Eyona ngxaki iphambili nge-sumif kwiPython kukuba inokushwankathela amaxabiso ukuya kuthi ga kumda othile. Ukuba ufuna ukushwankathela amaxabiso ngaphezulu koluhlu olukhulu, kuya kufuneka usebenzise omnye umsebenzi onje ngobuninzi okanye 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), apho len([MCE_LOW,[MCE_HIGH]]))=len([[len([[len([[[[[[[[[[[len([])]]]]]]] ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[…],…,…, …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==indlela yokubala eyi-avareji enye apho uhlela ngenani amanqaku akho edatha ngokokuphindaphinda kwamanani ngokokunyuka kwamanani ngokokuphindaphinda kwamanani ngokokuhla kwamanani ngokokuphindaphinda kwamanani usebenzisa i-Counter yethala leencwadi eliqokelelweyo kwaye ubuyisela nokuba sesona siqalelo siqhelekileyo i-MCE ukuba ubude bedataseti yakho LEN isahlulo semodyuli intsalela RMD emva kolwahlulo ngesibini == zero OKANYE ubuyisela ezibini ezona zinto ziqhelekileyo MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(-(–(—))))))) KUNYE MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]kwaye ubala i-arithmetic yabo ithetha 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==ijiyometri enye ithetha umndilili wendlela yokubala apho uhlela ngokwamanani amanqaku awodwa ngokoluhlu lwamanani ngokokuqokelelwa kwamanani ngokoluhlu lwamanani ngokwamanani ngokokuqokelelwa kwamanani ngokwamanani ngokokuqokelelwa kwamanani ngokwamanani ngokokuqokelelwa kwamanani ngokwamanani emva koko ubuyisele nokuba sesinye isiqalelo esiqhelekileyo MGE ukuba ubude bedataseti yakho LEN isahlulo semodyuli intsalela RMD emva kolwahlulo ngesibini == zero OKANYE wena retu rn izinto ezimbini eziqhelekileyo MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1AND MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4)))+1]emva koko ubala i-arithmetic yabo ithetha ukuthi AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllMethomethMedOfAllElementOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllMethomethFirstMethomethFirstMethomethMedOfAllMethomethFirstElements apho i-len(MGES)=inani lejometri ithetha kwiseti yedatha
Le yikhowudi yePython eyenza ikholamu entsha D kwi-pandas DataFrame. Umhlathi omtsha D uqulathe isimbuku samaxabiso kumhlathi A, kodwa kuphela ukuba ixabiso elikuluhlu B likhulu kunexabiso elikuluhlu C.
Sumif
I-Sumif yilayibrari yePython yokubala izishwankathelo zedatha. Ingasetyenziselwa ukubala isixa, umndilili, ubuncinci, ubuninzi, okanye ipesenti yoluhlu lwamaxabiso.
Yenza iikholamu
KwiPython, unokwenza iikholamu kwi-dataframe ngokusebenzisa umhlathi () umsebenzi. Isivakalisi somhlathi () simi ngolu hlobo lulandelayo:
ikholamu(igama, idatha)
apho igama ligama lomqolo kunye nedatha yidatha ofuna ukuyibeka kulo kholamu.
Sebenza ngedatha kunye neekholamu
KwiPython, ungasebenza ngedatha kwikholamu ngokusebenzisa i-dict () umsebenzi. Lo msebenzi uthatha njengengxoxo yawo uluhlu lwamagama ekholamu, kwaye ibuyisela into yesichazi-magama. Iqhosha ngalinye kwesi sichazi-magama ligama lekholamu, kwaye ixabiso ngalinye lixabiso elihambelanayo kwiseti yedatha.
Umzekelo, ukwenza into yesichazi-magama equlethe amaxabiso asuka kwiseti yedatha "idatha" kwikholamu "igama" kunye "nobudala", ungasebenzisa le khowudi ilandelayo:
idatha = [ 'igama' , 'ubudala' ] dict ( data )
