# Applying a Function to Each Element in a Column
import pandas as pd

data = {
    'temperature_celsius': [0, 20, 30, 40]
}
df = pd.DataFrame(data)
#######################################
def celsius_to_fahrenheit(celsius):
    return (celsius * 9/5) + 32
#######################################
df['temperature_fahrenheit'] = df['temperature_celsius'].apply(celsius_to_fahrenheit)
print(df)

# Applying a Lambda Function
import pandas as pd

data = {
    'names': ['Alice', 'Bob', 'Charlie', 'David']
}
df = pd.DataFrame(data)
df['name_length'] = df['names'].apply(lambda x: len(x))
print(df)

# Applying a Function to Rows
import pandas as pd

data = {
    'math': [90, 80, 70, 60],
    'science': [85, 75, 65, 55],
    'english': [88, 78, 68, 58]
}
df = pd.DataFrame(data)
#######################################
def total_score(row):
    return row['math'] + row['science'] + row['english']
#######################################
df['total_score'] = df.apply(total_score, axis=1)
print(df)

# Using apply with Additional Arguments
import pandas as pd

data = {
    'values': [1, 2, 3, 4]
}
df = pd.DataFrame(data)
######################################
def add_constant(x, constant):
    return x + constant
######################################
df['values_plus_10'] = df['values'].apply(add_constant, constant=10)
print(df)