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16,300 | if you open up the %bub djfodf folder for this classwhich you downloaded earlier in the earlier sectionyou should find zuipojqzoc filego ahead and double-click on that it should open right up in canopy if you have everything installed properlyand it should look little bit something like the following screenshotnew ... |
16,301 | understanding python code the first example that want to give you of some python code is right here the following block of code represents some real python code that we can actually run right within this view of the entire notebook pagebut let' zoom in now and look at that code |
16,302 | let' take look at what' going on we have list of numbers and list in pythonkind of like an array in other languages it is designated by these square bracketswe have this data structure of list that contains the numbers through and then to iterate through every number in that listwe'll say gpsovncfsjomjtu /vncfst that' ... |
16,303 | you'll notice that within this gps blockwe have tab of one within that entire blockand for every ovncfsjomjtu /vncfst we will execute all of this code that' tabbed in by one tab stop we'll print the numberand the comma just means that we're not going to do new line afterwards we'll print something else right after itan... |
16,304 | so againthe point want to make is that whitespace is important you will designate blocks of code that run togetheryou knowsuch as gps loop or jguifo statementsusing indentation or tabsso remember that alsopay attention to your colons too you'll notice that lot of these clauses begin with colon importing modules python ... |
16,305 | data structures let' move on to data structures if you need to pause and let things sink in little bitor you want to play around with these little bit morefeel free to do so the best way to learn this stuff is to dive in and actually experimentso definitely encourage doing thatand that' why ' giving you working ipython... |
16,306 | the output of the previous code example is as followsgo back to the original example there now you can also slice lists if you want to take subset of listthere' very simple syntax for doing soy the output of the above code example is as followspre colon iffor exampleyou want to take the first three elements of listever... |
16,307 | the output is as followsyou might want to keep this ipython/jupyter notebook file around it' good referencebecause sometimes it can get confusing as to whether the slicing operator includes that element or if it' up to or including it or not so the best way is to just play around with it here and remind yourself negati... |
16,308 | the append function if you want to just add one more thing to that listyou can use the bqqfoe function so just want to stick the number at the endthere we goybqqfoe the output of the above code is as followscomplex data structures you can also have complex data structures with lists so you don' have to just put number... |
16,309 | so will return element remember that had in it observe the previous exampleand we start counting from so element will actually be the second element in the listor the number in this casealrightthe sort function finallylet' have built-in sort function that you can use [tpsu so if start with list [which is and ... |
16,310 | the output of the previous code is as followswe can say can still use mfohuimfo on that to say that there are three elements in that tupleand even thoughif you're not familiar with the term uvqmfa uvqmf can actually contain as many elements as you want even though it sounds like it' latin based on the number thre... |
16,311 | we can create new list that contains two tuples so in the preceding examplewe have our tuple of and our tuple of then we make list of those two tuples and we get back this structurewhere we have square brackets indicating list that contains two tuples indicated by parenthesesand one thing that tuples are common... |
16,312 | dictionaries finallythe last data structure that we'll see lot in python is dictionaryand you can think of that as map or hash table in other languages it' way to basically have sort of mini-databasesort of key/value data store that' built into python so let' sayi want to build up little dictionary of star trek ships a... |
16,313 | we can also see what happens if you try to look up something that doesn' exist wellwe can use the hfu function on dictionary to safely return an entry so in this case&oufsqsjtf does have an entry in my dictionaryit just gives me back ,jslbut if call the / ship on the dictionaryi never defined the captain of thatso it... |
16,314 | python basics part in addition to python basics part let us now try to grasp more python concepts in detail functions in python let' talk about functions in python like with other languagesyou can have functions that let you repeat set of operations over and over again with different parameters in pythonthe syntax for ... |
16,315 | the output of the preceding code is as followsnow have function called % pnfuijohefg% pnfuijohand it will take two parametersone that 'll call and the other 'll call yand if happeni can actually pass in function for one of these parameters sothink about that for minute look at this example with bit more sense here% pnf... |
16,316 | in this example% pnfuijoh will pass in this lambda function as the first parameterwhich computes the cube of and the parameter so what' this really doing under the hoodthis mbnceb function is function of itself that gets passed into the in % pnfuijoh in the previous exampleand here is going to be this will return of yw... |
16,317 | the output of the previous code is as follows'bmtf the other thing we can do is use jtwhich is sort of the same thing as equal it' more python-ish representation of equalityso is the same thing as jtbut this is considered the more pythonic way of doing it so jtcomes back as 'bmtf because is not ifjgfmtfmppq jgjtqs... |
16,318 | the output of the previous code is as followsfor examplewe can use this range operator to automatically define list of numbers in the range so if we say gpsy in sbohf sbohfwill produce list of through and by saying for in that listwe will iterate through every individual entry in that list and print it out ... |
16,319 | the output of the previous code is as followswe can also saystart with and xijmf print it out and then increment by this will go through over and over againincrementing until it' less than at which point we break out of the xijmf loop and we're done so it does the same thing as this first example herebut jus... |
16,320 | now this shouldn' be too hard there are examples in this notebook of doing all that stuffall you have to do is put it together and get it to run sothe point is not to give you something that' hard just want you to actually get some confidence in writing your own python code and actually running it and seeing it operate... |
16,321 | more options than just the ipython/jupyter notebook want to make sure that you know there' more than one way to run python code nowthroughout this bookwe'll be using the ipython/jupyter notebook format but in the real worldyou're not going to be running your code as notebook you're going to be running it as standalone ... |
16,322 | running python scripts in command prompt can actually run the script in command prompt if go to toolsi can go to canopy command promptand that will open up command window that has all the necessary environment variables already in place for running python can just type qzuipo uftuqz and run the scriptand out comes my ... |
16,323 | using the canopy ide moving backi can also run the script from within the ide so from within canopyi can go to the run menu can either go to run run fileor click on the little play iconand that will also execute my scriptand see the results at the bottom in the output windowas shown in the following screenshotso that' ... |
16,324 | for examplei could say tuvggmake it mjtu calland have and now can say mfo tuvgg and that will give me can saygpsyjotuvgg qsjouyand we get output as so you can see you can kind of makeup scripts as you go down in the interactive prompt at the bottom and execute things one thing at time in this exampletuvgg is ... |
16,325 | now if do want to reset this environmentif want to get rid of tuvgg and start all overthe way you do that is you go up to the run menu here and you can say restart kerneland that will strike you over with blank slateso now have new python environment that' clean slateand in this casewhat did call ittype tuvgg and tuvgg... |
16,326 | so there you have itthree ways of running python codethe ipython/jupyter notebookwhich we'll use throughout this book just because it' good learning toolyou can also run scripts as standalone script filesand you can also execute python code in the interactive command prompt so there you have itand there you have three ... |
16,327 | statistics and probability refresherand python practice in this we are going to go through few concepts of statistics and probabilitywhich might be refresher for some of you these concepts are important to go through if you want to be data scientist we will see examples to understand these concepts better we will also ... |
16,328 | types of data alrightif you want to be data scientistwe need to talk about the types of data that you might encounterhow to categorize themand how you might treat them differently let' dive into the different flavors of data you might encounterthis will seem pretty basicbut we've got to start with the simple stuff and ... |
16,329 | numerical data let' start with numerical data it' probably the most common data type basicallyit represents some quantifiable thing that you can measure some examples are heights of peoplepage load timesstock pricesand so on things that varythings that you can measurethings that have wide range of possibilities now the... |
16,330 | categorical data the second type of data that we're going to talk about is categorical dataand this is data that has no inherent numeric meaning most of the timeyou can' really compare one category to another directly things like genderyes/no questionsracestate of residenceproduct categorypolitical partyyou can assign ... |
16,331 | ordinal data the last category that you tend to hear about with types of data is ordinal dataand it' sort of mixture of numerical and categorical data common example is star ratings for movie or musicor what have you in this casewe have categorical data in that could be through starswhere might represent poor and might... |
16,332 | how about if you're reading your overall health on scale of to where those choices correspond to the categories poormoderategoodand excellentwhat do you thinkthat' good example of ordinal data that' very much like our movie ratings dataand againdepending on how you model thatyou could probably treat it as discrete nume... |
16,333 | mean the meanas you probably knowis just another name for the average to calculate the mean of datasetall you have to do is sum up all the values and divide it by the number of values that you have sum of samples/number of samples let' take this examplewhich calculates the mean (averagenumber of children per house in m... |
16,334 | againall do is take the datasort it numericallyand take the center point if you have an even number of data pointsthen the median might actually fall in between two data points it wouldn' be clear which one is actually the middle in that caseall you do istake the average of the two that do fall in the middle and consid... |
16,335 | mode finallywe'll talk about mode this doesn' really come up too often in practicebut you can' talk about mean and median without talking about mode all mode meansis the most common value in dataset let' go back to my example of the number of kids in each house how many of each value are there the mode is if just look ... |
16,336 | using meanmedianand mode in python let' start doing some real coding in python and see how you compute the meanmedianand mode using python in an ipython notebook file so go ahead and open up the fbo fejbo pefjqzoc file from the data files for this section if you' like to follow alongwhich definitely encourage you to d... |
16,337 | once do thati compute the average of those data pointsor the mean by just calling oqnfbo on jodpnft which is my list of data it' just that simple let' go ahead and run that make sure you selected that code block and then you can hit the play button to actually execute itand since there is random component to these inc... |
16,338 | go ahead and select the code block and hit play it will actually create new graph for us as followsif you're not familiar with histograms or you need refresherthe way to interpret this is that for each one of these buckets that we've discretized our data into is showing the frequency of that data sofor examplearound , ... |
16,339 | the following is the output of the preceding code vu we don' expect to see lot of outliers because this is nice normal distribution median and mean will be comparable when you don' have lot of weird outliers analyzing the effect of outliers just to prove pointlet' add in an outlier we'll take donald tr... |
16,340 | calculating mode using the scipy package finallylet' look at mode we will just generate bunch of random integers of them to be precisethat range between and we're going to create bunch of fake ages for people bhft oqsboepnsboejou ijhi tj[ bhft your output will be randombut should look something like the fol... |
16,341 | nowscipykind of like numpyis bunch of like handy-dandy statistics functionsso we can import tubut from scipy using the following syntax it' little bit different than what we saw before gspntdjqzjnqpsutubut tubutnpef bhft the code meansfrom the tdjqz package import tubutand ' just going to refer to the package as tubut... |
16,342 | the output for randomizing the equation is as distribution is as followsmake sure you selected that code block and then you can hit the play button to actually execute it in this casethe mode ended up being the number which occurred times vu pef ftvmu npef bssbz dpvou bssbz soit' very simple concept you can do it f... |
16,343 | some exercises ' going to give you little assignment in this section if you open up fbo fejbo&yfsdjtfjqzoc filethere' some stuff you can play with want you to roll up your sleeves and actually try to do this in the filewe have some random -commerce data what this data represents is the total amount spent per transacti... |
16,344 | variance let' look at histogrambecause variance and standard deviation are all about the spread of the datathe shape of the distribution of dataset take look at the following histogramlet' say that we have some data on the arrival frequency of airplanes at an airportfor exampleand this histogram indicates that we have ... |
16,345 | measuring variance we usually refer to variance as sigma squaredand you'll find out why momentarilybut for nowjust know that variance is the average of the squared differences from the mean to compute the variance of datasetyou first figure out the mean of it let' say have some data that could represent anything let' s... |
16,346 | let' look at what happens thereso (- ) is positive and (- ) ends up being much smaller numberthat is because that' much closer to the mean of also ( ) turned out to be close to the meanonly but as we get up to the positive outlier( ends up being that gives us( to find the actual variance valuewe just take the average o... |
16,347 | identifying outliers with standard deviation here' histogram of the actual data we were looking at in the preceding example for calculating variance now we see that the number occurred twice in our datasetand then we had one one and one the standard deviation is usually used as way to think about how to identify outlie... |
16,348 | population variance versus sample variance there is little nuance to standard deviation and varianceand that' when you're talking about population versus sample variance if you're working with complete set of dataa complete set of observationsthen you do exactly what told you you just take the average of all the square... |
16,349 | as we've seenpopulation variance is usually designated as sigma squared ( )with sigma (sas standard deviationand we can say that is the summation of each data point minus the meanmusquaredthat' the variance of each sample squared over nthe number of data points and we can express it with the following equationx denotes... |
16,350 | analyzing standard deviation and variance on histogram let' write some code here and play with some standard deviation and variances so if you pull up the ue%fw bsjbodfjqzoc file ipython notebookand follow along with me here please dobecause there' an activity at the end that want you to try what we're going to do her... |
16,351 | we have , data points centered around with normal distribution and standard deviation of measure of the spread of this datayou can see that the most common occurrence is around and as we get further and further from thatthings become less and less likely the standard deviation point of that we specified is around and a... |
16,352 | try it yourself want you to dive in here and actually play around with itmake it realso try out different parameters on generating that normal data rememberthis is measure of the shape of the distribution of the dataso what happens if change that center pointdoes it matterdoes it actually affect the shapewhy don' you t... |
16,353 | probability density functions let' talk about probability density functionsand we've used one of these already in the book we just didn' call it that let' formalize some of the stuff that we've talked about for examplewe've seen the normal distribution few timesand that is an example of probability density function the... |
16,354 | howeveras you get between two and three standard deviations (- to - and to )we're down to just little bit over ( %to be preciseas you get out beyond three standard deviations (- and sthen we're much less than sothe graph is just way to visualize and talk about the probabilities of the given data point happening againa ... |
16,355 | for exampleyou can plot normal probability density function of continuous data on the black curve shown in the graphbut if we were to quantize that into discrete dataset like we would do with histogramwe can say the number occurs some set number of timesand you can actually say the number has little over chance of occu... |
16,356 | so we can create uniform distribution by using the numpy sboepnvojgpsn function the preceding code saysi want uniformly distributed random set of values that ranges between and and want of them if then create histogram of those valuesyou can see it looks like the following there' pretty much an equal chance o... |
16,357 | solet' look at the following examplegspntdjqztubutjnqpsuopsn jnqpsunbuqmpumjcqzqmpubtqmu oqbsbohf qmuqmpu opsnqeg in the preceding examplewe're creating list of values for plotting that range between - and with an increment of in between them by using the bsbohf function so those are the values on the gr... |
16,358 | in the above codewe use the sboepnopsnbm function of the numpy packageand the first parameter nvrepresents the mean that you want to center the data around tjhnb is the standard deviation of that datawhich is basically the spread of it thenwe specify the number of data points that we want using normal probability dist... |
16,359 | to do that in pythonjust like we had function in tdjqztubut for opsnqegwe also have an fyqpoqegor an exponential probability distribution function to do that in pythonwe can do the same syntax that we did for the normal distribution with an exponential distribution here as shown in the following code blockgspntdjqz... |
16,360 | binomial probability mass function we can also visualize probability mass functions this is called the binomial probability mass function againwe are going to use the same syntax as beforeas shown in the following codegspntdjqztubutjnqpsufyqpo jnqpsunbuqmpumjcqzqmpubtqmu oqbsbohf qmuqmpu fyqpoqeg so inst... |
16,361 | poisson probability mass function lastlythe other distribution function you might hear about is poisson probability mass functionand this has very specific application it looks lot like normal distributionbut it' little bit different the idea here isif you have some information about the average number of things that h... |
16,362 | the odds of seeing visitors on given dayit turns outcomes out to about or probability very interesting alrightso those are some common data distributions you might run into in the real world remember we used probability distribution function with continuous databut when we're dealing with discrete datawe use probabilit... |
16,363 | the following graph is an example for income distributionthe preceding image shows an example of income distribution data for exampleat the th percentile we can say that of the data pointswhich represent people in americamake less than $ , yearand one percent make more than that converselyif you're one-percenteryou're ... |
16,364 | quartiles percentiles are also used in the context of talking about the quartiles in distribution let' look at normal distribution to understand this better here' an example illustrating percentile in normal distribution |
16,365 | looking at the normal distribution in the preceding imagewe can talk about quartiles quartile ( and quartile ( in the middle are just the points that contain together of the dataso are on left side of the median and are on the right side of the median the median in this example happens to be near the mean for exampleth... |
16,366 | in this examplewhat we're going to do is generate some data centered around zerothat is with mean of zerowith standard deviation of and ' going to make data points with that distribution thenwe're going to plot histogram and see what we come up with the generated histogram looks very much like normal distributio... |
16,367 | want to compute the th percentilewhich gives me the point at which of the data is less than that value we can easily do that with the following codeoqqfsdfoujmf wbmthere is the output of that code vu the th percentile of this data turns out to be so it' around hereand basicallyat that point less th... |
16,368 | moments nextlet' talk about moments moments are fancy mathematical phrasebut you don' actually need math degree to understand itthough intuitivelyit' lot simpler than it sounds it' one of those examples where people in statistics and data science like to use big fancy terms to make themselves sound really smartbut the ... |
16,369 | now when we get to the third and fourth momentsthings get little bit trickierbut they're still concepts that are easy to grasp the third moment is called skewand it is basically measure of how lopsided distribution is you can see in these two examples above thatif have longer tail on the leftnow then that is negative s... |
16,370 | the fourth moment is called kurtosis wowthat' fancy wordall that really isis how thick is the tail and how sharp is the peak so againit' measure of the shape of the data distribution here' an exampleyou can see that the higher peak values have higher kurtosis value the topmost curve has higher kurtosis than the bottomm... |
16,371 | computing moments in python let' play around in python and actually compute these moments and see how you do that to play around with thisgo ahead and open up the pnfoutjqzocand you can follow along with me here let' again create that same normal distribution of random data againwe're going to make it centered around ... |
16,372 | this gives the following output in our example vu the output turns out to be very close to zerojust like we would expect for normally distributed data centered around zero sothe world makes sense so far now we find the second momentwhich is just another name for variance we can do that with the fol... |
16,373 | and here' the output vu indeedit does turn out to be zero kurtosis reveals our data distribution in two linked waysthe shape of the tailor the how sharp the peak if just squish the tail down it kind of pushes up that peak to be pointierand likewiseif were to push down that distributionyou can imagine... |
16,374 | we looked at the types of data distributions (uniform distributionnormal or gaussian distributionexponential probability distributionbinomial probability mass functionpoisson probability mass functionin general and how to visualize them using python we analyzed the concepts of percentiles and moments and saw how to com... |
16,375 | matplotlib and advanced probability concepts after going through some of the simpler concepts of statistics and probability in the previous we're now going to turn our attention to some more advanced topics that you'll need to be familiar with to get the most out of the remainder of this book don' worrythey're not too ... |
16,376 | crash course in matplotlib your data is only as good as you can present it to other peoplereallyso let' talk about plotting and graphing your data and how to present it to others and make your graphs look pretty we're going to introduce matplotlib more thoroughly and put it through its paces 'll show you few tricks on ... |
16,377 | so tying it back into last look at probability density functionshere we are plotting normal probability density function using nbuqmpumjc so we just call qzqmpu' qmpu method to set up our plotand then we display it using qmutipx when we run the previous codewe get the following outputthat' what we geta pretty little g... |
16,378 | in this examplei' calling my original function of just normal distributionbut ' going to render another normal distribution here as wellwith mean around and standard deviation of theni' going to show those two together so you can see how they compare to each other you can see that by defaultnbuqmpumjc chooses diffe... |
16,379 | you'll want to change that to an actual path that exists on your machine if you're following along you probably don' have tfst='sbol folder on your system remember too that if you're on linux or macosinstead of backslash you're going to use forward slashesand you're not going to have drive letter with all of these pyth... |
16,380 | in this examplefirst get the axes using qmubyft once have these axes objectsi can adjust them by calling tfu@ymjni can set the range from - to and with set tfu@zmjni set the range from to you can see in the below outputthat my values are ranging from to and goes from to can also have explicit control over where the ... |
16,381 | byfttfu@zujdlt byfthsje qmuqmpu opsnqeg qmuqmpu opsnqeg qmutipx by executing the above codei get nice little grid lines that makes it little bit easier to see where specific point isalthough it clutters things up little bit it' little bit of stylistic choice there changing line types and colors what if ... |
16,382 | so you see in the preceding codethere' actually an extra parameter on the qmpu functions at the end where can pass little string that describes the style of line in this first examplewhat cindicates is want bluesolid line the stands for blueand the dash means solid line for my second qmpu functioni' going to plot it in... |
16,383 | the preceding code gives you dashed red line as line style as shown in the following graph imagei can also do dash dot combination (byft qmubyft byfttfu@ymjn byfttfu@zmjn byfttfu@yujdlt byfttfu@zujdlt byfthsje qmuqmpu opsnqeg cqmuqmpu opsnqeg qmutipx |
16,384 | you get an output that looks like the following graph imagesothose are the different choices there could even make it green with vertical slashes ( byft qmubyft byfttfu@ymjn byfttfu@zmjn byfttfu@yujdlt byfttfu@zujdlt byfthsje qmuqmpu opsnqeg cqmuqmpu opsnqeg qmutipx |
16,385 | 'll get the following outputhave some fun with that if you wantexperiment with different valuesand you can get different line styles labeling axes and adding legend something you'll do more often is labeling your axes you never want to present data in vacuum you definitely want to tell people what it represents to do t... |
16,386 | into the legendyou pass in basically list of what you want to name each graph somy first graph is going to be called sneetchesand my second graph is going to be called gacksand the mpd parameter indicates what location you want it atwhere represents the lower right-hand corner let' go ahead and run the codeand you shou... |
16,387 | ebuboqbsbohf qmuboopubuf )&%":* &"-*;&%= *$ -%$ ,#"$ /= )&/& & * "/ &yz bsspxqspqt ejdu bsspxtuzmf yzufyu qmuqmpu ebub qmuymbcfm qmuzmbcfm ujnf nzpwfsbmmifbmui in this exampleyou call qmuylde which puts matplotlib in xkcd mode after you do thatthings will just have style with kind of comic book font an... |
16,388 | generating pie charts nowto go back to the real worldwe can remove xkcd mode by calling sdefgbvmut on matplotliband we can get back to normal mode here if you want pie chartall you have to do is call qmuqjf and give it an array of your valuescolorslabelsand whether or not you want items explodedand if soby how much he... |
16,389 | generating bar charts if want to generate bar chartthat is also very simple it' kind of similar idea to the pie chart let' look at the following code wbmvft dpmpst qmucbs sbohf wbmvftdpmps dpmpst qmutipx 've defined an array of values and an array of colorsand just plot the data the above code plots from the rang... |
16,390 | generating scatter plots scatter plot is something we'll see pretty often in this book sosay you have couple of different attributes you want to plot for the same set of people or things for examplemaybe we're plotting ages against incomes for each personwhere each dot represents person and the axes represent different... |
16,391 | generating histograms finallywe'll remind ourselves how histogram works we've already seen this plenty of times in the book let' look at the following codejodpnft oqsboepnopsnbm qmuijtu jodpnftqmutipx in this examplei call normal distribution centered on , with standard deviation of , with , data ... |
16,392 | we define outliers in box-and-whisker plot as anything beyond times the interquartile rangeor the size of the box sowe take the size of the box times and up to that point on the dotted whiskerswe call those parts outer quartiles but anything outside of the outer quartiles is considered an outlierand that' what the line... |
16,393 | you can see that the graph is showing the box that represents the inner of all dataand then we have these outlier lines where we can see little crosses (they may be circles in your versionfor each individual outlier that lies in that range try it yourself alrightthat' your crash course in matplotlib time to get your ha... |
16,394 | covariance and correlation nextwe're going to talk about covariance and correlation let' say have two different attributes of something and want to see if they're actually related to each other or not this section will give you the mathematical tools you need to do soand we'll dive into some examples and actually figur... |
16,395 | socovariance and correlation give us means of measuring just how tight these things are correlated would expect very low correlation or covariance for the data in the left scatter plotbut very high covariance and correlation for the data in the right scatter plot so that' the concept of covariance and correlation it me... |
16,396 | correlation correlation normalizes everything by the standard deviation of each attribute (just divide the covariance by the standard deviations of both variables and that normalizes thingsby doing soi can say very clearly that correlation of - means there' perfect inverse correlationso as one value increasesthe other ... |
16,397 | mfo sfuvsoepu ef@nfbo ef@nfbo covarianceagainis defined as the dot productwhich is measure of the angle between two vectorsof vector of the deviations from the mean for given set of data and the deviations from the mean for another given set of data for the same data' data points we then divide that by in this casebe... |
16,398 | so just as sanity check here we'll start off by scatter plotting this stuffyou'll see that it tends to cluster around the middle because of the normal distribution on each attributebut there' no real relationship between the two for any given page speed is wide variety of amount spentand for any given amount spent ther... |
16,399 | herewe are keeping things little bit randombut we are creating real relationship between these two sets of values for given userthere' real relationship between the page speeds they encounter and the amount that they spend if we plot that outwe can see the following outputyou can see that there' actually this little cu... |
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