我正在执行一些数据分析任务，使用此python脚本可以得到我想要的结果，但是它可能非常慢，可能是由于for循环所致，我必须处理数百万个数据行，有没有办法将此脚本更改为快速？

df=df.sort_values(by='ts')
df = df.set_index(pd.DatetimeIndex(df['ts']))
df = df.rename(columns={'ts': 'Time'})
x2=df.groupby(pd.Grouper(freq='1D',base=30,label='right'))
for name,df1 in x2:
    df1_split=np.array_split(df1,2)
    df_first=df1_split[0]
    df_second=df1_split[1]
    length_1=len(df_first)
    length_2=len(df_second)
    if len(df_first)>=5000:
        df_first_diff_max=abs(df_first['A'].diff(periods=1)).max()
        if df_first_diff_max<=10:
            time_first=df_first['Time'].values[0]
            time_first=pd.DataFrame([time_first],columns=['start_time'])
            time_first['End_Time']=df_first['Time'].values[-1]
            time_first['flag']=1
            time_first['mean_B']=np.mean(df_first['B'])
            time_first['mean_C']=np.mean(df_first['C'])
            time_first['mean_D']=np.mean(df_first['D'])
            time_first['E']=df_first['E'].values[0]
            time_first['F']=df_first['F'].values[0]
            result.append(time_first)
    if len(df_second)>=5000:
        df_second_diff_max=abs(df_second['A'].diff(periods=1)).max()
        if df_second_diff_max<=10:
            print('2')
            time_first=df_second['Time'].values[0]
            time_first=pd.DataFrame([time_first],columns=['start_time'])
            time_first['End_Time']=df_second['Time'].values[-1]
            time_first['flag']=2
            time_first['mean_B']=np.mean(df_second['B'])
            time_first['mean_C']=np.mean(df_second['C'])
            time_first['mean_D']=np.mean(df_second['D'])
            time_first['E']=df_second['E'].values[0]
            time_first['F']=df_second['F'].values[0]
            result.append(time_first)

final=pd.concat(result)