assignment problem code in python

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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

• Solving Blending Problem in Python using Gurobi
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The Assignment Problem, a NumPy function?

Since an assignment problem can be posed in the form of a single matrix, I am wondering if NumPy has a function to solve such a matrix. So far I have found none. Maybe one of you guys know if NumPy/SciPy has an assignment-problem-solve function?

Edit: In the meanwhile I have found a Python (not NumPy/SciPy) implementation at http://software.clapper.org/munkres/ . Still I suppose a NumPy/SciPy implementation could be much faster, right?

• optimization
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• What a shame it was not implemented with numpy. Not only might it be faster, but the algorithm must be much easier to express with numpy as well. –  u0b34a0f6ae Sep 9, 2009 at 12:33

7 Answers 7

There is now a numpy implementation of the munkres algorithm in scikit-learn under sklearn/utils/linear_assignment_.py its only dependency is numpy. I tried it with some approximately 20x20 matrices, and it seems to be about 4 times as fast as the one linked to in the question. cProfiler shows 2.517 seconds vs 9.821 seconds for 100 iterations.

• 6 This will be included in scipy as scipy.optimize.linear_sum_assignment from version 0.18. –  joeln Jan 14, 2016 at 3:41
• 1 The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead. –  min2bro Dec 7, 2019 at 8:56

I was hoping that the newer scipy.optimize.linear_sum_assignment would be fastest, but (perhaps not surprisingly) the Cython library (which does not have pip support) is significantly faster, at least for my use case:

UPDATE: using munkres v1.1.2 and scipy v1.5.0 achieves the following results:

• I just tried the library for my case and got 25x speedup using the Cython version! –  Marcus V. Apr 5, 2018 at 13:25
• I also tried the cython version and got a massive speedup. Just had to learn to install from source since it apparently lacks pip support –  kevinkayaks Sep 12, 2018 at 23:43
• 2 The implementation of scipy.optimize.linear_sum_assignment has been revised and implemented from scratch in C++. The new implementation will be available in SciPy 1.4.0 and should provide substantial improvements in speed. –  fuglede Jul 19, 2019 at 12:32
• Yes..Newer implementation of scipy.optimize.linear_sum_assignment is very fast. A 1000 x 1000 cost matrix takes 70ms to solve. –  Abhilash Awasthi Jun 6, 2020 at 10:35

No, NumPy contains no such function. Combinatorial optimization is outside of NumPy's scope. It may be possible to do it with one of the optimizers in scipy.optimize but I have a feeling that the constraints may not be of the right form.

NetworkX probably also includes algorithms for assignment problems.

• 11 There is an implementation scipy.optimize.linear_sum_assignment from scipy version 0.18. –  joeln Jan 14, 2016 at 3:41

Yet another fast implementation, as already hinted by @Matthew: scipy.optimize has a function called linear_sum_assignment . From the docs:

The method used is the Hungarian algorithm, also known as the Munkres or Kuhn-Munkres algorithm.

https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.optimize.linear_sum_assignment.html

There is an implementation of the Munkres' algorithm as a python extension module which has numpy support. I've used it successfully on my old laptop. However, it does not work on my new machine - I assume there is a problem with "new" numpy versions (or 64bit arch).

As of version 2.4 (released 2019-10-16), NetworkX solves the problem through nx.algorithms.bipartite.minimum_weight_full_matching . At the time of writing, the implementation uses SciPy's scipy.optimize.linear_sum_assignment under the hood, so expect the same performance characteristics.

In addition to the solver in scipy.optimize.linear_sum_assignment already mentioned in some of the other answers, SciPy (as of 1.6.0) also comes with a sparsity-friendly solver in scipy.sparse.csgraph.min_weight_full_bipartite_matching .

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Linear Sum Assignment Solver

This section describes the linear sum assignment solver , a specialized solver for the simple assignment problem, which can be faster than either the MIP or CP-SAT solver. However, the MIP and CP-SAT solvers can handle a much wider array of problems, so in most cases they are the best option.

Cost matrix

The costs for workers and task are given in the table below.

The following sections present a Python program that solves an assignment problem using the linear sum assignment solver.

Import the libraries

The code that imports the required library is shown below.

Define the data

The following code creates the data for the program.

The array is the cost matrix , whose i , j entry is the cost for worker i to perform task j . There are four workers, corresponding to the rows of the matrix, and four tasks, corresponding to the columns.

Create the solver

The program uses the linear assignment solver, a specialized solver for the assignment problem.

The following code creates the solver.

Add the constraints

The following code adds the costs to the solver by looping over workers and tasks.

Invoke the solver

The following code invokes the solver.

Display the results

The following code displays the solution.

The output below shows the optimal assignment of workers to tasks.

The following graph shows the solution as the dashed edges in the graph. The numbers next to the dashed edges are their costs. The total wait time of this assignment is the sum of the costs for the dashed edges, which is 265.

In graph theory, a set of edges in a bipartite graph that matches every node on the left with exactly one node on the right is called a perfect matching .

The entire program

Here is the entire program.

Solution when workers can't perform all tasks

In the previous example, we assumed that all workers can perform all tasks. But this not always the case - a worker might be unable to perform one or more tasks for various reasons. However, it is easy to modify the program above to handle this.

As an example, suppose that worker 0 is unable to perform task 3. To modify the program to take this into account, make the following changes:

• Change the 0, 3 entry of the cost matrix to the string 'NA' . (Any string will work.) cost = [[90, 76, 75, 'NA'], [35, 85, 55, 65], [125, 95, 90, 105], [45, 110, 95, 115]]
• In the section of the code that assigns costs to the solver, add the line if cost[worker][task] != 'NA': , as shown below. for worker in range(0, rows): for task in range(0, cols): if cost[worker][task] != 'NA': assignment.AddArcWithCost(worker, task, cost[worker][task]) The added line prevents any edge whose entry in the cost matrix is 'NA' from being added to the solver.

After making these changes and running the modified code, you see the following output:

Notice that the total cost is higher now than it was for the original problem. This is not surprising, since in the original problem the optimal solution assigned worker 0 to task 3, while in the modified problem that assignment is not allowed.

To see what happens if more workers are unable to perform tasks, you can replace more entries of the cost matrix with 'NA' , to denote additional workers who can't perform certain tasks:

When you run the program this time, you get a negative result:

This means there is no way to assign workers to tasks so that each worker performs a different task. You can see why this is so by looking at the graph for the problem (in which there are no edges corresponding to values of 'NA' in the cost matrix).

Since the nodes for the three workers 0, 1, and 2 are only connected to the two nodes for tasks 0 and 1, it not possible to assign distinct tasks to these workers.

The Marriage Theorem

There is a well-known result in graph theory, called The Marriage Theorem , which tells us exactly when you can assign each node on the left to a distinct node on the right, in a bipartite graph like the one above. Such an assignment is called a perfect matching . In a nutshell, the theorem says this is possible if there is no subset of nodes on the left (like the one in the previous example ) whose edges lead to a smaller set of nodes on the right.

More precisely, the theorem says that a bipartite graph has a perfect matching if and only if for any subset S of the nodes on the left side of the graph, the set of nodes on the right side of the graph that are connected by an edge to a node in S is at least as large as S.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2023-01-18 UTC.

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Adding sequence constraints to the assignment problem- Python

This is an online assignment problem and yet can be considered as an assignment problem with a sequence. Assume that workers are coming into the system sequentially and I want to assign a task to them based on their cost for each task. I have set up the python model without the sequence constraint, I just couldn't figure out how to mandate the order: This example from the OR-Tools: In the example there are four workers (numbered 0-3) and four tasks (numbered 0-3).

The costs of assigning workers to tasks are shown in the following table.

The problem is to assign each worker to at most one task, with no two workers performing the same task while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task.

Assume that I want to force an order/ sequence. Say worker 0 has to be assigned first worker 2, then worker 3... So in practice:

• worker 0 assigned to task 3 (lowest cost)
• worker 1 assigned to task 0 (lowest cost)
• worker 2 assigned to task 2 (task 3 has the lowest cost but since it is already assigned to worker 0 it is not available)
• worker 3 assigned to task 1

MIP solution The following sections describe how to solve the problem using the MIP solver as an assignment problem without enforcing the order.

Import the libraries The following code imports the required libraries.

The following code declares the MIP solver.

The following code creates the data for the problem.

The following code creates binary integer variables for the problem.

The following code creates the constraints for the problem.

HERE I want to add another constraint(s) that forces the order

The following code creates the objective function for the problem.

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

Invoke the solver The following code invokes the solver and prints the solution.

• assignment-problem

• $\begingroup$ You posted data for 4 tasks and 4 resources whereas you mentioned workers are more than tasks, could you please explain and also what constraint you want to include ? Is it just that 1Task per worker or other constraints are there too ? And is objective that you want to minimize total cost of Tasks wrt workers they are assigned to (That's what I made out from explanation). And also what are problems you facing as you've included almost all the program so aren't you getting desired OP ? $\endgroup$ –  user3237357 Sep 1, 2021 at 7:36

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scipy.optimize.linear_sum_assignment ¶

Solve the linear sum assignment problem.

The linear sum assignment problem is also known as minimum weight matching in bipartite graphs. A problem instance is described by a matrix C, where each C[i,j] is the cost of matching vertex i of the first partite set (a “worker”) and vertex j of the second set (a “job”). The goal is to find a complete assignment of workers to jobs of minimal cost.

Formally, let X be a boolean matrix where \(X[i,j] = 1\) iff row i is assigned to column j. Then the optimal assignment has cost

s.t. each row is assignment to at most one column, and each column to at most one row.

This function can also solve a generalization of the classic assignment problem where the cost matrix is rectangular. If it has more rows than columns, then not every row needs to be assigned to a column, and vice versa.

The method used is the Hungarian algorithm, also known as the Munkres or Kuhn-Munkres algorithm.

New in version 0.17.0.

• http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html
• Harold W. Kuhn. The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly , 2:83-97, 1955.
• Harold W. Kuhn. Variants of the Hungarian method for assignment problems. Naval Research Logistics Quarterly , 3: 253-258, 1956.
• Munkres, J. Algorithms for the Assignment and Transportation Problems. J. SIAM , 5(1):32-38, March, 1957.
• https://en.wikipedia.org/wiki/Hungarian_algorithm

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Python3 code for solving a generalized assignment problem.

stinodego/GeneralizedAssignment

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THIS CODE IS NOT BEING MAINTAINED.

Feel free to use this code for whatever purpose you'd like. I will not offer any support.

GeneralizedAssignment

This Python3 code may be used for solving an instance of the generalized assignment problem.

The generalized assignment problem

The generalized assignment problem is described quite well on Wikipedia .

This code actually allows for further generalization, multiple agents to perform a single task (regulated by a task budget).

The implementation

The implementation is a simple depth-first search algorithm. Therefore, it does not work well for very large problems.

The depth-first search expands most promising nodes first. True maximum assignment is guaranteed when algorithm is allowed to complete. Otherwise, the assignment printed last may be used as a best guess.

The code makes use of frozendict to keep track of the set of assignments. Simply install it using the following command: python -m pip install frozendict

Running the code

Solving your assignment problem is easy. Just specify your assignment problem at the bottom of the file, then run it. An example problem specification is given, to make clear what syntax is expected.

The code offers a few features:

• Optional 'hard assignment' initializes the assignment with certain agents assigned to certain tasks
• Optional 'fair' parameter maximizes the profits related to the least profitable task (and thus equalizes the profits among tasks).
• Optional 'complete' parameter requires agents and tasks to fully use their budgets.
• 'verbose' option prettily prints the assignment information after the code finishes. May be turned off.
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Optimizing Job Assignments with Python: A Greedy Approach

In this article, we will learn the skill of job assignment which is a very important topic in the field of Operations Research. For this, we will utilize Python programming language and the Numpy library for the same. We will also solve a small case on a job assignment.

Job assignment involves allocating tasks to workers while minimizing overall completion time or cost. Python’s greedy algorithm, combined with NumPy, can solve such problems by iteratively assigning jobs based on worker skills and constraints, enabling efficient resource management in various industries.

Recommended: Maximizing Cost Savings Through Offshore Development: A Comprehensive Guide

Recommended: Delivery Route Optimization using Python: A Step-by-Step Guide

What is a Job Assignment?

Let us understand what a job assignment is with an example. In our example, three tasks have to be completed. Three workers have different sets of skills and take different amounts of time to complete the above-mentioned tasks. Now our goal is to assign the jobs to the workers to minimize the period of completing the three tasks.

Now, we solve the above problem using the concepts of Linear programming. Now there are certain constraints as well, each worker can be assigned only a single job at a time. Our objective function is the sum of all the time taken by the workers and minimize it. Let us now solve this problem using the power of the Numpy library of Python programming language.

Let us now look at the output of the problem.

From the output, we can see that The assignment is complete and optimized. Let us now look at a small case and understand the job assignment further.

A Real-World Job Assignment Scenario

Continuing with the example of assigning workers some jobs, in this case, a company is looking to get some work done with the help of some freelancers. There are 15 jobs and we have 10 freelancers. We have to assign jobs to workers in such a way, that we minimize the time as well as the cost of the whole operation. Let us now model this in the Python programming language.

This problem is solved using the greedy algorithm. In short, the greedy algorithm selects the most optimal choice available and does not consider what will happen in the future while making this choice. In the above code, we have randomly generated data on freelancer details. Let us now look at the output of the code.

Thus, we complete our agenda of job assignment while minimizing costs as evidenced by the output.

Assigning jobs optimally is crucial for maximizing productivity and minimizing costs in today’s competitive landscape. Python’s powerful libraries like NumPy make it easy to implement greedy algorithms and solve complex job assignment problems, even with larger sets of jobs and workers. How could you adapt this approach to accommodate dynamic changes in job requirements or worker availability?

Recommended: Splitting Lists into Sub-Lists in Python: Techniques and Advantages

Recommended: Object Detection with OpenCV: A Step-by-Step Tutorial

Episode 207: Decomposing Software Problems & Avoiding the Trap of Clever Code

The real python podcast, jun 07, 2024 55m.

How do you effectively break a software problem into individual steps? What are signs you’re writing overly clever code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Episode Sponsor:

We discuss an article about de-warping images of book pages. We both found the piece a good study on decomposing a complex software problem.

Christopher discusses an article titled “Clever code is probably the worst code you could write.” Early in a programming career, it’s easier to write complex and difficult-to-read code. The real challenge is progressing towards writing clearer and readable code.

We also share several other articles and projects from the Python community, including a news roundup, what the __pycache__ folder is for in Python, what’s new in Django 5.1, a discussion about software engineering hiring and firing, a project for setting up repeated tasks, and a simple way to create reusable template components in Django.

This episode is sponsored by Sentry.

Course Spotlight: Efficient Iterations With Python Iterators and Iterables

In this video course, you’ll learn what iterators and iterables are in Python. You’ll learn how they differ and when to use them in your code. You’ll also learn how to create your own iterators and iterables to make data processing more efficient.

• 00:00:00 – Introduction
• 00:02:18 – PEP 667: Consistent Views of Namespaces (Accepted)
• 00:03:08 – PEP 649 Re-targeted to 3.14
• 00:03:50 – Untold Stories From 6 Years Working on Python Packaging
• 00:04:38 – What Is the __pycache__ Folder in Python?
• 00:09:57 – Sponsor: Sentry
• 00:11:04 – What’s New in Django 5.1
• 00:17:48 – Page Dewarping
• 00:26:55 – Video Course Spotlight
• 00:28:26 – Clever Code Is Probably the Worst Code You Could Write
• 00:33:19 – Software Engineering Hiring and Firing
• 00:51:22 – Metronomes: An Easy Way to Set Up Regular Tasks
• 00:52:26 – django-web-components: Create reusable template components in Django
• 00:54:03 – Thanks and goodbye
• PEP 667: Consistent Views of Namespaces (Accepted)
• PEP 649 Re-targeted to 3.14 – Python Enhancement Proposal 649: Deferred Evaluation Of Annotations Using Descriptors has been re-targeted to the Python 3.14 release
• Untold Stories From 6 Years Working on Python Packaging – Sumana gave the closing keynote address at PyCon US this year and this posting shares all the links and references from the talk.

Show Links:

• What Is the __pycache__ Folder in Python? – In this tutorial, you’ll explore Python’s __pycache__ folder. You’ll learn about when and why the interpreter creates these folders, and you’ll customize their default behavior. Finally, you’ll take a look under the hood of the cached .pyc files.
• What’s New in Django 5.1 – Django 5.1 has gone alpha so the list of features targeting this release has more or less solidified. This article introduces you to what is coming in Django 5.1.
• Page Dewarping – This article shows the techniques behind a page flattening algorithm. It starts with images of a book’s page which are curled from the spine of the book, and creates a resulting PDF that is a flat version.
• Clever Code Is Probably the Worst Code You Could Write – When you come across a clever bit of code, it is hard not to admire it, but often times, clear, readable code is the hardest code to write.

Discussion:

• Software Engineering Hiring and Firing – This article is a deep dive on the hiring and firing practices in the software field, and unlike most articles focuses on senior engineering roles. It isn’t a “first job” post, but a “how the decision process works” article.
• Metronomes: An Easy Way to Set Up Regular Tasks
• django-web-components: A simple way to create reusable template components in Django

Level Up Your Python Skills With These Courses:

For Loops in Python (Definite Iteration)

Get Started With Django: Build a Portfolio App

Efficient Iterations With Python Iterators and Iterables

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
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• Dictionaries

Python Operators

Precedence and associativity of operators in python.

• Python Arithmetic Operators
• Difference between / vs. // operator in Python
• Python - Star or Asterisk operator ( * )
• What does the Double Star operator mean in Python?
• Division Operators in Python
• Modulo operator (%) in Python
• Python Logical Operators
• Python OR Operator
• Difference between 'and' and '&' in Python
• not Operator in Python | Boolean Logic

Ternary Operator in Python

• Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

• Walrus Operator in Python 3.8
• Increment += and Decrement -= Assignment Operators in Python
• Merging and Updating Dictionary Operators in Python 3.9
• New '=' Operator in Python3.8 f-string

Python Relational Operators

• Comparison Operators in Python
• Python NOT EQUAL operator
• Difference between == and is operator in Python
• Chaining comparison operators in Python
• Python Membership and Identity Operators
• Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

• OPERATORS: These are the special symbols. Eg- + , * , /, etc.
• OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

• Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

• P – Parentheses
• E – Exponentiation
• M – Multiplication (Multiplication and division have the same precedence)
• D – Division
• A – Addition (Addition and subtraction have the same precedence)
• S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

• x % 10 -> yields the last digit
• x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

• Logical not
• logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

• Bitwise NOT
• Bitwise Shift
• Bitwise AND
• Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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3. An Informal Introduction to Python ¶

In the following examples, input and output are distinguished by the presence or absence of prompts ( >>> and … ): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command.

You can toggle the display of prompts and output by clicking on >>> in the upper-right corner of an example box. If you hide the prompts and output for an example, then you can easily copy and paste the input lines into your interpreter.

Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, # , and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples.

Some examples:

3.1. Using Python as a Calculator ¶

Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>> . (It shouldn’t take long.)

3.1.1. Numbers ¶

The interpreter acts as a simple calculator: you can type an expression at it and it will write the value. Expression syntax is straightforward: the operators + , - , * and / can be used to perform arithmetic; parentheses ( () ) can be used for grouping. For example:

The integer numbers (e.g. 2 , 4 , 20 ) have type int , the ones with a fractional part (e.g. 5.0 , 1.6 ) have type float . We will see more about numeric types later in the tutorial.

Division ( / ) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use % :

With Python, it is possible to use the ** operator to calculate powers [ 1 ] :

The equal sign ( = ) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:

If a variable is not “defined” (assigned a value), trying to use it will give you an error:

There is full support for floating point; operators with mixed type operands convert the integer operand to floating point:

In interactive mode, the last printed expression is assigned to the variable _ . This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example:

This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior.

In addition to int and float , Python supports other types of numbers, such as Decimal and Fraction . Python also has built-in support for complex numbers , and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j ).

3.1.2. Text ¶

Python can manipulate text (represented by type str , so-called “strings”) as well as numbers. This includes characters “ ! ”, words “ rabbit ”, names “ Paris ”, sentences “ Got your back. ”, etc. “ Yay! :) ”. They can be enclosed in single quotes ( '...' ) or double quotes ( "..." ) with the same result [ 2 ] .

To quote a quote, we need to “escape” it, by preceding it with \ . Alternatively, we can use the other type of quotation marks:

In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters:

If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote:

There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds.

String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...''' . End of lines are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. The following example:

produces the following output (note that the initial newline is not included):

Strings can be concatenated (glued together) with the + operator, and repeated with * :

Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated.

This feature is particularly useful when you want to break long strings:

This only works with two literals though, not with variables or expressions:

If you want to concatenate variables or a variable and a literal, use + :

Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one:

Indices may also be negative numbers, to start counting from the right:

Note that since -0 is the same as 0, negative indices start from -1.

In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring:

Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced.

Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s :

One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n , for example:

The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j , respectively.

For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2.

Attempting to use an index that is too large will result in an error:

However, out of range slice indexes are handled gracefully when used for slicing:

Python strings cannot be changed — they are immutable . Therefore, assigning to an indexed position in the string results in an error:

If you need a different string, you should create a new one:

The built-in function len() returns the length of a string:

Strings are examples of sequence types , and support the common operations supported by such types.

Strings support a large number of methods for basic transformations and searching.

String literals that have embedded expressions.

Information about string formatting with str.format() .

The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here.

3.1.3. Lists ¶

Python knows a number of compound data types, used to group together other values. The most versatile is the list , which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type.

Like strings (and all other built-in sequence types), lists can be indexed and sliced:

Lists also support operations like concatenation:

Unlike strings, which are immutable , lists are a mutable type, i.e. it is possible to change their content:

You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later):

Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list . Any changes you make to the list through one variable will be seen through all other variables that refer to it.:

All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list:

Assignment to slices is also possible, and this can even change the size of the list or clear it entirely:

The built-in function len() also applies to lists:

It is possible to nest lists (create lists containing other lists), for example:

3.2. First Steps Towards Programming ¶

Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows:

This example introduces several new features.

The first line contains a multiple assignment : the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right.

The while loop executes as long as the condition (here: a < 10 ) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to).

The body of the loop is indented : indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount.

The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this:

The keyword argument end can be used to avoid the newline after the output, or end the output with a different string:

Table of Contents

• 3.1.1. Numbers
• 3.1.2. Text
• 3.1.3. Lists
• 3.2. First Steps Towards Programming

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ValueError: setting an array element with a sequence

Im trying to training a Random Forest Classifier to predict movie success based on various features.

Im using tmdb_5000_movies.csv data set. code as below df_movies = pd.read_csv(‘tmdb_5000_movies.csv’) df_credits= pd.read_csv(‘tmdb_5000_credits.csv’)

df_movies.rename(columns={‘id’: ‘movie_id’}, inplace=True) ## Rename the ‘id’ column to ‘movie_id’

Merge the DataFrames on ‘movie_id’ with specified suffixes

df_merged = pd.merge(df_movies, df_credits, on=‘movie_id’, suffixes=(‘_movie’, ‘_credit’))

def extract_genres(json_str): try: genres = json.loads(json_str.replace(“'”, “"”)) # Replace single quotes with double quotes for valid JSON genre_names = [genre[‘name’] for genre in genres] return genre_names except (json.JSONDecodeError, TypeError): return

Apply the function to the ‘genres’ column

df_merged[‘genres’] = df_merged[‘genres’].apply(extract_genres)

Handle keywords

df_merged.iloc[0][‘keywords’]

def extract_keywords(text): L = for i in ast.literal_eval(text): L.append(i[‘name’]) return L

Apply the function to the ‘keywords’ column

df_merged[‘keywords’] = df_merged[‘keywords’].apply(extract_keywords)

Handle cast

df_merged.iloc[0][‘cast’]

Function to convert string to list of keyword names and keeping top 4 cast

def convert_cast(text):

df_merged[‘cast’] = df_merged[‘cast’].apply(convert_cast)

Handle crew

df_merged.iloc[0][‘crew’]

Function to Extract The Director Name

def get_director(text): L = for i in ast.literal_eval(text): if i[‘job’] == ‘Director’: L.append(i[‘name’]) break return L

df_merged[‘crew’] = df_merged[‘crew’].apply(get_director)

Converting overview to list

df_merged.iloc[0][‘overview’]

Remove spaces from strings

def remove_spaces(text): if isinstance(text, list): return [t.replace(" “, “”) for t in text] elif isinstance(text, str): return text.replace(” ", “”) else: return text

Apply the function to remove spaces

df_merged[‘overview’] = df_merged[‘overview’].apply(remove_spaces) df_merged[‘genres’] = df_merged[‘genres’].apply(remove_spaces) df_merged[‘keywords’] = df_merged[‘keywords’].apply(remove_spaces) df_merged[‘cast’] = df_merged[‘cast’].apply(remove_spaces) df_merged[‘crew’] = df_merged[‘crew’].apply(remove_spaces)

Drop the ‘homepage’ column

df_merged.drop(columns=[‘homepage’], inplace=True)

Preprocess the data

categorical_features = [‘genres’, ‘original_language’, ‘production_countries’, ‘spoken_languages’, ‘status’] label_encoder = LabelEncoder()

for feature in categorical_features: df_merged[feature] = label_encoder.fit_transform(df_merged[feature].astype(str))

Prepare the features and target variable

X = df_merged.drop([‘title_movie’, ‘vote_average’, ‘vote_count’], axis=1) y = df_merged[‘vote_average’].apply(lambda x: 1 if x >= 6 else 0) # Assuming a vote_average >= 6 is considered a success

Split the data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train a Random Forest Classifier

rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42) rf_classifier.fit(X_train, y_train)

I’m getting the below error. ValueError: setting an array element with a sequence.

The problem as you have posed it does not have enough information for us to resolve.

It’s not just that most of the code is hard to read because it isn’t formatted, and because you have clearly omitted a bunch of it, but you have given us dozens of lines of code without any hint as to which of these lines causes the error.

Also, the top result of a Google search is this: python - ValueError: setting an array element with a sequence - Stack Overflow

It would be very helpful to explain why this didn’t work for you, particularly since I suspect that the undefined variable you have, pd , is in fact pandas .

when entering your scripts onto this forum, please follow these guidelines so that your code will appear as it does on your Python editor for easier reading and interpretation.

Can you elaborate on the source of the reported issue? The editor will list the line number from where the exception is being generated. Please describe the source of the error.

Here is an example of what generates the stated exception. When you define an array, it should be X x Y dimensions. For example, with X = 3, and Y = 5, you will have a 3 x 5 dimensional array. If you don’t have a value for a given cell, you have to assign it a value of 0 and NOT leave it blank.

Here is a visual that may help in understanding this concept a little bit better:

In figure (a) the the first row has three columns. The second row has two columns. It needs to have three columns. However, the value has to be set to 0 if it has no value. It cannot be left blank. Otherwise, a ValueError: setting an array element with a sequence exception will be generated. Refer to figure (b) for the correct way of defining an array.

Therefore, somewhere in your script, an array or an array like object is being defined as per the characteristics of figure (a) .

Okay, and where in the code does this appear to occur? (Do you understand what a traceback is, and why it is useful?)

Related Topics

Version 1.90 is now available! Read about the new features and fixes from May.

Python debugging in VS Code

The Python extension supports debugging through the Python Debugger extension for several types of Python applications. For a short walkthrough of basic debugging, see Tutorial - Configure and run the debugger . Also see the Flask tutorial . Both tutorials demonstrate core skills like setting breakpoints and stepping through code.

For general debugging features such as inspecting variables, setting breakpoints, and other activities that aren't language-dependent, review VS Code debugging .

This article mainly addresses Python-specific debugging configurations , including the necessary steps for specific app types and remote debugging.

Python Debugger Extension

The Python Debugger extension is automatically installed along with the Python extension for VS Code. It offers debugging features with debugpy for several types of Python applications, including scripts, web apps, remote processes and more.

To verify it's installed, open the Extensions view ( ⇧⌘X (Windows, Linux Ctrl+Shift+X ) ) and search for @installed python debugger . You should see the Python Debugger extension listed in the results.

You can refer to the extension's README page for information on supported Python versions.

Initialize configurations

A configuration drives VS Code's behavior during a debugging session. Configurations are defined in a launch.json file that's stored in a .vscode folder in your workspace.

Note : To change debugging configuration, your code must be stored in a folder.

To initialize debug configurations, first select the Run view in the sidebar:

If you don't yet have any configurations defined, you'll see a button to Run and Debug and a link to create a configuration (launch.json) file:

To generate a launch.json file with Python configurations, do the following steps:

Select the create a launch.json file link (outlined in the image above) or use the Run > Open configurations menu command.

Select Python Debugger from the debugger options list.

A configuration menu will open from the Command Palette allowing you to choose the type of debug configuration you want to use for our Python project file. If you want to debug a single Python script, select Python File in the Select a debug configuration menu that appears.

Note : Starting a debugging session through the Debug Panel, F5 or Run > Start Debugging when no configuration exists will also bring up the debug configuration menu, but will not create a launch.json file.

The Python Debugger extension then creates and opens a launch.json file that contains a pre-defined configuration based on what you previously selected, in this case, Python File . You can modify configurations (to add arguments, for example), and also add custom configurations.

The details of configuration properties are covered later in this article under Standard configuration and options . Other configurations are also described in this article under Debugging specific app types .

Additional configurations

By default, VS Code shows only the most common configurations provided by the Python Debugger extension. You can select other configurations to include in launch.json by using the Add Configuration command shown in the list and the launch.json editor. When you use the command, VS Code prompts you with a list of all available configurations (be sure to select the Python option):

See Debugging specific app types for details on all of these configurations.

During debugging, the Status Bar shows the current configuration and the current debugging interpreter. Selecting the configuration brings up a list from which you can choose a different configuration:

By default, the debugger uses the same interpreter selected for your workspace, just like other features of Python extension for VS Code. To use a different interpreter for debugging specifically, set the value for python in launch.json for the applicable debugger configuration. Alternately, use the Python interpreter indicator on the Status Bar to select a different one.

Basic debugging

If you're only interested in debugging a Python script, the simplest way is to select the down-arrow next to the run button on the editor and select Python Debugger: Debug Python File .

If you're looking to debug a web application using Flask, Django or FastAPI, the Python Debugger extension provides dynamically created debug configurations based on your project structure under the Show all automatic debug configurations option, through the Run and Debug view.

But if you're looking to debug other kinds of applications, you can start the debugger through the Run view by clicking on the Run and Debug button.

When no configuration has been set, you'll be given a list of debugging options. Here, you can select the appropriate option to quickly debug your code.

Two common options are to use the Python File configuration to run the currently open Python file or to use the Attach using Process ID configuration to attach the debugger to a process that is already running.

For information about creating and using debugging configurations, see the Initialize configurations and Additional configurations sections. Once a configuration is added, it can be selected from the dropdown list and started using the Start Debugging button ( F5 ).

Command line debugging

The debugger can also be run from the command line, if debugpy is installed in your Python environment.

Install debugpy

You can install debugpy using python -m pip install --upgrade debugpy into your Python environment.

Tip : While using a virtual environment is not required, it is a recommended best practice. You can create a virtual environment in VS Code by opening the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ) and running the Python: Create Virtual Environment command ( ).

Command line syntax

The debugger command line syntax is as follows:

From the command line, you could start the debugger using a specified port (5678) and script using the following syntax. This example assumes the script is long-running and omits the --wait-for-client flag, meaning that the script will not wait for the client to attach.

You would then use the following configuration to attach from the VS Code Python Debugger extension.

Note : Specifying host is optional for listen , by default 127.0.0.1 is used.

If you wanted to debug remote code or code running in a docker container, on the remote machine or container, you would need to modify the previous CLI command to specify a host.

The associated configuration file would then look as follows.

Note : Be aware that when you specify a host value other than 127.0.0.1 or localhost you are opening a port to allow access from any machine, which carries security risks. You should make sure that you're taking appropriate security precautions, such as using SSH tunnels, when doing remote debugging.

Command line options

Note : [<arg>] can be used to pass command-line arguments along to the app being launched.

Debugging by attaching over a network connection

Local script debugging.

There may be instances where you need to debug a Python script that's invoked locally by another process. For example, you may be debugging a web server that runs different Python scripts for specific processing jobs. In such cases, you need to attach the VS Code debugger to the script once it's been launched:

Run VS Code, open the folder or workspace containing the script, and create a launch.json for that workspace if one doesn't exist already.

In the script code, add the following and save the file:

Open a terminal using Terminal: Create New Terminal , which activates the script's selected environment.

In the terminal, install the debugpy package .

In the terminal, start Python with the script, for example, python3 myscript.py . You should see the "Waiting for debugger attach" message that's included in the code, and the script halts at the debugpy.wait_for_client() call.

Switch to the Run and Debug view ( ⇧⌘D (Windows, Linux Ctrl+Shift+D ) ), select the appropriate configuration from the debugger dropdown list, and start the debugger.

The debugger should stop on the debugpy.breakpoint() call, from which point you can use the debugger normally. You also have the option of setting other breakpoints in the script code using the UI instead of using debugpy.breakpoint() .

Remote script debugging with SSH

Remote debugging allows you to step through a program locally within VS Code while it runs on a remote computer. It is not necessary to install VS Code on the remote computer. For added security, you may want or need to use a secure connection, such as SSH, to the remote computer when debugging.

Note : On Windows computers, you may need to install Windows 10 OpenSSH to have the ssh command.

The following steps outline the general process to set up an SSH tunnel. An SSH tunnel allows you to work on your local machine as if you were working directly on the remote in a more secure manner than if a port was opened for public access.

On the remote computer:

Enable port forwarding by opening the sshd_config config file (found under /etc/ssh/ on Linux and under %programfiles(x86)%/openssh/etc on Windows) and adding or modifying the following setting:

Note : The default for AllowTcpForwarding is yes, so you might not need to make a change.

If you had to add or modify AllowTcpForwarding , restart the SSH server. On Linux/macOS, run sudo service ssh restart ; on Windows, run services.msc , select OpenSSH or sshd in the list of services, and select Restart .

On the local computer:

Create an SSH tunnel by running ssh -2 -L sourceport:localhost:destinationport -i identityfile user@remoteaddress , using a selected port for destinationport and the appropriate username and the remote computer's IP address in user@remoteaddress . For example, to use port 5678 on IP address 1.2.3.4, the command would be ssh -2 -L 5678:localhost:5678 -i identityfile [email protected] . You can specify the path to an identity file, using the -i flag.

Verify that you can see a prompt in the SSH session.

In your VS Code workspace, create a configuration for remote debugging in your launch.json file, setting the port to match the port used in the ssh command and the host to localhost . You use localhost here because you've set up the SSH tunnel.

Starting debugging

Now that an SSH tunnel has been set up to the remote computer, you can begin your debugging.

Both computers: make sure that identical source code is available.

Both computers: install debugpy .

Remote computer: there are two ways to specify how to attach to the remote process.

In the source code, add the following lines, replacing address with the remote computer's IP address and port number (IP address 1.2.3.4 is shown here for illustration only).

The IP address used in listen should be the remote computer's private IP address. You can then launch the program normally, causing it to pause until the debugger attaches.

Launch the remote process through debugpy, for example:

This starts the package myproject using python3 , with the remote computer's private IP address of 1.2.3.4 and listening on port 5678 (you can also start the remote Python process by specifying a file path instead of using -m , such as ./hello.py ).

Local computer: Only if you modified the source code on the remote computer as outlined above , then in the source code, add a commented-out copy of the same code added on the remote computer. Adding these lines makes sure that the source code on both computers matches line by line.

Local computer: switch to the Run and Debug view ( ⇧⌘D (Windows, Linux Ctrl+Shift+D ) ) in VS Code, select the Python Debugger: Attach configuration

Local computer: set a breakpoint in the code where you want to start debugging.

Local computer: start the VS Code debugger using the modified Python Debugger: Attach configuration and the Start Debugging button. VS Code should stop on your locally set breakpoints, allowing you to step through the code, examine variables, and perform all other debugging actions. Expressions that you enter in the Debug Console are run on the remote computer as well.

Text output to stdout, as from print statements, appears on both computers. Other outputs, such as graphical plots from a package like matplotlib, however, appear only on the remote computer.

During remote debugging, the debugging toolbar appears as below:

On this toolbar, the disconnect button ( ⇧F5 (Windows, Linux Shift+F5 ) ) stops the debugger and allows the remote program to run to completion. The restart button ( ⇧⌘F5 (Windows, Linux Ctrl+Shift+F5 ) ) restarts the debugger on the local computer but does not restart the remote program. Use the restart button only when you've already restarted the remote program and need to reattach the debugger.

Set configuration options

When you first create launch.json , there are two standard configurations that run the active file in the editor in either the integrated terminal (inside VS Code) or the external terminal (outside of VS Code):

The specific settings are described in the following sections. You can also add other settings, such as args , that aren't included in the standard configurations.

Tip : It's often helpful in a project to create a configuration that runs a specific startup file. For example, if you want to always launch startup.py with the arguments --port 1593 when you start the debugger, create a configuration entry as follows:

Provides the name for the debug configuration that appears in the VS Code dropdown list.

Identifies the type of debugger to use; leave this set to debugpy for debugging Python code.

Specifies the mode in which to start debugging:

• launch : start the debugger on the file specified in program
• attach : attach the debugger to an already running process. See Remote debugging for an example.

Provides the fully qualified path to the python program's entry module (startup file). The value ${file} , often used in default configurations, uses the currently active file in the editor. By specifying a specific startup file, you can always be sure of launching your program with the same entry point regardless of which files are open. For example:

You can also rely on a relative path from the workspace root. For example, if the root is /Users/Me/Projects/MyProject then you can use the following example:

Provides the ability to specify the name of a module to be debugged, similarly to the -m argument when run at the command line. For more information, see Python.org

The full path that points to the Python interpreter to be used for debugging.

If not specified, this setting defaults to the interpreter selected for your workspace, which is equivalent to using the value ${command:python.interpreterPath} . To use a different interpreter, specify its path instead in the python property of a debug configuration.

Alternately, you can use a custom environment variable that's defined on each platform to contain the full path to the Python interpreter to use, so that no other folder paths are needed.

If you need to pass arguments to the Python interpreter, you can use the pythonArgs property.

Specifies arguments to pass to the Python interpreter using the syntax "pythonArgs": ["<arg 1>", "<arg 2>",...] .

Specifies arguments to pass to the Python program. Each element of the argument string that's separated by a space should be contained within quotes, for example:

If you want to provide different arguments per debug run, you can set args to ${command:pickArgs} . This will prompt you to enter arguments each time you start a debug session.

stopOnEntry

When set to true , breaks the debugger at the first line of the program being debugged. If omitted (the default) or set to false , the debugger runs the program to the first breakpoint.

Specifies how program output is displayed as long as the defaults for redirectOutput aren't modified.

There is more than one way to configure the Run button, using the purpose option. Setting the option to debug-test , defines that the configuration should be used when debugging tests in VS Code. However, setting the option to debug-in-terminal , defines that the configuration should only be used when accessing the Run Python File button on the top-right of the editor (regardless of whether the Run Python File or Debug Python File options the button provides is used). Note : The purpose option can't be used to start the debugger through F5 or Run > Start Debugging .

Allows for the automatic reload of the debugger when changes are made to code after the debugger execution has hit a breakpoint. To enable this feature set {"enable": true} as shown in the following code.

Note : When the debugger performs a reload, code that runs on import might be executed again. To avoid this situation, try to only use imports, constants, and definitions in your module, placing all code into functions. Alternatively, you can also use if __name__=="__main__" checks.

Specifies whether to enable subprocess debugging. Defaults to false , set to true to enable. For more information, see multi-target debugging .

Specifies the current working directory for the debugger, which is the base folder for any relative paths used in code. If omitted, defaults to ${workspaceFolder} (the folder open in VS Code).

As an example, say ${workspaceFolder} contains a py_code folder containing app.py , and a data folder containing salaries.csv . If you start the debugger on py_code/app.py , then the relative paths to the data file vary depending on the value of cwd :

redirectOutput

When set to true (the default for internalConsole), causes the debugger to print all output from the program into the VS Code debug output window. If set to false (the default for integratedTerminal and externalTerminal), program output is not displayed in the debugger output window.

This option is typically disabled when using "console": "integratedTerminal" or "console": "externalTerminal" because there's no need to duplicate the output in the debug console.

When omitted or set to true (the default), restricts debugging to user-written code only. Set to false to also enable debugging of standard library functions.

When set to true , activates debugging features specific to the Django web framework.

When set to true and used with "console": "externalTerminal" , allows for debugging apps that require elevation. Using an external console is necessary to capture the password.

When set to true , ensures that a Pyramid app is launched with the necessary pserve command .

Sets optional environment variables for the debugger process beyond system environment variables, which the debugger always inherits. The values for these variables must be entered as strings.

Optional path to a file that contains environment variable definitions. See Configuring Python environments - environment variable definitions file .

If set to true , enables debugging of gevent monkey-patched code .

When set to true , activates debugging features specific to the Jinja templating framework.

Breakpoints and logpoints

The Python Debugger extension supports breakpoints and logpoints for debugging code. For a short walkthrough of basic debugging and using breakpoints, see Tutorial - Configure and run the debugger .

Conditional breakpoints

Breakpoints can also be set to trigger based on expressions, hit counts, or a combination of both. The Python Debugger extension supports hit counts that are integers, in addition to integers preceded by the ==, >, >=, <, <=, and % operators. For example, you could set a breakpoint to trigger after five occurrences by setting a hit count of >5 For more information, see conditional breakpoints in the main VS Code debugging article.

Invoking a breakpoint in code

In your Python code, you can call debugpy.breakpoint() at any point where you want to pause the debugger during a debugging session.

Breakpoint validation

The Python Debugger extension automatically detects breakpoints that are set on non-executable lines, such as pass statements or the middle of a multiline statement. In such cases, running the debugger moves the breakpoint to the nearest valid line to ensure that code execution stops at that point.

Debugging specific app types

The configuration dropdown provides various different options for general app types:

Specific steps are also needed for remote debugging and Google App Engine. For details on debugging tests, see Testing .

To debug an app that requires administrator privileges, use "console": "externalTerminal" and "sudo": "True" .

Flask debugging

As you can see, this configuration specifies "env": {"FLASK_APP": "app.py"} and "args": ["run", "--no-debugger"] . The "module": "flask" property is used instead of program . (You may see "FLASK_APP": "${workspaceFolder}/app.py" in the env property, in which case modify the configuration to refer to only the filename. Otherwise, you may see "Cannot import module C" errors where C is a drive letter.)

The "jinja": true setting also enables debugging for Flask's default Jinja templating engine.

If you want to run Flask's development server in development mode, use the following configuration:

Troubleshooting

There are many reasons why the debugger may not work. Sometimes the debug console reveals specific causes, but the main reasons are as follows:

Make sure the Python Debugger extension is installed and enabled in VS Code by opening the Extensions view ( ⇧⌘X (Windows, Linux Ctrl+Shift+X ) ) and searching for @installed python debugger .

The path to the python executable is incorrect: check the path of your selected interpreter by running the Python: Select Interpreter command and looking at the current value:

You have "type" set to the deprecated value "python" in your launch.json file: replace "python" with "debugpy" instead to work with the Python Debugger extension.

There are invalid expressions in the watch window: clear all expressions from the Watch window and restart the debugger.

If you're working with a multi-threaded app that uses native thread APIs (such as the Win32 CreateThread function rather than the Python threading APIs), it's presently necessary to include the following source code at the top of whichever file you want to debug:

If you are working with a Linux system, you may receive a "timed out" error message when trying to apply a debugger to any running process. To prevent this, you can temporarily run the following command:

• Python environments - Control which Python interpreter is used for editing and debugging.
• Testing - Configure test environments and discover, run, and debug tests.
• Settings reference - Explore the full range of Python-related settings in VS Code.
• General debugging - Learn about the debugging features of VS Code.