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PythonML
Aggregation Functions in Data Manipulation and Database Queries
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -
Python Automation and Machine Learning for ICs                                                           http://www.globalsino.com/ICs/        


Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

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Aggregation functions in data manipulation and database queries are used to perform a calculation on a set of values to return a single value. They are commonly used in SQL for working with databases but also appear in various data processing libraries and frameworks. Some of the key aggregation functions often used are:

  • SUM(): Calculates the total sum of a numeric column.
  • AVG(): Computes the average value of a numeric column.
  • COUNT(): Returns the count of entries in a column. It can be used to count all rows or only unique entries (often specified as COUNT(*) for all rows or COUNT(DISTINCT column_name) for unique values).
  • MAX(): Finds the maximum value in a column.
  • MIN(): Finds the minimum value in a column.
  • GROUP_CONCAT(): Concatenates values from multiple rows into a single string, often separated by a delimiter. It's useful for aggregating data into a compact form for display.
  • VAR_POP() and VAR_SAMP(): Calculate the population variance and sample variance, respectively.
  • STDDEV_POP() and STDDEV_SAMP(): Compute the population standard deviation and sample standard deviation, respectively.
  • COUNT(DISTINCT): Counts distinct values in a column, useful for counting unique entries.
  • countDistinct(): It aggregates data by counting unique entries, which is very useful for understanding the diversity or variety within a dataset. It is more commonly found in programming languages or specific data manipulation tools and libraries, such as Apache Spark or certain business intelligence tools. It's often referred to in a slightly different form in SQL and some other data manipulation languages. In many SQL dialects, the function to count distinct values is typically expressed as COUNT(DISTINCT column_name). This is the standard syntax used to count the number of unique non-null values in a column.

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