original_docstring: 
      INPUT:
      df - the pandas dataframe you want to search
      col1 - the column name you want to look through
      col2 - the column you want to count values from
      look_for - a list of strings you want to search for in each row of df[col]
  
      OUTPUT:
      new_df - a dataframe of each look_for with the count of how often it shows up
      
docstring: the pandas dataframe you want to search
  col1 - the column name you want to look through
  col2 - the column you want to count values from
  look_for - a list of strings you want to search for in each row of df[col]
  
  a dataframe of each look_for with the count of how often it shows up
docstring_tokens: the pandas dataframe you want to search col1 - the column name you want to look through col2 - the column you want to count values from look_for - a list of strings you want to search for in each row of df [ col ] a dataframe of each look_for with the count of how often it shows up
code: def total_count(df, col1, col2, look_for):
      '''
      INPUT:
      df - the pandas dataframe you want to search
      col1 - the column name you want to look through
      col2 - the column you want to count values from
      look_for - a list of strings you want to search for in each row of df[col]
  
      OUTPUT:
      new_df - a dataframe of each look_for with the count of how often it shows up
      '''
      new_df = defaultdict(int)
      #loop through list of ed types
      for val in look_for:
          #loop through rows
          for idx in range(df.shape[0]):
              #if the ed type is in the row add 1
              if val in df[col1][idx]:
                  new_df[val] += int(df[col2][idx])
      new_df = pd.DataFrame(pd.Series(new_df)).reset_index()
      new_df.columns = [col1, col2]
      new_df.sort_values('count', ascending=False, inplace=True)
      return new_df
code_tokens: def total_count ( df , col1 , col2 , look_for ) : new_df = defaultdict ( int ) for val in look_for : for idx in range ( df . shape [ 0 ] ) : if val in df [ col1 ] [ idx ] : new_df [ val ] += int ( df [ col2 ] [ idx ] ) new_df = pd . DataFrame ( pd . Series ( new_df ) ) . reset_index ( ) new_df . columns = [ col1 , col2 ] new_df . sort_values ( 'count' , ascending = False , inplace = True ) return new_df
short_docstring: INPUT:
  df - the pandas dataframe you want to search
  col1 - the column name you want to look through
  col2 - the column you want to count values from
  look_for - a list of strings you want to search for in each row of df[col]
short_docstring_tokens: INPUT : df - the pandas dataframe you want to search col1 - the column name you want to look through col2 - the column you want to count values from look_for - a list of strings you want to search for in each row of df [ col ]
comment: '''
      INPUT:
      df - the pandas dataframe you want to search
      col1 - the column name you want to look through
      col2 - the column you want to count values from
      look_for - a list of strings you want to search for in each row of df[col]
  
      OUTPUT:
      new_df - a dataframe of each look_for with the count of how often it shows up
      '''$SEP$#loop through list of ed types$SEP$#loop through rows$SEP$#if the ed type is in the row add 1