Grouping Result Sets

-

 Eliminating Duplicates: Sometimes, a SELECT statement result set may contain duplicate values, which may not be desirable. To eliminate duplicates from the result set, we use the DISTINCT keyword in the SELECT statement. For example, SELECT DISTINCT country FROM author ORDER BY 1 retrieves a list of unique countries from the author table.


Grouping Results: We can group the result set based on matching values in one or more columns using the GROUP BY clause. This allows us to aggregate data and perform calculations on groups of rows. For example, SELECT country, COUNT(*) FROM author GROUP BY country groups the result set by country and calculates the count of authors from each country.


Renaming Derived Columns: When using functions like COUNT() to calculate values in the result set, the column names may not be directly available. In such cases, we can assign a column alias using the AS keyword to provide a meaningful name for the derived column. For example, SELECT country, COUNT(*) AS author_count FROM author GROUP BY country.


Restricting Results with HAVING: To further restrict the result set based on aggregated values, such as counts, we use the HAVING clause. Unlike the WHERE clause, which applies to individual rows, the HAVING clause filters groups of rows defined by the GROUP BY clause. For example, HAVING COUNT(country) > 4 filters the result set to include only those countries with more than four authors.


By applying these techniques, we can manipulate and refine the result set to extract the desired information effectively from the database.






Comments

Post a Comment

Popular posts from this blog

Lila's Journey to Becoming a Data Scientist: Her Working Approach on the First Task

-
  Education and Skill Acquisition: Lila, with an economics background, decides to transition to data science. Enrolls in the IBM Data Science Professional Certificate for comprehensive training. Gains proficiency in statistics, machine learning, and programming languages. Building a Strong Foundation: Develops a deep understanding of data manipulation and visualization using Python libraries. Visualization for Storytelling: Learns to create informative visualizations, aiding in effective communication of findings. Hands-On Experience: Engages in Kaggle competitions and personal data projects for practical experience. Establishes a GitHub profile to showcase her projects and skills. Data Wrangling and Preprocessing: Focuses on data cleaning, preprocessing, and handling missing data and outliers. Communication and Storytelling: Hones data storytelling skills using tools like Matplotlib and Plotly. Networking and Collaboration: Actively participates in data science communities, attend...
Read more

Notes on Hiring for Data Science Teams

-
  Unicorn Myth in Hiring: Companies often seek individuals with a myriad of skills for data science roles. Ideal candidates possess domain-specific knowledge, excel in data analysis, and have strong presentation and storytelling skills. However, finding such a "unicorn" is rare, and the focus should shift to other essential qualities. Resonance with Company DNA: The key is to identify applicants who resonate with the company's values and business domain. While analytics skills can be taught, passion for the business is intrinsic and contributes significantly to productivity. Curiosity Matters: Curiosity is a top priority in hiring for a data science team. Candidates should demonstrate a broad curiosity, showing interest in various aspects of their surroundings beyond just data science. Sense of Humor: A lighthearted approach is crucial, as being too serious may hinder collaboration and creativity. A sense of humor aids in navigating challenges and fosters a positive work ...
Read more

switch functions

-
  Nested IF Function : The  nested IF  function allows you to perform tests with multiple conditions and return different values based on the results of those tests. It is called “nested” because one IF function is embedded inside another IF function. Syntax of a generic nested IF formula: =IF(condition1, result1, IF(condition2, result2, IF(condition3, result3, result4) ) ) Each subsequent IF function is embedded into the  value_if_false  argument of the previous function. SWITCH Function : The  SWITCH  function is a more compact alternative to nested IF statements. It compares an expression against a list of values and returns the result according to the first matching value. Syntax of the SWITCH function: =SWITCH(expression, value1, result1, [default or value2, result2], ... [default or valueN, resultN]) It has four arguments: expression : The value to compare against the list of values. valueN : A value com...
Read more