- 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
Conditional Statements
a = 20 if a >= 22: print("if") elif a >= 21: print("elif") else: print("else")
Again, nothing remarkable here, just need to learn the syntax. Here, we should also mention spacing. Python is picky about indentation - you must start a newline after each conditional statemen (it is the same for the iterators above) and indent the same number of spaces for every statement within the scope of that condition.
a = 23 if a >= 22: print("if") print("greater than or equal 22") elif a >= 21: print("elif") else: print("else")
a = 23 if a >= 22: print("if") print("greater than or equal 22") elif a >= 21: print("elif") else: print("else")
Four spaces are customary, but you can use whatever you like. Consistency is necessary.
Python has another type of conditional expression that is very useful. Suppose your program is processing user input or data from a file. You don’t always know for sure what you are getting in that case, and this can lead to problems. The ‘try/except’ conditional can solve them!
a = "1" try: b = a + 2 except: print(a, " is not a number")
Here, we have tried to add a number and a string. That generates an exception - but we have trapped the exception and informed the user of the problem. This is much preferable to the programming crashing with some cryptic error like:
a = "1" b = a + 2
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