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Profit and Linear Optimization using Pandas

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by Abdul Rawoof A R dot icon Updated: Jul 31, 2023

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Linear programming is a subset of optimization and method. It locates an optimal value for a linear objective function using a varying decision. 

 

Python can also be used to optimize parameters in a model to best-fit data, increase the profitability of a potential engineering design, or meet other objectives that can be described mathematically with the variables and equations in Python. The optimization process's essential goal is to find variables' values that minimize or maximize the objective function while satisfying the constraints. This result is called an optimal solution or optimal result. The optimizations generally have three components: objective functions, decision variables, and constraints. There are two different types of optimization or solving methods in Python, 

  • Linear optimization: It is a method that includes techniques known as tabu search and scatter search. 
  • Nonlinear optimization: It is a method that includes genetic algorithms. 


Types of optimization models: 

  • Linear Programming(LP). 
  • Nonlinear Programming(NLP). 
  • Constraint Programming(CP). 
  • Mixed integer Programming(MIP). 


Here is an example of how to implement profit and linear optimization in Pandas: 

Fig : Preview of the output that you will get on running this code from your IDE.

Code

In this solution we're using Pandas and numpy libraries.

pandas python optimize profit, linear optimization

PythondotLines of Code : 23dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import numpy as np 
import pandas as pd

dates = pd.date_range('20130101',periods=6) 
df = pd.DataFrame(np.random.randn(6,3),index=dates,columns=['Value1', 'Value2', 'Profit']) 
df['Profit'] = df['Profit']*100 

print(df.to_string())

# create list of all possible value pairs
vals = [[i,j] for i in df.Value1 for j in df.Value2]

# create list of profits from all possible value pairs
total_profit = [df['Profit'].loc[(df.Value1 > i) & (df.Value2 >= j)].sum() for i, j in vals]

# get index of maximum profit
max_index = total_profit.index(max(total_profit))

# get values that correspond to max profit
vals[max_index]

Out[9]: [-0.51914224014959032, -0.73918945103973344]

Instructions

Follow the steps carefully to get the output easily.

  1. Install pandas on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add required dependencies and import them in Python file.
  4. Run the file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for 'profit and linear optimization using pandas' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2021.3.
  2. The solution is tested on Python 3.9.7.
  3. Pandas version-v1.5.2.
  4. numpy version-v1.24.0.


Using this solution, we are able to implement profit and linear optimization using pandas with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to implement profit and linear optimization using pandas.

Dependent Libraries

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
      Security
        License
          Reuse

            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

                      Support
                        Quality
                          Security
                            License
                              Reuse

                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
                                    Security
                                      License
                                        Reuse

                                          You can also search for any dependent libraries on kandi like 'pandas' and 'numpy'.

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