old_contents: from s2v4 import *
  
  def find_max(data_sample, col):
  	temp_list = []
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	return max(temp_list) # why not just do max of prices or some other variable we've already created by creating lists of column data? Maybe this is more reusable? 
  
  def find_min(data_sample, col):
  	temp_list = []
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	return min(temp_list)
  
  
  def find_max_min(data_sample, col, m='max'):
  	temp_list = []
  	val = 0
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	if m == "max":
  		val = max(temp_list)
  	elif m == "min":
  		val = min(temp_list)
  	else: # hopefully it doesn't come to this
  		pass
  	return val
  
  print("Max / Min... MIN", find_max_min(data_from_csv[1:], 2, 'min'))
  print("Max:", find_max(data_from_csv[1:], 2))
  print("Min", find_min(data_from_csv[1:], 2))
  
new_contents: from s2v4 import *
  
  def find_max(data_sample, col):
  	temp_list = []
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	return max(temp_list) # why not just do max of prices or some other variable we've already created by creating lists of column data? Maybe this is more reusable? 
  
  def find_min(data_sample, col):
  	temp_list = []
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	return min(temp_list)
  
  
  def find_max_min(data_sample, col, m='max'):
  	temp_list = []
  	val = 0
  	for row in data_sample:
  		price = float(row[col])
  		temp_list.append(price)
  	if m == "max":
  		val = max(temp_list)
  	elif m == "min":
  		val = min(temp_list)
  	else: # hopefully it doesn't come to this
  		pass
  	return val
  
  # print("Max / Min... MIN", find_max_min(data_from_csv[1:], 2, 'min'))
  # print("Max:", find_max(data_from_csv[1:], 2))
  # print("Min", find_min(data_from_csv[1:], 2))
  
  
  # NumPy max and min
  price = my_csv['priceLabel']
  price_in_float = [float(x) for x in price]
  numpy_max = numpy.amax(price_in_float)
  numpy_min = numpy.amin(price_in_float)
  # print(numpy_max)
  # print(numpy_min)
subject: Add NumPy min/max function