Hovedproblemet med sumif i Python er, at det kun kan summere værdier op til en vis grænse. Hvis du skal summere værdier over et større område, skal du bruge en anden funktion som max eller 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
liedToListOfAllModeValuesInDataset), hvor len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[[len([])]]]]]]] ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[...],…,…, …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==nulORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==en vægtet gennemsnitsberegningsmetode, hvorved du sorterer dine datapunkter enten stigende eller faldende efter antallet af værdier, der opstår i stigende antal eller faldende. ved at bruge samlingsbibliotekets Counter-klasse returnerer du enten et mest almindeligt element MCE, hvis dit datasæts længde LEN modulo division rester RMD efter division gennem to == nul ELLER du returnerer to mest almindelige elementer MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(–(–(—))))))))OG MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]så beregner du deres aritmetiske middelværdi AMEAN=(AMEAN_(forEachElementInList=[AMEAN_(forEachElementI) nList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToListOfAllWeightedValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[ len([[len([[[[[[[[[[[[len([])]]]]]]]]]])],[len ([])]],[…], …,…,…,…)==numberOfWeightedValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==nulORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==én geometrisk gennemsnitlig gennemsnitsberegningsmetode, hvorved du sorterer dine datapunkter i overensstemmelse med deres taltallige værdier, der enten stiger i numerisk tal i henhold til deres entydige talværdier i henhold til deres numeriske værdier, der er stigende i numerisk tal. så returnerer du enten det mest almindelige element MGE, hvis dit datasæts længde LEN modulo division resterende RMD efter division gennem to == nul ELLER du retu rn to mest almindelige elementer MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1OG MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4)))+1]så beregner du deres aritmetiske middelværdi AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),amean_(forEachElementInList) hvor len(MGES)=antal geometriske middelværdier i datasættet
Dette er en Python-kode, der opretter en ny kolonne D i en pandas DataFrame. Den nye kolonne D indeholder summen af værdierne i kolonne A, men kun hvis værdien i kolonne B er større end værdien i kolonne C.
Sumif
Sumif er et Python-bibliotek til beregning af opsummeringer af data. Det kan bruges til at beregne summen, gennemsnittet, minimum, maksimum eller percentil af en liste over værdier.
Opret kolonner
I Python kan du oprette kolonner i en dataramme ved at bruge funktionen column(). Syntaksen for kolonne() er som følger:
kolonne (navn, data)
hvor navn er navnet på kolonnen, og data er de data, du vil lægge i kolonnen.
Arbejd med data og kolonner
I Python kan du arbejde med data i kolonner ved at bruge dict()-funktionen. Denne funktion tager som argument en liste over kolonnenavne og returnerer et ordbogsobjekt. Hver nøgle i denne ordbog er et kolonnenavn, og hver værdi er en tilsvarende værdi fra datasættet.
For eksempel, for at oprette et ordbogsobjekt, der indeholder værdierne fra datasættet "data" i kolonnerne "navn" og "alder", kan du bruge følgende kode:
data = [ 'navn' , 'alder' ] dict ( data )
