Read "Streaming Systems" 1&2, Streaming 101 Read "F1, a distributed SQL database that scales" Read "Zanzibar, Google’s Consistent, Global Authorization System" Read "Spanner, Google's Globally-Distributed Database" Read "Designing Data-intensive applications" 12, The Future of Data Systems IOS development with Swift Read "Designing Data-intensive applications" 10&11, Batch and Stream Processing Read "Designing Data-intensive applications" 9, Consistency and Consensus Read "Designing Data-intensive applications" 8, Distributed System Troubles Read "Designing Data-intensive applications" 7, Transactions Read "Designing Data-intensive applications" 6, Partitioning Read "Designing Data-intensive applications" 5, Replication Read "Designing Data-intensive applications" 3&4, Storage, Retrieval, Encoding Read "Designing Data-intensive applications" 1&2, Foundation of Data Systems Three cases of binary search TAMU Operating System 2 Memory Management TAMU Operating System 1 Introduction Overview in cloud computing 2 TAMU Operating System 7 Virtualization TAMU Operating System 6 File System TAMU Operating System 5 I/O and Disk Management TAMU Operating System 4 Synchronization TAMU Operating System 3 Concurrency and Threading TAMU Computer Networks 5 Data Link Layer TAMU Computer Networks 4 Network Layer TAMU Computer Networks 3 Transport Layer TAMU Computer Networks 2 Application Layer TAMU Computer Networks 1 Introduction Overview in distributed systems and cloud computing 1 A well-optimized Union-Find implementation, in Java A heap implementation supporting deletion TAMU Advanced Algorithms 3, Maximum Bandwidth Path (Dijkstra, MST, Linear) TAMU Advanced Algorithms 2, B+ tree and Segment Intersection TAMU Advanced Algorithms 1, BST, 2-3 Tree and Heap TAMU AI, Searching problems Factorization Machine and Field-aware Factorization Machine for CTR prediction TAMU Neural Network 10 Information-Theoretic Models TAMU Neural Network 9 Principal Component Analysis TAMU Neural Network 8 Neurodynamics TAMU Neural Network 7 Self-Organizing Maps TAMU Neural Network 6 Deep Learning Overview TAMU Neural Network 5 Radial-Basis Function Networks TAMU Neural Network 4 Multi-Layer Perceptrons TAMU Neural Network 3 Single-Layer Perceptrons Princeton Algorithms P1W6 Hash Tables & Symbol Table Applications Stanford ML 11 Application Example Photo OCR Stanford ML 10 Large Scale Machine Learning Stanford ML 9 Anomaly Detection and Recommender Systems Stanford ML 8 Clustering & Principal Component Analysis Princeton Algorithms P1W5 Balanced Search Trees TAMU Neural Network 2 Learning Processes TAMU Neural Network 1 Introduction Stanford ML 7 Support Vector Machine Stanford ML 6 Evaluate Algorithms Princeton Algorithms P1W4 Priority Queues and Symbol Tables Stanford ML 5 Neural Networks Learning Princeton Algorithms P1W3 Mergesort and Quicksort Stanford ML 4 Neural Networks Basics Princeton Algorithms P1W2 Stack and Queue, Basic Sorts Stanford ML 3 Classification Problems Stanford ML 2 Multivariate Regression and Normal Equation Princeton Algorithms P1W1 Union and Find Stanford ML 1 Introduction and Parameter Learning

Simple file IO

2016-07-20

File IO

Most common operations are file read and write. First, let’s open a file.

Open file

f = open(filename, mode) filename is filename or path to file. And mode can be read, write or append(‘r’,’w’,’a’). Then f is the file handler.

Close file

Alway remember to close file and free memory.

f.close()

Mode

  • “r”: Open a file for read only
  • “w”: Open a file for writing. If file already exists its data will be cleared before opening. Otherwise new file will be created
  • “a”: Opens a file in append mode i.e to write a data to the end of the file
  • “wb”: Open a file to write in binary mode
  • “rb”: Open a file to read in binary mode

Write to file

Note that write will not add \n automatically, which is default to print.

f = open('myfile.txt', 'w')    # open file for writing
f.write('this is first line\n')   # write a line to the file
f.write('this is second line\n')  # write one more line
f.close()

Read file

There are three modes:

  • read([number]): Return specified number of characters from the file. if omitted it will read the entire contents of the file.
  • readline(): Return the next line of the file.
  • readlines(): Read all the lines as a list of strings in the file
Read all file content
f = open('myfile.txt', 'r')
f.read()
'this is first line\nthis is second line\n'
f.close()
Read all lines
f = open('myfile.txt','r')
f.readlines()
['this is first line\n', 'this is second line\n']
f.close()
Read one line
f = open('myfile.txt','r')
f.readline()
'this is first line\n'
f.close()

Append

f = open('myfile.txt','a')
f.write('this is third line\n')
f.close()

Access every line

f = open('myfile.txt','r')
for line in f:
    print line,
this is first line
this is second line
this is third line
f.close()

with open

with open('myfile.txt','r') as f:
    for line in f:
        print line,
this first line
this second line
this is third line
with open('myfile.txt','r') as f:
    for line in f.readlines():
        print line,
this is first line
this is second line
this is third line

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