import numpy as np import pandas as pd def read_asc(filepath): usecols = [0, 4] # 仅读取第0列和第4列 chunksize = 100000 # 每次读取100000行数据 for chunk in pd.read_csv(filepath, skiprows=4, encoding="gbk", engine='python', sep=' ', delimiter=None, index_col=False, header=None, skipinitialspace=True, usecols=usecols, chunksize=chunksize): file = np.array(chunk) data = read_message(file) filter_step_size(data) def read_message(file): mask = file[:, 1] == "107" data = file[mask] return data def filter_step_size(data): diff = np.diff(data[:, 0].astype(float)) # 将第0列转换为浮点数类型 mask = np.logical_and(0.090 < diff, diff < 0.110) success_sum = np.count_nonzero(mask) fail_sum = len(mask) - success_sum result = np.column_stack((data[:-1, 0], data[1:, 0], diff)) result = result print("步长通过数: {}".format(success_sum)) print("步长未通过数: {}".format(fail_sum)) print("未通过前一项值：未通过后一项值：差值：") print(result) if name == 'main': read_asc("E:\package\databin 7-12-2023 2-23-05 pm Messages File.asc") 修改这段代码，循环读取xlsx文件中第三列除去第一行的所有数据，将读取的数据依次赋值给read_message方法中的mask，并执行整段代码，每次赋值后打印的结果能明显区分

import numpy as np
import pandas as pd

def read_xlsx(filepath):
df = pd.read_excel(filepath)
data = df.iloc[1:, 2].values
for value in data:
mask = read_message(value)
filter_step_size(mask)

def read_message(value):
mask = value == "107"
return mask

def filter_step_size(mask):
success_sum = np.count_nonzero(mask)
fail_sum = len(mask) - success_sum
result = np.column_stack((mask[:-1], mask[1:]))
result = result.astype(int)

print("步长通过数: {}".format(success_sum))
print("步长未通过数: {}".format(fail_sum))
print("未通过前一项值：未通过后一项值：")
print(result)

if __name__ == '__main__':
read_xlsx("E:\package\databin 7-12-2023 2-23-05 pm Messages File.xlsx")