study memo
Date: 2018-05-20
#Chapter 16. MAPPINGS AND DICTIONARIES map a key to a data value：
viewpoints:
semantics The key can be any type of Python object that computes a consistent hash value. The value referenced by the key can be any type of Python object.
Working with a dict is similar to working with a sequence. Items are inserted into the dict, found in the dict and removed from the dict. A dict object has member methods that return a sequence of keys, or values, or ( key , value ) tuples suitable for use in a for statement. Unlike a sequence, a dict does not preserve order. Instead of order， hash() to identify each item’s place in the dict with a rapid calculation of the key’s hash value. dict A dict can be thought of as a container of ( key : value ) pairs. A dict can be called an associative array. sequence by key, instead of index keys must be immutable objects ( eg. tuple, string and frozenset)
literal values diceRoll = { (1,1): “snake eyes”, (6,6): “box cars” } myBoat = { “NAME”:”KaDiMa”, “LOA”:18, “SAILS”:[“main”,”jib”,”spinnaker”] } theBets = { }
operations [] only »> d= {} »> d[2] = [ (1,1) ] »> d[3] = [ (1,2), (2,1) ] »> d {2: [(1, 1)], 3: [(1, 2), (2, 1)]} »> d[2] [(1, 1)] »> d[“2 or 3”] = d[2] + d[3] »> d {‘2 or 3’: [(1, 1), (1, 2), (2, 1)], 2: [(1, 1)], 3: [(1, 2), (2, 1)]}
String Formatting with Dictionaries.
>>> myBoat = { "NAME":"KaDiMa", "LOA":18,"SAILS":["main","jib","spinnaker"] }
>>> print "%(NAME)s, %(LOA)d" % myBoat
KaDiMa, 18 String Formatting with Dictionaries.
comparison operators NOT ( ‘<’ , ‘<=’ , ‘>’ , ‘>=’, ‘==’ , ‘!=’ )
( in, not in )
statements The FOR statement iterates through the keys of the dictionary. for key in someDictionary.keys(): # or simply someDictionary print key, colors[key] for value in someDictionary.values(): process the value
The del statement removes items from a dict del someDictionary[key0]
built-in functions dict(), len(), max(), min(), sum(), any(), all(), enumerate(), sorted()
methods clear(), pop(), setdefault(), update(), copy(), get(), items(), keys(), values()
Advanced Parameter Handling For Functions Unlimited Number of Positional Argument Values, *extras， TUPLE def printf( format, *vals ): print format % vals
Unlimited Number of Keyword Argument Values, **extras, DICT def rtd( **args ): if "rate" in args and "time" in args:
args['distance'] = args['rate']*args['time'] elif "rate" in args and "distance" in args:
args['time']= args['distance']/args['rate'] elif "time" in args and "distance" in args:
args['rate']= args['distance']/args['time'] else:
raise Exception( "%r does not compute" % ( args, ) ) return args
Evaluation with a Container Instead of Individual Argument Values
def avg3( a, b, c ): … return (a+b+c)/3.0 … data= ( 4, 3, 2 ) avg3( *data ) 3.0 d={ ‘a’:5, ‘b’:6, ‘c’:9 } avg3( **d ) 6.666666666666667
avg3( 2, b=3, **{‘c’:4} ) 3.0
#Chapter 17. SETs simple containers of data values, irrespective of order or any key. With a sequence, objects are identified by position. With a mapping, objects are identified by some key. With a set, objects stand for themselves.
A set is mutable, which means that it cannot be used as a key for a dict frozenset, is an immutable
no literal values for set objects, only with factory functions: set(), frozenset() set(iterable), Transforms the given iterable (sequence, file, frozenset or set) into a set # usually a tuple
set( (“hello”, “world”, “of”, “words”, “of”, “world”) )
set([‘world’, ‘hello’, ‘words’, ‘of’]) fib=set( (1,1,2,3,5,8,13) ) prime=set( (2,3,5,7,11,13) ) nullset = set()
Union, Intersection, Difference, Symmetric Difference
fib=set( (1,1,2,3,5,8,13) ) prime=set( (2,3,5,7,11,13) ) fib | prime set([1, 2, 3, 5, 7, 8, 11, 13]) fib - prime set([8, 1]) fib ^ prime set([1, 7, 8, 11])
‘<’, ‘<=’, ‘>’, ‘>=’ are implemented by ⊂, ⊆, ⊃, ⊇ ‘in’, ‘not in’ by ∈ and ∈/
even= set( range(2,38,2) ) odd= set( range(1,37,2) ) zero= set( (0,’00’) ) for n in even|odd|zero: print n
Exception Handling
def avgReport( someList ): try: print “Start avgReport” m= avg(someList) print “Average+15%=”, m*1.15 except TypeError, ex: print “TypeError: “, ex except ZeroDivisionError, ex: print “ZeroDivisionError: “, ex finally: print “Finish avgReport”
#Chapter 20. FILES file is a container for data objects, encoded as a sequence of bytes. Files include more than disk drives and network interfaces. All GNU/Linux operating systems make all devices available through a standard file-oriented interface.
File Organization and Structure:
The POSIX standard, however, considers a file to be nothing more than a sequence of bytes.
TEXT File, equence of variable length lines; each line terminated with a newline character.
built-in file objects and their methods for reading and writing lines of data
BINARY, byte-oriented files, ibrary modules like pickle and csv
Built-in Functions:
open(filename, [mode], [buffering]), file(filename, [mode], [buffering])
mode:
open-mode: r w a
operation: + for read & write, (nothing) for only
text-handleing: (nothing) for text, b for bytes, U for universal newline
buffering: 0 no-buffer, 1 buffering, n buffer-size
os.path()
os.path.join()
Statements:
source = open( "someFile.txt", "r" )
# with file("somefile","r") as source:
for line in source:
# process line
source.close()
Methods:
read([size]) readline([size]) readlines([hint])
flush() write(string) writelines(list) truncate([size])
seek(offset, [whence]) tell()
close() fileno() isatty()
Attributes:
file.closed -> boolean
file.mode -> string
file.name -> string
file.encoding -> string
Examples:
# read file: /etc/password pswd = file( "/etc/passwd", "r" ) for aLine in pswd
fields= aLine.split( ":" )
print fields[0], fields[1] pswd.close()
# read file CSV stock, lastPrice, date, time, change, openPrice, daysHi, daysLo, volume "^DJI",10623.64,"6/15/2001","4:09PM",-66.49,10680.81,10716.30,10566.55,N/A "AAPL",20.44,"6/15/2001","4:01PM",+0.56,20.10,20.75,19.35,8122800 "CAPBX",10.81,"6/15/2001","5:57PM",+0.01,N/A,N/A,N/A,N/A
qFile= file( “quotes.csv”, “r” ) for q in qFile: try: stock, price, date, time, change, opPrc, dHi, dLo, vol\ = q.strip().split( “,” ) print eval(stock), float(price), date, time, change, vol except ValueError: pass qFile.close()
data= [] with file( “quotes.csv”, “r” ) as qFile: for q in qFile: fields= tuple( q.strip().split( “,” ) ) if len(fields) == 9: data.append( fields )
def priceVolume(a): return a[1], a[8]
data.sort( key=priceVolume ) for stock, price, date, time, change, opPrc, dHi, dLo, vol in data: print stock, price, date, time, change, volume
+/-,Ticker,Price,Price Change,Current Value,Links,# Shares,P/E,Purchase Price, -0.0400,CAT,54.15,-0.04,2707.50,CAT,50,19,43.50, -0.4700,DD,45.76,-0.47,2288.00,DD,50,23,42.80, 0.3000,EK,46.74,0.30,2337.00,EK,50,11,42.10, -0.8600,GM,59.35,-0.86,2967.50,GM,50,16,53.90,
invest= 0 current= 0 with open( “display.csv”, “rU” ) as quotes: titles= quotes.next().strip().split( ‘,’ ) for q in quotes: values= q.strip().split( ‘,’ ) data= dict( zip(titles,values) ) print data invest += float(data[“Purchase Price”])float(data[”# Shares”]) current += float(data[“Price”])float(data[”# Shares”]) print invest, current, (current-invest)/invest
#chapter 23. Advanced List Comprehensions
aa= [ (x,2*x+1) for x in range(10) if x%3 == 0 ] aa [(0, 1), (3, 7), (6, 13), (9, 19)]
Sequence Processing Functions: map(), filter() and reduce() The map() and filter() each apply some function to a sequence to create a new sequence. The reduce() function applies a function which will reduce the sequence to a single value.
map(function, sequence, [...])
# def map( aFunction, aSequence ):
# return [ aFunction(v) for v in aSequence ]
>>> map( int, [ "10", "12", "14", 3.1415926, 5L ] )
[10, 12, 14, 3, 5]
filter(function, sequence)
# def filter( aFunction, aSequence ):
# return [ v for v in aSequence if aFunction(v) ]
>>> def over9(m)
... return m > 9
...
>>> filter( over9, map( int, [ "10", "12", "14", 3.1415926, 5L ] ))
[10, 12, 14]
reduce(function, sequence, [initial=0])
# def reduce( aFunction, aSequence, init= 0 ): r= init
# for s in aSequence:
# r = aFunction( r, s )
# return r
>>> def plus( a, b ):
... return a+b
>>> reduce( plus, [1, 3, 5, 7, 9] ) 25
zip(sequence, [sequence...])
>>> zip( range(5), range(1,12,2) )
[(0, 1), (1, 3), (2, 5), (3, 7), (4, 9)]
list.sort(key=xxx)
deco= [ ((a[2],a[3]),a) for a in jobData ]
deco.sort()
The Lambda - unnamed, simple function
lambda form. This is a kind of anonymous, one-use-only function body in places where we only need a very, very simple function.
>>> def timesX( x ):
... return lambda a: x*a
...
>>> t2= timesX(2)
>>> t2(5)
10
>>> t3= timesX(3)
>>> t3(5)
15
>>> map( timesX(3), range(5) )
[0, 3, 6, 9, 12]
Multi-Dimensional Arrays or Matrices
m1 = [ [1, 2, 3, 0], [4, 5, 6, 0], [7, 8, 9, 0] ]
m2 = [ [2, 4, 6, 0], [1, 3, 5, 0], [0, -1, -2, 0] ]
m3= [ 4*[0] for i in range(3) ]
for i in range(3):
for j in range(4):
m3[i][j]= m1[i][j]+m2[i][j]
for row in m3:
print row
for row in m3:
for col in row
print "%2d" % col