Masalah utama dengan sumif dalam Python ialah ia hanya boleh menjumlahkan nilai sehingga had tertentu. Jika anda perlu menjumlahkan nilai dalam julat yang lebih besar, anda perlu menggunakan fungsi lain seperti maks atau 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), di mana len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[[[]]]]]]]]]]] ]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[…],…,…, …,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==satu kaedah pengiraan purata wajaran di mana anda mengisih setiap titik data anda sama ada secara menurun mengikut nilai nombornya mengikut masa yang menurun mengikut nilai nombor secara menurun mengikut nilai nombornya mengikut masa yang sama. menggunakan kelas Kaunter perpustakaan koleksi maka anda mengembalikan sama ada satu elemen paling biasa MCE jika panjang set data anda LEN pembahagian modulo baki RMD selepas pembahagian hingga dua == sifar ATAU anda mengembalikan dua elemen paling biasa MCEs=[MCE_LOW=(LEN/2)-(( RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(–(–(—))))))))DAN MCE_HIGH=(LEN/2 )+((RMD)/4)*(-((RMD)/4)))+(–)]kemudian anda mengira min aritmetiknya 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 ([])]],[…], …,…,…,…)==numberOfWeightedValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==satu pustaka min purata nombor geometrik bersama-sama menggunakan kaedah pengiraan purata kelas berbilang bersama-sama dengan anda menyusun nilai data anda mengikut kaedah pengiraan purata kelas atau bersama-sama dengan anda mengisih data anda mengikut kaedah pengiraan nilai berbilang atau mengikut bersama-sama. maka anda mengembalikan sama ada satu elemen paling biasa MGE jika panjang set data anda LEN modulo bahagian baki RMD selepas pembahagian melalui dua == sifar ATAU anda retu rn dua elemen paling biasa MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1DAN MGE_HIGH=(LEN/2)+((RMD)/4 )*(-((RMD)/4)))+1]kemudian anda mengira min aritmetiknya AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfTheAllFiElementrstCameraMetrikOlehMetrikOlehMetrikOlehMetrikOfTheLandLastOneExceptForTheFirstAndLastOne) dengan len(MGES)=bilangan cara geometri dalam set data
Ini ialah kod Python yang mencipta lajur D baharu dalam DataFrame panda. Lajur D baharu mengandungi jumlah nilai dalam lajur A, tetapi hanya jika nilai dalam lajur B lebih besar daripada nilai dalam lajur C.
Sumif
Sumif ialah perpustakaan Python untuk mengira ringkasan data. Ia boleh digunakan untuk mengira jumlah, purata, minimum, maksimum atau persentil senarai nilai.
Buat lajur
Dalam Python, anda boleh membuat lajur dalam kerangka data dengan menggunakan fungsi column(). Sintaks untuk column() adalah seperti berikut:
lajur (nama, data)
di mana nama ialah nama lajur dan data ialah data yang anda ingin letakkan dalam lajur itu.
Bekerja dengan data dan lajur
Dalam Python, anda boleh bekerja dengan data dalam lajur dengan menggunakan fungsi dict(). Fungsi ini mengambil senarai nama lajur sebagai hujahnya dan mengembalikan objek kamus. Setiap kunci dalam kamus ini ialah nama lajur dan setiap nilai ialah nilai yang sepadan daripada set data.
Contohnya, untuk mencipta objek kamus yang mengandungi nilai daripada set data "data" dalam lajur "nama" dan "umur", anda boleh menggunakan kod berikut:
data = [ 'nama' , 'umur' ] dict ( data )
