Pandas df.iterrows() 并行化

As @Khris said in his comment, you should split up your dataframe into a few large chunks and iterate over each chunk in parallel. You could arbitrarily split the dataframe into randomly sized chunks, but it makes more sense to divide the dataframe into equally sized chunks based on the number of processes you plan on using. Luckily someone else has already figured out how to do that part for us:

# don't forget to import
import pandas as pd
import multiprocessing

# create as many processes as there are CPUs on your machine
num_processes = multiprocessing.cpu_count()

# calculate the chunk size as an integer
chunk_size = int(df.shape[0]/num_processes)

# this solution was reworked from the above link.
# will work even if the length of the dataframe is not evenly divisible by num_processes
chunks = [df.iloc[df.index[i:i + chunk_size]] for i in range(0, df.shape[0], chunk_size)]

这将创建一个列表，其中包含我们的数据框.现在我们需要将它与一个操作数据的函数一起传递到我们的池中.

def func(d):
   # let's create a function that squares every value in the dataframe
   return d * d

# create our pool with `num_processes` processes
pool = multiprocessing.Pool(processes=num_processes)

# apply our function to each chunk in the list
result = pool.map(func, chunks)

for i in range(len(result)):
   # since result[i] is just a dataframe
   # we can reassign the original dataframe based on the index of each chunk
   df.iloc[result[i].index] = result[i]

def func(d):
   for row in d.iterrow():
      idx = row[0]
      k = row[1]['Chromosome']
      start,end = row[1]['Bin'].split('-')

      sequence = sequence_from_coordinates(k,1,start,end) #slow download form http
      d.set_value(idx,'GC%',gc_content(sequence,percent=False,verbose=False))
      d.set_value(idx,'G4 repeats', sum([len(list(i)) for i in g4_scanner(sequence)]))
      d.set_value(idx,'max flexibility',max([item[1] for item in dna_flex(sequence,verbose=False)]))
   # return the chunk!
   return d