An Introduction to Python: File I/O

Introduction to Python: File I/O

An Introduction to Python: File I/O

File I/O

Table of Contents


 

Let's do things with files!

File I/O in C/C++ required including several header files and
defining a series of stream or file objects. Python is similar,
but simpler. The open builtin function
and the Python File object provide a
very convenient interface.

Let's create a file quickly and dump some text into it.
In this case a list of names that we'll enter at the commandline.

    # Let's create a file and write it to disk.
    filename = "test.dat"
    # Let's create some data:
    done = 0
    namelist = []
    while not done:
        name = raw_input("Enter a name:")
        if type(name) == type(""):
            namelist.append(name)
        else:
            break
    
    # Create a file object:
    # in "write" mode
    FILE = open(filename,"w")
    
    # Write all the lines at once:
    FILE.writelines(namelist)
    
    # Alternatively write them one by one:
    for name in namelist:
        FILE.write(name)
    
    FILE.close()
    

Notice that we simply have to use the open
function to open a file, and give it the "r" argument to indicate we want
to open the file for reading.
The value returned from this function is a file object,
which we can use to read (or write, by specified the "w" flag).
From there you can either read the entire file with a
fileobject.read() method, or read a
line at a time with readline().

If you find opening a file and remember to close it to be a bit of a pain
you could try this little hack:

    # Hackish way of avoiding having to call file.close()
    
    """
     0 means buffer size of 0
     or unbuffered:
    
     1 means line buffered
    
     Some other number >1 means use
     a buffer that size (4096 would mean use a buffer
     of 4096 bytes)
    
     Defaults to -1, which indicates the platform
     default
    """
    FILE = open(filename, "w", 0) 
    
    FILE.writelines(namelist)
    
    """
    Now when your program exits without
    calling FILE.close() your data will
    still be written to disk.
    
    Yeah...  It kind of an ugly way to do things.
    
    """
    

The above only works because you've selected unbuffered IO. Depending on
the platform you're on unbuffered IO may be the default.

However, unbuffered IO is not the write way to ensure that files get
cleaned up and closed properly. Instead Python2.6 gives us a very
nice way to manage file objects with Context Managers:

    By using your file object as a context manager as soon as your execution leaves
    your with block the file will be closed properly.

    Now let's go over some useful utility functions:

      import os.path
      # os.path - The key to File I/O
      os.path.exists("bob.txt")
      os.path.isfile("bob.txt") # Does bob.txt exist?  Is it a file, or a directory?
      os.path.isdir("bob")
      os.path.isabs("/home/me/bob.txt") # Is it an absolute path to this file?
      
      # Creating cross platform paths
      # This will be slightly different on each platform
      currentdir = os.curdir
      imagedir = os.path.join(currentdir, "images")
      
      # Let's say I have a full path, and yet I want to store records based
      # on the name of the file:
      longpath = "/home/me/python/somefiles/junk/notjunk/blah/bingo.txt"
      shortpath = os.path.basename(longpath)
      
      # Get the type of shortpath:
      print "Type of",shortpath,"is", os.path.splitext(shortpath)[1]
      
      # os.path.walk can be used to traverse directories recursively
      # to apply changes to a whole tree of files.
      def callback( arg, dirname, fnames ):
          sum = 0
          for file in fnames:
              sum += os.path.getsize(file)
          arg.append(sum)
      
      arglist = []
      os.path.walk("./",callback,arglist)
      
      sum = 0
      for value in arglist:
          sum += value
      
      print "Size of directory:",sum
      

    Here is a more advanced example, which reads in the following file, "hw0grades.txt", which contains the current grades for the first (Zeroth) cs373 homework:

      1883 46.5
      Alpha Quadrant 62
      Araldor 47
      ASPWizKid 48.5
      bamboozle 12.5
      banana 42.5
      Barney 47.5
      Ben Kenobi 32.5
      bfavre 49.5
      Bib-B 47.5
      Big-P 47.5
      bikerider 38.5
      brit1 42.5
      buan 35.5
      cheese 47.5
      ChinDaddy 56
      Clockwisefoot 47.5
      clutch 38.5
      crzyfk 51.5
      Dave 32
      dbrown 49.5
      Eggman63 47.5
      eidiki 47
      elf2582 58
      epsilonought 58
      fats403 60
      fuzzy 56
      gilman 60
      HealeySt 30
      HockeyBoy 48.5
      HUMAN#31B8A 62
      Iconoclast 29
      IlliniSgEp 24
      Isles311 54.5
      jorin 58
      klee
      kvltbm 46.5
      KWO 60.5
      Lastone 18
      lcoker 49.5
      liren 42.5
      ljn35 54.5
      Lock 37
      Mist1 58.5
      Mist2 58.5
      moses 42.5
      NiceMan 60.5
      og4l 51.5
      photon 51
      Pootie-Tang 32
      Rollo Tamasi 27
      Sammy 43.5
      Shodan 37
      skv9 56
      Slayn 37
      spidey 38.5
      spoonman 43.5
      spoonman2530 54.5
      summy 43.5
      The First Amigo 33
      The Second Amigo33
      The Third Amigo 33
      the257 44.5
      TooDumb373 42.5
      Very Sleepy 63.5
      Very Sleepy 63.5
      Very Sleepy 63.5
      washu 60
      xtreme 51
      ziggy43 19
      zorba
      20.5
      56.5
      56.5
      58.5
      58.5
      35.5
      41

    Now, since we know some basics of files, we can go ahead and read them a bit, and go ahead and compute somethings about my grade:

      # Let's get the average and our grades:
      US = {"Sammy":0,"summy":0,"spoonman":0}
      HWAVE = 0
      TOTAL = 0
      FILES = ["hw0grades.txt"]
      
      STUDENTS = 0
      
      # Get the Data
      for file in FILES:
          infile = open(file,"r")
          while infile:
              line = infile.readline()
              for person in US:
                  if line.find(person) >= 0 and len(line.split()[0])==len(person):
                      US[person] = float( line.split()[1] )
              s = line.split()
              n = len(s)
              if n == 0:
                  break
              try:
                  TOTAL += float( s[ n-1 ] )
              except:
                  pass
              STUDENTS += 1
      
      # Compute the Average
      print TOTAL, STUDENTS
      HWAVE = TOTAL / ( STUDENTS * (1.0) )
      
      # Assume the average is C
      # Define grade ranges:
      C = HWAVE
      Cmax = C + HWAVE * .05
      Cmin = C - HWAVE * .05
      Bmax = Cmax + HWAVE * .1
      Bmin = Cmax
      Amin = Bmax
      Dmax = Cmin
      Dmin = Cmin - HWAVE * .1
      Emax = Dmin
      # Print out some STATS:
      print "The AVERAGE for this homework:", HWAVE
      print "The A range:", ">="+str(Amin)
      print "The B range:", Bmax,"-", Bmin
      print "The C range:", Cmax,"-", Cmin
      print "The D range:", Dmax,"-", Dmin
      print "The E range:", "<"+str(Emax)
      
      # Assign grades to US:
      for person in US:
          if US[person] >= Amin:
              print person,"(",US[person],")","probably got an A on this assignment."
          elif Bmax > US[person] >= Bmin:
              print person,"(",US[person],")","probably got a B on this assignment."
          elif Cmax > US[person] >= Cmin:
              print person,"(",US[person],")","probably got a C on this assignment."
          elif Dmax > US[person] >= Dmin:
              print person,"(",US[person],")","probably got a D on this assignment."
          else:
              print person,"(",US[person],")","probably got a E on this assignment."
      

    This example is a little more complicated, but it's definitely more real world. A very common use for Python is doing file and string parsing tasks like this. It's not as well known for this task as Perl, although it really is quite suitable for this sort of thing (and your chances of being able to maintain this code in the future is better).

    Finally, I want to talk about pickling things. Python has a facility for compressing native objects into strings for transmission across the network, or to write to disk.

    You might refer to this technique as a method of "Object Persistance" or something similar, and you'd be right. If you have data you'd like to save between sessions, like an objects personal state, just pickle it. This could be a simple way to create a basic file format for your programs datafiles, or it could enable faster crash recovery for a large program. You might even use it while playing at the commandline to save something temporarily.

    There are quite a few modules besides Pickle that can be used to do this in Python, but Pickle is the fundamental building block for many of them. Larger projects with more data might decide to use Shelves, which are a lot like dictionaries that store pickled objects.

    Follow along with this simple example, and remember that there is more to Pickling then what I've got below.

      # Let's define a class quick:
      class Student:
          def __init__(self, name="", school="", major="", year="", gpa=-1):
              self.name = name
              self.school = school
              self.major = major
              self.year = year
              self.gpa = gpa
      
          def __repr__(self):
              """
              Return official string representation of object
      
              Called by repr() builtin function and
              by print if __str__ is not defined.
              """
              string = ["++++++++++++++++++++++",
                       "+" + name,
                       "+" + school,
                       "+" + major,
                       "+" + year,
                       "+" + gpa,
                       "++++++++++++++++++++++"]
      
              return "\n".join(string)
      
          def __str__(self):
              """
              Return unofficial string representation of an object.
      
              Called by print
              """
              return self.__repr__()
      
      # Now I'll create a student:
      Mike = Student("Mike", "UIUC", "CS", "Senior", 0.6)
      
      # Now, let's Pickle Mike
      mike_the_pickle = pickle.dumps(Mike)
      print mike_the_pickle
      
      # Now let's clone mike!
      Mike2 = pickle.loads(mike_the_pickle)
      if type(Mike) == type(Mike2):
          print "Mike and Mike2 are of type:", (str(type(Mike)))
      
      # Create A Pickler
      P.pickle.Pickler("pickle.dat")
      # Write to your Pickler
      P.dump(Mike)
      # OR, combine the last 2 lines
      pickle.dump(Mike, "pickle.dat")
      
      # Now, Create an Unpickler, and get Mike back
      U = pickle.Unpickler("pickle.dat")
      # Read from the pickler
      Mike = U.load()
      # Do it in one line
      Mike2 = pickle.load("pickle.dat")
      
      # Just something to keep in mind:
      # For more sophisticated usage, you may want to use the Shelve module,
      # or other Database modules.

    原文链接: https://www.cnblogs.com/lexus/archive/2012/06/06/2537577.html

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