Power BI: Data Transformation and Visualization

This comprehensive guide provides an in-depth look into Power BI, a powerful business intelligence tool from Microsoft. It details the step-by-step process of installing and utilizing Power BI Desktop, covering essential data manipulation techniques such as text, numerical, date, and time transformations. The sources further explore advanced concepts like merging and appending queries, managing data relationships through primary and foreign keys, and understanding different cardinalities. Finally, the guide concludes with a focus on data visualization, demonstrating the creation of various charts and filters, and the process of publishing dashboards to Power BI service.

Mastering Power BI: Data Analysis and Visualization

Power BI, developed by Microsoft, is a powerful business analytics tool designed for analyzing and visualizing data in insightful and interactive ways. It has gained popularity due to its user-friendly interface and robust features. Power BI is suitable for business analysts, data analysts, data scientists, or anyone who wants to work efficiently with data, providing necessary skills and knowledge to become proficient in data handling.

Key Capabilities and Features Power BI allows users to transform, clean, analyze, and visualize data. It enables effortless data gathering from various platforms, including Excel, CSV files, different databases like MySQL, Postgres, Oracle, or other datasets. It is noted for its strong visualization capabilities, offering a wide range of charts such as bar plots, pie charts, and stack plots. Unlike Excel, Power BI has the capacity to work with large datasets and offers numerous deployment options. The end result of working with Power BI is often the creation of interactive and visually appealing dashboards.

Installation and Interface To install Power BI Desktop for Windows, users typically download the executable file from Microsoft’s website. Once installed, its user interface is very similar to Excel, making it easy for Excel users to adapt. Power BI also offers tutorials, blogs, and forums for support. While desktop usage is common, Power BI reports can also be created and viewed on mobile phones. A company domain email address is generally required for login, though free business emails can be created for this purpose.

Data Handling and Transformation Power BI provides various data connectors to import data from diverse sources. These include:

Files: Excel workbooks, Text/CSV files, XML, JSON, and PDF. Data can also be pulled from folders.
Databases: SQL Server, Oracle, Postgres, MySQL, and other databases.
Power Platform: Existing datasets loaded in Power Platform can be accessed.
Cloud Services (Azure): Azure SQL Database and other Azure options are available.
Online Services: Google Analytics, GitHub, LinkedIn Sales Navigator, and many more.
Other: Data can be scrapped from the web, or connected to Hadoop, Spark, R script, and Python script.

Power BI offers extensive tools for data transformation:

Text Tools: Used for text manipulations like converting to lower/upper case, trimming whitespace, replacing values, combining values (concatenate), finding specific text, formatting text, and extracting specific parts of text using delimiters (e.g., username from an email address). These tools can either transform the existing column or add a new column with the transformed data.
Numerical Tools: Used for mathematical operations, statistics (maximum, median, average, standard deviation, count), rounding values, and applying filters. These can be applied by adding a new column or transforming an existing one.
Date and Time Tools: Essential for analyzing time-based patterns, such as identifying peak order times or days. They allow extraction of year, month, day, age calculations, and conversion of time formats (e.g., 24-hour to 12-hour). Regional settings may need adjustment for proper date parsing.
Pivoting and Unpivoting: These techniques allow converting rows to columns (pivoting) and columns to rows (unpivoting) to restructure data for easier analysis.
Conditional Columns: New columns can be created based on specified conditions, similar to conditional statements in programming.
Creating Tables: Users can manually create tables within Power BI by entering data directly.

DAX (Data Analysis Expressions) DAX is a collection of functions, operators, and constants used in Power BI to create new data or transform existing data.

Purpose: DAX is used to calculate complex formulas, create measures, develop time intelligence calculations, and dynamically or statically analyze data.
Calculated Columns vs. Measures:
Calculated Columns: Create a new column in the data model, adding static data that consumes memory and updates when new data is added. They work row by row.
Measures: Dynamically calculate values at runtime, primarily for aggregations like sum, count, or average, and are used to create visual reports. They do not consume memory for each row. Measures can be implicit (automatically created by Power BI) or explicit (user-defined).
DAX Functions: Broadly categorized into:
Date and Time: Work on date-related calculations (e.g., NOW, YEAR, WEEKDAY).
Text Functions: Manipulate text strings (e.g., CONCATENATE, FIND, FORMAT, LEFT, LEN, LOWER, REPLACE, RIGHT, TRIM, UPPER).
Informative Functions: Provide information about data types and handle errors (e.g., IFERROR, IFNA).
Filter Functions: Filter data based on conditions (e.g., FILTER, CALCULATETABLE).
Aggregation Functions: Compute aggregate values (e.g., SUM, COUNT, AVERAGE, MIN, MAX).
Time Intelligence Expressions: Analyze data over time periods.
Logical Functions: Implement conditional logic (e.g., IF, AND, OR, NOT, SWITCH).
Math and Trigonometric Functions: Perform mathematical calculations (e.g., ABS, SIN, COS, TAN).
Statistical Functions: Used for statistical calculations (e.g., percentile, standard deviation).
Financial Functions: Aid in financial computations.
DAX Syntax: Typically involves a column name, an equals sign, a function, and then references to table and column names (e.g., ColumnName = Function(TableName[ColumnName])).
Operators: Used in DAX formulas for various purposes:
Arithmetic: +, -, *, / for mathematical operations.
Comparison: >, <, =, >=, <=, <> for comparing values, returning true/false.
Logical: AND, OR, NOT for combining or negating conditions.
Concatenation: & for joining text from multiple columns.
Reference: TableName[ColumnName] for referencing specific columns.
Parentheses: () for controlling execution order of formulas.
Miscellaneous: : (colon) for separating elements in date and time.

Data Modeling and Relationships Data modeling is crucial for connecting different tables and sources of data within Power BI, especially in companies with diverse datasets (e.g., product, sales, customer details).

Merge and Append Queries:
Merge: Combines two tables based on a common key (like a primary key and foreign key), increasing the number of columns, similar to SQL joins (inner, left, right, full, anti-joins).
Append: Stacks rows from multiple tables with similar columns into one table, increasing the number of rows.
Keys:
Primary Key: A unique identifier for each record in a table (e.g., product ID, Aadhaar card number).
Foreign Key: A column in one table that refers to the primary key in another table, allowing for duplicate values.
Cardinality: Describes the nature of the relationship between two tables based on primary and foreign keys.
One-to-one (1:1): Both tables have unique primary keys related to each other.
One-to-many (1:*): One table has a primary key, and the other has a foreign key that can be repeated multiple times.
Many-to-one (*:1): The reverse of one-to-many, where the foreign key is on the “many” side and the primary key is on the “one” side.
Many-to-many (:): Both tables have foreign keys that can be repeated.
Cross-Filter Direction: Defines the flow of data filtering between related tables (single or double direction).
Managing Relationships: Power BI can automatically detect relationships. Users can manually manage and edit these relationships, including setting cardinality and cross-filter direction, and activating/deactivating multiple relationships between tables.

Data Visualization Visualization is a critical step in Power BI, revealing patterns and trends that are not apparent in raw row and column data.

Dashboard Elements: The report section is where visuals are built using fields (columns from tables) that can be dragged and dropped.
Visual Types: Power BI offers a wide array of built-in visuals:
Charts: Stacked bar, stacked column, clustered bar, clustered column, line, area, pie, scatter, donut, funnel, map, tree map.
Matrices: Powerful tools for visualizing data across different parameters and dimensions, allowing drill-down into subcategories.
Cards: Number cards (for highlighting single large numbers) and multi-row cards (for multiple pieces of information).
KPI Visuals: Show key performance indicators, often with trend lines, useful for comparing current and past performance.
Custom Visuals: Users can import additional visuals from the Power BI marketplace (e.g., boxplot, flow map, calendar).
Formatting and Customization: Visuals can be extensively formatted, including changing font size, colors, titles, background, borders, data labels, and themes.
Filtering:
Filter Pane: Allows applying filters on a specific visual, on the current page, or across all pages. Advanced filtering options like “greater than” or “less than” are available.
Slicers: Interactive tools for filtering data across the entire dashboard or different pages. They can display data as lists, dropdowns, or ranges (e.g., date sliders).
Sync Slicers: Allows the same filter to be applied consistently across multiple pages.
Interactivity Tools:
Buttons: Can be added to navigate between pages or trigger other actions.
Bookmarks: Capture the current state of a report page (e.g., filters applied, visuals visible) allowing users to return to that view.
Images: Can be inserted for branding (e.g., logos) or icons.

Publishing and Sharing Once a dashboard is complete, it can be published to Power BI service, which typically requires a user to be signed in. Published reports retain their interactivity and can be viewed online, shared with co-workers, or even published to the web without security if desired. Power BI also allows creating a mobile layout for dashboards, optimizing them for phone viewing.

Power BI: Data Analysis from Gathering to Visualization

Data analysis is a critical process for extracting insights and patterns from raw data to inform decision-making, and Power BI serves as a powerful business analytics tool to facilitate this. It involves several key steps, from data gathering and cleaning to sophisticated analysis and visualization.

The Role of a Data Analyst

A data analyst’s primary responsibility is to gather, interpret, process, and clean data, ultimately representing it in a graphical format. This graphical representation allows business strategists to understand the information better and use it to grow their business. Power BI is designed to provide the necessary skills and knowledge to become proficient in working efficiently with data.

Key Steps in Data Analysis using Power BI

Data Gathering (Data Connectors): Power BI offers extensive data connectors that allow users to effortlessly gather data from various platforms. These sources include:

Files: Excel workbooks, Text/CSV files, XML, JSON, and PDF. Data can also be pulled from folders.
Databases: SQL Server, Oracle, Postgres, and MySQL are among many databases from which data can be extracted.
Power Platform: Existing datasets loaded in Power Platform can be directly accessed.
Cloud Services (Azure): Azure SQL Database and other Azure options enable data retrieval from the cloud.
Online Services: Google Analytics, GitHub repositories, and LinkedIn Sales Navigator are examples of online services that can connect to Power BI.
Other: Data can be obtained by scrapping from the web, or connecting to Hadoop, Spark, R scripts, and Python scripts.

Data Transformation and Cleaning: Once data is gathered, Power BI provides robust tools for cleaning and processing it. This includes:

Text Tools: Used for manipulations such as converting text to lower or upper case, trimming whitespace, replacing values, combining values (concatenate), finding specific text, formatting text, and extracting parts of text using delimiters (e.g., username from an email address). These tools can either transform an existing column or add a new one with the transformed data.
Numerical Tools: Applicable for mathematical operations, statistics (maximum, median, average, standard deviation, count), rounding values, and applying filters. Like text tools, they can transform existing columns or create new ones.
Date and Time Tools: Essential for analyzing time-based patterns (e.g., peak order times or days). They allow extraction of year, month, day, and age calculations, and conversion of time formats (e.g., 24-hour to 12-hour). Regional settings may need adjustment for proper date parsing.
Pivoting and Unpivoting: These techniques allow restructuring data by converting rows to columns (pivoting) or columns to rows (unpivoting) for easier analysis.
Conditional Columns: New columns can be created based on specified conditions, similar to conditional statements in programming.
Creating Tables: Users can manually create tables within Power BI by entering data directly.

Data Analysis Expressions (DAX): DAX is a collection of functions, operators, and constants used in Power BI to create new data or transform existing data.

Purpose: DAX is used to calculate complex formulas, create measures, develop time intelligence calculations, and dynamically or statically analyze data.
Calculated Columns vs. Measures:
Calculated Columns: Create a new column in the data model, adding static data that consumes memory and updates when new data is added. They work row by row.
Measures: Dynamically calculate values at runtime, primarily for aggregations like sum, count, or average, and are used to create visual reports. They do not consume memory for each row. Measures can be implicit (automatically created by Power BI) or explicit (user-defined).
DAX Functions: Broadly categorized into Date and Time, Text, Informative, Filter, Aggregation, Time Intelligence, Logical, Math and Trigonometric, Statistical, and Financial functions.
DAX Syntax: Typically involves a column name, an equals sign, a function, and then references to table and column names (e.g., ColumnName = Function(TableName[ColumnName])).
Operators: Used in DAX formulas, including arithmetic (+, -, *, /), comparison (>, <, =, >=, <=, <>), logical (AND, OR, NOT), concatenation (&), reference (TableName[ColumnName]), and parentheses () for controlling execution order.

Data Modeling and Relationships: Data modeling is crucial for connecting different tables and sources, especially in companies with diverse datasets (e.g., product, sales, customer details).

Merge and Append Queries:
Merge: Combines two tables based on a common key, increasing the number of columns, similar to SQL joins (inner, left, right, full, anti-joins).
Append: Stacks rows from multiple tables with similar columns into one table, increasing the number of rows.
Keys: Primary keys are unique identifiers, while foreign keys can be duplicated and refer to a primary key in another table.
Cardinality: Describes the relationship type between tables (one-to-one, one-to-many, many-to-one, many-to-many).
Cross-Filter Direction: Defines the flow of data filtering between related tables (single or double direction).
Managing Relationships: Power BI can automatically detect relationships, and users can manually manage and edit them, including setting cardinality and cross-filter direction.

Data Visualization: Visualization is a critical step in data analysis within Power BI, as it reveals patterns and trends not apparent in raw row and column data.

Dashboard Elements: Visuals are built in the report section by dragging and dropping fields (columns from tables).
Visual Types: Power BI offers a wide range of built-in visuals, including stacked bar, stacked column, clustered bar, clustered column, line, area, pie, scatter, donut, funnel, map, tree map, matrices, cards (number and multi-row), and KPI visuals. Users can also import custom visuals from the Power BI marketplace.
Formatting and Customization: Visuals can be extensively formatted, including changing font size, colors, titles, background, borders, data labels, and themes.
Filtering: Filters can be applied via the filter pane (on specific visuals, pages, or all pages) or interactive slicers (displaying data as lists, dropdowns, or ranges). Slicers can also be synced across multiple pages.
Interactivity Tools: Buttons can be added for page navigation or other actions, and bookmarks capture report states to allow users to return to specific views. Images can be inserted for branding or icons.

Publishing and Sharing: Completed dashboards can be published to Power BI service, requiring login, to be viewed online, shared with co-workers, or published to the web without security. Power BI also supports creating mobile layouts for dashboards, optimizing them for phone viewing.

Power BI: Mastering Data Visualization and Reporting

Data visualization is a crucial step in data analysis, transforming raw data into insightful and interactive visual representations to reveal patterns and trends that are not apparent in simple rows and columns. Power BI, a business analytics tool developed by Microsoft, is designed to facilitate this process, offering powerful features for visualizing data.

The Importance of Data Visualization

Visualizing data helps users see new things and discover patterns that might otherwise be missed. When data is presented in a graphical format, business strategists can better understand the information and use it to grow their business. Power BI provides the necessary skills and knowledge to become proficient in efficiently working with and visualizing data.

Key Aspects of Data Visualization in Power BI

Report Section and Visuals:

The primary area for creating visuals in Power BI is the report section.
Users can build visuals by dragging and dropping fields (columns from tables) from the “Fields” pane on the right-hand side.
Power BI offers a user-friendly interface with a wide range of interactive and powerful features for visualization.

Types of Visuals: Power BI includes many built-in chart types and allows for the import of custom visuals:

Bar and Column Charts: Stacked bar, stacked column, clustered bar, and clustered column charts are available for comparing values across categories.
Line and Area Charts: Used to show trends over time or categories.
Pie and Donut Charts: Represent parts of a whole. A donut chart can become a pie chart by reducing its inner radius to zero.
Scatter Plot: Displays relationships between two numerical variables.
Funnel Chart: Shows stages in a linear process.
Maps: Allows visualization of data geographically, using locations like countries or continents. Bubbles on the map can represent values, with their size corresponding to a measure like population. A “flow map” visual can also be imported to show destinations and origins or flows between regions.
Tree Maps: Display hierarchical data in a set of nested rectangles, where the size of each rectangle is proportional to its value. An existing chart, like a donut chart, can easily be converted into a tree map.
Matrices: A powerful tool for visualizing data on different parameters and dimensions, allowing for hierarchical drilling down from categories (e.g., continents) to subcategories (e.g., countries).
Cards: Used to highlight specific numeric information or text.
Number Cards: Display a single large number, such as total population or average values.
Multi-row Cards: Show multiple pieces of information, like sum of population, average life expectancy, and average GDP, in one visual.
Text Cards: Display textual information, such as the top-performing category based on an order quantity filter.
KPI (Key Performance Indicator) Visuals: Allow for showing performance metrics, often with a trend graph in the background, like the sum of population over time or company profit/loss.
Slicers: Interactive filtering tools that allow users to filter data across the entire dashboard or specific pages. Slicers can display data as a list, a dropdown, or a range slider (e.g., for years). They can also be synchronized across multiple pages.
Tables: Simple tabular representations of data.
Custom Visuals: Users can import additional visuals from the Power BI marketplace (AppSource) to enhance their dashboards.

Formatting and Customization: Power BI provides extensive options for customizing the appearance of visuals and dashboards:

Canvas Settings: Users can change the background color or add images to the canvas background to match a particular theme. Transparency can also be adjusted.
Themes: Different built-in themes are available, and users can also create their own custom themes.
Gridlines: Can be added to help arrange visuals neatly on the canvas.
Object Locking: Visuals can be locked in place to prevent accidental movement.
Axis Formatting: Users can change font size, colors, define ranges (minimum/maximum), and customize titles for X and Y axes.
Data Labels: Can be turned on or off to display specific values directly on the chart, with customizable colors and positions.
Colors: Colors of bars, slices (in donut charts), and text can be customized. Conditional formatting can be applied, for instance, to show a gradient of colors based on value (e.g., light blue for lowest to dark blue for highest).
Borders and Shadows: Visuals can have customizable borders and shadows to make the dashboard more interactive and visually appealing.
Spacing and Padding: Adjusting inner and outer padding for elements within charts helps control visual spacing.
Titles: Visual titles can be customized in terms of text, color, and font.

Filtering and Interactivity:

Filter Pane: Filters can be applied to individual visuals, to all visuals on a specific page, or to all visuals across all pages. Advanced filtering options include operators like “less than” or “greater than”.
Buttons: Can be added to dashboards for various actions, such as page navigation. Users can define the destination page for a button.
Bookmarks: Capture the current state of a report (including filters, sort order, and visible visuals), allowing users to return to specific views easily. Bookmarks can be linked to buttons for navigation.
Images: Logos or other icons can be added to the dashboard for branding or aesthetic purposes.

Publishing and Mobile View:

Mobile Layout: Dashboards created on desktops can be optimized for phone viewing by arranging elements within a mobile grid layout. This allows for scrolling and resizing visuals to fit mobile screens.
Publishing: Once a dashboard is complete and satisfactory, it can be published to the Power BI service for online viewing and sharing with co-workers. Reports can also be published to the web without security for public viewing.

Power BI Data Modeling: Relationships and Cardinality

Data modeling is a crucial aspect of data analysis in Power BI, particularly when dealing with information from various sources. It involves connecting different tables and managing the relationships between them to enable comprehensive and accurate data visualization and analysis.

Purpose and Importance of Data Modeling

Data modeling is essential because companies often have data stored in separate tables or databases, such as sales, product, and customer details. Creating relationships between these disparate tables allows for a unified view and accurate visualization of the data, which is vital for data analysis. Without proper data modeling, tables remain independent, and it becomes difficult to see relationships between them, leading to inaccurate or incomplete data display.

Key Concepts in Data Modeling

Primary Key: A column that contains unique values and is not repeated or duplicated within a table. For example, a product ID in a product table or an Aadhaar card number are primary keys because each is unique to a single entity.
Foreign Key: A column that can contain duplicate values and acts as a clone of a primary key from another table. For instance, a customer key in a sales table might appear multiple times if a customer buys several products, making it a foreign key, whereas the same customer key in the customer data table would be a primary key.

Relationships and Cardinality

Relationships are built between tables based on common primary and foreign keys. Power BI can automatically detect these relationships upon data load. The type of relationship between tables is known as cardinality:

One-to-One (1:1): Occurs when both tables involved in the relationship have unique primary keys in the joined columns. For example, an employee ID in an employee details table and the same employee ID in a bonus table, where both IDs are unique in their respective tables, form a one-to-one relationship.
One-to-Many (1:N): This is a common relationship where one table contains a primary key, and the related column in another table is a foreign key with multiple occurrences. An example is a product table with unique product IDs (primary key) linked to a sales table where product IDs can repeat for multiple sales (foreign key). The data flow typically goes from the ‘one’ side (primary key) to the ‘many’ side (foreign key).
Many-to-One (N:1): This is the inverse of one-to-many, where the foreign key is in the first table and the primary key is in the second.
Many-to-Many (N:N): This relationship occurs when both related columns in two tables are foreign keys, meaning values can repeat in both. It is generally advised to create this type of relationship rarely.

Cross-Filter Direction: This refers to the direction of data flow between tables in a relationship.

Single Direction: Data flow is from the primary key side to the foreign key side (1 to Many).
Double Direction (Both): Data flow is bidirectional, allowing filtering from either side (primary key to foreign key and vice versa). This enables a third connected table to access data more easily, even if it doesn’t have a direct relationship.

Managing and Editing Relationships in Power BI

Power BI offers tools to manage and edit relationships:

Automatic Detection: Power BI can automatically detect and create relationships between tables when data is loaded, especially if common column names or keys exist.
Manual Creation: Users can manually create relationships by dragging and dropping common keys between tables in the ‘Model’ view.
Editing Relationships: Existing relationships can be edited to change their type (cardinality) or cross-filter direction. For instance, a user can modify a relationship from one-to-many to many-to-many or change its filter direction.
Activation/Deactivation: Only one active relationship can exist between two tables at any given time. If multiple potential relationships exist, others will appear as dotted lines, indicating they are deactivated. To activate a deactivated relationship, another active relationship between the same tables must be deactivated first.

Proper data modeling ensures that relationships are correctly defined, leading to accurate data analysis and visualization in dashboards.

DAX Functions for Data Analysis and Power BI

DAX, which stands for Data Analysis Expressions, is a powerful functional language used in Power BI to create custom calculations for data analysis and visualization. It includes a library of functions, operators, and constants that can be used to perform dynamic aggregations and define new computed columns and measures within your data models.

Purpose and Application of DAX Functions

DAX functions are essential for transforming and analyzing data beyond what simple transformations can achieve. They allow users to:

Create calculated columns: These are new columns added to a table, where each row’s value is computed based on a DAX formula. Calculated columns are static and consume memory, updating when new data is added to the model.
Create measures: Measures are dynamic calculations that aggregate data, such as sums, averages, or counts, and are evaluated at query time, making them efficient for reporting and dashboard interactions. They do not consume memory until used in a visual.
Calculate complex formulas: DAX enables the creation of sophisticated calculations, including time intelligence calculations, to group data and derive insights.
Analyze data dynamically and statically: DAX expressions provide flexibility for various analytical needs.

Categories of DAX Functions

DAX functions are broadly categorized to handle different types of data and analytical needs:

Date and Time Functions: Used for operations on date and time data, such as extracting parts of a date (year, month, day), calculating age, or finding differences between dates. Examples include NOW(), YEAR(), WEEKDAY(), DATE_DIFFERENCE().
Text Functions: Used to manipulate text strings, such as concatenating text, changing case, trimming whitespace, or finding specific substrings. Examples include CONCATENATE(), FIND(), FORMAT(), LEFT(), RIGHT(), LEN(), LOWER(), UPPER(), REPLACE(), and TRIM().
Informative Functions: Provide information about data types or handle errors, like checking for text, even/odd numbers, or missing data. Examples include ISERROR() or ISNA().
Filter Functions: Work based on specified conditions to filter data, often used with CALCULATE or FILTER to modify contexts. Examples include SUMX (sum if condition) or COUNTX (count if condition).
Aggregation Functions: Used to summarize data, such as SUM, COUNT, AVERAGE, MIN, and MAX.
Time Intelligence Functions: Specialized functions that enable calculations over time periods, essential for trend analysis.
Logical Functions: Implement conditional logic, evaluating expressions based on true/false conditions. Examples include IF(), AND(), OR(), NOT(), and SWITCH().
Math and Trigonometric Functions: Perform mathematical operations like absolute value, square root, exponents, or trigonometric calculations such as sine, cosine, and tangent. Examples include ROUNDUP(), ROUNDDOWN().
Statistical Functions: Used for statistical calculations like percentile or standard deviation.
Financial Functions: Help compute financial calculations.
Other Functions: A category for functions that don’t fit into the above, such as NOW() or GOOD().

DAX Syntax

The general syntax for a DAX expression typically involves:

Column Name: The name of the new calculated column or measure being created.
Equals Sign (=): Indicates that the column or measure is defined by the subsequent expression.
Function: The DAX function to be used (e.g., SUM, COUNT, IF).
Table Name (optional for measures, often needed for calculated columns): Specifies the table containing the data.
Column Reference: The specific column on which the function operates, often enclosed in square brackets [].

Example: Total Price = SUM(‘Order Items'[Price])

Practical Examples of DAX Functions

LEN(): To find the number of digits or characters in a column, such as digit count of ID = LEN(‘Zomato Asia Africa'[Restaurant ID]).
LEFT() / RIGHT(): To extract a specified number of characters from the beginning or end of a text string. For instance, creating a “Short Day” column from “Day Name” using short day = LEFT(‘Customer Data'[Day Name], 3) to get “THU” from “Thursday”.
LOWER() / UPPER(): To convert text in a column to lowercase or uppercase. For example, LOWER(‘Customer Data'[Day Name]) converts “THU” to “thu”.
Concatenation (&): To combine values from multiple columns into one, like creating a full name: ‘Customer Data'[Prefix] & ” ” & ‘Customer Data'[First Name] & ” ” & ‘Customer Data'[Last Name].
DATE_DIFFERENCE(): To calculate the difference between two dates, useful for determining age. For example, DATE_DIFFERENCE(‘Customers Data'[Birth Date], TODAY(), YEAR) to get age in years.
IF(): To apply conditional logic. For instance, creating a payment data column: IF(‘O list order payments'[Payment Value] > 100, “High Price”, “Low Price”).
Arithmetic Operators (+, -, *, /): Used for mathematical calculations on column values.
Comparison Operators (>, <, =, etc.): Used to compare values, yielding true/false results, often within conditional statements.

DAX functions are fundamental for performing advanced data manipulation and aggregation, enabling users to derive deeper insights from their data in Power BI.

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By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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Power BI: Data Transformation and Visualization

Mastering Power BI: Data Analysis and Visualization