pe_scrip = int(df_sort['Heading scrip'].astype(object).iloc[[1]])
gap = 100
start_dt = pd.to_datetime("2021-09-23 09:15:59")
DTE_time = pd.to_timedelta(7, unit='d')
DTE_time
pe_list = [str(pe_scrip),str(pe_scrip+gap),str(pe_scrip),str(pe_scrip-gap),str(pe_scrip)]
pe_list
for list in range(len(pe_list)):
var = list
b = []
for i in range(len(df_sort)):
find_df = ((df_sort['Heading scrip'].iloc[i] == pe_list[var]) & (df_sort['Option'].iloc[i] == 'PE')
& (df_sort['Date_time'].iloc[i] == start_dt) & (df_sort['DTE'].iloc[i] == DTE_time))
b.append(find_df)
# display([b])
pe_df = df_sort[b]
display(pe_df)
I'm using Concat function it doesn't help me, please someone help me where should I use concat function, or how can I concat these all 5 dataframes.
When I using pd.concat - enter image description here
Dataset :
Ticker Date Time Open High Low Close Volume Open Interest Option Expiry Symbol Heading scrip Date_time DTE
133561 BANKNIFTY23SEP2136900CE.NFO 2021-09-23 09:15:59 349.95 440.10 349.85 398.60 109250 328725 CE 2021-09-23 BANKNIFTY 36900 2021-09-23 09:15:59 0 days
139190 BANKNIFTY23SEP2137600PE.NFO 2021-09-23 09:15:59 550.00 550.00 225.00 412.45 33900 95850 PE 2021-09-23 BANKNIFTY 37600 2021-09-23 09:15:59 0 days
132813 BANKNIFTY23SEP2136800CE.NFO 2021-09-23 09:15:59 477.95 527.00 462.25 480.55 71450 244000 CE 2021-09-23 BANKNIFTY 36800 2021-09-23 09:15:59 0 days
139942 BANKNIFTY23SEP2137700PE.NFO 2021-09-23 09:15:59 350.00 517.95 350.00 500.35 16750 86675 PE 2021-09-23 BANKNIFTY 37700 2021-09-23 09:15:59 0 days
132099 BANKNIFTY23SEP2136700CE.NFO 2021-09-23 09:15:59 560.05 614.35 551.00 572.00 15425 93075 CE 2021-09-23 BANKNIFTY 36700 2021-09-23 09:15:59 0 days
140693 BANKNIFTY23SEP2137800PE.NFO 2021-09-23 09:15:59 578.95 612.00 545.95 590.70 3575 87050 PE 2021-09-23 BANKNIFTY 37800 2021-09-23 09:15:59 0 days
131425 BANKNIFTY23SEP2136600CE.NFO 2021-09-23 09:15:59 653.00 709.60 644.15 659.00 10700 70300 CE 2021-09-23 BANKNIFTY 36600 2021-09-23 09:15:59 0 days
141444 BANKNIFTY23SEP2137900PE.NFO 2021-09-23 09:15:59 709.90 709.90 625.15 676.10 3975 40100 PE 2021-09-23 BANKNIFTY 37900 2021-09-23 09:15:59 0 days
130686 BANKNIFTY23SEP2136500CE.NFO 2021-09-23 09:15:59 750.95 809.85 737.55 757.10 18525 378825 CE 2021-09-23 BANKNIFTY 36500 2021-09-23 09:15:59 0 days
142194 BANKNIFTY23SEP2138000PE.NFO 2021-09-23 09:15:59 801.00 801.00 730.45 771.30 9650 163850 PE 2021-09-23 BANKNIFTY 38000 2021-09-23 09:15:59 0 days
130165 BANKNIFTY23SEP2136400CE.NFO 2021-09-23 09:15:59 896.00 896.00 837.15 858.05 625 19350 CE 2021-09-23 BANKNIFTY 36400 2021-09-23 09:15:59 0 days
142944 BANKNIFTY23SEP2138100PE.NFO 2021-09-23 09:15:59 889.00 890.05 847.05 874.05 600 34225 PE 2021-09-23 BANKNIFTY 38100 2021-09-23 09:15:59 0 days
129664 BANKNIFTY23SEP2136300CE.NFO 2021-09-23 09:15:59 965.05 965.05 933.90 962.65 625 17325 CE 2021-09-23 BANKNIFTY 36300 2021-09-23 09:15:59 0 days
143690 BANKNIFTY23SEP2138200PE.NFO 2021-09-23 09:15:59 943.25 973.90 943.25 972.45 325 29625 PE 2021-09-23 BANKNIFTY 38200 2021-09-23 09:15:59 0 days
129214 BANKNIFTY23SEP2136200CE.NFO 2021-09-23 09:15:59 1124.25 1124.25 1124.25 1124.25 25 6000 CE 2021-09-23 BANKNIFTY 36200 2021-09-23 09:15:59 0 days
128779 BANKNIFTY23SEP2136100CE.NFO 2021-09-23 09:15:59 1176.30 1176.30 1176.30 1176.30 25 4625 CE 2021-09-23 BANKNIFTY 36100 2021-09-23 09:15:59 0 days
128153 BANKNIFTY23SEP2136000CE.NFO 2021-09-23 09:15:59 1281.85 1281.85 1231.70 1253.65 2075 52025 CE 2021-09-23 BANKNIFTY 36000 2021-09-23 09:15:59 0 days
145792 BANKNIFTY23SEP2138500PE.NFO 2021-09-23 09:15:59 1262.10 1262.15 1262.10 1262.15 150 31600 PE 2021-09-23 BANKNIFTY 38500 2021-09-23 09:15:59 0 days
126104 BANKNIFTY23SEP2135500CE.NFO 2021-09-23 09:15:59 1752.55 1753.50 1752.45 1752.45 75 8825 CE 2021-09-23 BANKNIFTY 35500 2021-09-23 09:15:59 0 days
139191 BANKNIFTY23SEP2137600PE.NFO 2021-09-23 09:16:59 409.00 409.00 372.95 380.65 30375 95850 PE 2021-09-23 BANKNIFTY 37600 2021-09-23 09:16:59 0 days
