【对比Python】分组子集连续区间比上期
任务：计算各股票最长连涨天数
Python
| 1 | import pandas as pd |
| 2 | def con_rise(stock:pd.DataFrame): |
| 3 | rise_day_list = [] |
| 4 | rise_num = 0 |
| 5 | shift_1 = stock['CL']>stock['CL'].shift(1) |
| 6 | for bl in shift_1: |
| 7 | if bl == False: |
| 8 | rise_num = 0 |
| 9 | else: |
| 10 | rise_num+=1 |
| 11 | rise_day_list.append(rise_num) |
| 12 | return max(rise_day_list) |
| 13 | stock_file = "E:\\txt\\StockRecords.txt" |
| 14 | stock_info = pd.read_csv(stock_file,sep="\t") |
| 15 | stock_info.sort_values(by='DT',inplace=True) |
| 16 | stock_group = stock_info.groupby(by='CODE') |
| 17 | max_rise_list = [] |
| 18 | for index,stock_g in stock_group: |
| 19 | code = stock_g.iloc[0]['CODE'] |
| 20 | max_rise_list.append([code,con_rise(stock_g)]) |
| 21 | max_rise_df = pd.DataFrame(max_rise_list,columns=['CODE','con_rise']) |
| 22 | print(max_rise_df) |
集算器
| A | B | ||
| 1 | E:\\txt\\StockRecords.txt | ||
| 2 | =file(A1).import@t() | ||
| 3 | =A2.sort(DT) | ||
| 4 | =A3.group(CODE) | ||
| 5 | =A4.new(CODE,func(A6,~):con_rise) | ||
| 6 | func | ||
| 7 | =(num=0,A6.max(num=if(CL>CL[-1],if(#==1,0,num+1),0))) |
单支股票的连续区间比上期思路：如果大于前一天的股价则加1，不大于则置0，最后查看序列中的最大值即可。把单支股票的计算方式写成函数，每支股票的表当做参数传入即可。集算器可以方便的在循环函数中调用函数得到结果。同样的思路，Pandas的代码看起来就复杂的多。