Applying a function to dataframe with index and columns names as arguments

Question

Consider an empty dataframe (df) with countries for index and status for columns.

df = pd.DataFrame({'Country': ['Albania', 'France', 'Mexico', 'Russia'], 
                               'On': [0, 0, 0, 0],
                               'Off': [0, 0, 0, 0]}).set_index('Country')

How do I apply a function to each cell in the dataframe with the index name and column name as arguments to the function? I would like to replace row(x) and column(x) with syntax that would make this work.

df = df.apply(lambda x : function(row(x), column(x)))

Thank you

Answer 1

You can do something like that

def fct(row):
    # You can get index with row.name
    print(row.name)
    
    # You can set separately row["On"] and row["Off"]
    print(row["On"])
    print(row["Off"])
    row["On"] = row["On"] + 1
    row["Off"] = row["Off"] + 2
    return row
    
    
dff = df.apply(fct, axis=1)

Edit: If you definitely need to use a function dependant of the row and column, would this solutions work for you?

def f2(row, col):
    # ...

def fct(row):
    # You can get index with row.name
    row_name = row.name
    for column_name in row.index.values:
        row[v] = f2(row_name, column_name)
    return row
    
    
dff = df.apply(fct, axis=1)