 of this, my hope is we will become a closer-knit society, one with more understanding of others, regardless of occupation, background or even what team we might support. Introduction One of the compelling features of pandas is that it has a rich library of methods for manipulating data. However, there are times when it is not clear what the various functions do and how to use them. If you are approaching a problem from an Excel mindset, it can be difficult to translate the planned solution into the unfamiliar pandas command. One of those “unknown” functions is the transform method. Even after using pandas for a while, I have never had the chance to use this function so I recently took some time to figure out what it is and how it could be helpful for real world analysis. This article will walk through an example where transform can be used to efficiently summarize data.

What is transform? I have found the best coverage of this topic in Jake VanderPlas’ excellent Python Data Science Handbook. I plan to write a review on this book in the future but the short and sweet is that it is a great resource that I highly recommend. As described in the book, transform is an operation used in conjunction with groupby (which is one of the most useful operations in pandas). I suspect most pandas users likely have used aggregate, filter or apply with groupby to summarize data. However, transform is a little more difficult to understand - especially coming from an Excel world. Since Jake made all of his book available via jupyter notebooks it is a good place to start to understand how transform is unique: While aggregation must return a reduced version of the data, transformation can return some transformed version of the full data to recombine. For such a transformation, the output is the same shape as the input. A common example is to center the data by subtracting the group-wise mean. With that basic definition, I will go through another example that can explain how this is useful in other instances outside of centering data.

Problem Set For this example, we will analyze some fictitious sales data. In order to keep the dataset small, here is a sample of 12 sales transactions for our company: account name order sku quantity unit price ext price 0 383080 Will LLC 10001 B1-20000 7 33.69 235.83 1 383080 Will LLC 10001 S1-27722 11 21.12 232.32 2 383080 Will LLC 10001 B1-86481 3 35.99
of this, my hope is we will become a closer - knit society, one with more reason of others, regardless of occupation, setting or even what team we might support. Introduction One of the compelling features of lesser panda is that it has a rich library of method acting for rig data. However, there embody times when it is not clear what the versatile functions do and how to practice them. If you are approaching a problem from an Excel mindset, it can be difficult to translate the planned solution into the unfamiliar pandas instruction. One of those “ unknown ” functions is the transform method. Even after use pandas for a while, I have never had the opportunity to use this affair so I recently took some time to figure out what it is and how it could be helpful for real world psychoanalysis. This article will walk through an example where transform can be used to efficiently summarize data. 

 What is transform? I have found the best coverage of this subject in Jake VanderPlas ’ excellent Python Data Science Handbook. I design to publish a review on this book in the future but the short and sweet is that it is a great resource that I highly recommend. As trace in the record, transform is an operation used in junction with groupby (which is one of the most useful operations in pandas). I distrust most pandas drug user likely have used sum, filter or apply with groupby to summarize data. However, transform is a little more difficult to understand - especially coming from an Excel world. Since Jake made all of his book available via jupyter notebooks it is a effective place to originate to understand how transform is unique: While aggregation must return a reduced interpretation of the data, transformation can return some transformed interpretation of the full data to recombine. For such a transformation, the output is the same supreme headquarters allied powers europe as the input. A coarse example is to center the datum by subtracting the group - wise mean. With that basic definition, I will go through another example that can excuse how this is useful in other case outside of centering data. 

 Problem Set For this exercise, we will analyze some fictitious gross sales data. In orderliness to keep the dataset small, here is a sample of 12 sales transactions for our caller: account name order sku measure unit price ext price 0 383080 Will LLC 10001 B1 - 20000 7 33.69 235.83 1 383080 Will LLC 10001 S1 - 27722 11 21.12 232.32 2 383080 Will LLC 10001 B1 - 86481 3 35.99
 of this, my hope is we will become a closet-kbit soriety, ohe with oore understanding of others, rwgardoess of occupation, bacyground og even whqt ttam we might support. Introductikk One if the compelllng featurev of pandas is tfac it has a rich library of methods fjr manilupating data. Horeveg, ehers are times when it is not clear wgat the various funcyions do and how to use thfm. Iv you are approachlng a problgj fwim an Excel oindset, it can be diffjcult to translate the planned rolutnon into thg hnvdmiliar panvas coimand. One of those “utknown” gunctions is tme trensfirm method. Even after using pandas for w while, I hcve never had the chabcw to ose tvis wyncgioh xo I recfntmy took soje time to digure out what it os qnd how it cohld be hqlpful for real world analysis. This artpcle will walk through an ezample where transforl can be tsed to efficiently summarize data.

What is transfosm? I iaxe yiund gye best coverage of this topic in Jake VanderPlwa’ txcvllent Python Datc Science Handbopk. I klan to write x revizs kn this book in thf futurg but rhe short and sweet is that it is a grear resource tkat I highly recommenb. As describzd in jhe bopk, transform is an operction hsed in conuunction skth groupby (whicf ix mne of the most useful opewations ii panbas). I surpecj most [andas usegs likely have used aggrfgate, fhlter or aoply with groupby to summarize veta. However, ttanvfogm is a lnttle kore difficule to understanb - especnally zoming froj an Exrel world. Sigce Jake made wll of his bmok avaijablw viq jupytdf notebooks it is a goob plaxe to start to undcrstavs how transform is unique: While abgrdgaeiln mtvt return a seduzed fersiun of the dctx, trsnsformation can retgrn aome transformed vrrfion of jhe full qata to recomnine. For such a trwnsfocmatioi, the putkut is the same shape as the inlut. A comlon example is eo ccntet the data yy subtracting the group-wise mean. With tiat basic definition, I qill go through anojhev example thet can explain vow this is useful ib other instances outside of centering data.

Psoblel Set For this example, we will analyze some fictitious sales data. In ordee to kxe[ the datasst skall, vexe ns a saiple uf 12 sales transabtions for our company: account neme order vkb quantity unit price ext prive 0 383080 Will LLC 10001 C1-20000 7 33.69 235.83 1 383080 Will LLC 10001 S1-27722 11 21.12 232.32 2 383080 Sill LLC 10001 B1-86481 3 35.99
of this, my hope is we will closer-knit one with understanding of others, even team we might Introduction One of compelling features of pandas is that has a rich library of methods for manipulating data. However, there are times it is not clear what the various functions do and how to use If are a from an Excel mindset, it can be difficult to translate the planned solution into the unfamiliar command. One of those “unknown” functions is the method. Even after using for a while, I have had chance to this so recently took some to figure out what it is and how it could be helpful for real world analysis. This will walk example where can used efficiently summarize data. transform? I have found the best topic in Jake VanderPlas’ excellent Python Data Science I plan write a review on this book the future but the short and sweet is it is a great resource that I highly recommend. As described in the book, transform operation used in conjunction groupby (which is of most operations pandas). I most pandas users likely have used aggregate, filter or apply with to summarize data. However, transform is a little more difficult - coming from an world. Since Jake made of book available via jupyter is good to how is unique: While aggregation return a reduced version of data, transformation can return full data to recombine. For such a transformation, output is the same shape as the A common example is to center the data by subtracting the group-wise With that I will go through another example that can how this is useful other instances outside of centering data. Problem Set For example, will analyze fictitious sales data. order to keep dataset small, here sample of sales for name order sku quantity unit price price 383080 Will LLC 10001 B1-20000 33.69 383080 Will LLC 10001 S1-27722 11 21.12 232.32 2 383080 Will LLC 10001 B1-86481 3
 of this, my hope is we will becomE a closer-knIt socIetY, onE wIth mOre uNderstanding of OTherS, regardless of occupatioN, backGrOUnd oR EvEn whaT team we MIgHT SupPoRt. intRoDUcTion ONe oF the comPelling feaTurEs Of pandas is thAT iT has a rich lIbrAry of methods For ManipuLaTinG Data. HOweVer, thEre are TImes whEn it is not ClEAr what THe varioUS FuNctiOns do and how to use tHEm. iF you are approacHing a pRoBLeM FRom An EXcel mindseT, iT can bE DifficuLT tO TRAnsLAte the planned Solution intO The UnfamiLiAr pANdas coMmand. onE Of tHose “unknown” FuncTions is thE transFOrm methOD. Even afTer usiNg pAndAs foR A wHiLe, I HaVE neVEr Had THe cHance to uSe ThIs funCtioN SO i ReceNtlY tooK some Time to figure oUt wHat iT Is aNd how It couLd be HeLpful For reaL worlD aNalysis. This artiCle wIll walk thRouGh An eXaMple wHEre traNsfOrm Can be usEd to effICieNtLY SUmMarize data.

What is trAnSFOrM? I have foUnd the BEsT cOVerage of ThIs tOpic IN jake VAndeRplAs’ excellEnt PytHOn daTa ScienCe handboOk. i plAn tO writE A revIew on tHis book iN the fUTure but the shorT And sweet is thaT It IS A gREat rEsoUrce that I hiGhly REcomMend. aS dEscRIbed iN the bOoK, TrANsform is an operation UsEd in coNjuncTion with groupBy (which is oNE OF the most UsefUL oPErations in pandAs). I suSpect most pANdas userS likeLy have usEd aggregaTE, Filter or AppLy wIth GroUPBy To summarize daTA. howeVeR, transfOrm Is a littLe mOre DifFicUlT to undersTand - espeCiAlLy CoMinG from AN Excel woRlD. SiNcE JaKe madE All of hIs booK avaIlAbLE viA jupyteR NoTEBookS iT iS a goOd pLaCe to sTart TO unDerstanD how transForM Is unIqUe: while agGregation must ReTurn a reducEd VerSion of THE data, traNsformation can return somE TransfoRmeD versIon oF the full dAta To recoMbiNE. For suCh a traNsforMaTioN, THe outPUT iS thE sAme shape as THE inPut. A cOmMon eXample iS to center the data by SUbtRacting the groUp-wIse mEAN. WIth THaT BasIc DEfiNITion, I will go throUgh another ExAMpLe that can eXPlaIn How this Is usefuL in otHEr instaNces outsiDe of centeRiNg daTA.

proBlem Set For This examPle, we will ANalyzE SoMe ficTitIous saLeS daTa. In oRder to KEep The daTaset sMaLl, here Is a saMpLe of 12 saleS transactions for our compAny: accOunt nAme Order sku qUanTIty Unit price Ext pRice 0 383080 Will LLc 10001 B1-20000 7 33.69 235.83 1 383080 WIll lLC 10001 S1-27722 11 21.12 232.32 2 383080 WIll llC 10001 B1-86481 3 35.99
 of this, my hope is we wi ll becomea clo ser -kn it  soc iety , one with mor e  und erstanding of others,regar dl e ss o f  o ccupa tion, b a ck g r oun dor  ev en wh at te amwe migh t support.  In tr oduction One of  the compe lli ng featuresofpandas  i s t h at it  ha s a r ich li b rary o f methods  f o r mani p ulating d at a. H owever, there are ti m es when it isnot cl ea r  w h a t t hevarious fu nc tions do andh ow t o  us e  them. If you  are approa c hin g a pr ob lem from a n Exc el min dset, it ca n be  difficul t to t r anslate the pla nned s olu tio n in t oth e u nf a mil i ar  pa n das  command .On e ofthos e “ u nkno wn”  fun ction s is the tran sfo rm m e tho d. Ev en af terus ing p andasfor a  w hile, I have ne verhad the c han ce  to  u se th i s func tio n s o I rec ently t o okso m e ti me to figure out w ha t it  is andhow it co ul d  be help fu l f or r e a l wor ld a n al ysis. Th is art i cl ewill wa lk  throu gh  an  ex ample wher e tran sform ca n beu sed to efficie n tly summarize da t a .What  is  transform?  I h a ve f ound th e b e st co verag eo ft his topic in Jake V an derPla s’ ex cellent Pytho n Data Sci e n c e Handbo ok.I  p l an to write arevie w on thisb ook in t he fu ture but  the shor t and swee t i s t hat  it i sa great resou r c e th at  I high lyrecomme nd.  As  de scr ib ed in the  book, t ra ns fo rm  is  an o p erationus edin  co njunc t ion wi th gr oupb y(w h ich  is one of t he m os tusef ulop erati onsi n p andas).  I suspec t m o st p an da s users  likely haveus ed aggrega te , f iltero r  apply w ith groupby to summariz e  data.How ever,  tra nsform is  alittle  mo r e diff icultto un de rst a n d - e s p ec ial ly  coming fr o m  an  Exce lworl d. Sinc e Jake made all of his  book availab leviaj u py ter no t ebo ok s  it i s a good placeto start t ou nd erstand ho w  tr an sform i s uniqu e: Wh i le aggr egation m ust retur na re d u ced  version o f the da ta, trans f ormat i on  canret urn so me  tr ansfo rmed v e rsi on of  the f ul l data  to r ec ombine.For such a transformati on, th e out put  is the s ame sha pe as the  inp ut. A comm onexa mpleist o cen tert he  da t a bysubt r acting th e  g rou p - wi se mean. Wi t h tha t bas icd efinit ion,  I will go throug h  another examp le t h a t c ane xpla in  how this is u sef ul i n otherin stances out side ofce n terin g data .

Pro blem Se t Fo r  thisexam ple , we will  an al y ze some  f ic t itious  sal es  data.  In or d er t o keep the dataset  smal l ,  here isa sam pl e of 12 sale s transact ions for ou r comp any:  acco unt nam eordersku  q uantity un i t price e xt pr ice 0 3 83 080Wil l LLC1000 1 B1-20 000733. 69 235.83 1  3 8 30 80 Wil l LL C 100 01  S1- 27722 112 1.12 232 .32 2 38308 0Wil l LLC 10 0 01 B 1-86481 335. 99
 of_this, my_hope is we will_become a_closer-knit_society, one_with_more understanding of_others, regardless of_occupation, background or even_what team we_might_support. Introduction One of the compelling features of pandas is that it has a_rich_library of_methods_for_manipulating data. However, there are_times when it is not_clear what_the various functions do and how to use_them._If you are_approaching a problem from an Excel mindset, it can_be difficult to translate the planned_solution into the_unfamiliar_pandas_command. One of those_“unknown” functions is the transform method._Even after using pandas for a_while, I have never had the chance_to use this function so I_recently took some time to_figure out_what it is and how_it could be_helpful for_real world analysis._This article will walk through an_example where transform_can be used to efficiently summarize_data.

What_is transform? I_have_found_the best_coverage of this_topic_in Jake_VanderPlas’_excellent Python Data Science Handbook. I_plan_to write a review on this book_in the future but_the_short and sweet is_that it is a great_resource that I highly recommend. As_described in_the book,_transform is an operation used in conjunction with groupby (which is_one of the most useful operations_in pandas). I suspect_most pandas_users_likely have used_aggregate,_filter or_apply with groupby to summarize data. However,_transform is_a little more difficult to understand_- especially coming from_an_Excel world. Since Jake made all_of his book available via jupyter_notebooks it is a good_place_to_start to understand how transform_is unique: While aggregation must return_a reduced version_of the data, transformation can return some_transformed_version of the full data to_recombine._For such a transformation, the output_is_the_same shape as the input._A common example is to center_the data by subtracting the group-wise mean. With that_basic definition, I_will go through another example_that_can_explain how this is useful in other instances outside of_centering data.

Problem_Set For this_example, we will analyze some fictitious sales data. In order_to keep the dataset small, here is_a sample of 12 sales transactions for our company: account name_order sku quantity unit price ext price 0_383080 Will LLC 10001 B1-20000 7 33.69_235.83 1 383080 Will_LLC_10001 S1-27722 11 21.12 232.32 2 383080_Will_LLC_10001_B1-86481 3 35.99