Question

I have a DataFrame with a few columns. One columns contains a symbol for which currency is being used, for instance a euro or a dollar sign. Another column contains a budget value. So for instance in one row it could mean a budget of 5000 in euro and in the next row it could say a budget of 2000 in dollar.

In pandas I would like to add an extra column to my DataFrame, normalizing the budgets in euro. So basically, for each row the value in the new column should be the value from the budget column * 1 if the symbol in the currency column is a euro sign, and the value in the new column should be the value of the budget column * 0.78125 if the symbol in the currency column is a dollar sign.

I know how to add a column, fill it with values, copy values from another column etc. but not how to fill the new column conditionally based on the value of another column.

Any suggestions?

Answer 1

You probably want to do

df['Normalized'] = np.where(df['Currency'] == '$', df['Budget'] * 0.78125, df['Budget'])