. To get started, you can visit the Google Cloud website: https://cloud.google.com/ and sign up for a free trial. Once you have a Google Cloud account, you can request access to Med-PaLM 2. Here are the steps on how to get started with using Med-PaLM: 1. Check if Med-PaLM is available in your country. Med-PaLM is currently only available in the following countries: United States Canada United Kingdom Australia New Zealand Singapore India Japan South KoreaYou can check the Med-PaLM website: https://sites.research.google/med-palm/ for the latest list of supported countries. 2. Create a Google Cloud Platform (GCP) account. Med-PaLM is a cloud-based service, so you will need to create a GCP account in order to use it. You can do this by going to the GCP website: https://cloud.google.com/ and clicking on the "Create Account" button. 3. Enable the Med-PaLM API. Once you have created a GCP account, you will need to enable the Med-PaLM API. You can do this by going to the API Library: https://console.cloud.google.com/apis/library and searching for "Med-PaLM". Click on the "Enable" button to enable the API. 4. Create a Med-PaLM service account. A service account is a special type of account that can be used to access GCP resources. You will need to create a service account in order to use Med-PaLM. You can do this by going to the IAM & Admin: https://console.cloud.google.com/iam-admin/ page and clicking on the "Create Service Account" button. 5. Download the Med-PaLM credentials. Once you have created a service account, you will need to download the credentials. The credentials will be a JSON file that contains your service account's email address and private key. You can download the credentials by clicking on the "Download JSON" button. 6. Set up the Med-PaLM client library. There are client libraries available for a variety of programming languages. You will need to install the client library for the language that you are using. You can find the client libraries on the Med-PaLM website: https://sites.research.google/med-palm/. 7. Initialize the Med-PaLM client. Once you have installed the client library, you can initialize the Med-PaLM client. The client will need your service account's email address and private key in order to authenticate with Med-PaLM. You can initialize the client by using the following code: import medpalm client = medpalm.Client(    email="your_service_account_email_address",    key_file="your_service_account_private_key.json" ) 8. Start using Med-PaLM! Once you have initialized the Med-PaLM client, you can start using it to access Med-PaLM's capabilities. For example, you can use Med-PaLM to answer medical questions, generate text, and translate languages. Key Features Med-PaLM 2 has a number of key features that make it a valuable tool for the healthcare industry. These features include: Accuracy: Med-PaLM 2 is highly accurate in answering medical questions. It has been shown to achieve an accuracy of 85% on a variety of medical question answering datasets. Expertise: Med-PaLM 2 is trained on a massive dataset of medical text and code. This gives it a deep understanding of medical concepts and terminology. Versatility: Med-PaLM 2 can be used for a variety of purposes in the healthcare industry. It can answer questions, generate text, translate languages, and write different kinds of creative content. Scalability: Med-PaLM 2 is scalable and can be used to process large amounts of data. This makes it a valuable tool for research and clinical applications. Conclusion Med-PaLM 2 is a powerful LLM that has the potential to revolutionize the healthcare industry. It can be used to improve medical research, clinical decision support, health education, drug discovery, and personalized medicine. Med-PaLM 2 is still under development, but it has already demonstrated the potential to make a significant impact on healthcare. 

DataPro is a weekly, expert-curated newsletter trusted by 120k+ global data professionals. Built by data practitioners, it blends first-hand industry experience with practical insights and peer-driven learning.Make sure to subscribe here so you never miss a key update in the data world. IntroductionChatGPT is slowly becoming a pivotal player in software development.  It is being used by countless developers to help produce quality and robust code.  However, many of these developers are using ChatGPT for text-based programming languages like C++ or Java.  There are few, if any, tutorials on how ChatGPT can be utilized to write Ladder Logic code.  As such, this tutorial is going to be dedicated to exploring how and why ChatGPT can be used as a tool for traditional Ladder Logic programmers.Why use ChatGPT for Ladder Logic?The first step in learning how to leverage ChatGPT is to learn why to use the system.  First of all, ChatGPT is not a programmer, nor is it designed to replace programmers in any way, shape, or form.  However, it can be a handy tool for people that are not sure how to complete a task, need to produce some code in a crunch, and so on.  To effectively use ChatGPT, a person will have to know how to properly produce a statement, refine that statement, and, if necessary, write subsequent statements that have the right amount of information for ChatGPT to effectively produce a result.  In other words, a ChatGPT user still has to be competent, but when used correctly, the AI system can produce code much faster than a human can, especially if the human is inexperienced at a given task.In terms of industrial automation, ChatGPT can be an especially attractive tool.  It is no secret that many PLC programmers are not formally trained developers.  It is common for many PLC programmers to be maintenance technicians, electricians, or other types of engineers.  In any case, it is common for many people who are forced to write complex PLC software to have little more than previous experience guiding them.  As such, when faced with a complex situation with little to no resources available, the programmer can often be lost with no hope of finding a solution.  This is where ChatGPT can be utilized as a user can pose questions and task the system with finding solutions.  With that, how do we use ChatGPT at a basic level?  How to use ChatGPT?The key behind using ChatGPT is writing what are called prompts.  In a lay sense, a prompt is a query or command that ChatGPT runs.  In other words, a prompt can be thought of as a task you ask ChatGPT to do.  For example, a person could input the following prompt into ChatGPT:Tell me how to install a Beckhoff PLC.In this case, ChatGPT will give you a high-level overview of the basics of installing a Beckhoff PLC.  However, a prompt like this will usually not return any useful results unless you want general, high-level information.  Generally, to get a viable answer, you’ll need to hone the prompt with detailed information and exceptions.  Learning to write effective prompts is a skill that is in the early stages of development.  It is important to remember that ChatGPT is a novel tool. This means that the IT and automation industry as a whole is still learning how to use it effectively.  However, a general flow of writing and refining prompts can be summarized in Figure 1.Figure 1 – Prompt Refining FlowThis is just a general logic flow for massaging a prompt to get the desired result.  However, sometimes it is necessary and easier to run subsequent prompts instead of refining existing ones.  Either way, it may take a couple of tries to get what you need out of ChatGPT.  Regardless, to explore how to use ChatGPT, let’s start by creating a basic AND gate. AND ConfigurationAs everyone knows, an AND configuration is simply two contacts in a series.  For the coil to turn on, both contacts have to be on at the same time.  This a relatively simple Ladder Logic program to write and understand, so we’re going to use it as a test for ChatGPT. To begin, we’re going to use the following as a prompt:Write a ladder logic program for an AND gateWhen this prompt is run, it’ll generate the following network.  NETWORK--| |--[ ]---[ ]---( )                                                                                                                                  |          |     |----[ ]---( )Figure 2 – AND Gate: Attempt 1 With Description In this example, an and gate is produced, but it also generates an extra rung that wasn’t asked for or necessary for the task at hand.  In this case, it is not easy to determine what the extra run does or why it was included; as such, this is a prime example of needing to refine the prompts to only produce what is needed.  To do this, let’s refine the prompt a little more to match the following:Write only the necessary contacts and coils for an AND gate in Ladder LogicWhen this query is run, it will produce the network in Figure 2:NETWORK--| |--[ ]---[ ]---( ) Figure 3 – Refined AND Gate: Attempt 2 With DescriptionIn this refined attempt, ChatGPT produces a very simple network of contacts in series.  There are no extra rungs or ambiguous instructions. In short, the moral of this exercise is that we started with a general prompt that produced a working result but a result that needed to be refined. We then refined that prompt and produced a high-quality network with only the necessary instructions for a working AND gate.  With that, let’s look at something a little more complex, timer networks. Timer ProgramsAs every PLC programmer knows, most PLC programs will usually include a timer of some type.  So, with that in mind, let’s explore how ChatGPT handles timer programs.  To begin, let’s have ChatGPT generate a simple 10-minute timer.  To do this, we can use the following query:Write a ladder logic program for a 10 minute timer.Depending on your usage of ChatGPT, you may get a result without the variables labeled or even in Structured Text.  However, the prompt can be adjusted to produce the desired output by using subsequent prompts that are akin to the following:I need this in ladder logicor,assign the variables in the diagramAs stated before, you won’t have to re-write the initial prompt. All you have to do is run these statements as if they were your initial prompt.  However, depending on the prompts you run, your result should be something akin to the following:VAR    Timer: TON;         (* Timer for the 10-minute duration *)    StartButton: BOOL;  (* Input for starting the timer *)    Done: BOOL;         (* Output to indicate when the timer is done *) END_VAR NETWORK -----[StartButton]----[/Timer]-----   |                            |   |                            |   |                           | ---|----------------------------|---- NO                           |                               |                               |                              ---[Done]---In the above ladder logic program:StartButton represents the input contact that starts the timer./Timer represents the timer coil, indicating that the timer is active and counting.Done represents the output contact, which becomes closed (energized) when the timer reaches 10 minutes.When the StartButton input contact is activated, and the timer starts counting. Once the timer reaches 10 minutes, the Done output contact will close, indicating that the timer is done. When the StartButton input contact is deactivated, the timer stops counting, and the Done output contact opens.Please note that the specific syntax and representation of timers may vary depending on the PLC programming software or the manufacturer's conventions. Adjust the syntax and parameters as per the specific requirements of your PLC system.As anyone who has programmed in Ladder Logic before can see, this isn’t standard notation for a timer function.  However, you probably noticed that each prompt will generate a relatively short description of how the snippet is supposed to work.  If you read the subsequent output, you can piece together the symbolic nature of the snippet.  With that, let’s move on to something a little more advanced.  Let’s explore building a cascading timer network that will have one timer set for 10 minutes and another set for 15, and when the final timer turns on, it will power an LED.  To do this, we can input the following prompt,Write a cascading timer Ladder Logic Network that has one timer set for 10 minutes and the other set for 15 minutes. Turn on an LED when both timers turn on. Give the necessary variables as well.This prompt will produce the following:VAR    Timer1: TON;     // Timer 1 (10 minutes)    Timer2: TON;     // Timer 2 (15 minutes)    LED: BOOL;       // Output LED END_VAR NETWORK    // Timer 1 (10 minutes)    Timer1(IN:= TRUE, PT:= T#10M);  // Set Timer 1 to 10 minutes duration    // Timer 2 (15 minutes)    Timer2(IN:= Timer1.Q, PT:= T#15M);  // Start Timer 2 when Timer 1 completes    // LED turns on when both timers complete    LED := Timer1.Q AND Timer2.Q; END_NETWORKIn this ladder logic program:Timer1 is a timer variable of type TON set for 10 minutes duration.Timer2 is a timer variable of type TON set for 15 minutes duration.LED is a boolean variable representing the output LED.Here's a breakdown of the logic:Timer1 is triggered to start with the input IN:= TRUE and is set to a preset time of 10 minutes (PT:= T#10M).Timer2 is started when Timer1 completes its timing sequence (IN:= Timer1.Q), and it is set to a preset time of 15 minutes (PT:= T#15M).The LED is turned on (LED := Timer1.Q AND Timer2.Q) when both Timer1 and Timer2 have completed their timing sequences.You can adjust the timing durations of the timers according to your specific requirements in your PLC programming software. Additionally, you can connect the LED output variable to the appropriate LED indicator in your setup.As can be seen, this isn’t our desired output.  We want Ladder Logic, and this is Structured Text.  In short, ChatGPT got confused.  If you read the description, ChatGPT thinks its result is Ladder Logic.  In all, this isn’t what we were looking for, so we can add a subsequent prompt that reads the following:I need this program in Ladder LogicThis prompt will produce the following output:NETWORK --|----[ ]----[/]---[ ]----[/]---[ ]----[/]---[ ]--+ |              |              |              | +---[ ]----[/]---[ ]----[/]---[ ]---[/]---[ ]--+    Timer1         Timer2    +---[ ]---+    +---[ ]---+    |         |    |         |    +---[/]---+    +---[/]---+      LED            LEDIn this ladder logic program:Each [ ] represents a normally open (NO) contact./ represents a normally closed (NC) contact.The timers Timer1 and Timer2 are cascaded to achieve the desired timing sequence.The LED output is controlled based on the completion of both timers.Here's a breakdown of the logic:Timer1 is a timer with a preset time of 10 minutes.Timer2 is a timer with a preset time of 15 minutes.When Timer1 completes its timing sequence, it energizes the output coil [/], and the LED associated with Timer1 turns on.Timer2 is enabled when the output coil [/] of Timer1 is energized.When Timer2 completes its timing sequence, it energizes the output coil [/], and the LED associated with Timer2 turns on.The LED output is controlled by the parallel combination of Timer1's output coil [/] and Timer2's output coil [/]. The LED turns on only when both timers have been completed.Please note that the ladder logic diagram provided is a visual representation, and the actual implementation may vary depending on the PLC programming software or hardware you are using.This output is closer to what we’re looking for, but depending on a person's experience level, this probably won’t be much help.  This particular diagram isn’t very descriptive of how everything is supposed to fit together, so if a person had to translate this to a specific programming software, this diagram would probably take a while to fully digest and implement.  As such, we’re going to have to add another prompt that will be as follows,I need the variables assigned to the proper contacts.This subsequent prompt will produce the following:NETWORK --|----[StartButton]----[/Timer1.Enabled]---[Timer1.TimerDN]----[/Timer2.Enabled]---[Timer2.TimerDN]--+ |                                                                                             | +---[Timer1.Timer.Q]----[/LED1]---+---[Timer2.Timer.Q]----[/LED2]---+                                   |                                   |                                    +---[/LED]--------------------------+In this ladder logic program:StartButton is a boolean variable representing the input contact [StartButton] that starts the timer sequence.Timer1.Enabled and Timer2.Enabled are boolean variables representing the input contacts [/Timer1.Enabled] and [/Timer2.Enabled] that enable the respective timers.Timer1.TimerDN and Timer2.TimerDN are boolean variables representing the output contacts [Timer1.TimerDN] and [Timer2.TimerDN] that indicates the completion of the respective timers.Timer1.Timer.Q and Timer2.Timer.Q are boolean variables representing the output contacts [Timer1.Timer.Q] and [Timer2.Timer.Q] that indicates the running state of the respective timers.LED1 and LED2 are boolean variables representing the output contacts [/LED1] and [/LED2] that control the LEDs associated with Timer1 and Timer2.LED is a boolean variable representing the output contact [/LED] that controls the final LED indicating the completion of both timers.Please note that the actual variable names may vary depending on the PLC programming software or hardware you are using. You can assign these variables in your PLC programming software and connect them to the appropriate input and output terminals to implement the desired cascading timer logicThis result is much better as it shows how to wire the network together while the description gives a clear overview of how everything works.  With this, a person with a basic knowledge of Ladder Logic will be able to piece this together and port the rungs to other PLC programming programs. SummaryChatGPT can be leveraged as an excellent tool to help Ladder Logic developers with concocting specialized programs.  However, ChatGPT is NOT a replacement for programmers, and to effectively use the system, a person must be skilled enough to write descriptive prompts and interpret the results.  This means that though ChatGPT is an excellent tool, it does not have the intuition nor the skill to fully replace a programmer.A big part of using ChatGPT is learning to write and refine prompts as well as subsequent follow-up prompts.  These prompts are a developing art form that probably will be the next iteration of software development.  For now, the art of using ChatGPT and similar systems is novel, and there aren’t any definitive standards that govern how to effectively use these yet, especially when it comes to graphical programming such as Ladder Logic.  When used by a knowledgeable person that has a basic idea of PLC programming and ChatGPT, it can be a great way of getting over hurdles that could take hours or days to solve. Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming

Our Data Engineering Byte Newsletter gives data engineers and practitioners what they often lack today: clear, real-world insights—where every byte tells a story.Subscribe here to stay ahead in data engineeringIn today's data-driven world, the ability to build accurate and efficient data models is paramount for businesses and individuals alike. However, the process of constructing a data model can often be complex and daunting, requiring specialized knowledge and technical skills. But what if there was a way to simplify this process and make it accessible to a wider audience? Enter ChatGPT, a powerful language model developed by OpenAI. In this article, we will explore how ChatGPT can be leveraged to build data models easily, using a practical example. By harnessing the capabilities of ChatGPT, you'll discover how data modeling can become a more approachable and intuitive task for everyone, regardless of their technical background.Build Data Model with ChatGPTConsider data modeling as the process of drawing diagrams for software applications that provide an overview of all the data pieces they include. The data flow is depicted in the diagram using text and symbols. It serves as a model for creating a new database that will allow a company to utilize the data efficiently for its needs. The primary objective of the data model is to establish an overall picture of the types of data that are used, how they are kept in the system, the relationships between the data entities, and the various ways in which they can be arranged and grouped. The norms and procedures for gathering feedback from the business stakeholders are taken into consideration when building data models.The Data Model functions as a better understanding of what is designed, much like a roadmap or blueprint might. It offers a comprehensive review of the standardized methodologies and schema to define and manage data in a way that is common and uniform throughout the organization. According to the level of abstraction, there are three different types of data models.Conceptual Data Model: It provides a helicopter view of the system description, its organization, and business rules to be considered. Initial project requirements are captured using the conceptual model. It mainly consists of the business entities, their constraints and characteristics, and the relationship between them for data integrity and security requirements.Logical Data Model: The logical data model provides detailed insights into the concepts and relationship which consists of data attributes and the relationship between the entities. It is very much useful for data-driven projects or initiatives.Physical Data Model: It provides an overview of how the data should be stored physically within the database. It is a final design to implement the relational database including the relationship using the primary and foreign keys.                           Image 1 : Types of Data Modelling TechniquesThe data model was created using a variety of data modeling methodologies, as seen in the graphic above. The most popular data modeling technique utilized by any corporate organization is entity relationship modeling, also known as dimensional modeling. Erwin Data Modeler, ER/Studio, Archi, and other tools are available on the market to construct data models utilizing these data modeling methodologies. The data Modelling technique mainly involves below steps :  Identify the entitiesFind the entity propertiesCreate a relationship between the entitiesCorrelated attributes to the entityDefine the degree of normalization to improve the performanceValidate and Finalise the data modelLet’s start with creating a data model using chatGPT. The goal is to ask chatGPT to start with the data modeling activities for the anti-money laundering(AML) system of a banking domain: Image 1: The data model for the banking system, Part 1  Image 2: Data Modelling  for AML Process for Bank As you can see in the image, once we provide an input to the chatGPT, it provides a step-by-step process of building the data model. The first step is to understand the AML regulations and identify the stakeholders for the system to capture the requirements. Once the stakeholders are identified, the next step is to define the data modeling goals including the list of data sources, and perform the data profiling. Once data profiling steps are done, the next activity is to create a conceptual, logical, and physical data model.Now, Let’s check with chatGPT to create a conceptual model with all the entities. Image 3: Conceptual Data Model, Part 1                 Image 4 : AML Conceptual ModelAfter the input, chatGPT responds with the list of actors, entities, and relationships between the entities to define the conceptual model. With this information, we can have a high-level overview of the system by building the conceptual data model. Let’s ask chatGPT to build the logical data model once the conceptual data model is ready:Image 5: AML data model for ERwin Tool                  Image 6 : AML Logical Data Model, Part 2 As you can see in the above image, step by step process to create a logical data model is to open the Erwin Tool and create a new data model. In the new data model, add all entities, their attributes, and the relationship between the entities. Once entities are defined, set up primary and foreign keys for all entities and validate the data model. After the validation, adjust the review comments and finalize the logical data model and generate the documentation for the same.Next, Let’s ask chatGPT if it can add new customer information to the existing conceptual model.                  Image 5 : AML Logical Data Model with Customer Information As we can see in the above image, chatGPT asks to first identify the source information and create an entity and attributes for the same. Once it is done, we have to define the cardinality to understand how entities are related to each other. Then define primary and foreign key relationships, data model validation and generate documentation.ConclusionIn this article, we understood the importance of building the data model and step by step process to create the data model. Later in this article, we also checked how to use chatGPT to create conceptual and logical data models.Author BioSagar Lad is a Cloud Data Solution Architect with a leading organisation and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.Link - Medium , Amazon , LinkedIn 

Make sure to subscribe our BIPro Newsletter so you never miss a key update in the data world. Join over 35K+ BI lovers and get tips from those who’ve cracked tough data challenges! In this article by Anita Graser, the author of Learning QGIS Third Edition, we will cover the following topics:Selecting features with the mouseSelecting features using expressionsSelecting features using Spatial queries(For more resources related to this topic, see here.)Selecting features with the mouseThe first group of tools in the Attributes toolbar allows us to select features on the map using the mouse. The following screenshot shows the Select Feature(s) tool. We can select a single feature by clicking on it or select multiple features by drawing a rectangle. The other tools can be used to select features by drawing different shapes: polygons, freehand areas, or circles around the features. All features that intersect with the drawn shape are selected. Holding down the Ctrl key will add the new selection to an existing one. Similarly, holding down Ctrl + Shift will remove the new selection from the existing selection.Selecting features by expressionThe second type of select tool is called Select by Expression, and it is also available in the Attribute toolbar. It selects features based on expressions that can contain references and functions using feature attributes and/or geometry. The list of available functions is pretty long, but we can use the search box to filter the list by name to find the function we are looking for faster. On the right-hand side of the window, we will find Selected Function Help, which explains the functionality and how to use the function in an expression. The Function List option also shows the layer attribute fields, and by clicking on Load all unique values or Load 10 sample values, we can easily access their content. As with the mouse tools, we can choose between creating a new selection or adding to or deleting from an existing selection. Additionally, we can choose to only select features from within an existing selection. Let's have a look at some example expressions that you can build on and use in your own work:Using the lakes.shp file in our sample data, we can, for example, select big lakes with an area bigger than 1,000 square miles using a simple attribute query, "AREA_MI" > 1000.0, or using geometry functions such as $area > (1000.0 * 27878400). Note that the lakes.shp CRS uses feet, and we, therefore, have to multiply by 27,878,400 to convert from square feet to square miles. The dialog will look like the one shown in the following screenshot.We can also work with string functions, for example, to find lakes with long names, such as length("NAMES") > 12, or lakes with names that contain the s or S character, such as lower("NAMES") LIKE '%s%', which first converts the names to lowercase and then looks for any appearance of s.Selecting features using spatial queriesThe third type of tool is called Spatial Query and allows us to select features in one layer based on their location, relative to the features in a second layer. These tools can be accessed by going to Vector | Research Tools | Select by location and then going to Vector | Spatial Query | Spatial Query. Enable it in Plugin Manager if you cannot find it in the Vector menu. In general, we want to use the Spatial Query plugin, as it supports a variety of spatial operations such as crosses, equals, intersects, is disjoint, overlaps, touches, and contains, depending on the layer's geometry type.Let's test the Spatial Query plugin using railroads.shp and pipelines.shp from the sample data. For example, we might want to find all the railroad features that cross a pipeline; we will, therefore, select the railroads layer, the Crosses operation, and the pipelines layer. After clicking on Apply, the plugin presents us with the query results. There is a list of IDs of the result features on the right-hand side of the window, as you can see in the following screenshot. Below this list, we can select the Zoom to item checkbox, and QGIS will zoom to the feature that belongs to the selected ID. Additionally, the plugin offers buttons to directly save all the resulting features to a new layer.SummaryThis article introduced you to three solutions to select features in QGIS: selecting features with mouse, using spatial queries, and using expressions.Resources for Article:Further resources on this subject:Editing attributes [article]Server Logs [article]Improving proximity filtering with KNN [article]