[Reading notes] Python data Analysis (i) Preparation

1. Data structures in Python: matrices, arrays, data frames, multiple tables interconnected by key columns (SQL PRIMARY key, foreign key), time series

2. Python-interpreted language, programmer time and CPU time measurement, high-frequency trading system

3. Global interpreter lock Gil, global interpreter lock mechanism to prevent the interpreter from executing multiple python bytecode instructions at the same time

Cpython can inherit OpenMP implementation of parallel processing loops and greatly increase the speed of numerical algorithms

4. Numpy, pandas,matplotlib,ipython,scipy

Numpy:python Scientific Computing Base Library, as a container for passing data between algorithms, Numpy arrays are more efficient than python built-in data structures, and low-level languages such as C can manipulate data in Numpy arrays directly

• Fast and efficient multidimensional array object Ndarray
• Mathematical operations of array elements and arrays as a whole
• Array-based dataset tools for reading and writing on hard disks
• Linear algebra, Fourier transform, random number generation
• C, C + +, FORTRAN code integration into Python tools

Pandas: A large number of data structures and functions that handle structured data

• Precise indexing, reshaping, slicing, chopping, aggregating, selecting subsets
• High performance time series features and tools

Matplotlib: The most popular library for plotting data graphs

Ipython: Enhanced Python Shell provides a robust and efficient environment for interactive and exploratory computing

• Interactive data processing and plotting
• An HTML notebook similar to Mathematica, connected via a Web browser Ipython
• QT Framework-based GUI console with drawing, multi-line editing, syntax highlighting
• Infrastructure for interactive parallel and distributed computing

SCIPY: Scientific Computing Toolkit

• Scipy.integrate: Numerical integration and differential equation solvers
• SCIPY.LINALG: Extended linear algebra routines and matrix decomposition provided by NUMPY.LINALG
• Scipy.optimize: function optimizer and Root lookup algorithm
• Scipy.signal: Signal Processing tools
• Scipy.sparse: coefficient matrix and coefficient linear system solution
• Scipy.stats: standard continuous and discrete probability distributions, statistical tests, etc.
• Scipy.weave: Tools for accelerating array computing with inline C + + code

[Reading notes] Python data Analysis (i) Preparation