https://www.cnblogs.com/everfight/p/pandas_select_rows.html
https://blog.csdn.net/midion9/article/details/89000131
https://blog.csdn.net/fengqiaoxian/article/details/80415354
万能的pandas能处理excel的20多万行数据。xlsxwriter处理不了20多万行，但是能处理五万行数据，能处理的最多的数据都没测试过。
一、读取excel：
Pandas中根据列的值选取多行数据：
#选取等于某些值的行记录 用
df.loc[df[‘column_name’] == some_value]
二、写入excel：
写入excel主要通过pandas构造DataFrame，调用to_excel方法实现。
三、代码实现。
注意excel格式要正确：第一行可以是数据的含义，从第二行开始是得处理的数据。
from pandas import DataFrame, read_excel, ExcelWriter
def readData():
excelFile = r’C:\Users\sparrow\Desktop\PupilData(20191011073414).xlsx’
data = DataFrame(read_excel(excelFile))
df1 = data[['Time(s)', 'LeftPupil(mm)', 'RightPupil(mm)', 'AveragePupil(mm)']]
df2 = df1.loc[df1['Time(s)'] >= 147].loc[df1['Time(s)'] <= 156.1]
df3 = df1.loc[df1['Time(s)'] >= 286].loc[df1['Time(s)'] <= 294.1]
df4 = df1.loc[df1['Time(s)'] >= 313].loc[df1['Time(s)'] <= 324.1]
df5 = df1.loc[df1['Time(s)'] >= 324].loc[df1['Time(s)'] <= 364.1]
df6 = df1.loc[df1['Time(s)'] >= 372].loc[df1['Time(s)'] <= 397.1]
df7 = df1.loc[df1['Time(s)'] >= 497].loc[df1['Time(s)'] <= 505.1]
df8 = df1.loc[df1['Time(s)'] >= 591].loc[df1['Time(s)'] <= 630.1]
df9 = df1.loc[df1['Time(s)'] >= 640].loc[df1['Time(s)'] <= 663.1]
writer = ExcelWriter('output3.xlsx')
df2.to_excel(writer, '1')
df3.to_excel(writer, '2')
df4.to_excel(writer, '3')
df5.to_excel(writer, '4')
df6.to_excel(writer, '5')
df7.to_excel(writer, '6')
df8.to_excel(writer, '7')
df9.to_excel(writer, '8')
writer.save()
print('ok')
readData()
来瞧瞧运行结果：
