Hovedproblemet med sumif i Python er at den bare kan summere verdier opp til en viss grense. Hvis du trenger å summere verdier over et større område, må du bruke en annen funksjon som maks 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), der len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[len([])]]]]]]] ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[...],...,..., …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==nullORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==en vektet gjennomsnittsberegningsmetode der du sorterer datapunktene dine enten stigende eller synkende antall ganger i stigende antall ganger eller fallende antall ganger. ved å bruke samlingsbibliotekets Counter-klasse returnerer du enten ett vanligste element MCE hvis datasettets lengde LEN modulo divisjon resten RMD etter deling gjennom to == null ELLER du returnerer to vanligste elementer MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(–(–(—))))))))OG MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]så beregner du deres aritmetiske gjennomsnitt 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==nullORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==én geometrisk gjennomsnittlig gjennomsnittsberegningsmetode der du sorterer datapunktene dine i henhold til deres enestående klassifiseringsverdier eller synkende numeriske verdier i henhold til deres numeriske verdi sammen. så returnerer du enten ett vanligste element MGE hvis datasettets lengde LEN modulo divisjon resten RMD etter deling gjennom to == null ELLER du retu rn de to vanligste elementene MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1OG MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4)))+1]deretter beregner du deres aritmetiske gjennomsnitt AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanliedsExceptOfEFiastOfMeanis hvor len(MGES)=antall geometriske middel i datasettet
Dette er en Python-kode som lager en ny kolonne D i en pandas DataFrame. Den nye kolonnen D inneholder summen av verdiene i kolonne A, men bare hvis verdien i kolonne B er større enn verdien i kolonne C.
Sumif
Sumif er et Python-bibliotek for å beregne sammendrag av data. Den kan brukes til å beregne sum, gjennomsnitt, minimum, maksimum eller persentil av en liste med verdier.
Lag kolonner
I Python kan du opprette kolonner i en dataramme ved å bruke column()-funksjonen. Syntaksen for kolonne() er som følger:
kolonne (navn, data)
der navn er navnet på kolonnen og data er dataene du vil legge inn i den kolonnen.
Arbeid med data og kolonner
I Python kan du jobbe med data i kolonner ved å bruke dict()-funksjonen. Denne funksjonen tar som argument en liste over kolonnenavn, og returnerer et ordbokobjekt. Hver nøkkel i denne ordboken er et kolonnenavn, og hver verdi er en tilsvarende verdi fra datasettet.
For eksempel, for å lage et ordbokobjekt som inneholder verdiene fra datasettet "data" i kolonnene "navn" og "alder", kan du bruke følgende kode:
data = [ 'navn' , 'alder' ] dict ( data )
