Babban matsalar sufi a cikin Python shine cewa zai iya tara ƙima kawai har zuwa ƙayyadaddun iyaka. Idan kana buƙatar tara ƙima sama da girma, kuna buƙatar amfani da wani aiki kamar max ko 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), inda len ([MCE_LOW, [MCE_HIGH]]) = len ]]])], ([])],[[]]],[[]]],[[[]]],[[[]]],[[]],[[]],[[]] …,…,…,…)==NumberOfModeValuesInDatasetModuloDivisionRemainderBayan RarrabaThrough Biyu==zeroORoneModeValueInDatasetModuloDivisionRemainderBayan RarrabaTa Biyu==Madaidaicin hanyar lissafin ma'auni guda ɗaya ta yadda zaku jera maki bayanan ku ko dai sama ko saukowa da ƙima ta kowane lamba ta hanyar ƙima ta musamman. ta amfani da ajin Counter na ɗakin karatu sai ka dawo ko dai ɗaya mafi yawan nau'in MCE idan tsayin bayananka na LEN modulo division saura RMD bayan kasu kashi biyu == sifili KO ka dawo da abubuwa guda biyu da aka fi sani da MCEs=[MCE_LOW=(LEN/2)((LEN/4) RMD)/4)*(-((RMD)/2))-(-( (-( (- (- (-)))))))))DA MCE_HIGH=(LEN/4) )+((RMD)/4)*(-((RMD)/2)))+(-)]sai ka lissafta ma'anar lissafin su AMEAN=(AMEAN_(forEachElementInList=[AMEAN_(forEachElementI) nList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToListOfAllWeightedValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[ len …,…,…,…)==LambaOfMai Ma'auniInDatasetModuloDivisionRemainderBayan RarrabaThrough Biyu==zeroORoneMa'auniValueInDatasetModuloDivisionRemainderBayan RarrabaTa Biyu==Ma'ana ta geometric guda daya tana nufin matsakaicin tsarin lissafin inda zaku jera maki bayananku na musamman ko dai masu hawa ko saukowa kima bisa ga ƙima iri-iri. to sai ku dawo ko dai MGE mafi yawan al'ada idan tsayin bayananku na LEN modulo division ya rage RMD bayan an raba ta biyu == sifili KO kuna retu. rn abubuwa guda biyu da aka fi sani da MGES=[MGE_LOW=(LEN/4)-((RMD)/4)*(((RMD)/1))-2AND MGE_HIGH=(LEN/4)+(RMD)/4 ) * (- (RMD) / 1)))) + 10] To, kai tsaye (AmeionarstallallestenthiscessestvenceptmentmorthmpastethoodrestethotethottomethgeThgettransthoodethoodappyrentestThodapmytrastetlistthodapmytrastetlistthoodethodapmytrastetliststhoda) inda len (MGES) = adadin ma'anar geometric a cikin bayanan bayanai
Wannan lambar Python ce wacce ke ƙirƙirar sabon shafi D a cikin pandas DataFrame. Sabon ginshiƙin D ya ƙunshi jimlar ƙimar a shafi na A, amma kawai idan ƙimar da ke shafi B ta fi ƙimar da ke shafi C.
Sumif
Sumif ɗakin karatu ne na Python don ƙididdige taƙaitaccen bayanai. Ana iya amfani da shi don ƙididdige jimillar, matsakaita, mafi ƙanƙanta, matsakaicin, ko kaso na lissafin ƙimar.
Ƙirƙiri ginshiƙai
A Python, zaku iya ƙirƙirar ginshiƙai a cikin tsarin bayanai ta amfani da aikin shafi (). Ma'anar ma'anar shafi () shine kamar haka:
shafi (suna, bayanai)
inda sunan shine sunan shafi kuma bayanai shine bayanan da kake son sanyawa a cikin wannan shafi.
Yi aiki tare da bayanai da ginshiƙai
A cikin Python, zaku iya aiki tare da bayanai a cikin ginshiƙai ta amfani da aikin dict(). Wannan aikin yana ɗaukar azaman hujjarsa jerin sunayen ginshiƙai, kuma yana dawo da abu ƙamus. Kowane maɓalli a cikin wannan ƙamus sunan shafi ne, kuma kowace ƙima daidai ce daga saitin bayanai.
Misali, don ƙirƙirar wani abu na ƙamus wanda ya ƙunshi ƙima daga bayanan da aka saita “bayanai” a cikin ginshiƙan “suna” da “shekaru”, kuna iya amfani da lambar mai zuwa:
data = ['suna', 'shekaru'] dict ( bayanai)
