python pandas aggregate functions

pandas is a powerful library for data manipulation and analysis in python. one of its key features is the ability to perform aggregate functions on data, allowing you to summarize and analyze datasets efficiently. in this tutorial, we will explore python pandas aggregate functions with code examples.
aggregate functions in pandas are used to perform operations on data, typically to obtain summary statistics or derive new insights. these functions are often applied to groups of data, allowing for group-wise analysis.
the sum() function is used to calculate the sum of values in a dataframe or series.
the mean() function calculates the average of values in a dataframe or series.
the min() function returns the minimum value in a dataframe or series.
the max() function returns the maximum value in a dataframe or series.
the count() function calculates the number of non-null values in a dataframe or series.
the agg() function allows you to apply multiple aggregate functions simultaneously.
the groupby() function in pandas is often used in conjunction with aggregate functions to perform group-wise operations. here's a simple example:
you can also define custom aggregate functions using the agg() method. here's an example:
pandas aggregate functions are powerful tools for summarizing and analyzing data in python. whether you need to calculate basic statistics or perform more complex group-wise operations, pandas provides a flexible and efficient framework for data manipulation.
this tutorial covers some common aggregate functions, the groupby() operation, and how to create custom aggregate functions. experiment with these concepts to gain a deeper understanding of how to leverage pandas for your data analysis tasks.
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