- Introduction to Python
- Getting started with Python and the IPython notebook
- Functions are first class objects
- Data science is OSEMN
- Working with text
- Preprocessing text data
- Working with structured data
- Using SQLite3
- Using HDF5
- Using numpy
- Using Pandas
- Computational problems in statistics
- Computer numbers and mathematics
- Algorithmic complexity
- Linear Algebra and Linear Systems
- Linear Algebra and Matrix Decompositions
- Change of Basis
- Optimization and Non-linear Methods
- Practical Optimizatio Routines
- Finding roots
- Optimization Primer
- Using scipy.optimize
- Gradient deescent
- Newton’s method and variants
- Constrained optimization
- Curve fitting
- Finding paraemeters for ODE models
- Optimization of graph node placement
- Optimization of standard statistical models
- Fitting ODEs with the Levenberg–Marquardt algorithm
- 1D example
- 2D example
- Algorithms for Optimization and Root Finding for Multivariate Problems
- Expectation Maximizatio (EM) Algorithm
- Monte Carlo Methods
- Resampling methods
- Resampling
- Simulations
- Setting the random seed
- Sampling with and without replacement
- Calculation of Cook’s distance
- Permutation resampling
- Design of simulation experiments
- Example: Simulations to estimate power
- Check with R
- Estimating the CDF
- Estimating the PDF
- Kernel density estimation
- Multivariate kerndel density estimation
- Markov Chain Monte Carlo (MCMC)
- Using PyMC2
- Using PyMC3
- Using PyStan
- C Crash Course
- Code Optimization
- Using C code in Python
- Using functions from various compiled languages in Python
- Julia and Python
- Converting Python Code to C for speed
- Optimization bake-off
- Writing Parallel Code
- Massively parallel programming with GPUs
- Writing CUDA in C
- Distributed computing for Big Data
- Hadoop MapReduce on AWS EMR with mrjob
- Spark on a local mahcine using 4 nodes
- Modules and Packaging
- Tour of the Jupyter (IPython3) notebook
- Polyglot programming
- What you should know and learn more about
- Wrapping R libraries with Rpy
Variables
Variables in Python are defined and typed for you when you set a value to them.
my_variable = 2 print(my_variable) type(my_variable)
2
int
This makes variable definition easy for the programmer. As usual, though, great power comes with great responsibility. For example:
my_varible = my_variable+1 print (my_variable)
2
“If you leave out word, spell-check will not put the word in you” – Taylor Mali, The the impotence of proofreading
If you accidentally mistype a variable name, Python will not catch it for you. This can lead to bugs that can be hard to track - so beware.
Types and Typecasting
The usual typecasting is available in Python, so it is easy to convert strings to ints or floats, floats to ints, etc. The syntax is slightly different than C:
a = "1" b = 5 print(a+b)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-3-6463279979e9> in <module>() 1 a = "1" 2 b = 5 ----> 3 print(a+b) TypeError: cannot concatenate 'str' and 'int' objects
a = "1" b = 5 print(int(a)+b)
Note that the typing is dynamic. I.e. a variable that was initally say an integer can become another type (float, string, etc.) via reassignment.
a = "1" type(a) print(type(a)) a = 1.0 print(type(a))
Python has some other special data types such as lists, tuples and dictionaries that we will address later.
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