How To Use .map() In Pandas (Python)

How To Use .map() In Pandas (Python)

This video shows how to map functions to columns of pandas data frames using .map(). The .map() function operates on pandas series and applies a function or dictionary mapping to each element of the series. Since the columns of data frames are series, it provides a convenient way of generating new columns by applying functions or correspondences elementwise on existing columns.

This video shows how to map functions to columns of pandas data frames using .map(). The .map() function operates on pandas series and applies a function or dictionary mapping to each element of the series. Since the columns of data frames are series, it provides a convenient way of generating new columns by applying functions or correspondences elementwise on existing columns.

Code used in this Python Code Clip:

import pandas as pd

data = pd.DataFrame({"power_level": [12000, 16000, 4000, 1500, 3000, 2000, 1600, 2000], "uniform color": ["orange", "blue", "black", "orange", "purple", "green", "orange", "orange"], "species": ["saiyan","saiyan","saiyan","half saiyan", "namak","human","human","human"]}, index = ["Goku","Vegeta", "Nappa","Gohan", "Piccolo","Tien","Yamcha", "Krillin"])

data

Use .map() to apply a function to a pandas Series Data Frame columns are Series

def my_function(x): if x > 10000: return("high") if x > 2000: return("med") return ("low")

data["power_level"].map(my_function)

To map Series values based on key: value correspondence *Pass a mapping dictionary to .map() *

correspondence = {"saiyan": "alien", "namak":"alien", "human":"earthling", "half saiyan": "earthling"}

data["species"].map(correspondence)

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