Dynamically checking values in Dataframe

Question

Good Morning,

I have a dataframe with 20+ columns. I'd like to add a new column 'Pattern Exists' such that it is set to 1 if a non-zero value exists in a subset of columns, otherwise it's set to 0. On any row, once a cell with non-zero value is found, we can set 'Pattern Exists' to 1 and move onto the next row.

Is there a way to do this with list comprehension?

df_raw['Pattern Exists'] = 0 ## initiating the column to 0 ##

for row in df_raw.index: ## iterating on dataframe rows ##
  for ptrn in pattern_accuracy['Pattern'].to_list(): ## checking values in the desired columns ##
    if df_raw[ptrn][row] != 0:
      df_raw['Pattern Exists'].loc[row] = 1
      break

Answer 1

You could do it with one line:

df_raw['Pattern Exists'] = (df_raw[pattern_accuracy['Pattern'].to_list()] != 0).any(axis=1)

The output would be of bool type, you can convert it to int by adding .astype(int)