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How to Maximize Revenue using Pandas in Python

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by Abdul Rawoof A R dot icon Updated: Mar 24, 2023

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In Python, the money that is produced by carrying out normal business operations and is calculated by multiplying the average sales price of the product by the number of items sold is called Revenue, and it is the total sum of money from which other costs and expenses are subtracted to calculated net income. 


You need to give a lot of details. Still, I assume you consider the "perfect foresight" revenue maximization problem. I.e., we know at the start how the price will evolve over the horizon, and this problem is quite easy to solve, but as far as we can tell, our problem needs to be solved. We could make an arbitrarily large revenue by buying an infinite Number of units at a low price and selling them at a high price. We need to add a constraint that you can only start with a finite amount of cash and that we can only sell stock we own (this is not strictly true. It is possible to "short-sell," where you sell stuff now on the expectation its value will fall (when you will have to buy it again later). Ignoring short-selling shenanigans, we can formulate an optimization problem as a Linear Program. 


Libraries that we are using to find the maximum Revenue in Python: 

  • Pandas: It is used to work with data sets and has functions for analyzing, exploring, manipulating data, and cleaning. 
  • NumPy: It is used to work with arrays and has functions for working in the domain of linear algebra and matrices. 
  • PuLP: It can be used to solve linear programming problems, and it is also used to solve optimization problems and has uses in various industries like Food, Transportation, etc. 


Here is an example of how to maximize Revenue using Pandas in Python: 

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

Code

In this solution we're using Pandas, NumPy and PuLP libraries.

How to maximize revenue - Python

PythondotLines of Code : 62dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import pandas as pd
import numpy as np
from pulp import *

# Problem Data
df = pd.DataFrame({
    'Time': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'Price': [44, 100, 40, 110, 77, 109, 65, 93, 89, 49]})

times = list(df.Time)
times_plus_1 = times + [times[-1] + 1]

# Instantiate maximisation problem
prob = LpProblem("numpy_constraints", LpMaximize)

# Create the problem vairables
# Cash in bank and stock-level at start of each interval
Cash = pulp.LpVariable.dicts("Cash", times_plus_1, cat='Continuous', lowBound=0)
Stock = pulp.LpVariable.dicts("Stock", times_plus_1, cat='Continuous', lowBound=0)

# Amount bought during interval
Buy = pulp.LpVariable.dicts("Buy", times, cat='Continuous')

# Add Objective to problem - cash at end of period modelled
prob += Cash[times_plus_1[-1]]

# Add constraints
# Start with a single dollar in the bank & no stock
prob += Cash[times[0]] == 1.0
prob += Stock[times[0]] == 0.0

# Cash & stock update rules
for t in times:
    prob += Cash[t+1] == Cash[t] - Buy[t]*df.Price[t]
    prob += Stock[t+1] == Stock[t] + Buy[t]

# Solve
prob.solve()

# Check when we bought when:
Buy_soln = np.array([Buy[t].varValue for t in times])
print("Buy_soln:")
print(Buy_soln)

Stock_soln = np.array([Stock[t].varValue for t in times_plus_1])
print("Stock_soln:")
print(Stock_soln)

Cash_soln = np.array([Cash[t].varValue for t in times_plus_1])
print("Cash_soln:")
print(Cash_soln)

Buy_soln:
[ 0.02272727 -0.02272727  0.05681818 -0.05681818  0.08116883 -0.08116883
  0.13611389 -0.13611389  0.          0.        ]
Stock_soln:
[0.         0.02272727 0.         0.05681818 0.         0.08116883
 0.         0.13611389 0.         0.         0.        ]
Cash_soln:
[ 1.         0.         2.2727273  0.         6.25       0.
  8.8474026  0.        12.658591  12.658591  12.658591 ]

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 'how to maximize revenue 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.
  5. PuPL version-2.7.0.


Using this solution, we are able to maximize revenue using pandas in python 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 maximize revenue using pandas in python.

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.

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                                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
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                                          pulpby coin-or

                                          Python doticonstar image 1683 doticonVersion:2.7.0doticon
                                          License: Others (Non-SPDX)

                                          A python Linear Programming API

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                                                    pulpby coin-or

                                                    Python doticon star image 1683 doticonVersion:2.7.0doticon License: Others (Non-SPDX)

                                                    A python Linear Programming API
                                                    Support
                                                      Quality
                                                        Security
                                                          License
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                                                              You can also search for any dependent libraries on kandi like 'pandas', 'numpy'and 'pulp'.

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