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In this case, the idx tells us it s an index, and then it s followed by the table name and column names You could also use ixc to denote a clustered index, if the primary key of the table isn t also the clustered index As with any of the naming standards, the specifics aren t as important as having the standard in the first place

The rst print() statement prints the column titles, and the second print() statement prints hyphens under each title This second statement s str format() call is slightly subtle The string we give to be printed is "", that is, the empty string we get the hyphens by printing the empty string padded with hyphens to the given widths Next we use a for in loop to print the details of each user, extracting the key for each user s dictionary item in sorted order For convenience we create the user variable so that we don t have to keep writing users[key] throughout the rest of the function In the loop s rst call to strformat() we set the name variable to the user s name in surname, forename (and optional initial) form We access items in the user named tuple by name Once we have the user s name as a single string we print the user s details, constraining each column, (name, ID, username) to the widths we want The complete program (which differs from what we have reviewed only in that it has some initial comment lines and some imports) is in generate_usernamespy The program s structure read in a data le, process each record, write output is one that is very frequently used, and we will meet it again in the next example

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Keeping these characteristics of the target platform in mind, we can evaluate the design along the three dimensions of flexibility, efficiency, and simplicity Flexibility It is desirable for the high level design to be adaptable to a variety of different implementation requirements, and certainly all the important ones The rest of this section provides a partial checklist of factors that affect flexibility

Suppose we have a bunch of data les containing numbers relating to some processing we have done, and we want to produce some basic statistics to give us some kind of overview of the data Each le uses plain text (ASCII encoding) with one or more numbers per line (whitespace-separated) Here is an example of the kind of output we want to produce:

count mean median mode std dev = 183 = 13056 = 4300 = [500, 700, 5000] = 23501

Is the decomposition flexible in the number of tasks generated Such flexibility allows the design to be adapted to a wide range of parallel computers Is the definition of tasks implied by the task decomposition independent of how they are scheduled for execution Such independence makes the load balancing problem easier to solve Can the size and number of chunks in the data decomposition be parameterized Such parameterization makes a design easier to scale for varying numbers of PEs Does the algorithm handle the problem's boundary cases A good design will handle all relevant cases, even unusual ones For example, a common operation is to transpose a matrix so that a distribution in terms of blocks of matrix columns becomes a distribution in terms of

Here, we read 183 numbers, with 5, 7, and 50 occurring most frequently, and with a sample standard deviation of 23501 The statistics themselves are held in a named tuple called Statistics:

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