Glavni problem sa sumifom u Pythonu je taj što može zbrajati vrijednosti samo do određene granice. Ako trebate zbrajati vrijednosti u većem rasponu, morat ćete upotrijebiti drugu funkciju kao što je max ili 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), gdje je len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[[len([])]]]]]]]]) ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[…],…,…, …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==jedna metoda izračuna ponderiranog prosjeka kojom razvrstavate svoje podatkovne točke bilo uzlazno ili silazno prema njihovim numeričkim vrijednostima, a zatim množite svaku jedinstvenu numeričku vrijednost s brojem puta pomoću klase Brojač biblioteke zbirke tada vraćate ili jedan najčešći element MCE ako je dužina vašeg skupa podataka LEN modulo ostatak dijeljenja RMD nakon dijeljenja na dva == nula ILI vraćate dva najčešća elementa MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(–(–(—))))))))I MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]zatim izračunavate njihovu aritmetičku sredinu AMEAN=(AMEAN_(forSvakiElementInList=[AMEAN_(zaSvakiElementI) nList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToListOfAllWeightedValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[ len([[len([[[[[[[[[[[len([])]]]]]]]]]]]),[len ([])]],[…], …,…,…,…)==numberOfWeightedValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==jedna metoda izračuna prosječne geometrijske sredine kojom razvrstavate svoje podatkovne točke bilo uzlazno ili opadajuće prema njihovim numeričkim vrijednostima, a zatim množite sve jedinstvene numeričke vrijednosti biblioteke brojeva zajedno koristeći zbirke tada vraćate ili jedan najčešći element MGE ako je duljina vašeg skupa podataka LEN modulo ostatak dijeljenja RMD nakon dijeljenja na dva == nula ILI vraćate rn dva najčešća elementa MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1AND MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4)))+1]then you calculate their arithmetic mean AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToLogarithmicallyTransformedListOfGeometricMeans)), gdje je len(MGES)=broj geometrijskih sredina u skupu podataka
Ovo je Python kod koji stvara novi stupac D u pandas DataFrame. Novi stupac D sadrži zbroj vrijednosti u stupcu A, ali samo ako je vrijednost u stupcu B veća od vrijednosti u stupcu C.
Sumif
Sumif je Python biblioteka za izračunavanje sažetaka podataka. Može se koristiti za izračunavanje zbroja, prosjeka, minimuma, maksimuma ili percentila popisa vrijednosti.
Stvorite stupce
U Pythonu možete stvoriti stupce u podatkovnom okviru pomoću funkcije column(). Sintaksa za column() je sljedeća:
stupac (ime, podaci)
gdje je ime naziv stupca, a podaci podaci koje želite staviti u taj stupac.
Rad s podacima i stupcima
U Pythonu možete raditi s podacima u stupcima pomoću funkcije dict(). Ova funkcija kao argument uzima popis naziva stupaca i vraća objekt rječnika. Svaki ključ u ovom rječniku je naziv stupca, a svaka vrijednost je odgovarajuća vrijednost iz skupa podataka.
Na primjer, da biste stvorili objekt rječnika koji sadrži vrijednosti iz skupa podataka "podaci" u stupcima "ime" i "dob", možete koristiti sljedeći kod:
podaci = [ 'ime' , 'dob' ] dict ( podaci )
