Inkinga enkulu nge-sumif ku-Python ukuthi ingakwazi ukuhlanganisa amanani kuze kufike kumkhawulo othile. Uma udinga ukuhlanganisa amanani phezu kobubanzi obukhulu, uzodinga ukusebenzisa omunye umsebenzi ofana nobukhulu noma ubuncane.
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), lapho len([MCE_LOW,[MCE_HIGH]]))=len([[len([[len([[[[[[[[[[[len([])]]]]]]] ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[…],…,…, …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==indlela yokubala enesisindo esisodwa lapho uhlela amaphuzu akho edatha ngokwenombolo ngayinye ngokwezinombolo bese wehla ngokwezinombolo izikhathi eziningana bese wehla ngokwezinombolo usebenzisa i-Counter class yeqoqo lomtapo wezincwadi bese ubuyisela into eyodwa evamile kakhulu i-MCE uma ubude bedathasethi yakho LEN yengxenye yemodulo esele RMD ngemva kokuhlukanisa kabili == zero NOMA ubuyisela izici ezimbili ezivame kakhulu MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(-(–(—))))))) KANYE MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]bese ubala izibalo zabo zisho ukuthi 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==zeroORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==ijiyomethri eyodwa isho isilinganiso sendlela yokubala lapho uhlela amanani akho ngokwezinombolo ngokwezinombolo, bese uhlela inani lezinombolo ngokwezinombolo bese ubuyisela into eyodwa evame kakhulu i-MGE uma ubude bedathasethi yakho ye-LEN modulo division isalela i-RMD ngemva kokuhlukanisa kabili == zero NOMA u-retu rn izakhi ezimbili ezivame kakhulu MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1AND MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4))))+1]bese ubala izibalo zabo ezichaza ukuthi AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllMethomethMedOfAllElementOfAllElementsExcept ForTheFirstAndLastOne lapho i-len(MGES)=inombolo yezindlela zejometri kudathasethi
Lena ikhodi yePython eyenza ikholomu entsha D ku-pandas DataFrame. Ikholomu entsha iqukethe isamba samanani kukholomu A, kodwa kuphela uma inani elikukholomu B likhulu kunenani elikukholomu C.
Sumif
I-Sumif ilabhulali ye-Python yokubala izifinyezo zedatha. Ingasetshenziswa ukubala isamba, isilinganiso, ubuncane, ubukhulu, noma iphesenti lohlu lwamanani.
Dala amakholomu
Ku-Python, ungakha amakholomu kuhlaka lwedatha ngokusebenzisa ikholomu() umsebenzi. I-syntax yekholomu() imi kanje:
ikholomu(igama, idatha)
lapho igama kuyigama lekholomu futhi idatha iyidatha ofuna ukuyibeka kuleyo kholomu.
Sebenza ngedatha namakholomu
Ku-Python, ungasebenza nedatha kumakholomu ngokusebenzisa umsebenzi we-dict(). Lo msebenzi uthatha njengempikiswano yawo uhlu lwamagama ekholomu, bese ubuyisela into yesichazamazwi. Ukhiye ngamunye kulesi sichazamazwi yigama lekholomu, futhi inani ngalinye liyinani elihambisanayo elisuka kusethi yedatha.
Isibonelo, ukuze udale into yesichazamazwi equkethe amanani asuka kudatha esethelwe "idatha" kumakholomu "igama" kanye "nobudala", ungasebenzisa ikhodi elandelayo:
idatha = [ 'igama' , 'age' ] dict ( idatha)
