Python's Data Processing learning (II.)

This article refers to the book "Head First Python" by Paul Barry, which can be downloaded from the http://python.itcarlow.ie/site. If there is any fallacy in this article, I hope to enlighten you

two. code Module1. Prepare to learn (1) data read

With

Open (james.txt) as JAF: #打开文件 data = Jaf.readline () #读数据行 James =d Ata.strip (). Split (', ') #将数据转换为列表

Description: Data.strip (). Split (', ') is called a method string chain, and strip () is applied to data rows in it, removing all whitespace characters from the string, and processing the result by split (', ') of the second method, split (', ') Indicates that the result is split in form, returning the list. (2) Data cleansing definition function sanitize () to unify the list format of each contestant's performance into the mins.secs format

def sanitize (time_string): if '-' in time_string: splitter = '-' if ': ' In Time_string: splitter = ': ' else: return (time_string) (mins,secs) = t Ime_string.split (splitter) return (mins + '. ' + secs)

Description: Split is a built-in function that represents the decomposition of a string(3) Conversion List---Deduction list For example, the General list conversion method and the way to use the derivation list:

clean_mikey = [] #列表创建for each_t in Mikey: #迭代 clean_mikey.append (sanit Ize (each_t)) #转换与追加

Equivalent to

Clean_mikey = [Sanitize (each_t) for each_t in Mikey]

Description: Sanitize () is a custom data cleansing function, and the built-in function sorted is to sort the entire list(4) Delete duplicate data--not in list operation method:

unique_james = []for each_t in James: if each_t not in Unique_james: Unique_j Ames.append (each_t)

Collection Action method: (The Python collection is characterized by the unordered nature of the data items in the collection, and does not allow duplicates) example: 

distances = Set (James)

(5) "Shard" To access multiple list items in a list

print (sorted (set ([Sanitize (T)] for T in James])) [0:3]

)

(6) Change multiple repeating code to function

def get_coach_data (filename): try: with open (filename) as AF: re Turn (Data.strip (). Split (', ')) except IOError as Ioerr: print (' File error: ' + str (ioerr)) ' c7> return (None)

2. Customizing Data Objects(1) New data format, james2.txt,julie2.txt,mikey2.txt,sarah2.txt, files are opened as follows: (full name, date of birth, training score)

James Lee,2002-3-14,2-34,3:21,2.34,2.45,3.01,2:01,2:01,3:10,2-22,2-01,2.01,2:16julie Jones, 2002-8-17,2.59,2.11,2:11,2:23,3-10,2-23,3:10,3.21,3-21,3.01,3.02,2:59sarah Sweeney, 2002-6-17,2:58,2.58,2:39,2-25,2-55,2:54,2.18,2:55,2:55,2:22,2-21,2.22mikey McManus, 2002-2-24,2:22,3.01,3:01,3.02,3:02,3.02,3:22,2.49,2:38,2:40,2.22,2-31

(2) Data extraction: (Take Sarah for example)

Sarah = get_coach_data (' sarah2.txt ') (SARAH_NAME,SARAH_DOB) = Sarah.pop (0), Sarah.pop (0)pop (0) The call deletes and returns the first item in the list and assigns a value to the specified variable name and date of birth

(3) using a dictionary to correlate data, a dictionary is a built-in data structure that allows data to be associated with keys rather than numbers so that the in-memory data is aligned with the structure of the actual data. For example, key associated data name--> Sarah Sweeney dob--> 2002-6-17 times--> 2:58,2.58,2:39,2-25,2-55,2:54,2.18,2:55,2:55,2:22,2-21,2.22 How to create a dictionary: Brace creation: Cleese = {} factory function: Palin = di CT () adds data in two ways:cleese[' Name '] = ' John Cleese 'palin = {' Name ': ' Michael Palin '}(4) Application:sarah_data = {}sarah_data[' Name '] = Sarah.pop (0)sarah_data[' DOB ' = Sarah.pop (0)sarah_data[' times ' = Sarahprint (sarah_data[' Name ' + ' s fastest times is: "+ str (sorted (set[sanitize (t) for T in sarah_data[' Times ']) ) [0:3]))(5) Complete the creation of the dictionary at once and return to the dictionary

def get_coach_data (filename): Try: With open (filename) as F: data = F.readline () Templ = Data.strip (). Split (', ') return ({' Name ': Templ.pop (0), ' DOB ': Templ.pop (0), ' Times ': Str (sorted (Set ([Sanitize (t) for T in Templ])) [0:3])}) except IOError as Ioerr: print (' File error: ' + str (ioerr)) return (None)  

(6) package the code and its data in a class

class Athlete: def __init__ (self,a_name,a_dob,a_times=[]): self.name = A_name Self.dob=a_dob Self.times=a_times     def top3 (self): return (sorted (set ([Sanitize (t) for T in Self.times])) [0:3] )     def get_coach_data (filename):        Try With open (filename) as F: data = F.readline () Templ = Data.strip (). Split (', ') Return (athlete (templ.pop (0), Templ.pop (0), Templ)      Except IOError as Ioerr: print (' File error: ' + str (ioerr))Return (None)

(7) Class call and result output

James = Get_coach_data (' james2.txt ')

Result output: James Lee's fastest times is: [' 2.01 ', ' 2.16 ', ' 2.22 '] The next lesson about class inheritance

Python's Data Processing Learning (ii)