Conditionally fill column values based on another columns value in pandas
You probably want to do
df['Normalized'] = np.where(df['Currency'] == '$', df['Budget'] * 0.78125, df['Budget'])
Similar results via an alternate style might be to write a function that performs the operation you want on a row, using row['fieldname'] syntax to access individual values/columns, and then perform a DataFrame.apply method upon it
This echoes the answer to the question linked here: pandas create new column based on values from other columns
def normalise_row(row): if row['Currency'] == '$' ... ... ... return resultdf['Normalized'] = df.apply(lambda row : normalise_row(row), axis=1)
An option that doesn't require an additional import for numpy:
df['Normalized'] = df['Budget'].where(df['Currency']=='$', df['Budget'] * 0.78125)