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Advanced Python and Application

課程時長:3天

Chap1: Python Basis and advanced Functions

1.1 Anaconda and Pycharm setup and path set

  1) Anaconda+python3.6
  2) Pycharm
  3) Training

1.2 type, variable, string, format

  1) Print
  2) type
  3) Variable, constant,
  4) ord(), chr(), str
  5) Format
  6) Training

1.3 list and tuple

  1) List
  2) Tuple
  3) Training

1.4 if, while, for

  1) If <condition1>
  2) for
  3) while
  4) Training

1.5 dict and set

  1) In
  2) get(), set()
  3) pop(), add, remove
  4) Training

1.6 File I/O operator

  1) open
  2) read
  3) write
  4) seek()
  5) Training

1.7 OOP

  1) Inheritance
  2) Polymorphism
  3) Static classes
  4) Static functions
  5) Decorators
  6) Training: test1.7.py

1.8 Python advanced function

  1) Inner function
   MathematicsSetLogical judgementReflectIO operationpass, def, return
  2) Generator
  3) Iterable, Iterator
  4) decorate, high-order function, function nest
  5) JSON and PICKLE
  6) Training

Chap2 Python advanced: the use of libraries

2.1 Standard library

  1. Itertools
   Training

  2. Functools:Partial, wraps, total_ordering, cmp_to_key
   Training

  3. Re
   Training

  4. Subprocess: call, check_call, check_output, Popen+PIPE
   Training

  5. Pdb、traceback
   Training

  6. Pprint
   Training

  7. Logging
   Training

  8. Threading and multiprocessing
   Training

  9. Urllib/urllib2/httplib
   Training

  10. Os/sys
   Training

  11. Queue
   Training

  12. Pickle/cPickles
   Training

  13. Hashlib md5,sha
   Training

  14. Cvs
   Training

  15. Timeit
   Training

2.2 Third libraries

  1. numpy, scipy
   Training

  2. PIL

  3. lxml

  4. Pandas

  5. matplotlib

  6. scrapy: crawler

  7. Machine Learning Libraries

  8. Natural Language Processing Libraries

Chap3 Python advanced application (Pandas, Matplotlib, Scrapy)

3.1 Data Analysis with Pandas

  3.1.1 Data Cleaning:
   Training

  3.1.2 Using vectorized data in pandas
   Training

  3.1.3 Data Wrangling
   Training

  3.1.4 Aggregate Operations
   Training

  3.1.5 Analyzing time series
  Training

3.2 Data Visualization

  3.2.1 Plotting diagrams with matplotlib
   Training

  3.2.2 Using matplotlib from within pandas
   Training

  3.2.3 Creating quality diagrams
   Training

  3.2.4 Visualizing data in Jupyter notebooks
   Training

  3.2.5 Other visualization libraries in Python--- PIL
   Training

3.3 Python for the web

  3.3.1 Packages for web processing
   Training

  3.3.2 Web Crawling
   Training

  3.3.3 Parsing HTML and XML
   Training: test3.3.3.py

  3.3.4 Filling web forms automatically
   Training: test3.3.4.py

  3.3.5 Integrative Case Training
   1) Taobao.py
   2) Douban.py
   3) Jiepai.py
   4) Maoyan.py

3.4 Python for maintenance scripting

  3.4.1 Raising and catching exceptions correctly

  3.4.2 Organizing code into modules and packages

  3.4.3 Understanding symbol tables and accessing them in code

  3.4.4 Picking a testing framework and applying TDD in Python

Chap4 Python with Machine Learning and NLP

4.1 Python with Machine Learning:

  4.1.1 regression:SGD, SVR Ensemble, Ridge, SVR

  4.1.2 Classification:SGD, Kernel Approximation, KNeighbors, LinearSVC, SVM, Na?ve Bayes, Decision Tree, Random Forest

  4.1.3 Clustering:KMeans, Spectral Clustering , GMM, MeanSHift VBGMM, MiniBatch Kmeans, SOM

  4.1.4 Dimension Reduction:PCA, LDA, LLE, Isomap, Spectral Embedding

  4.1.5 Series Data Mining:HMM, GMM, DTW, DNN, TDNN

4.2 Python with NLP:

  4.2.1 Word Segmentation

  4.2.2 Part of Speech tagging: POS

  4.2.3 Word Vectorization: Word2vec

  4.2.4 Semantic Similarity

  4.2.5 CRF++

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