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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