Error in dividing one column by another column in the same dataframe

Question

I have three columns like follows: Sales Population Income Price 14 48777.0 285.0 nan 17 12550.0 1.0 nan 24 15664.0 14.0 14 9 23796.0 24.0 0 20 40149.0 63.0 nan 75 39489.0 32.0 nan I need to convert everything to per capita terms, that is for each element in sales, I need to divide it by the corresponding population. For example, for sales, I want to divide it by population to get per capita sales: Per capita sales 14/48777 17/12550 ... The command I use is: data['Per Capita Sales'] = data['Sales'] / data['Population'] But I got the following error: TypeError: ufunc 'true_divide' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' Does anyone know how to fix this? I have searched online how to divide one column by another column element-wise, but did not find a safe way to do it... Since the columns with values "nan" and "0" are in the numerator, I don't know why there could be an error... Thank you very much for your help! JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

Most probably, at least one of the columns involved is of string type. Instead of converting them only for the division, I propose to convert them to numbers in advance, for all other operations: data.Sales = pd.to_numeric(data.Sales) data.Population = pd.to_numeric(data.Population) As I see, all rows contain valid data, so the above (shortened) form should be enough. But if your data were "dirty" (some entries not convertible to number), you should add errors='coerce' parameter. Then your original command to divide should work. If you are not sure about types of columns in your DataFrame, execute: data.info() Or maybe you load your DataFrame from a file, e.g. calling read_csv? In this case include dtype parameter (a dictionary in the form {"column name" : type}) to force the required types of particular columns.