![Python: High-Level Data Type
Lt(list): [ d1, d2, …, dn ]
Ex: [ 1, 3.14, True, 'a', [1], (2,3), {'B+':3.5} ]
Tuple (tuple): ( d1, d2, ..., dn, )
Ex: ( 1, 3.14, True, 'a', [1], (2,3), {'B+':3.5} )
Dictionary (dict): { k1:v1 , k2:v2 , ..., kn:vn }
Ex: { 'A':4 , 'B+':3.5 , 3:'B' }
Set (set): set([ d1, d2, ..., dn ])
Ex: set([ 4, 3.5, 'B'])](https://image.slidesharecdn.com/python-171220133716/85/Python-Training-Tutorial-for-Frreshers-9-320.jpg)
![Python: List Comprehensions
>>>[ i for i in range(5)]
→ [ 0, 1, 2, 3, 4 ]
>>>[ i for i in range(5) if i <> 3 ]
→ [ 0, 1, 2, 4 ]
>>>[ (i, j) for i in range(3) for j in range(i) ]
→ [ (1,0), (2,0), (2,1) ]](https://image.slidesharecdn.com/python-171220133716/85/Python-Training-Tutorial-for-Frreshers-12-320.jpg)
![PuLP: Results
Check status: pulp.LpStatus[PB.status]
Ex: pulp.LpStatus[prob.status]
Optimal cost: pulp.value(PB.objective)
Ex: pulp.value(prob.objective)
Optimal solution: DV.varValue
Ex: x1.varValue
or pulp.value(x1)
](https://image.slidesharecdn.com/python-171220133716/85/Python-Training-Tutorial-for-Frreshers-21-320.jpg)
Python Training Tutorial for Frreshers
- 1. Python Tutorial http://www.pythontraininginbangalore.in Mob : 9513332301 / 02 / 03
- 2. Architecture Programming Language: Python Interface: PuLP Optimization Solvers: GLPK, CPLEX, COIN, etc.
- 3. Python Syllabus Introduction Why do we need Python? Program structure Execution steps Interactive Shell Executable or script files. User Interface or IDE Memory management and Garbage collections Object creation and deletion Object properties Data Types and Operations Numbers Strings List Tuple Dictionary Other Core Types
- 4. Python Syllabus Statements and Syntax Assignments, Expressions and prints If tests and Syntax Rules While and For Loops Iterations and Comprehensions File Operations and Functions Opening a file Using Files Other File tools Function definition and call Function Scope Arguments Function Objects Anonymous Functions Modules and Packages Module Creations and Usage Module Search Path Module Vs. Script Package Creation and Importing
- 5. Python Syllabus Classes and Exceptional Handling Classes and instances Classes method calls Inheritance and Compositions Static and Class Methods Bound and Unbound Methods Operator Overloading Polymorphism Default Exception Handler Catching Exceptions Raise an exception User defined exception Advanced Concepts Decorators Generators Iterators Co-routines Standard Library Modules References Excerces Roadmap with Python
- 6. Python Python is a programming language. Python runs on Windows, Linux/Unix, Mac OS X. Python is free to use.
- 7. Python: Features High-level data structures Ex: list, tuple, dictionary Object-oriented Interpreter Standard library & Third party modules Ex: pulp, numpy, matplotlib
- 8. Python: Basic Data Type Integer (int) Ex: 1, 2, 3, 5, 8, 13, 21 Float (float) Ex: 3.14, 2.71828 Boolean (bool) Ex: True, False String (str) Ex: 'Python', ”Sucha”
- 9. Python: High-Level Data Type Lt(list): [ d1, d2, …, dn ] Ex: [ 1, 3.14, True, 'a', [1], (2,3), {'B+':3.5} ] Tuple (tuple): ( d1, d2, ..., dn, ) Ex: ( 1, 3.14, True, 'a', [1], (2,3), {'B+':3.5} ) Dictionary (dict): { k1:v1 , k2:v2 , ..., kn:vn } Ex: { 'A':4 , 'B+':3.5 , 3:'B' } Set (set): set([ d1, d2, ..., dn ]) Ex: set([ 4, 3.5, 'B'])
- 10. Python: Flow Control - If If statement if boolean: command elif boolean: command else: command
- 11. Python: Loop - For, While For loop for var in sequence: command While loop while boolean: command
- 12. Python: List Comprehensions >>>[ i for i in range(5)] → [ 0, 1, 2, 3, 4 ] >>>[ i for i in range(5) if i <> 3 ] → [ 0, 1, 2, 4 ] >>>[ (i, j) for i in range(3) for j in range(i) ] → [ (1,0), (2,0), (2,1) ]
- 13. PuLP & GLPK PuLP is an LP modeler written in Python. PuLP can generate LP files, and calls solvers to solve linear problems. Supported solvers are GLPK, COIN, CPLEX, and GUROBI. The GLPK (GNU Linear Programming Kit) package is intended for solving large-scale linear programming (LP), mixed integer programming (MIP), and other related problems.
- 14. PuLP: Import Module Import module >>>import pulp >>>pulp.pulpTestAll() >>>from pulp import * >>>pulpTestAll() Following slides assume the first import method.
- 15. PuLP: Create Decision Variables DV = pulp.LpVariable(name_str, lowbound, upbound, category) For lowbound and upbound, No bound → None . category ∈{ pulp.LpContinuous, pulp.LpInteger, pulp.LpBinary } Ex: x ∈[0, ∞) x = pulp.LpVariable('Var X', 0, None, pulp.LpContinuous)
- 16. PuLP: Formulate Problem PB = pulp.LpProblem(name_str, sense) sense ∈{ pulp.LpMinimize, pulp.LpMaximize } Ex: maximization problem prob = pulp.LpProblem('Benefit', pulp.LpMaximize)
- 17. PuLP: Add Objective Function PB += linear_function, objective_name_str linear_function is in the form of c1*DV1 + c2*DV2 + … + cn*DVn Ex: Cost: 2*DV1 – 1.5*DV2 prob += 2*x1 – 1.5*x2, 'Cost'
- 18. PuLP: Add Constraints PB += linear_constraint , constraint_name_str linear_constraint is in the form of a1*DV1 + a2*DV2 + … + an*DVn == a0 or a1*DV1 + a2*DV2 + … + an*DVn <= a0 or a1*DV1 + a2*DV2 + … + an*DVn >= a0 Ex: Con1: 5*DV1 + 6*DV2 <= 7 prob += 5*x1 + 6*x2 <= 7, 'Con1' or prob += 2*x1 + 6*x2 <= 7 – 3*x1, 'Con1'
- 19. PuLP: Write .lp File PB.writeLP(filename_str) Ex: write to Benefit.lp prob.writeLP('Benefit.lp')
- 20. PuLP: Solve PB.solve() Ex: prob.solve() // Solved by COIN solver PB.solve(pulp.GLPK()) //Solved by GLPK solver Ex: prob.solve(pulp.GLPK())
- 21. PuLP: Results Check status: pulp.LpStatus[PB.status] Ex: pulp.LpStatus[prob.status] Optimal cost: pulp.value(PB.objective) Ex: pulp.value(prob.objective) Optimal solution: DV.varValue Ex: x1.varValue or pulp.value(x1)
- 22. Python Training Second Floor and Third Floor, 5/3 BEML Layout, Varathur Road,Kundalahalli Gate, Bangalore – 560066