Data aggregation is a term known from relational databases. In a database query, we can group data by the value in a column or columns. We can then perform various operations on each of these groups. The pandas DataFrame has similar capabilities. We will generate data held in a Python dict and then use this data to create a pandas DataFrame. We will then practice the pandas aggregation features:
Seed the NumPy random generator to make sure that the generated data will not differ between repeated program runs. The data will have four columns:
Weather(a string)Food(also a string)Price(a random float)Number(a random integer between one and nine)
The use case is that we have the results for some sort of a consumer-purchase research, combined with weather and market pricing, where we calculate the average of prices and keep a track of the sample size and parameters:
import pandas as pd from numpy.random import seed from numpy.random import rand from...