Iteration over the rows of a Pandas DataFrame as dictionaries

Defining a separate function for this will be inefficient, as you are applying row-wise calculations. More efficient would be to calculate a new series, then iterate the series:

df = pd.DataFrame({'length':[1,2,3,'test'], 'width':[10, 20, 30,'hello']})

df2 = df.iloc[:].apply(pd.to_numeric, errors='coerce')

error_str = 'Error : length and width should be int or float'
print(*(df2['length'] * df2['width']).fillna(error_str), sep='\n')

10.0
40.0
90.0
Error : length and width should be int or float

You can try:

for k, row in df.iterrows():
    myfunc(**row)

Here k is the dataframe index and row is a dict, so you can access any column with: row["my_column_name"]

one clean option is this one:

for row_dict in df.to_dict(orient="records"):
    print(row_dict['column_name'])