Here is my code for converting one column field of a data frame to time data type:

col_value = df.select(df.columns[0]).first()[0]


start_time = time.time()
col_value = datetime.strftime(col_value, "%Y-%m-%d %H:%M:%S") \
           if isinstance(col_value, datetime) \
           else col_value
end_time = time.time()
elapsed_time = end_time - start_time
print(elapsed_time) 

The elapsed_time value is 0.00011396408081054688, which make sense it should cost little effort.

However, after I put this code inside a Python loop, things turn strange. Here is the code

for col in df.columns:
  col_value = df.select(col).first()[0]

  start_time = time.time()
  col_value = datetime.strftime(col_value, "%Y-%m-%d %H:%M:%S") \
              if isinstance(col_value, datetime) \
              else col_value
  end_time = time.time()
  elapsed_time = end_time - start_time
  print(elapsed_time)

After finishing running this code, I found the elapsed_time increased to 5 seconds!

Then I remove the time convert logic and re-do the statistics again including the whole loop:

loop_start_time = time.time()
for col in col_list:
  start_time = time.time()
  # Nothing was done here
  end_time = time.time()
  elapsed_time = end_time - start_time
  print(col, elapsed_time)

loop_end_time = time.time()
loop_elapsed_time = loop_end_time - loop_start_time
print(f"Loop time cost {loop_elapsed_time} seconds")

It looks like each round without logic will cost more than 5 seconds as well, but the whole loop only costs 0.0026 second.

Why did this happen? Did I miss something? What is the correct why to measure the cost of each Python statement and function?