CODEWITHSUNDEEP
pandasdataframe
Karun
Karun

Reputation: 621

Element-wise multiplication of multiple column with a specific column on a row by row basis

enter image description here .

{0: {"Nov '18": 0.1666471015536077, "Dec '18": 0.5403863967743445, "Jan '19": 0.5362702245675458, "Feb '19": 0.3538342118892141, "Mar '19": 0.6068213241958712, "Apr '19": 0.6959594096743349, "May '19": 0.682575498865738}, 1: {"Nov '18": 0.2993902407933448, "Dec '18": 0.44429158402908286, "Jan '19": 0.3729695419273137, "Feb '19": 0.3980823560973494, "Mar '19": 0.3200835471705221, "Apr '19": 0.29763667231002056, "May '19": 0.2840070502525354}, 2: {"Nov '18": 0.5337308848310992, "Dec '18": 0.013817091931355035, "Jan '19": 0.07008689274226004, "Feb '19": 0.10680130054564026, "Mar '19": 0.06818860955654642, "Apr '19": 0.004524516700862339, "May '19": 0.004338865464848797}, 'xTrader (838)': {"Nov '18": 75319.0, "Dec '18": 42484.39, "Jan '19": 40484.71, "Feb '19": 40470.29, "Mar '19": 66609.0, "Apr '19": 71057.87999999999, "May '19": 89627.88}}



{0: {'Owner': 'system_voy', 'App': 'Voyager', 'LOB': 'Risk Management: Capital Markets', 'Transit': '83534', "Nov '18": 0.1666471015536077, "Dec '18": 0.5403863967743445, "Jan '19": 0.5362702245675458, "Feb '19": 0.3538342118892141, "Mar '19": 0.6068213241958712, "Apr '19": 0.6959594096743349, "May '19": 0.682575498865738, "Jun '19": 0.7032990347937492}, 1: {'Owner': 'eu\\xtradereod', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0.2993902407933448, "Dec '18": 0.44429158402908286, "Jan '19": 0.3729695419273137, "Feb '19": 0.3980823560973494, "Mar '19": 0.3200835471705221, "Apr '19": 0.29763667231002056, "May '19": 0.2840070502525354, "Jun '19": 0.2929727958768866}, 2: {'Owner': 'eu\\system_xtrader2', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0.5337308848310992, "Dec '18": 0.013817091931355035, "Jan '19": 0.07008689274226004, "Feb '19": 0.10680130054564026, "Mar '19": 0.06818860955654642, "Apr '19": 0.004524516700862339, "May '19": 0.004338865464848797, "Jun '19": 0.0027272448226331497}, 3: {'Owner': 'mr-tech', 'App': 'FRTB', 'LOB': 'Risk Management: Capital Markets', 'Transit': '83534', "Nov '18": 4.021308836676355e-06, "Dec '18": 7.853538029670704e-05, "Jan '19": 0.015370002324550705, "Feb '19": 0.11787934038028858, "Mar '19": 1.5161864573662851e-07, "Apr '19": 1.0092819280702894e-06, "May '19": 9.714219073341933e-06, "Jun '19": 1.1635748117981739e-07}, 4: {'Owner': 'eu\\system_xtsup_prd', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0, "Dec '18": 0, "Jan '19": 0, "Feb '19": 0.021433060967667138, "Mar '19": 0, "Apr '19": 0, "May '19": 0.016256659135696943, "Jun '19": 0}, 5: {'Owner': 'xt-tech', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0.00022774976090734464, "Dec '18": 2.212229303038311e-06, "Jan '19": 0.004022482749891066, "Feb '19": 0.00011334322251753845, "Mar '19": 0.0036268312234368394, "Apr '19": 4.7611888584087586e-05, "May '19": 0.0103897652257289, "Jun '19": 0.0010008081492497863}, 6: {'Owner': 'ad\\watb', 'App': 'CVATrader', 'LOB': 'Capital Markets: RMG', 'Transit': '91707', "Nov '18": 0, "Dec '18": 0, "Jan '19": 0.0012585476083139418, "Feb '19": 0.0017582009987088963, "Mar '19": 0.001275486891583217, "Apr '19": 0.0015783820251811566, "May '19": 0.0006181777165474082, "Jun '19": 0}, 7: {'Owner': 'ad\\xustev', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0, "Dec '18": 0.0014241796556178747, "Jan '19": 2.2308080124760536e-05, "Feb '19": 9.818589861410218e-05, "Mar '19": 4.049343394433275e-06, "Apr '19": 0.00025239811908896236, "May '19": 0.0006735771849304808, "Jun '19": 0}, 8: {'Owner': 'ad\\cvatrader', 'App': 'CVATrader', 'LOB': 'Capital Markets: RMG', 'Transit': '91707', "Nov '18": 0, "Dec '18": 0, "Jan '19": 0, "Feb '19": 0, "Mar '19": 0, "Apr '19": 0, "May '19": 0.0011116369831956367, "Jun '19": 0}, 9: {'Owner': 'ad\\mccloske', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 0, "Dec '18": 0, "Jan '19": 0, "Feb '19": 0, "Mar '19": 0, "Apr '19": 0, "May '19": 1.905495170508051e-05, "Jun '19": 0}, 10: {'Owner': 'anonymous', 'App': 'xTrader', 'LOB': 'Capital Markets: Global Markets', 'Transit': '75088', "Nov '18": 1.752204286133488e-09, "Dec '18": 0, "Jan '19": 0.0, "Feb '19": 0, "Mar '19": 0, "Apr '19": 0, "May '19": 0, "Jun '19": 0}, 'xTrader (838)': {'Owner': 0.0, 'App': 0.0, 'LOB': 0.0, 'Transit': 0.0, "Nov '18": 75319.0, "Dec '18": 42484.39, "Jan '19": 40484.71, "Feb '19": 40470.29, "Mar '19": 66609.0, "Apr '19": 71057.87999999999, "May '19": 89627.88, "Jun '19": 0.0}}

Upvotes: 1

Views: 83

Answers (2)

GZ0
GZ0

Reputation: 4273

Another alternative to update the dataframe in-place is to operate on the underlying numpy ndarray directly.

df.values[:, :-1] *= df.values[:, [-1]]

If you want to create a new dataframe rather than updating in-place, you could do

df_new = df.iloc[:, :-1].mul(df.iloc[:, -1], axis=0).join(df.iloc[:, -1])

Upvotes: 0

anky
anky

Reputation: 75080

IIUC, you need df.mul() with df.iloc[] :

data.iloc[:,:-1]=data.iloc[:,:-1].mul(data.iloc[:,-1],axis=0)
print(data)

                    0             1             2  xTrader (838)
Apr '19  49453.400218  21149.430945    321.502565       71057.88
Dec '18  22957.986431  18875.456930    587.010722       42484.39
Feb '19  14319.773167  16110.508395   4322.279605       40470.29
Jan '19  21710.744523  15099.563744   2837.447527       40484.71
Mar '19  40419.761583  21320.444993   4541.975094       66609.00
May '19  61177.794903  25454.949819    388.883313       89627.88
Nov '18  12551.693042  22549.773546  40200.076515       75319.00

Note: the sum of the provided data is 0.9981205987006647 , this is the reason why the sum of each row doesn't match the last. Else, this logic should work.

Upvotes: 1

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