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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
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Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
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Understanding SQL Not Equal Operator

Ayushi Trivedi
Ayushi Trivedi Last Updated : 10 Jun, 2024
5 min read

Introduction

In SQL, comparison operators are crucial for querying databases. They help compare values and filter data based on conditions. The SQL Not Equal operator is one of the most used. It excludes specific data from query results, making it vital for database management. This operator refines data retrieval, ensuring you get relevant information. Whether dealing with numbers, text, or dates, the Not Equal operator is indispensable.

Overview

  • Understand the syntax and usage of the SQL Not Equal (<>) operator.
  • Learn how to effectively filter data using the SQL Not Equal operator.
  • Explore scenarios where the SQL Not Equal operator is advantageous in database queries.
  • Understand the impact of NULL values on comparisons with the SQL Not Equal operator.
  • Discover best practices for optimizing performance when using the SQL Not Equal operator in SQL queries.
Understanding SQL Not Equal Operator

Table of contents

SQL Not Equal Operator Syntax

The SQL not equal operator (<>) is used to compare values and retrieve records where a specified column is not equal to a particular value. It is commonly used in SQL queries to filter data based on inequality conditions.

Standard Syntax: <>

The standard syntax for the SQL Not Equal operator is <>. This follows the ISO standard. It is widely recommended for consistency and compatibility across different SQL databases.

Example:

SELECT * FROM customers WHERE age <> 30;

This query selects all customers whose age is not 30.

Alternate Syntax: !=

An alternative syntax for the Not Equal operator is !=. While this is also common, it doesn’t follow the ISO standard. However, it functions the same way as <>.

Example:

SELECT * FROM customers WHERE age != 30;

This query also selects all customers whose age is not 30.

Usage Scenarios

Let us no explore some usage scenarios of SQL Not Equal.

Filtering Data with SQL Not Equal

The Not Equal operator is perfect for filtering data. You can use it to exclude specific values from your query results.

Example:

SELECT * FROM employees WHERE department <> 'HR';

This query retrieves all employees who are not in the HR department.

Excluding Specific Records

You can use the Not Equal operator to exclude specific records. This is useful when you need to remove certain data from your results.

Example:

SELECT * FROM orders WHERE order_status <> 'Cancelled';

This query returns all orders except those that are canceled.

Combining with Other Conditions

The Not Equal operator works well with other conditions. You can combine it with other operators to refine your queries further.

Example:

SELECT * FROM products WHERE price <> 100 AND stock > 50;

This query selects all products that do not cost 100 and have more than 50 in stock.

Performance Considerations

We will now look into some performance considerations of SQL Not Equal operator.

Comparison with Equality Operator

The Not Equal operator performs differently compared to the Equality operator. While both are useful, they impact performance in various ways.

Impact on Query Performance

Using the Not Equal operator can sometimes slow down queries. This is because it requires the database engine to check each record to see if it meets the exclusion criteria.

Example:

SELECT * FROM sales WHERE region <> 'East';

This query may take longer than an equality comparison because it must evaluate each record.

Best Practices for Optimal Performance

To optimize performance, consider the following best practices:

  • Use Indexes: Ensure the columns used with the Not Equal operator are indexed.
  • Combine Conditions Wisely: Combine Not Equal with other conditions to reduce the number of records evaluated.
  • Limit Results: Use the LIMIT clause to restrict the number of returned records if possible.

Example:

SELECT * FROM transactions WHERE status <> 'Failed' AND amount > 50 LIMIT 100;

This query is optimized by limiting the results and combining conditions.

SQL Not Equal Operator and NULL Values

The not equal operator in SQL compares values where a column is not equal to a specific value, but handling NULL values is crucial as comparisons will not return true.

Handling NULL Values in Comparisons

The Not Equal operator handles NULL values uniquely. Comparisons involving NULL values do not return true or false but rather NULL.

Example:

SELECT * FROM employees WHERE department <> NULL;

This query will not return any results because NULL comparisons do not work as expected.

Impact on Query Results

When dealing with NULL values, it’s crucial to handle them explicitly. Use the IS NULL or IS NOT NULL operators to manage NULL comparisons.

Example:

SELECT * FROM employees WHERE department IS NOT NULL AND department <> 'Sales';

This query retrieves all employees with a non-null department that is not ‘Sales’.

Real-World Use Cases

The SQL Not Equal operator is widely used in various real-world applications. For instance, in e-commerce platforms, it helps exclude certain product categories from sales reports. It’s also useful in customer relationship management (CRM) systems to filter out inactive customers from marketing campaigns. Additionally, it can help in finance applications to exclude specific transaction types when generating financial statements.

In healthcare databases, the Not Equal operator can exclude certain patient records, such as those not requiring follow-up. In education management systems, it can help filter out students who are not enrolled in specific courses.

Common Scenarios in Data Analysis

In data analysis, the SQL Not Equal operator is crucial for refining datasets. Analysts often use it to exclude outliers or irrelevant data points from their analyses. For example, when analyzing sales data, excluding orders from test markets ensures the accuracy of results.

In survey analysis, it helps exclude incomplete or invalid responses, leading to cleaner data. In social media analysis, it can filter out posts or comments from bots or spam accounts, providing more accurate insights.

The Not Equal operator also helps in comparing performance metrics by excluding specific time periods or data sources. This leads to more focused and relevant analyses.

Conclusion

The SQL Not Equal operator is a crucial tool for filtering and refining data in SQL queries. It allows users to exclude specific values, leading to more precise and relevant results. Whether used in e-commerce, healthcare, or data analysis, mastering this operator enhances data management and analysis capabilities. By understanding its syntax, usage scenarios, and performance considerations, you can efficiently handle complex data conditions and make informed decisions.

Frequent Asked Questions

Q1. What is the SQL Not Equal operator?

A. The SQL Not Equal operator (<>) is used to compare values and retrieve records where a specified column is not equal to a particular value.

Q2. What are some best practices for optimizing performance when using the Not Equal operator?

A. To optimize performance, consider indexing columns used with the Not Equal operator, combining conditions wisely, and limiting the number of returned records using the LIMIT clause.

Q3. In which real-world applications is the SQL Not Equal operator commonly used?

A. The SQL Not Equal operator is widely used in e-commerce for excluding specific product categories, in CRM systems for filtering out inactive customers, and in data analysis for refining datasets by excluding outliers.

beginnersSQLSQL Not Operator
Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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Mastering the Not Equal Operator in SQL: Syntax, Usage, and Practical Examples
Mastering the Not Equal Operator in SQL: Syntax, Usage, and Practical Examples
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