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Can’t Compare Float and Numerical Bin Values Tableau: Why & Solutions?

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Can’t compare float and numerical bin values in Tableau? This is a common issue when working with histograms and bins in Tableau, especially when dealing with decimal values. At COMPARE.EDU.VN, we break down the reasons behind this limitation and offer several effective solutions, including a hidden Tableau function that simplifies the process. Learn how to overcome this obstacle and create accurate, insightful visualizations. Explore data visualization and Tableau tips for similar challenges.

1. Understanding the Core Issue: Why Can’t You Directly Compare Float and Numerical Bin Values in Tableau?

When working with Tableau, you might encounter difficulties when trying to directly compare float (decimal) and numerical bin values. This arises due to the way Tableau handles these different data types and how they interact within calculated fields and visualizations.

1.1 Data Type Mismatch

  • Inherent Differences: Float values represent numbers with decimal points, allowing for a high degree of precision. Numerical bin values, on the other hand, typically represent discrete categories or ranges.
  • Tableau’s Handling: Tableau treats these data types differently. When you attempt to compare them directly, Tableau might not know how to reconcile the continuous nature of float values with the discrete nature of bin values.

1.2 Floating-Point Arithmetic Limitations

  • Precision Problems: Floating-point numbers are stored in a way that can sometimes lead to small inaccuracies due to the way computers represent decimal numbers in binary format.
  • Impact on Comparisons: When comparing float values, these tiny inaccuracies can cause unexpected results, especially when binning or categorizing data.

1.3 Binning Process Challenges

  • Discretization Issues: Binning involves grouping continuous data (like floats) into discrete categories. This process can introduce its own set of challenges, particularly when the bin sizes are not well-defined or when dealing with the edge cases of float values.
  • Inconsistent Grouping: If the binning process isn’t carefully managed, float values that should logically fall into the same bin might end up being separated due to floating-point inaccuracies.

1.4 Limitations with Default Bins

  • Discrete Nature: Default bins in Tableau are discrete, meaning they represent distinct, separate categories. This makes it difficult to perform calculations or comparisons that require a continuous range of values.
  • Reference Line Restrictions: One significant limitation is the inability to add reference lines to histograms created using default bins. This restricts the ability to add visual benchmarks for analysis.

1.5 The Role of Calculated Fields

  • Type Conversion Attempts: When you create calculated fields to manipulate or compare float and bin values, Tableau needs to determine how to handle the type conversion.
  • Unexpected Outcomes: If the calculation isn’t explicitly defined to account for the differences in data types and potential floating-point issues, you might get unexpected or incorrect results.

2. Practical Solutions: How to Overcome the Comparison Issue in Tableau

When faced with the challenge of comparing float and numerical bin values in Tableau, several strategies can help you overcome this obstacle and achieve accurate, insightful visualizations.

2.1 Multiplying Decimal Values

  • The Technique: Multiply the decimal values by 100, 1000, or even higher to remove the decimal places and effectively convert them into integers.
  • Why It Works: This approach eliminates the floating-point precision issues that can arise when Tableau performs calculations on decimal numbers.
  • Example: If you have values ranging from 0.01 to 1.00, multiplying by 100 transforms them into integers from 1 to 100, making binning and comparisons more accurate.

2.2 Using the FLOOR or CEILING Function

  • The Technique: Apply the FLOOR() or CEILING() function to round the float values down or up to the nearest integer, respectively.
  • Why It Works: This method converts the continuous float values into discrete integers, making them directly comparable to numerical bin values.
  • Example: If you want to group values into bins of size 5, you can use FLOOR([Your Float Value] / 5) * 5 to assign each value to the appropriate bin.

2.3 Creating Custom Calculated Fields

  • The Technique: Develop your own calculated fields to define how the binning and comparisons should be performed.
  • Why It Works: This approach gives you full control over the binning process, allowing you to account for floating-point issues and ensure consistent grouping.
  • Example: You can create a calculated field that first multiplies the float values by 100, then uses the FLOOR() function to assign them to specific bins.

2.4 Utilizing the SYS_NUMBIN() Function

  • The Technique: Employ the hidden SYS_NUMBIN() function in Tableau, which takes a measure and a bin size as arguments.
  • Why It Works: This function simplifies the binning process, automatically handling many of the underlying complexities.
  • Syntax: SYS_NUMBIN([Your Measure], [Bin Size])
  • Example: SYS_NUMBIN([Urban Population], 0.05)

2.5 Adjusting Bins for Floating-Point Issues

  • The Technique: Modify your bin calculations to account for potential floating-point inaccuracies.
  • Why It Works: This ensures that values are consistently grouped into the correct bins, even when dealing with small decimal differences.
  • Example: If you notice that values are slightly off, you can add or subtract a small constant to shift the bin boundaries.

2.6 Combining Techniques

  • The Technique: Use a combination of the above methods to create a robust and accurate binning solution.
  • Why It Works: This approach addresses multiple aspects of the comparison issue, ensuring that your visualizations are both precise and insightful.
  • Example: You might multiply the float values by 100, use the SYS_NUMBIN() function, and then adjust the bin boundaries to fine-tune the grouping.

3. Step-by-Step Guide: Creating Custom Bins with Calculated Fields

Creating custom bins with calculated fields in Tableau provides greater control over how your data is grouped and analyzed. This step-by-step guide walks you through the process, ensuring accuracy and flexibility.

3.1 Understanding the Data

  • Examine Your Data: Before you start, take a close look at your data to understand its range, distribution, and any potential issues like floating-point inaccuracies.
  • Identify Key Fields: Determine the measure you want to bin (e.g., sales, population, or any numerical value) and the desired bin size (e.g., 5, 10, 20).

3.2 Creating the Calculated Field

  1. Open Tableau: Launch Tableau Desktop and connect to your data source.
  2. Create a New Calculated Field:
    • Right-click in the Data pane and select “Create Calculated Field”.
    • Give your calculated field a descriptive name (e.g., “Sales Bin”).

3.3 Writing the Calculation

  • Basic Binning Formula: 
    FLOOR([Your Measure] / [Bin Size]) * [Bin Size]
    • Replace [Your Measure] with the actual name of your measure field.
    • Replace [Bin Size] with the desired bin size (e.g., 5, 10).
  • Handling Floating-Point Issues: 
    FLOOR(([Your Measure] * 100) / ([Bin Size] * 100)) * [Bin Size]
    • This formula multiplies the measure and bin size by 100 to eliminate decimal places, then divides back to the original scale.
  • Using Parameters for Dynamic Bin Sizes:
    • Create a parameter called “Bin Size Parameter” with the data type “Integer” or “Float”, depending on your needs.
    • Use the parameter in your calculated field: 
      FLOOR([Your Measure] / [Bin Size Parameter]) * [Bin Size Parameter]
    • This allows users to adjust the bin size dynamically.

3.4 Applying the Calculated Field

  1. Drag the Calculated Field: Drag the calculated field (e.g., “Sales Bin”) from the Data pane to the Rows or Columns shelf.
  2. Convert to Discrete: Right-click on the calculated field on the shelf and select “Discrete”. This will treat the bins as distinct categories.
  3. Create a Histogram: If you want to create a histogram, drag the original measure to the Rows or Columns shelf and change the mark type to “Bar”.

3.5 Customizing the Bins

  • Edit the Calculated Field: To modify the bin boundaries or handle edge cases, edit the calculated field and adjust the formula.
  • Add Conditional Logic: Use IF statements to create custom binning rules based on specific criteria. 
    IF [Your Measure] > 100 THEN "High"
ELSEIF [Your Measure] > 50 THEN "Medium"
ELSE "Low"
END

3.6 Validating the Results

  • Check the Distribution: Examine the distribution of values in your bins to ensure they are grouped as expected.
  • Compare to Default Bins: If possible, compare your custom bins to Tableau’s default bins to verify the accuracy of your calculations.

4. The Hidden Gem: Leveraging the SYS_NUMBIN() Function in Tableau

Tableau has a hidden function called SYS_NUMBIN() that simplifies bin creation. This function isn’t documented in Tableau’s official resources, but it can be incredibly useful for creating continuous bins and addressing floating-point issues.

4.1 What is SYS_NUMBIN()?

  • Purpose: SYS_NUMBIN() is a built-in Tableau function that bins numerical data into discrete intervals.
  • Syntax: SYS_NUMBIN([Measure], [Bin Size])
    • [Measure] is the numerical field you want to bin.
    • [Bin Size] is the width of each bin.
  • Output: The function returns an integer representing the bin number for each value.

4.2 How to Use SYS_NUMBIN()

  1. Create a Calculated Field:
    • In Tableau, right-click on the Data pane and select “Create Calculated Field”.
    • Give your calculated field a descriptive name (e.g., “Binned Sales”).
  2. Enter the Formula:
    • Use the SYS_NUMBIN() function in the calculated field: 
      SYS_NUMBIN([Sales], 10)
      • This example bins the “Sales” measure into intervals of 10.
  3. Adjust for Floating-Point Issues:
    • If you encounter floating-point issues, multiply the measure by 100 or 1000 before binning: 
      SYS_NUMBIN([Sales] * 100, 1000)
  4. Create Continuous Bins:
    • To create continuous bins, multiply the result of SYS_NUMBIN() by the bin size and add the bin size: 
      (SYS_NUMBIN([Sales], 10) * 10) + 10

4.3 Advantages of Using SYS_NUMBIN()

  • Simplicity: SYS_NUMBIN() simplifies the binning process, reducing the need for complex calculations.
  • Continuous Bins: It allows you to create continuous bins, which can be used for reference lines, bands, and distributions.
  • Flexibility: You can use SYS_NUMBIN() in conjunction with other calculations to create custom binning rules.

4.4 Example: Creating a Histogram with SYS_NUMBIN()

  1. Create the Binned Field:
    • Create a calculated field using SYS_NUMBIN(): 
      (SYS_NUMBIN([Sales], 10) * 10) + 10
  2. Drag to Columns:
    • Drag the calculated field to the Columns shelf.
    • Right-click on the field and select “Discrete”.
  3. Drag the Measure to Rows:
    • Drag the original measure (e.g., “Sales”) to the Rows shelf.
    • Change the aggregation to “Count Distinct”.
  4. Create the Histogram:
    • Tableau will automatically create a histogram showing the distribution of sales values in each bin.

4.5 Limitations of SYS_NUMBIN()

  • Undocumented: As an undocumented function, SYS_NUMBIN() may not be supported in future versions of Tableau.
  • Integer Bins: The function creates integer bins, so you may need to adjust the results for continuous values.

5. Advanced Techniques: Creating Variable Width Bins

Creating variable width bins in Tableau allows you to tailor your data analysis to specific distributions and patterns. This technique is particularly useful when dealing with skewed data or when you want to highlight certain areas of interest.

5.1 Understanding Variable Width Bins

  • Definition: Variable width bins are bins that have different sizes, allowing you to group data more effectively based on its distribution.
  • Use Cases:
    • Analyzing skewed data distributions
    • Highlighting specific ranges of values
    • Creating custom categories based on business rules

5.2 Creating a Parameter for Bin Widths

  1. Create a Parameter:
    • In Tableau, right-click in the Data pane and select “Create Parameter”.
    • Name the parameter (e.g., “Bin Widths”).
    • Set the data type to “String”.
    • In the “Allowable values” section, select “List”.
    • Enter the bin widths as a comma-separated list (e.g., “10, 20, 30”).
  2. Create a Calculated Field:
    • Create a calculated field to split the parameter string into individual bin widths: 
      SPLIT([Bin Widths], ",", 1) // For the first bin width
SPLIT([Bin Widths], ",", 2) // For the second bin width
SPLIT([Bin Widths], ",", 3) // For the third bin width

5.3 Creating the Variable Width Bins

  1. Use IF Statements:
    • Create a calculated field using IF statements to define the bin ranges based on the bin widths: 
      IF [Your Measure] <= INT([Bin Width 1]) THEN "Bin 1"
ELSEIF [Your Measure] <= INT([Bin Width 1]) + INT([Bin Width 2]) THEN "Bin 2"
ELSE "Bin 3"
END
    • Replace [Your Measure] with the actual name of your measure field.
    • Replace [Bin Width 1], [Bin Width 2], and [Bin Width 3] with the calculated fields that extract the bin widths from the parameter.
  2. Adjust for Floating-Point Issues:
    • If you encounter floating-point issues, multiply the measure by 100 or 1000 before comparing: 
      IF [Your Measure] * 100 <= INT([Bin Width 1]) * 100 THEN "Bin 1"
ELSEIF [Your Measure] * 100 <= (INT([Bin Width 1]) + INT([Bin Width 2])) * 100 THEN "Bin 2"
ELSE "Bin 3"
END

5.4 Creating a Histogram with Variable Width Bins

  1. Drag the Calculated Field:
    • Drag the calculated field to the Columns shelf.
    • Right-click on the field and select “Discrete”.
  2. Drag the Measure to Rows:
    • Drag the original measure (e.g., “Sales”) to the Rows shelf.
    • Change the aggregation to “Count Distinct”.
  3. Create the Histogram:
    • Tableau will automatically create a histogram showing the distribution of sales values in each bin.

5.5 Example: Analyzing Health Expenditure with Variable Width Bins

  • Scenario: You want to analyze health expenditure per capita, but the data is skewed with a long tail.
  • Solution: Create variable width bins to highlight the distribution:
    • Bin 1: $0 – $100 (width: $100)
    • Bin 2: $100 – $500 (width: $400)
    • Bin 3: $500+ (width: variable)
  • Calculated Field: 
    IF [Health Exp/Capita] <= 100 THEN "Bin 1: $0 - $100"
ELSEIF [Health Exp/Capita] <= 500 THEN "Bin 2: $100 - $500"
ELSE "Bin 3: $500+"
END

6. Resolving Common Errors: Troubleshooting Binning Issues in Tableau

Binning in Tableau can sometimes lead to errors or unexpected results. Troubleshooting these issues effectively ensures accurate and insightful data analysis.

6.1 Incorrect Bin Sizes

  • Problem: The bins are not the size you expect, leading to inaccurate grouping of data.
  • Solution:
    • Double-check the bin size parameter or value in your calculated field.
    • Ensure that the bin size is appropriate for the range and distribution of your data.
    • Use parameters to allow users to dynamically adjust the bin size.

6.2 Floating-Point Precision Errors

  • Problem: Values are not being binned correctly due to floating-point precision issues.
  • Solution:
    • Multiply the measure and bin size by 100 or 1000 to eliminate decimal places.
    • Use the ROUND() function to round values to a specific number of decimal places.
    • Use the INT() function to convert values to integers before binning.

6.3 Null Values

  • Problem: Null values are causing issues with the binning process.
  • Solution:
    • Use the IFNULL() or ZN() function to replace null values with a default value (e.g., 0).
    • Filter out null values from your data before binning.

6.4 Data Type Mismatch

  • Problem: The data type of the measure and bin size are not compatible.
  • Solution:
    • Ensure that the measure and bin size have the same data type (e.g., both are integers or both are floats).
    • Use the INT() or FLOAT() function to convert the data types as needed.

6.5 Incorrect Aggregation

  • Problem: The aggregation of the measure is not correct, leading to inaccurate binning.
  • Solution:
    • Check the aggregation of the measure on the Rows or Columns shelf.
    • Use the appropriate aggregation function (e.g., SUM(), AVG(), COUNT()) for your data.

6.6 Using Table Calculations

  • Problem: Table calculations are interfering with the binning process.
  • Solution:
    • Ensure that the table calculation is computed correctly for the dimensions in your view.
    • Use the FIXED, INCLUDE, or EXCLUDE LOD expressions to control the scope of the table calculation.

6.7 Complex Calculations

  • Problem: Complex calculations are causing errors or unexpected results.
  • Solution:
    • Break down the calculation into smaller, more manageable steps.
    • Use comments to document your calculations and make them easier to understand.
    • Test each step of the calculation to identify the source of the error.

7. Best Practices: Optimizing Bin Creation for Performance and Accuracy

Optimizing bin creation in Tableau is crucial for ensuring both performance and accuracy in your data analysis. By following these best practices, you can create efficient and reliable binning solutions.

7.1 Choose the Right Bin Size

  • Consider Data Distribution: Analyze the distribution of your data to determine an appropriate bin size.
  • Avoid Too Few or Too Many Bins: Too few bins can obscure important patterns, while too many can make it difficult to identify trends.
  • Use Rules of Thumb: Consider using rules of thumb like Sturges’ rule or Freedman-Diaconis rule to determine an optimal bin size.

7.2 Use Parameters for Dynamic Bin Sizes

  • Flexibility: Allow users to dynamically adjust the bin size using parameters.
  • Experimentation: Enable users to experiment with different bin sizes to find the most insightful view of the data.
  • User Control: Give users control over the granularity of the analysis.

7.3 Optimize Calculations

  • Simplify Formulas: Use simple and efficient formulas for binning calculations.
  • Avoid Complex Logic: Minimize the use of complex IF statements or nested calculations.
  • Use Built-In Functions: Leverage Tableau’s built-in functions like FLOOR(), CEILING(), and SYS_NUMBIN() for optimized performance.

7.4 Test and Validate

  • Check Distribution: Verify that the bins are distributing the data as expected.
  • Compare to Default Bins: Compare your custom bins to Tableau’s default bins to ensure accuracy.
  • Use Sample Data: Test your binning solution with a sample of your data before applying it to the entire dataset.

7.5 Document Your Work

  • Add Comments: Document your calculations with comments to explain the logic and purpose of each step.
  • Use Descriptive Names: Give your calculated fields and parameters descriptive names that clearly indicate their function.
  • Create Documentation: Create a separate document or knowledge base article to describe your binning solution and its intended use.

7.6 Consider Performance

  • Minimize Data Processing: Reduce the amount of data processing required for binning by pre-processing data in a database or data preparation tool.
  • Use Extracts: Use Tableau extracts to improve performance when working with large datasets.
  • Optimize Data Sources: Optimize your data sources by using efficient data types, indexing, and partitioning.

8. Real-World Examples: Applying Binning Techniques to Various Datasets

Applying binning techniques to real-world datasets can provide valuable insights and enhance your data analysis. Here are several examples illustrating how to use binning in different scenarios.

8.1 Analyzing Customer Age

  • Dataset: Customer database with age information.
  • Objective: Understand the distribution of customers by age group.
  • Binning Technique:
    • Create bins of size 10 (e.g., 20-29, 30-39, 40-49).
    • Use the FLOOR() function to assign customers to age groups: 
      FLOOR([Age] / 10) * 10
  • Visualization:
    • Create a histogram showing the number of customers in each age group.
    • Analyze the distribution to identify key customer segments.

8.2 Analyzing Sales Revenue

  • Dataset: Sales transaction data with revenue amounts.
  • Objective: Identify the distribution of sales transactions by revenue range.
  • Binning Technique:
    • Create variable width bins to highlight different revenue ranges:
      • Bin 1: $0 – $100
      • Bin 2: $100 – $500
      • Bin 3: $500+
    • Use IF statements to assign transactions to revenue ranges: 
      IF [Revenue] <= 100 THEN "Bin 1: $0 - $100"
ELSEIF [Revenue] <= 500 THEN "Bin 2: $100 - $500"
ELSE "Bin 3: $500+"
END
  • Visualization:
    • Create a bar chart showing the number of transactions in each revenue range.
    • Analyze the distribution to identify key revenue segments.

8.3 Analyzing Website Traffic

  • Dataset: Website traffic data with session duration.
  • Objective: Understand the distribution of website sessions by duration.
  • Binning Technique:
    • Create bins of size 30 seconds (e.g., 0-29 seconds, 30-59 seconds, 60-89 seconds).
    • Use the FLOOR() function to assign sessions to duration groups: 
      FLOOR([Session Duration] / 30) * 30
  • Visualization:
    • Create a histogram showing the number of sessions in each duration group.
    • Analyze the distribution to identify key engagement patterns.

8.4 Analyzing Product Prices

  • Dataset: Product catalog with prices.
  • Objective: Identify the distribution of product prices.
  • Binning Technique:
    • Use the SYS_NUMBIN() function to create continuous bins: 
      (SYS_NUMBIN([Price], 10) * 10) + 10
  • Visualization:
    • Create a histogram showing the number of products in each price range.
    • Analyze the distribution to identify key price segments.

9. The Future of Binning: Emerging Trends and Technologies

The field of data analysis is constantly evolving, and binning techniques are no exception. Several emerging trends and technologies are shaping the future of binning, offering new possibilities for data exploration and insight generation.

9.1 Machine Learning-Based Binning

  • Concept: Using machine learning algorithms to automatically determine optimal bin sizes and boundaries.
  • Benefits:
    • Adaptive binning based on data characteristics
    • Improved accuracy and insight generation
    • Reduced manual effort
  • Techniques:
    • Clustering algorithms (e.g., K-means)
    • Decision tree algorithms
    • Neural networks

9.2 Adaptive Binning

  • Concept: Dynamically adjusting bin sizes based on the distribution of data.
  • Benefits:
    • Highlighting important patterns in skewed data
    • Improving the visualization of complex datasets
    • Tailoring binning to specific analytical needs
  • Techniques:
    • Variable width bins
    • Quantile-based binning
    • Density-based binning

9.3 Interactive Binning

  • Concept: Allowing users to interactively adjust bin sizes and boundaries in real-time.
  • Benefits:
    • Enhanced data exploration and discovery
    • Improved user engagement
    • Greater flexibility in data analysis
  • Tools:
    • Tableau parameters and actions
    • Custom JavaScript libraries
    • Interactive data visualization platforms

9.4 Automated Binning Recommendations

  • Concept: Providing automated recommendations for bin sizes and techniques based on data characteristics.
  • Benefits:
    • Reduced manual effort
    • Improved efficiency
    • Enhanced accuracy
  • Tools:
    • Data analysis platforms with built-in recommendation engines
    • Machine learning-based binning tools
    • Automated data preparation tools

9.5 Integration with Data Visualization Tools

  • Concept: Seamless integration of advanced binning techniques with data visualization tools like Tableau.
  • Benefits:
    • Improved data storytelling
    • Enhanced communication of insights
    • Greater ease of use
  • Tools:
    • Tableau extensions and APIs
    • Custom data visualization libraries
    • Integration with data science platforms

10. Frequently Asked Questions (FAQ) About Comparing Float and Numerical Bin Values in Tableau

1. Why can’t I directly compare float and numerical bin values in Tableau?

Float values are continuous and have decimal points, while numerical bin values are discrete categories. Tableau treats them differently, leading to comparison issues due to data type mismatch and floating-point arithmetic limitations.

2. How can I resolve floating-point precision errors when binning data in Tableau?

Multiply the float values by 100, 1000, or even higher to remove the decimal places, effectively converting them into integers and eliminating precision issues.

3. What is the SYS_NUMBIN() function in Tableau, and how can I use it?

SYS_NUMBIN() is a hidden Tableau function that simplifies bin creation. Use the syntax SYS_NUMBIN([Measure], [Bin Size]) to bin numerical data into discrete intervals. It’s useful for creating continuous bins.

4. How can I create variable width bins in Tableau?

Create a parameter for bin widths, use IF statements to define bin ranges based on these widths, and adjust for floating-point issues by multiplying the measure by 100 or 1000 before comparing.

5. What are some best practices for optimizing bin creation in Tableau?

Choose the right bin size, use parameters for dynamic bin sizes, optimize calculations, test and validate your results, document your work, and consider performance.

6. How can machine learning enhance binning techniques in the future?

Machine learning algorithms can automatically determine optimal bin sizes and boundaries, adapt binning based on data characteristics, and improve accuracy and insight generation.

7. What is adaptive binning, and how is it useful?

Adaptive binning dynamically adjusts bin sizes based on the distribution of data, highlighting important patterns in skewed data, improving the visualization of complex datasets, and tailoring binning to specific analytical needs.

8. How can I troubleshoot binning issues in Tableau?

Check for incorrect bin sizes, resolve floating-point precision errors, handle null values, address data type mismatches, correct aggregation, and ensure proper use of table calculations.

9. Can you provide a real-world example of applying binning techniques to a dataset?

For analyzing customer age, create bins of size 10 (e.g., 20-29, 30-39, 40-49) and use the FLOOR() function to assign customers to age groups. Visualize this with a histogram.

10. How can I create custom calculated fields for binning in Tableau?

Use the FLOOR() function with the formula FLOOR([Your Measure] / [Bin Size]) * [Bin Size]. Adjust for floating-point issues by multiplying the measure and bin size by 100 before applying the formula.

Overcoming the challenges of comparing float and numerical bin values in Tableau unlocks powerful insights. At COMPARE.EDU.VN, we strive to provide comprehensive guides and solutions to empower your data analysis journey.

Ready to take your data analysis skills to the next level? Visit COMPARE.EDU.VN for more in-depth comparisons, expert tips, and resources to help you make informed decisions. Contact us at 333 Comparison Plaza, Choice City, CA 90210, United States, Whatsapp: +1 (626) 555-9090 or explore our website at compare.edu.vn today.

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