Pandas Grouping and Aggregating
In this article, we are going to see grouping and aggregating using pandas. Grouping and aggregating will help to achieve data analysis easily using various functions. These methods will help us to the group and summarize our data and make complex analysis comparatively easy.
Aggregation in Pandas
Aggregation in pandas provides various functions that perform a mathematical or logical operation on our dataset and returns a summary of that function. Aggregation can be used to get a summary of columns in our dataset like getting sum, minimum, maximum, etc. from a particular column of our dataset. The function used for aggregation is agg(), the parameter is the function we want to perform.
Some functions used in the aggregation are:
Function Description:
- sum() :Compute sum of column values
- min() :Compute min of column values
- max() :Compute max of column values
- mean() :Compute mean of column
- size() :Compute column sizes
- describe() :Generates descriptive statistics
- first() :Compute first of group values
- last() :Compute last of group values
- count() :Compute count of column values
- std() :Standard deviation of column
- var() :Compute variance of column
- sem() :Standard error of the mean of column
👇EXAMPLE PROGRAMS 👇
👉Example programs on Pandas Grouping and Aggregating 👈
