comparison of values in a column of Dataframe-CodePudding

CODE:-

from datetime import date
from datetime import timedelta
from nsepy import get_history
import pandas as pd
import datetime



end1 = date.today()
start1 = end1 - timedelta(days=180)
stock = ['RELIANCE']#,'HDFCBANK','INFY','ICICIBANK','HDFC'] ,'TCS','KOTAKBANK','LT','SBIN','HINDUNILVR','AXISBANK',
         # 'ITC','BAJFINANCE','BHARTIARTL','ASIANPAINT','HCLTECH','MARUTI','TITAN','BAJAJFINSV','TATAMOTORS',
         # 'TECHM','SUNPHARMA','TATASTEEL','M&M','WIPRO','ULTRACEMCO','POWERGRID','HINDALCO','NTPC','NESTLEIND',
         # 'GRASIM','ONGC','JSWSTEEL','HDFCLIFE','INDUSINDBK','SBILIFE','DRREDDY','ADANIPORTS','DIVISLAB','CIPLA',
         # 'BAJAJ-AUTO','TATACONSUM','UPL','BRITANNIA','BPCL','EICHERMOT','HEROMOTOCO','COALINDIA','SHREECEM','IOC']



for stock in stock:
    vol = get_history(symbol=stock,
                  start=start1,
                  end=end1)


    d_vol = pd.concat([vol['Deliverable Volume']])
    symbol_s = pd.concat([vol['Symbol']])
    close = pd.concat([vol['Close']])

    df = pd.DataFrame(symbol_s)
    df['D_vol'] = d_vol
    pd.set_option('display.max_columns',10)
    pd.set_option('display.max_rows',200)
    print(df)

PRODUCT:-

              Symbol     D_vol
Date                          
2022-02-17  RELIANCE   2872619
2022-02-18  RELIANCE   1216161
2022-02-21  RELIANCE   2888548
2022-02-22  RELIANCE   3920789
2022-02-23  RELIANCE   1928054
2022-02-24  RELIANCE   7266877
2022-02-25  RELIANCE   3670287
2022-02-28  RELIANCE   5321030
2022-03-02  RELIANCE   6277829
2022-03-03  RELIANCE   2479449
2022-03-04  RELIANCE   2754095
2022-03-07  RELIANCE   4962748
2022-03-08  RELIANCE   5602260
2022-03-09  RELIANCE   4647153
2022-03-10  RELIANCE   4102149
2022-03-11  RELIANCE   3028604
2022-03-14  RELIANCE   1800381
2022-03-15  RELIANCE   3924279
2022-03-16  RELIANCE   2824434
2022-03-17  RELIANCE   5961590
2022-03-21  RELIANCE   3019258
2022-03-22  RELIANCE   5753732
2022-03-23  RELIANCE   4321837
2022-03-24  RELIANCE   3208900
2022-03-25  RELIANCE   3821279
2022-03-28  RELIANCE   2149565
2022-03-29  RELIANCE   2280912
2022-03-30  RELIANCE   4259734
2022-03-31  RELIANCE   3893403
2022-04-01  RELIANCE   1845831
2022-04-04  RELIANCE   1859891
2022-04-05  RELIANCE   3962890
2022-04-06  RELIANCE   3640461
2022-04-07  RELIANCE   4936384
2022-04-08  RELIANCE   4458242
2022-04-11  RELIANCE   2103573
2022-04-12  RELIANCE   4019726
2022-04-13  RELIANCE   3009160
2022-04-18  RELIANCE   3079230
2022-04-19  RELIANCE   6735937
2022-04-20  RELIANCE   5767347
2022-04-21  RELIANCE   5151153
2022-04-22  RELIANCE   3113096
2022-04-25  RELIANCE   4210856
2022-04-26  RELIANCE   2382379
2022-04-27  RELIANCE   4014016
2022-04-28  RELIANCE   6937716
2022-04-29  RELIANCE   4308908
2022-05-02  RELIANCE   1939501
2022-05-04  RELIANCE   4758487
2022-05-05  RELIANCE   4135949
2022-05-06  RELIANCE   4117945
2022-05-09  RELIANCE   3496083
2022-05-10  RELIANCE   4214002
2022-05-11  RELIANCE   3471644
2022-05-12  RELIANCE   5344949
2022-05-13  RELIANCE   4652716
2022-05-16  RELIANCE   3089139
2022-05-17  RELIANCE   4085578
2022-05-18  RELIANCE   3948750
2022-05-19  RELIANCE   3713450
2022-05-20  RELIANCE   5198311
2022-05-23  RELIANCE   3078691
2022-05-24  RELIANCE   3094755
2022-05-25  RELIANCE   2740033
2022-05-26  RELIANCE   3568836
2022-05-27  RELIANCE   3519149
2022-05-30  RELIANCE   2716871
2022-05-31  RELIANCE  19734107
2022-06-01  RELIANCE   2072536
2022-06-02  RELIANCE   4788206
2022-06-03  RELIANCE   5136072
2022-06-06  RELIANCE   1680712
2022-06-07  RELIANCE   2712556
2022-06-08  RELIANCE   3012536
2022-06-09  RELIANCE   4301647
2022-06-10  RELIANCE   4608963
2022-06-13  RELIANCE   4242241
2022-06-14  RELIANCE   3299932
2022-06-15  RELIANCE   1888736
2022-06-16  RELIANCE   3664860
2022-06-17  RELIANCE   5410573
2022-06-20  RELIANCE   2410448
2022-06-21  RELIANCE   1906058
2022-06-22  RELIANCE   2129069
2022-06-23  RELIANCE   2980195
2022-06-24  RELIANCE   3440303
2022-06-27  RELIANCE   4127406
2022-06-28  RELIANCE   2590698
2022-06-29  RELIANCE   3617967
2022-06-30  RELIANCE   5419202
2022-07-01  RELIANCE  16124097
2022-07-04  RELIANCE   8925205
2022-07-05  RELIANCE   3241427
2022-07-06  RELIANCE   5356881
2022-07-07  RELIANCE   6400200
2022-07-08  RELIANCE   5266144
2022-07-11  RELIANCE   2790542
2022-07-12  RELIANCE   2091493
2022-07-13  RELIANCE   3820557
2022-07-14  RELIANCE   3162562
2022-07-15  RELIANCE   2013822
2022-07-18  RELIANCE   3641338
2022-07-19  RELIANCE   3280412
2022-07-20  RELIANCE   5571093
2022-07-21  RELIANCE   4032523
2022-07-22  RELIANCE   2374481
2022-07-25  RELIANCE   5024203
2022-07-26  RELIANCE   2368186
2022-07-27  RELIANCE   2097378
2022-07-28  RELIANCE   2973888
2022-07-29  RELIANCE   3783646
2022-08-01  RELIANCE   3695549
2022-08-02  RELIANCE   3420282
2022-08-03  RELIANCE   3725408
2022-08-04  RELIANCE   3215051
2022-08-05  RELIANCE   4106312
2022-08-08  RELIANCE   2615697
2022-08-10  RELIANCE   2941793
2022-08-11  RELIANCE   2296573
2022-08-12  RELIANCE   2683554

PROBLEM:- See in above data frame there are around 180 rows. I have a list of 50 stocks but here I have displayed only one stock and its product and 49 comment out. Now from the above data frame column D_vol contains around 180 rows. My criteria is that the last row of column D_vol should contain highest number than previous 90 rows or more than 90 rows. And if this condition satisfy any stock then it should reflect on output table. ex:- if suppose RELIANCE contain 10000 as D_vol on 12-aug-2022 and before 12-aug it was contain less than 10000 for 90 days or more than 90 then that stock should reflect on my output table. thank you.

CodePudding user response：

You can use a condition like this:

result = list()
if df['D_vol'].iloc[-1] > max(df['D_vol'].iloc[-91:-1]):
    result.append(df)

Thus you get datasets where the last value is bigger than 90 previous ones.