Profit and Linear Optimization using Pandas
by Abdul Rawoof A R Updated: Feb 21, 2023
In Python, Linear programming is a subset of optimization. Linear programming is an optimization technique that tries to locate an optimal value for a linear objective function of linear constraints using a varying set of decisions in Python.
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.
In this solution we're using Pandas and numpy 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: [-0.51914224014959032, -0.73918945103973344]
Follow the steps carefully to get the output easily.
- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add required dependencies and import them in Python file.
- 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!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2021.3.
- The solution is tested on Python 3.9.7.
- Pandas version-v1.5.2.
- 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.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python 37415 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)
The fundamental package for scientific computing with Python.
Python 23030 Version:v1.24.2 License: Permissive (BSD-3-Clause)
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