pandas cut

Topics

• pandas

pd.cut allows us to bin values into discrete intervals.

Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins.

Example:

The grocery contains the prices of groceries. Based on the prices, we need to divide the groceries into three categories and label them as cheap, mid-priced, or expensive. Then calculate the average price for each category using the groupby function.

import pandas as pd
 
grocery = pd.read_csv("grocery.csv")
 
 
def find_avg_price():
    try:
        grocery["price_category"] = pd.cut(
            grocery["price"], bins=3, labels=["cheap", "mid-priced", "expensive"]
        )
        avg_prices = grocery.groupby("price_category").agg(
            avg_price=("price", "mean"))
 
        return list(avg_prices.index), list(avg_prices["avg_price"].round(2))
    except:
        pass

                avg_price
price_category
cheap            3.362060
mid-priced       6.391228
expensive       11.400000

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