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

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

This video shows how to apply functions to columns and rows pandas data frames using .apply(). The .apply() function operates on pandas series or data frames and applies a function to each element of a single series (such as each record in a column of a data frame) or to each row or column of a data frame. .apply() can be a useful way to generate aggregate statistics for each column or to generate new columns.

This video shows how to apply functions to columns and rows pandas data frames using .apply(). The .apply() function operates on pandas series or data frames and applies a function to each element of a single series (such as each record in a column of a data frame) or to each row or column of a data frame. .apply() can be a useful way to generate aggregate statistics for each column or to generate new 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, 300], "uniform color": ["orange", "blue", "black", "orange", "purple", "green", "orange", "orange","orange"], "species": ["saiyan","saiyan","saiyan","half saiyan", "namak","human","human","human","human"]}, index = ["Goku","Vegeta", "Nappa","Gohan", "Piccolo","Tien","Yamcha", "Krillin","Roshi"])

data

Use .apply() to apply a function to a Series (single column)

def my_function(x, h, l): if x > h: return("high") if x > l: return("med") return ("low")

data["power_level"].apply(my_function, args = [10000, 2000])

Apply a function to each column with axis = 0 Can be used to create new rows/summary rows

def mode(x): return x.mode()

data.apply(mode, axis = 0)

Apply a function to each row with axis = 1 Can be used to create new columns/summary columns

def max_str_len(x): return max([len(str(v)) for v in x])

data.apply(max_str_len, axis = 1)

Apply a function to each row, referencing column names:

def make_char_string(x): return(f"{x.species} with power level: {x.power_level}")

data.apply(make_char_string, axis = 1 )

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