These games and story problems are fun because they challenge your creativity and problem-solving skills alongside your Python knowledge. In addition to practice, you might learn something new. Python for Programmers Paul J. Deitel, Paul Deitel, Harvey Deitel This book was designed to teach Python to coders who are already skilled in a different programming language, but it also has good advanced materials for those who are looking to expand their Python knowledge.
Check out the chapters on numpy arrays, file handling, defining your own object classes, and more. Full of shortcuts and advice, this is a very dense book, so I recommend browsing the table of contents for topics that interest you.
They also have free videos on YouTube that include Python tricks, answered viewer questions, advice, and general Python news and talk. Categories: Learning Resources.
Tags: rse. This erroneous behavior results because an else statement is bound solely to the if statement that it directly follows; in the above code-block, an elif would have been the appropriate keyword for line 5. This example also underscores the danger of assuming that lack of a certain condition a False built-in Boolean type necessarily implies the fulfillment of a second condition a True for comparisons that seem, at least superficially, to be linked.
In writing code with complicated streams of logic conditionals and beyond , robust and somewhat redundant logical tests can be used to mitigate errors and unwanted behavior. A strategy for building streams of conditional statements into code, and for debugging existing codebases, involves i outlining the range of possible inputs and their expected outputs , ii crafting the code itself, and then iii testing each possible type of input, carefully tracing the logical flow executed by the algorithm against what was originally anticipated.
Recognition of edge-case behavior is useful, as a disproportionate share of errors occur near these cases; for instance, division by zero can crash a function if the denominator in each division operation that appears in the function is not carefully checked and handled appropriately. Though beyond the scope of this primer, note that Python supplies powerful error-reporting and exception-handling capabilities; see, for instance, Python Programming [ 66 ] for more information.
Supplemental Chapters 14 and 16 in S1 Text provide detailed examples of testing the behavior of code. Exercise 3 : Recall the temperature-conversion program designed in Exercises 1 and 2. Now, rewrite this code such that it accepts two arguments: the initial temperature, and a letter designating the units of that temperature. Have the function convert the input temperature to the alternative scale. In this example, we sort three randomly chosen integers:.
Whereas the if statement tests a condition exactly once and branches the code execution accordingly, the while statement instructs an enclosed block of code to repeat so long as the given condition the continuation condition is satisfied. In fact, while can be considered as a repeated if. This is the simplest form of a loop, and is termed a while loop Fig 3. This is mentioned because looping constructs should be carefully examined when comparing source code in different languages.
If the condition is true, the block is executed and then the interpreter effectively jumps to the while statement that began the block.
If the condition is false, the block is skipped and the interpreter jumps to the first statement after the block. The code below is a simple example of a while loop, used to generate a counter that prints each integer between 1 and inclusive :.
This code will begin with a variable, then print and increment it until its value is , at which point the enclosing while loop ends and a final string line 5 is printed. Crucially, one should verify that the loop termination condition can, in fact, be reached. If not—e. In many environments, such as a Unix shell, the keystroke Ctrl-c can be used as a keyboard interrupt to break out of the loop. Exercise 4 : With the above example as a starting point, write a function that chooses two randomly-generated integers between 0 and , inclusive, and then prints all numbers between these two values, counting from the lower number to the upper number.
Recursion is a subtle concept. A while loop is conceptually straightforward: a block of statements comprising the body of the loop is repeatedly executed as long as a condition is true. A recursive function, on the other hand, calls itself repeatedly, effectively creating a loop.
Recursion is the most natural programming approach or paradigm for solving a complex problem that can be decomposed into easier subproblems, each of which resembles the overall problem. Mathematically, problems that are formulated in this manner are known as recurrence relations , and a classic example is the factorial below.
Recursion is so fundamental and general a concept that iterative constructs for , while loops can be expressed recursively; in fact, some languages dispense with loops entirely and rely on recursion for all repetition. The key idea is that a recursive function calls itself from within its own function body, thus progressing one step closer to the final solution at each self-call.
The recursion terminates once it has reached a trivially simple final operation, termed the base case. Recall that the factorial of a natural number, n , is defined as: 1 This function can be compactly implemented in Python like so:. A call to this factorial function will return 1 if the input is equal to one, and otherwise will return the input value multiplied by the factorial of that integer less one factorial n Note that this recursive implementation of the factorial perfectly matches its mathematical definition.
This often holds true, and many mathematical operations on data are most easily expressed recursively. Python places the current function call on hold in the call stack while the newly-called function is evaluated.
This process continues until the base case is reached, at which point the function returns a value. Next, the previous function instance in the call stack resumes execution, calculates its result, and returns it. This process of traversing the call stack continues until the very first invocation has returned.
At that point, the call stack is empty and the function evaluation has completed. Defining recursion simply as a function calling itself misses some nuances of the recursive approach to problem-solving. Any difficult problem e. Only when the problem is trivially easy 1! Recursive approaches fundamentally differ from more iterative also known as procedural strategies: Iterative constructs loops express the entire solution to a problem in more explicit form, whereas recursion repeatedly makes a problem simpler until it is trivial.
Many data-processing functions are most naturally and compactly solved via recursion. However, if the argument is negative or is an even number, the base case will never be reached note that line 5 subtracts 2 , causing the function call to simply hang, as would an infinite loop.
A valid recursive function must progress towards—and eventually reach—the base case with every call. More information on recursion can be found in Supplemental Chapter 7 in S1 Text , in Chapter 4 of [ 40 ], and in most computer science texts. Exercise 5 : Consider the Fibonacci sequence of integers, 0, 1, 1, 2, 3, 5, 8, 13, …, given by 2 This sequence appears in the study of phyllotaxis and other areas of biological pattern formation see, e.
Now, write a recursive Python function to compute the n th Fibonacci number, F n , and test that your program works. Include an assert to make sure the argument is positive.
Doing so gets you one step closer to Lucas sequences, L n , which are a highly general class of recurrence relations. Exercise 6 : Many functions can be coded both recursively and iteratively using loops , though often it will be clear that one approach is better suited to the given problem the factorial is one such example. In this exercise, devise an iterative Python function to compute the factorial of a user-specified integer argument.
As a bonus exercise, try coding the Fibonacci sequence in iterative form. Is this as straightforward as the recursive approach? Note that Supplemental Chapter 7 in the S1 Text might be useful here. A staggering degree of algorithmic complexity is possible using only variables, functions, and control flow concepts. However, thus far, numbers and strings are the only data types that have been discussed. Such data types can be used to represent protein sequences a string and molecular masses a floating point number , but actual scientific data are seldom so simple!
Optical microscopy experiments yield thousands of images, each consisting of a large two-dimensional array of pixels, and each pixel has color information that one may wish to access [ 68 ].
A protein multiple sequence alignment can be considered as a two-dimensional array of characters drawn from a letter alphabet one letter per amino acid AA and a gap symbol , and a protein 3D structural alignment is even more complex. Phylogenetic trees consist of sets of species, individual proteins, or other taxonomic entities, organized as typically binary trees with branch weights that represent some metric of evolutionary distance.
Modern datasets are often quite heterogeneous, particularly in the biosciences [ 69 ], and therefore data abstraction and integration are often the major goals. The data challenges hold true at all levels, from individual RNA transcripts [ 70 ] to whole bacterial cells [ 71 ] to biomedical informatics [ 72 ]. In each of the above examples, the relevant data comprise a collection of entities, each of which, in turn, is of some simpler data type. This unifying principle offers a way forward.
The term data structure refers to an object that stores data in a specifically organized structured manner, as defined by the programmer. Python offers several built-in sequence data structures, including strings, lists, and tuples.
Thus, the tuple is especially useful in building data structures as higher-order collections. Data that are inherently sequential e. The tuple is surrounded by parentheses, and commas separate the individual elements. The empty tuple is denoted , and a tuple of one element contains a comma after that element, e. A parenthesized expression is therefore not made into a tuple unless it contains commas. At the Python interpreter, try the statements type 1 and type 1,.
How do the results differ? A tuple can contain any sort of object, including another tuple. This versatility makes tuples an effective means of representing complex or heterogeneous data structures. Note that any component of a tuple can be referenced using the same notation used to index individual characters within a string; e.
In general, data are optimally stored, analyzed, modified, and otherwise processed using data structures that reflect any underlying structure of the data itself. Thus, for example, two-dimensional datasets are most naturally stored as tuples of tuples. This abstraction can be taken to arbitrary depth, making tuples useful for storing arbitrarily complex data. For instance, tuples have been used to create generic tensor-like objects. These rich data structures have been used in developing new tools for the analysis of MD trajectories [ 18 ] and to represent biological sequence information as hierarchical, multidimensional entities that are amenable to further processing in Python [ 20 ].
As a concrete example, consider the problem of representing signal intensity data collected over time. If the data are sampled with perfect periodicity, say every second, then the information could be stored most compactly in a one-dimensional tuple, as a simple succession of intensities; the index of an element in the tuple maps to a time-point index 0 corresponds to the measurement at time t 0 , index 1 is at time t 1 , etc.
What if the data were sampled unevenly in time? Then each datum could be represented as an ordered pair, t , I t , of the intensity I at each time-point t ; the full time-series of measurements is then given by the sequence of 2-element tuples, like so:. Three notes concern the above code: i From this two-dimensional data structure, the syntax dataSet[i][j] retrieves the j th element from the i th tuple. In the example above, the first line alone is not a valid closed expression, and Python allows the expression to continue on to the next line; the lengthy dataSet expression was formatted as above in order to aid readability.
Once defined, a tuple cannot be altered; tuples are said to be immutable data structures. This rigidity can be helpful or restrictive, depending on the context and intended purpose. For instance, tuples are suitable for storing numerical constants, or for ordered collections that are generated once during execution and intended only for referencing thereafter e.
A mutable data structure is the Python list. This built-in sequence type allows for the addition, removal, and modification of elements. The syntactic form used to define lists resembles the definition of a tuple, except that the parentheses are replaced with square brackets, e.
A trailing comma is unnecessary in one-element lists, as [1] is unambiguously a list. The index is said to be out of range , and a valid approach would be to append the value via myList. Several functions and methods are available for lists, tuples, strings, and other built-in types. For lists, append , insert , and remove are examples of oft-used methods; the function len returns the number of items in a sequence or collection, such as the length of a string or number of elements in a list.
The OOP section, below, elaborates the relationship between functions and methods. Lists and tuples are examples of iterable types in Python, and the for loop is a useful construct in handling such objects.
Custom iterable types are introduced in Supplemental Chapter 17 in S1 Text. A Python for loop iterates over a collection, which is a common operation in virtually all data-analysis workflows. Recall that a while loop requires a counter to track progress through the iteration, and this counter is tested against the continuation condition.
In contrast, a for loop handles the count implicitly, given an argument that is an iterable object:. In the above loop, all elements in myData are of the same type namely, floating-point numbers.
This is not mandatory. Together with dynamic typing, operator overloading helps make Python a highly expressive programming language.
In each iteration of the above loop, the variable datum is assigned each successive element in myData ; specifying this iterative task as a while loop is possible, but less straightforward.
Assuming that each reading represents the average CO 2 production rate over the previous ten hours, calculate the total amount of CO 2 generated and the final ethanol concentration in grams per liter.
Note that Supplemental Chapters 6 and 9 might be useful here. Other distance metrics, such as the Mahalanobis and Manhattan distances, often appear in computational biology too. With your code in hand, note the ease with which you can adjust your entire data-analysis workflow simply by modifying a few lines of code that correspond to the definition of the distance function. As a bonus exercise, generalize your code to read in a list of points and compute the total path length. Supplemental Chapters 6, 7, and 9 might be useful here.
Dictionaries, also known as associative arrays or hashes in Perl and other common languages, consist of : pairs enclosed in braces. They are particularly useful data structures because, unlike lists and tuples, the values are not restricted to being indexed solely by the integers corresponding to sequential position in the data series.
Rather, the keys in a dictionary serve as the index, and they can be of any immutable data type strings, numbers, or tuples of immutable data. As another example, dictionaries can be used to create lookup tables for the properties of a collection of closely related proteins.
Each key could be set to a unique identifier for each protein, such as its UniProt ID e. Dictionaries are described in greater detail in Supplemental Chapter 10 in the S1 Text. Consider the task of representing genealogy: an individual may have some number of children, and each child may have their own children, and so on. There is no straightforward way to represent this type of information as a list or tuple. A better approach would be to represent each organism as a tuple containing its children.
Each of those elements would, in turn, be another tuple with children, and so on. A specific organism would be a node in this data structure, with a branch leading to each of its child nodes; an organism having no children is effectively a leaf. A node that is not the child of any other node would be the root of this tree. This intuitive description corresponds, in fact, to exactly the terminology used by computer scientists in describing trees [ 73 ].
Trees are pervasive in computer science. This document, for example, could be represented purely as a list of characters, but doing so neglects its underlying structure, which is that of a tree sections, sub-sections, sub-sub-sections, ….
The whole document is the root entity, each section is a node on a branch, each sub-section a branch from a section, and so on down through the paragraphs, sentences, words, and letters. A common and intuitive use of trees in bioinformatics is to represent phylogenetic relationships.
However, trees are such a general data structure that they also find use, for instance, in computational geometry applications to biomolecules e. Relaxing these requirements gives a graph [ 76 ], which is an even more fundamental and universal data structure: A graph is a set of vertices that are connected by edges.
Graphs can be subtle to work with and a number of clever algorithms are available to analyze them [ 77 ]. There are countless data structures available, and more are constantly being devised.
Advanced examples range from the biologically-inspired neural network, which is essentially a graph wherein the vertices are linked into communication networks to emulate the neuronal layers in a brain [ 78 ], to very compact probabilistic data structures such as the Bloom filter [ 79 ], to self-balancing trees [ 80 ] that provide extremely fast insertion and removal of elements for performance-critical code, to copy-on-write B-trees that organize terabytes of information on hard drives [ 81 ].
The object-oriented programming OOP paradigm, to which Python is particularly well-suited, treats these two features of a program as inseparable. Several thorough treatments of OOP are available, including texts that are independent of any language [ 83 ] and books that specifically focus on OOP in Python [ 84 ].
The core ideas are explored in this section and in Supplemental Chapters 15 and 16 in S1 Text. Most scientific data have some form of inherent structure, and this serves as a starting point in understanding OOP. For instance, the time-series example mentioned above is structured as a series of ordered pairs, t , I t , an X-ray diffraction pattern consists of a collection of intensities that are indexed by integer triples h , k , l , and so on.
What data structure might be capable of most naturally representing such an entity? A simple generic Python tuple or list is clearly insufficient. While valid, such a data structure will be difficult to use: The programmer will have to recall multiple arbitrary numbers list and sub-list indices in order to access anything, and extensions to this approach will only make it clumsier. Additionally, there are many functions that are meaningful only in the context of proteins, not all tuples.
Conversely, not all tuple methods would be relevant to this protein data structure, yet a function to find Court cases that reached a decision along party lines would accept the protein as an argument. In other words, the tuple mentioned above has no clean way to make the necessary associations.
This protein representation problem is elegantly solved via the OOP concepts of classes, objects, and methods. Briefly, an object is an instance of a data structure that contains members and methods. Members are data of potentially any type, including other objects. Unlike lists and tuples, where the elements are indexed by numbers starting from zero, the members of an object are given names, such as yearDiscovered. Methods are functions that typically make use of the members of the object.
Objects are constructed from class definitions, which are blocks that define what most of the methods will be for an object. The examples in the 'OOP in Practice' section will help clarify this terminology. Note that some languages require that all methods and members be specified in the class declaration, but Python allows duck punching , or adding members after declaring a class.
Adding methods later is possible too, but uncommon. Some built-in types, such as int , do not support duck punching.
During execution of an actual program, a specific object is created by calling the name of the class, as one would do for a function. Classes can be created from previously defined classes. In such cases, all properties of the parent class are said to be inherited by the child class. The child class is termed a derived class , while the parent is described as a base class.
For instance, a user-defined Biopolymer class may have derived classes named Protein and NucleicAcid , and may itself be derived from a more general Molecule base class. Class names often begin with a capital letter, while object names i. Within a class definition, a leading underscore denotes member names that will be protected.
Working examples and annotated descriptions of these concepts can be found, in the context of protein structural analysis, in ref [ 85 ]. All built-in string methods will be exposed for that object e.
The statement dir 1 will show all the methods and members of an int there are many! This example also illustrates the conventional OOP dot-notation, object.
For instance, protein1. In this example, the residues member is a list or tuple of objects, and an item is retrieved from the collection using an index in brackets. By effectively compartmentalizing the programming logic and implicitly requiring a disciplined approach to data structures, the OOP paradigm offers several benefits.
Indeed, a generally good practice is to discourage end-users from directly accessing and modifying all of the members of an object.
Using classes sidesteps these problems. If our Protein class does not define a max method, then no attempt can be made to calculate its maximum. If it does define an isoelectricPoint method, then that method can be applied only to an object of type Protein. A classic example of a data structure that is naturally implemented via OOP is the creation of a Human class.
Each Human object can be fully characterized by her respective properties members such as , , etc. A specific human being, e. Note the usage of self as the first argument in each method defined in the above code. The self keyword is necessary because when a method is invoked it must know which object to use. That is, an object instantiated from a class requires that methods on that object have some way to reference that particular instance of the class, versus other potential instances of that class.
A practical way to view the effect of self is that any occurrence of objName. This is one key deviation from the behavior of top-level functions, which exist outside of any class. When defining methods, usage of self provides an explicit way for the object itself to be provided as an argument self-reference , and its disciplined usage will help minimize confusion about expected arguments.
Then, we can use this Atom class in constructing another class to represent molecules:. If the above code is run, for example, in an interactive Python session, then note that the aforementioned dir function is an especially useful built-in tool for querying the properties of new classes and objects.
For instance, issuing the statement dir Molecule will return detailed information about the Molecule class including its available methods.
Exercise 9 : Amino acids can be effectively represented via OOP because each AA has a well-defined chemical composition: a specific number of atoms of various element types carbon, nitrogen, etc. For these reasons, the prototype of an l -amino acid can be unambiguously defined by the SMILES [ 88 ] string , where denotes the side-chain and indicates the l enantiomer. In this exercise, create an AA class and use it to define any two of the twenty standard AAs, in terms of their chemical composition and unique physical properties.
To extend this exercise, consider expanding your AA class to include additional class members e. Scientific data are typically acquired, processed, stored, exchanged, and archived as computer files. Most simply, the Python interpreter allows command-line input and basic data output via the print function. For real-time interaction with Python, the free IPython [ 89 ] system offers a shell that is both easy to use and uniquely powerful e.
Depending on which mode is specified, different methods of the file object will be exposed for use. Table 4 describes mode types and the various methods of a File object.
The following example opens a file named myDataFile. As for all lists, this object is iterable and can be looped over in order to process the data. Data can be extracted and processed via subsequent string operations on the list of lines drawn from the file. In fact, many data-analysis workflows commit much effort to the pre-processing of raw data and standardization of formats, simply to enable data structures to be cleanly populated.
For many common input formats such as. A common example of the need to read and extract information is provided by the PDB file format [ 22 ], which is a container for macromolecular structural data.
In addition to its basic information content—lists of atoms and their 3D coordinates—the standard PDB file format also includes a host of metadata loosely, data that describe other lower-level data, for instance in terms of syntax and schemas , such as the biopolymer sequence, protein superfamily, quaternary structures, chemical moieties that may be present, X-ray or NMR refinement details, and so on.
Indeed, processing and analyzing the rich data available in a PDB file motivates the Final Project at the end of this primer. Such , or heteroatom, lines in a PDB file correspond to water, ions, small-molecule ligands, and other non-biopolymer components of a structure; for example, glycerol lines are often found in cryo-crystallographic structures, where glycerol was added to crystals as a cryo-protectant.
Exercise 10 : The standard FASTA file-format, used to represent protein and nucleic acid sequences, consists of two parts: i The first line is a description of the biomolecule, starting with a greater-than sign in the first column; this sign is immediately followed by a non-whitespace character and any arbitrary text that describes the sequence name and other information e.
Then, write Python code to read in the sequence from the FASTA file and: i determine the relative frequencies of AAs that follow proline in the sequence; ii compare the distribution of AAs that follow proline to the distribution of AAs in the entire protein; and iii write these results to a human-readable file. The regular expression regex is an extensible tool for pattern matching in strings.
They are discussed at length in Supplemental Chapter 17 in S1 Text. Regexes entered the world of practical programming in the late s at Bell Labs and, like many tools of that era, they are powerful, flexible, and terse constructs. Fundamentally, a regex specifies a set of strings.
The simplest type of regex is a simple string with no special characters metacharacters. In Python, a regex match es a string if the string starts with that regex.
Python also provides a search function to locate a regex anywhere within a string. For clarity, we will say that a regex find s a string if the string is completely described by the regex, with no trailing characters. There is no find in Python but, for purposes of description here, it is useful to have a term to refer to a match without trailing characters. Locating strings and parsing text files is a ubiquitous task in the biosciences, e.
Yet regexes offer even greater functionality than may be initially apparent from these examples, as described below. First, we note that the following metacharacters are special in regexes: , and in most cases they do not find themselves. The and metacharacters known as anchors are straightforward, as they find the start and end of a line, respectively. While match looks for lines beginning with the specified regex, adding a to the end of the regex pattern will ensure that any matching line ends at the end of the regex.
This is why there is no find function in Python: it is easily achieved by adding a to a regex used in match. A a period find s literally any character. The metacharacters , , , and are special quantifier operators, used to specify repetition of a character, character class, or higher-order unit within a regex described below.
A after a character or group of characters find s that character zero or more times. One can comb through RNA-seq reads to find sequences that are 3'-polyadenylated by search ing for. The metacharacter is akin to , except that it find s one or more of the preceding character.
A find s the preceding character zero or one time. Most generally, the syntax find s the preceding character possibly from a character class between m and n times, inclusive. The regex to search for this sequence would be.
Characters enclosed in square brackets, , specify a character class. This functionality allows a regex to find any of a set of characters. A range of characters can be provided by separating them with a hyphen,. So, for instance, would find a word that starts with a capital letter. Multiple ranges can be specified, and would find PDB files in some search directory.
This could be corrected by escaping the with a backslash, as discussed below. The metacharacter can be used to negate a character class: would find any non-numeric character.
A regex might be. This monstrous regex should find a dollar sign, any number of zeros, one non-zero number, at least three numbers, a period, and two numbers. The requirement of a non-zero number followed by three numbers is not met in this case. But, there is a problem here: The metacharacter anchors the end of a line, and because no text can appear after the end of a line this regex will never match any text.
Furthermore, the is meant to find a literal period the decimal point , but in a regex it is a wildcard that find s any character. The metacharacter can be used to solve both of these problems: It notifies the regex engine to treat the subsequent character as a literal. In particular, find s any digit, find s whitespace, find s non-whitespace, and find s any alphanumeric character or underscore i.
Initially, the behavior of can be deceptive: is not equivalent to , as the former will find a lone pound symbol, a lone Euro symbol, or a dollar sign followed by a number. As an example, to match the and records in a PDB file, would work. The final metacharacters that we will explore are matched parentheses, , which find character groups. The utility of groups stems from the ability to use them as units of repetition.
Note that parentheses delimit the. Maybe your supervisor has told you that you need to learn programming for your next project. Maybe you've been looking at job ads and noticed just how many of them are asking for programming skills. Table of contents In chapter one, you'll learn why Python is a good choice for biologists and beginners alike.
You'll also learn how to install Python for your operating system and how to set up your programming environment, complete with links to all the free software you'll need. In chapter two, you'll learn how to manipulate text including DNA and protein sequences and how to fix errors in your programs.
Exercises: calculating AT content, splicing introns. In chapter three, you'll learn how to read and write data to and from files. In chapter four, you'll learn how to process many pieces of data in a single program and more advanced tools for sequence manipulation. Exercises: trimming adapter sequences, concatenating exons. In chapter five, you'll learn how to make Python even more useful by creating your own functions, including the best ways to test those functions in order to speed up development.
Exercises: Analyzing the amino acid composition of protein sequences. In chapter six, you'll learn how to write programs that can make smart decisions about how to handle data and how to make your programs follow complex rules. Exercises: filtering genes based on multiple criteria. In chapter seven, you'll learn an incredibly powerful tool for working with patterns in text - regular expressions - and how to use it to search in DNA and protein sequences.
Exercises: filtering accession names and calculating restriction fragment sizes. In chapter eight, you'll learn how to store huge amounts of data in a way that can still allows it to be retrived very efficiently. This allows simplification of much of the code from previous chapters. Exercises: translating DNA sequences to protein. In chapter nine, you'll learn how to make your Python programs work in harmony with existing tools, and how to polish up your programs so that they're ready for other people to use.
Exercises: counting k-mers, binning DNA sequences by length. About the author Dr. Martin Jones has been teaching biologists to write software for over five years and has taught everyone from postgraduates to PIs.
He is currently Lecturer in Bioinformatics at Edinburgh University. Get A Copy. Paperback , pages. More Details Other Editions 3. Friend Reviews. To see what your friends thought of this book, please sign up. To ask other readers questions about Python for Biologists , please sign up. Be the first to ask a question about Python for Biologists. Lists with This Book. This book is not yet featured on Listopia.
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