1 / 49

Python – Essential characteristics

Python – Essential characteristics. think Monty, not snakes!. Key Advantages: Open source & free (thank you Guido van Rossum!) Portable – works on Unix, Linux, Win32 & 64, MacOS etc. Easy to learn and logically consistent Lends itself to rapid development

kuper
Télécharger la présentation

Python – Essential characteristics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Python – Essential characteristics think Monty, not snakes! Key Advantages: • Open source & free (thank you Guido van Rossum!) • Portable – works on Unix, Linux, Win32 & 64, MacOS etc. • Easy to learn and logically consistent • Lends itself to rapid development • So, good for “quick and dirty” solutions & prototypes • But also suitable for full fledged applications • Hides many low-level aspects of computer architecture • Elegant support of object-orientation and data structures • Extensive library support – a strong standard library • Dynamic “duck typing” paradigm is very flexible • Language is minimalistic, only 31 keywords

  2. Python – Essential characteristics Some Disadvantages: • It's not very fast (but often better than PERL!) • Relatively inefficient for number crunching • Can have high memory overhead • Being “far from the metal” has disadvantages – systems or kernal programming is impractical • Dynamic typing can be both a blessing and a curse • Some key libraries are still developing (e.g. BioPython) • Version 3 breaks compatibility to prior versions • Some find the whitespace conventions annoying • Tends towards minimalism in favour of expressiveness

  3. Becoming a Pythonista Windows and MacOS X installers available at: www.python.org/getit Note that BNFO602 will be using version 2.73, not more recent 3.xx distributions Even if your machine supports 64 bit, a 32- bit install is generally a safer choice for compatibility Linux users may possibly need to download a source tarball and compile themselves

  4. A Python IDE for BNFO602 Windows, MacOS X, and Linux installers at: We are using the Free community edition www.jetbrains.com/pycharm An IDE is an Integrated Development Environment While not strictly required, IDEs ease and facilitate the creation and management of larger programs. IDLE is the built-in IDE and is another option Python can also be run interactively.

  5. Documents for Python For version 2.X, official documentation and tutorials are here: docs.python.org/2 docs.python.org/2 While a notable weakness of Python in the past, the online documentation and tutorials for Python are now quite good! StackOverflow.com also has good information: stackoverflow.com/tags/python/info

  6. No semicolon! Function Argument Keywords The Building Blocks of Python - Hello World! print "Hello World" Python 2.7 has only 31 keywords in the language. It is minimalistic.

  7. Does NOT use curly brackets to delimit statement blocks! Use colon after conditional statement Hello World! if True: print "Hello" print "World" Statement Block If statements are the sentences of Python, then statement blocks are analogous to paragraphs. Unlike PERL, python is somewhat fussy about how we use whitespaces (spaces, tabs, line breaks).....

  8. Comments begin with # Escape sequence for “tab” (but no variable interpolation as w/ PERL) Dummy statement Statement blocks are nested using whitespace #Demo of nested blocks print "Outer level" if True: print "\tMiddle level #1" if True: print "\t\tInner level" print "\tMiddle level #2" pass print "Outer level #2" Whitespace delimits statement blocks! Preferred practice is to use exactly four spaces Don't use tabs unless your editor maps these to spaces!

  9. Statement blocks can be nested Output Outer level Middle level #1 Inner level Middle level #2 Outer level #2 Yes, this is a trivial example. Note: scoping within these simple blocks is a little different than PERL as there is no “my” statement for local variables

  10. String delimiters String Integer Floating point Complex Boolean Null Data Types in Python Some basic data types "Hello World!" 42 3.1459 2+6j False, True None Some types, like strings, are hard-coded and cannot be directly changed! They are “immutable”

  11. list tuple dict Data Types in Python Some compound data types delimiters ["A", "C", "G", "T"] ("A", “C", "G", "T") {"A":"T", "C":"G", "G":"C", "T":"A"} A tuple is essentially an immutable list whereas a dict is like a PERL hash

  12. Identifiers begin with a letter or underscore Variables in Python Variables in Python are NOT associated to a type They are just identifiers that name some object dna_sequence = "AGCTAGC" seq_len = 9 symbols = ["A", "G", "C", "T"] empty_dict = {} symbols = {"A":"Adenine"} Declaration and definition are usually coincident

  13. Index notation always uses square brackets even if a tuple or a dict Data Types and identifiers A = [42, 32, 64] print A print "The answer is ", A[0] Output • [42, 32, 64] The answer is 42 Data types are actually implemented as a classes that know how to print their own instance objects. Later we'll see how to make our own classes and types

  14. operands subexpression expression operators Operators, Operands & Expressions var = 12 * 10 Expressions consist of valid combinations of operands and operators, and a sub-expression can act as an operand in an expression Very similar to PERL, but some operators vary, especially for the logical operators. Also string concatenation uses "+", not "."

  15. foo = somefunction(foo2) foo = somefunc(foo2) * foo3 foo = somefunc(foo2) + somefunc2(foo3) foo = somefunc(somefunc2(foo2)) All of the above are possibly legal Python expressions depending on the functions Expressions Expressions can use the result of a function (or the result of a method of a class) as an operand

  16. Addition • subtraction division multiplication concatenation 4 + 2 = 6 4 – 2 = -2 4 / 2 = 2 4 * 2 = 8 "4" + "2" = "42" = assignment Does NOT denote equivalence Use == for testing equivalence! Some Python Operators Common operators + - / * + Operators follow a strict order of operations: e.g. 2 + 7 * 2 = 16 See documentation for complete details

  17. The Assignment Operator Unlike in algebra, does not imply that both sides of the equation are equal! The following is a valid Python statement: var = var + 1 This also does the same thing: var += 1 *=, -=, /=,all work the same way. This says “take the current value of var and add one to it, then store the result back in var”

  18. Increment by shown amount Incrementing and Decrementing The following are functionally equivalent statements: var = var + 1 var += 1 Similarly: var = var - 1; var -= 1 But NOT: var++, ++var or var--, --var No PERL style autoincrement/decrement!

  19. equivalence operator The Equivalence Operator Python does have an equivalence operator Print "Is 2 equal to 4:", 2 == 4 print "Is 2 equal to 2:", 2 == 2 Output: Is 2 equal to 4: False Is 2 equal to 2: True Python has a built-in Boolean type! 0, Boolean False, None, empty lists, null strings, and empty dicts are all evaluated as false

  20. Comparison Operators The equivalence operator is just one of the comparison operators == equal to < less than > greater than <= less than or equal to >= greater than or equal to != or <> not equal to These are the comparison operators for everything Use caution when testing floating point numbers, especially for exact equivalence!

  21. Conditional expression note the colon Flow Control – if, else and conditional expressions Comparison operators enable program flow control dna = "GATCTCTT" dna2 = "GATCTCCC" if dna == dna2: print "Sequences identical:", dna else: print "Sequences different" Output: Sequences different

  22. Flow Control – if, else and conditional expressions Comparison operators at work #2 dna = "ATGCATC" if dna: print "Sequence defined" else: print "Sequence not defined" Output: Sequence defined non-None, non-zero, non-False, & non-empty results are logically “true”

  23. Flow Control – if, else andconditional expressions Comparison operators at work dna = "" if dna == "ATG": print "Sequence is ATG start codon" else: print "Sequence not defined" Output: Sequence not defined Remember, empty lists and null strings are logically equivalent to “false”

  24. Several elif blocks in a row is OK! Multi-way branching using elif dna = "ATG" if dna == "GGG": print "All Gs" elif dna == "AAA": print "All As" elif dna == "TTT": print "All Ts" elif dna == "CCC": print "All Cs" else print "Something else:", dna Output: Something else: ATG

  25. Conditional expression Loops with the while statement dna = "ATGCATC" while dna == "ATGCATC": print "The sequence is still", dna Output: The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC The sequence is still ATGCATC etc… while statements will execute their statement block forever unless the conditional expression becomes false. Therefore the variable tested in the conditional expression is normally manipulated within the statement block..

  26. returns the length of a string conditional expression More on “slice notation” later when discussing lists. Here we remove the last character of a string Loops with the while statement dna = "ATGCATGC" whilelen(dna): print "The sequence is:", dna dna = dna[0:-1] print "done" Output: The sequence is ATGCATGC The sequence is ATGCATG The sequence is ATGCAT The sequence is ATGCA The sequence is ATGC The sequence is ATG The sequence is AT The sequence is A done

  27. string concatenation and assignment len is one of several built-in functions Use break to simulate PERL until dna = "A" while True: • if len(dna) > 3: • break • print "The sequence is:", dna dna += "A" print "done" Output: The sequence is A The sequence is AA The sequence is AAA done There is no native “do-while” or “until” in Python Python is minimalistic

  28. Loops with the for statement nt_list = ("A", "C", "G", "T") for nt in nt_list: print "The nt is:", nt Output: The sequence is A The sequence is C The sequence is G The sequence is T for loops iterate over list-like (“iterable”) data types and are similar to PERL foreach, not the PERL or Cfor

  29. Caution! range in 2.x instantiates an actual list. Use xrange if iteration is big Loops with the for statement nt = ("A", "C", "G", "T") for index inrange(len(dna)): print "The nt is:", dna[index] Output: The sequence is A The sequence is C The sequence is G The sequence is T for loops can have a definite number of iterations typically using the range or xrange built-in function Try this example with a string instead of a list!

  30. “attribute” notation! These are methods specific to the string type, not of general utility like built-ins Data Types in Python - Strings Strings are string-like iterables with a rich collection of methods for their manipulation dna = "ACGT" Some useful methods are: join, split, strip, upper, lower, count dna = "ACGT" dna2 = dna.lower() # will give "acgt"

  31. Data Types in Python - Strings Strings are string-like iterables with a rich collection of methods for their manipulation dna = "ACGT" Some useful methods are: join, split, strip, upper, lower, count dna = "AACGTA" print dna.count(“A”) # will give 3

  32. Data Types in Python - Lists A list is simply a sequence of objects enclosed in square brackets that we can iterate through and access by index. They are array-like. ["A","G","C","T"] Unlike PERL, pretty much anything can be put into a list, including other lists!! Mirabile dictu! [42,"groovy", dna, 3.14, var1-var2, ["A", "G", "C", "T"]] Try printing item 5 from the above list….how does this differ from the result you would get in PERL?

  33. No “@” token to distinguish list variables!! Note that first element is index 0 Data Types in Python - lists A list is a powerful type for manipulating lists: bases = ["A","G","C","T"] list elements can be accessed by an index: index = 2 print bases[0], bases[index] Output: AC Assigning to a non-existent element raises an error exception There is no PERL-style “autovivication” (although we can fake this)

  34. min and max are built-ins Data Types in Python - Lists Lists also have rich collection of methods Some useful methods are: len, sort, reverse, in, max, min, count Note that some are built-in functions while others use attribute notation pi = 3.14 my_list = ["ACGT", 0, pi] print min(list) # will print 0

  35. attribute notation Data Types in Python - Lists Lists also have rich collection of methods Some useful methods are: len, sort, reverse, in, max, min, count Note that some are built-in functions while others use attribute notation my_list = ["A", "C", "G", "T"] my_list.reverse() print my_list # will print ["T", "G", "C", "A"]

  36. Data Types in Python - Lists Lists also have rich collection of methods Some useful methods are: len, sort, reverse, in, max, min, count my_list = ["A"] * 4 #init with 4 "A"s print my_list.count("A") # prints 4 my_list.append("C") if "C" in my_list: print 'The list contained "C"\n' testing for inclusion with in is a common operation with all iterable types

  37. Array “slices” can be assigned to a subarray Lists and slice notation Slices allow us to specify subarrays bases = ["A","G","C","T"] size = len(bases) # will be equal to four var1, var2, var3, var4 = bases #var1="A" & var2="G", etc. Slice indices refer to the space between elements! subarray = bases[0:2] #subarray = ["A","G"] subarray = bases[0:-1] #subarray = ["A","G","C"] subarray = bases[1:] #subarray = ["G","C","T"] subarray = bases[1:len(bases)] #subarray = ["G","C","T"]

  38. Slice notation can be used to modify a list! Try this on the previously defined bases list and see what happens Lists modification and methods Some useful list methods are: append, insert, del, sort, remove, count, reverse, etc. bases = ["A","G","C"] bases.append("T") # bases = ["A","G","C","T"] bases.sort() # bases = ["A","C","G","T"] num_of_As = bases.count("A") # num_of_As = 1 bases[:0] = ["a","g","c","t"]

  39. Data Types in Python - dictionaries a.k.a.dicts dicts are associative arrays similar to PERL hashes: complement = {"A" : "T", "C" : ”G", "G" : ”C”, "T" : ”A”} no PERL “%” token to distinguish hash identifiers!! The left hand is the dict key and must be unique, “hashable”, and “immutable” (this will become clearer later) On right hand is the associated value. It can be almost ANY type of object! Nice.

  40. Working with Dicts dicts are a preferred data type in Python #A dict for complementing a DNA nucleotide comp = {"A" : "T", "C" : "G", "G" : "C", "T" : "A"} print "complement of A is:", comp["A"] print "complement of C is:", comp["C”] It’s easy to add new pairs to the hash: comp["g"] = "c" Output: complement of A is: T complement of C is: G Or to delete pairs in the hash: comp.del("g")

  41. Other dict methods Some useful dict methods are: keys, values, items, del, in, copy, etc. #A hash for complementing a DNA nucleotide comp = {"A" : "T", "C" : "G", "G" : "C", "T" : "A"} print comp.keys() # might return.. ["A","C”,"G","T"] No assertion is made as to order of key/value pairs!

  42. iterate over both keys and values together! Dicts are iterable #Iterating over hashes comp = {"A": "T", "C" : "G", "G" : "C", "T" : "A"} for k, v in comp.items(): print 'complement of', k, 'is', v .items() returns a two-element tuple that is “unpacked” here into k and v Output could be: complement of A is T complement of C is G complement of G is C complement of T is A Or output could be: complement of C is G complement of A is T complement of T is A complement of G is C The point is that dicts are unordered, and no guarantees are made!!

  43. Tuples are essentially immutable lists In most read-only contexts, they work just like lists you just can't change their value nucleotides = ("A", "C","G", "T") for NT in nucleotides: print NT , "is a nucleotide symbol" tuples are delimited by () Packing and unpacking: (one, two, three) = (1, 2, 3) print one # prints 1 Why Tuples? The immutable nature of tuples means they do not need to support all list operations. They can therefore be implemented differently, are consequently more efficient for certain operations. And only immutable objects can serve as hash keys

  44. Sparse matrices An example of tuples as dict keys 3 0 -2 0 0 9 0 0 0 7 0 0 0 0 0 -5 Standard multidimensional array: matrix = [ [3,0,-2,0], [0,9,0,0], [0,7,0,0], [0,0,0,-5] ] print matrix[0][2] # This will print -2 # Not very memory efficient if there are many zero valued # elements in a very large matrix!!! Sparse matrix representation: matrix = { (0,0): 3, (0,2): -2, (1,1): 9, (2,1):7, (3,3):-5 } print matrix.get( (0,2), 0) # prints -2 # The get method here returns 0 if the key is undefined # Much more memory efficient, since zero values not stored

  45. Functions Q: Why do we need Functions? A: Because we are lazy! Functions are the foundation of reusable code Repeatedly typing out the code for a chore that is used over and over again (or even only a few times) would be a waste of time and space, and makes the code hard to read Functions in Python akin to subroutines in PERL as well as procedures in some other languages

  46. Capital letters OK A return value is optional, None is default if value isn’t specified or no explicit final return statement Functions Defining a function Minimally, all we need is a statement block of Python code that we have named def I_dont_do_much: #any code you like!! pass return Once defined, functions are called (“invoked”) just by stating its name, and passing any required arguments: I_dont_do_much()

  47. No messing with @_ weirdness like in PERL Functions Pythonhas several flexible ways to pass arguments to function. This example is just the most basic way! Warning! Python passes objects to functions by reference, never by copy. Changes to mutable objects in the function change the starting object!! def expand_name (amino_acid): convert = {"R" : "Arg", "A" : "Ala", etc.} if amino_acid in convert: three_letter = convert[amino_acid] else: three_letter = "Ukn" return three_letter expand_name(“R”) convert is local to the function (i.e. in lexical scope) Note indentation – line is not part of function definition, but rather is an invocation of the function Output:Arg

  48. Using external functions Python includes many useful libraries or, it can be code that you have written In Python its easy to use functions (or indeed other variables or objects) that are defined in some other file… Option 1: import module_name # use the module name when calling the function.. # i.e. module_name.function(arg) Option 2: from module_name import name1, name2, name3 # imports just the names you want # no need to refer to module name when calling Option 3: from module_name import * # imports all of the public names in a module

  49. Putting it all together - An in-class challenge Get Python up and running, try “Hello world!” then… Write a program that: Defines a function that generates random DNA sequences of some specified length given a dict describing the probability distribution of A, C, G, T -- should be familiar from BNFO601 You’ll need the rand function from the math library!! This is a real-world chore that is frequently encountered in bioinformatics

More Related