Dhibaatada ugu weyn ee sumif ee Python waa in ay soo koobeyso kaliya qiimaha ilaa xad go'an. Haddii aad u baahan tahay inaad soo koobto qiimayaal ka badan baaxad weyn, waxaad u baahan doontaa inaad isticmaasho hawl kale sida max ama min.
I have a dataframe that looks like this:
<code>df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 4, 5], 'C': [3, 4, 5, 6]})
A B C
0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6
</code>
I want to create a new column D that sums the values in column A if the value in column B is greater than the value in column C. So for row 0 it would be <code>1+2+3=6</code>, for row 1 it would be <code>2+3=5</code>, and so on. The expected output is:
<code> A B C D
0 1 2 3 6 # (1+2+3) since B > C for row 0 only
1 2 3 4 5 # (2+3) since B > C for row 1 only
2 3 4 5 0 # no values added since B <= C
3 4 5 6 0 # no values added since B <= C
sumif(B>C) sumif(B<=C) sumif(B>C)+sumif(B<=C) sumif() total of all rows without conditions (A) sum() total of all rows with conditions (D) sum() total of all rows with conditions (D)+sum() total of all rows without conditions (A)=total of all rows with and without conditions (=sum()) expected output (=sum()) actual output (=sum()) difference (=expected-actual) error (%) (=difference/expected*100%) error (%) (=difference/actual*100%) absolute error (%) (=error%*absolute value of difference or absolute value of error % whichever is smaller or equal to 100%) absolute error (%) if expected !=0 else absolute value of actual % whichever is smaller or equal to 100% relative error (%) if expected !=0 else absolute value of actual % whichever is smaller or equal to 100% relative error (%) if actual !=0 else absolute value of expected % whichever is smaller or equal to 100% relative percentage change from previous result on line i-1 to current result on line i (%); when previous result on line i-1 is 0 the relative percentage change equals infinity cumulative relative percentage change from start at line 1 up till end at line n (%); when any result along the way equals 0 the cumulative relative percentage change up till that point equals infinity cumulative percent change from start at line 1 up till end at line n (%); when any result along the way equals 0 the cumulative percent change up till that point equals infinity cumulative percent change from start at previous result on line i-1 up till current result on line i (%); when any result along the way equals 0 the cumulative percent change up till that point equals infinity running product from start at line 1 until end at current line i running product from start at previous result on line i-1 until end at current result on line i running quotient by dividing each number by its position index starting from left to right: first number divided by index position 1 ; second number divided by index position 2 ; third number divided by index position 3 etc until last number divided by index position n running quotient by dividing each number by its reverse position index starting from right to left: first number divided by index position n ; second number divided by index position n-1 ; third number divided by index position n-2 etc until last number divided by index position 1 square root (&#8730;x); same as x^0.5 cube root (&#8731;x); same as x^(1/3) factorial x! = x * (x - 1) * (x - 2)...* 2 * 1 = product[i=x..n](i), where x! = y means y factorials are multiplied together starting with y and going down sequentially towards but not including zero factorial which is defined as being equal to one: e.g. 10! = 10 * 9 * 8 ... * 2 * 1 = 3628800 and similarly 9! = 9 * 8 ... * 2 * 1 = 362880 combination formula used in probability theory / statistics / combinatorics / gambling / etc.: choose k items out of a set consisting out of n items without replacement and where order does not matter: combination(n items set , k items chosen)=(n!)/(k!*((n)-(k))!), where ! means factorial e.g.: combination(52 cards deck , 13 spades)=52!/13!39!, because there are 52 cards in a deck consisting out of 13 spades and 39 non spades cards permutation formula used in probability theory / statistics / combinatorics / gambling / etc.: choose k items out of a set consisting out of n items with replacement AND where order does matter: permutation(n items set , k items chosen)=(n!)/(k!), because there are 52 cards in a deck consisting out ouf 13 spades and 39 non spades cards standard deviation formula used in statistics which measures how spread apart numbers are within a data set around its mean average variance formula used in statistics which measures how spread apart numbers are within a data set correlation coefficient formula used in statistics which measures how closely related two variables are covariance formula used in statistics which measures how two variables move together median average calculation method whereby you sort your data points either ascendingly or descendingly according to their numerical values then you pick either one middle point if your dataset's length LEN modulo division remainder RMD after division through two == zero OR you pick two middle points MDPT_LOW=(LEN/2)-((RMD)/2)-((RMD)/4)*(-((RMD)/4)) AND MDPT_HIGH=(LEN/2)+((RMD)/4)*(-((RMD)/4)) then you calculate their arithmetic mean AMEAN=(MDPT_LOW+(MDPT_HIGH))/len([MDPT_LOW,[MDPT_HIGH]]), where len([MDPT_LOW,[MDPT_HIGH]])=len([[len([[len([[[[[[[[[[[[len([])]]]]]]]]]]])],[len([])]],[len([])]],[len([])]],[len([])]],[len([])]],[len ([])]],[len ([])]],[len ([])]],...,[...],...,[...],...,...,...,...,...,...,...,...,...,...,. ..,. ..,. ..,. ..,. ..,. ..,. . . . . . ])==numberOfMiddlePointsInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneMiddlePointInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==one mode average calculation method whereby you sort your data points either ascendingly or descendingly according to their numerical values then you count how often each unique numerical value occurs using collections library's Counter class then you return either one most common element MCE if your dataset's length LEN modulo division remainder RMD after division through two == zero OR you return two most common elements MCEs=[MCE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(-(-(-(--(-(-(-(---)))))))))))AND MCE_HIGH=(LEN/2)+((RMD)/4)*(-((RMD)/4)))+(--)]then you calculate their arithmetic mean AMEAN=(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodApp
beenToListOfAllModeValuesInDataset), halka len ([MCE_LOW,[MCE_HIGH]]) = len ([[len ]])],[len([])]],[len([])],[len([])] …,…,…,…)==NambarOfModeValuesInDatasetModuloQaybta HadhaagaKadib QaybintaLabada=eberORoneModeValueInDatasetModuloDivisionHadhaagaKadib Qaybinta Laba=hal qaab xisaabin celcelis miisaan leh oo aad u kala saartid dhibcaha xogtaada mid kor u kacaya ama hoos ugu dhufanaya tirada jeer ee tirada badan adiga oo isticmaalaya fasalka Counter-ka ee maktabadda ka dib waxaad soo celinaysaa mid ka mid ah walxaha ugu caansan MCE haddii dhererka xogtaada LEN modulo qaybteeda ay ka hadhsan tahay RMD ka dib marka loo qaybiyo laba == eber AMA aad soo celiso laba walxood ee ugu caansan MCEs=[MCE_LOW=(LEN/2)((LEN/4))" RMD)/4)*(-((RMD)/2))-(-( (-( (- (- (-))))))))))IYO MCE_HIGH=(LEN/4) )+((RMD)/4)*(-((RMD)/2)))+(-)]kadib waxaad xisaabinaysaa xisaabtooda macnaha AMEAN=(AMEAN_(forEachElementInList=[AMEAN_(forEachElementI) nList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToListOfAllWeightedValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[ len …,…,…,…)==TiradaOfQiimaha Miisaanka LehInDatasetModuloDivisionHadhaaKadibQaysintaLabada=eberORoneMiisaanValueInDatasetModuloDivisionRemainderKadib QaybintaLaba=Hal joometri celcelis ahaan habka xisaabinta taaso aad u kala saarayso xogtaada dhibcaha mid kor u kacaysa ama hoos u dhacaysa tiro ahaan ka dib waxaad soo celisaa mid ka mid ah walxaha ugu caansan MGE haddii xogtaada dhererka LEN modulo qaybteeda ay ka hartay RMD ka dib marka loo qaybiyo laba = eber AMA aad dib u soo celiso rn labada curiye ee ugu caansan MGES=[MGE_LOW=(LEN/4)-((RMD)/4)*((RMD)/1))-2IYO MGE_HIGH=(LEN/4)+((RMD)/4 ) * (- ((rmd) / 1))) + 10]))))) + XNUMX])))) + XNUMX])))) + XNUMX])))) + XNUMX])))) + XNUMX])))))))))))))) + XNUMX]))))) + XNUMX]))))) + XNUMX]))))) + XNUMX]))))) + XNUMX]))))))))))))))) + XNUMX]))) + XNUMX]))))))))) + XNUMX]))))) + XNUMX] Markaas waxaad xisaabisay macnaha macnaha guud = XNUMX ** halka len(MGES)=lambarka joomatari macneheedu ku jiro xogta
Kani waa koodka Python kaas oo ku abuura tiir cusub D ee pandas DataFrame. Tiirka cusub ee D wuxuu ka kooban yahay wadarta qiyamka tiirka A, laakiin waa haddii qiimaha tiirka B uu ka weyn yahay qiimaha tiirka C.
Sumif
Sumif waa maktabad Python si loo xisaabiyo koobitaanka xogta. Waxaa loo isticmaali karaa in lagu xisaabiyo wadarta, celceliska, ugu yar, ugu badnaan, ama boqolleyda liiska qiyamka.
Abuur tiirar
Python dhexdeeda, waxaad samayn kartaa tiirar ku jira qaab-dhismeed xogeed adiga oo isticmaalaya tiirka() shaqada. Ereyga tiirka() waa sida soo socota:
tiirka (magaca, xogta)
meesha magacu yahay magaca tiirka iyo xogtu waa xogta aad rabto inaad ku dhejiso tiirkaas.
Ku shaqee xogta iyo tiirarka
Python dhexdeeda, waxaad ku shaqayn kartaa xogta tiirarka adoo isticmaalaya shaqada dict(). Shaqadani waxay dood ahaan u qaadataa liiska magacyada tiirka, oo soo celisa shay qaamuus ah. Fure kasta oo qaamuuskan ku jira waa magac tiir, qiime kastana waa qiime u dhigma marka loo eego xogta la sameeyay.
Tusaale ahaan, si aad u abuurto shay qaamuus ah oo ka kooban qiyamka xogta lagu dejiyay "xogta" ee tiirarka "magaca" iyo "da'da", waxaad isticmaali kartaa koodka soo socda:
xogta = ['magac', 'da'] dict ( xogta)
