Python · .py and .ipynb · From $20
Python Assignment Help
Python is the one language you meet in two courses graded two different ways. A CS1 problem set where check50 runs your .py file line for line, and a data-science notebook where Otter-Grader re-runs every cell against hidden tests. We write code that clears both, written to your Python version and reproduced from a blank kernel before it reaches you.
Written to your version · Autograder-checked · Pay 50% after it runs
What we cover in Python
The whole stack, from a .py file to a graded notebook
Python coursework lives in two places at once. One half is the CS1 problem set, a .py file the autograder runs line for line and a style pass that costs real marks. The other half is the data-science notebook, a .ipynb that a grader re-executes from a clean kernel against tests you never see. We write against the same tools each world keys on: your CPython version, pandas and NumPy for the data spine, pytest or Otter for the tests, and the formatter and linters that enforce PEP 8.
The data work gets the reshape right, the groupby, the merge, and the .loc assignment that so often writes to a copy instead of the frame. Notebooks are pre-run from a blank Restart and Run All so they reproduce on Gradescope. Where the brief is web, Django or Flask come with migrations, the ORM, and templates wired the way a course expects, not a loose pile of files.
Python assignments we do
Four assignment shapes, start to graded
A CS1 .py problem set graded by an autograder
Small programs that the grader runs line for line: input validation, string work, an object-oriented class with __init__ and __str__, a re regex parser. The CS50P pattern, where check50 or pytest matches your output exactly and a style pass carries part of the grade. Trailing newlines and edge cases are handled before submit, because a single missing newline fails a check.
A Jupyter data-analysis notebook graded by Otter-Grader
Read a CSV with pd.read_csv, clean and reshape with pandas using groupby, merge, and .loc assignment, vectorize with NumPy broadcasting and boolean masks, then plot with Matplotlib or seaborn. The Data 8 and Data 100 shape, where public tests are visible but hidden tests decide the grade and the notebook must reproduce on Gradescope, not just on your laptop.
An intro machine-learning scikit-learn pipeline
A train_test_split, a Pipeline that wires preprocessing to an estimator, fit and predict, and the metrics the rubric scores: accuracy, precision and recall, or RMSE on a held-out set. The leakage trap, where preprocessing fitted on the full data inflates the score, is closed by fitting inside the pipeline so the test fold stays unseen.
A Django or Flask web application
A full-stack project rather than a single script: the CS50W Wiki reimplementation, a CRUD app, or a REST API. Models and migrations, views or routes, Jinja2 or Django templates, the ORM, and user auth. Flask for the lightweight version, Django for the full-feature one, with a milestone GitHub commit history built up across the project window.
Python problems we fix
Six Python failures that quietly cost marks
Each one has a known cause and a one-line fix. We name the mechanism, not just the traceback, so the same error stops coming back on the next problem set or notebook.
A pandas change that silently does nothing
Assigning into a chained slice like df[df.x > 0]["y"] = value writes to a temporary copy, not the frame, so the edit vanishes and you get a SettingWithCopyWarning. We do the selection and the assignment in one .loc call, df.loc[df.x > 0, "y"] = value, so the value actually lands and the warning clears.
ModuleNotFoundError when the grader runs your code
The package is installed globally or in a different virtual environment while the grader runs an isolated one, or pip and python point at different interpreters. We pin every dependency in requirements.txt, install into the venv, and confirm python and pip resolve to the same bin so the import works where it is graded.
A mutable default argument that leaks state
Writing def f(items=[]) builds the list once at definition, so it persists and accumulates across calls and corrupts later results. We default to None and build the list inside the function, def f(items=None): items = items or [], so each call starts clean.
IndentationError or TabError from mixed whitespace
In Python the indentation is the syntax, so one stray tab inside a space-indented block stops the file compiling. We use four spaces throughout per PEP 8 and let black normalize the file, so the parser stops rejecting it on the first run.
A KeyError, IndexError, or off-by-one walk past the end
Indexing a dict key that is not there, or looping to len(seq) inclusive, runs one step too far. We guard lookups with dict.get(key, default) or an in check, and iterate the sequence directly instead of an index that can overshoot.
It runs in my notebook but fails the autograder
The notebook leans on cells run out of order or on a stale kernel, so it passes for you and fails on Gradescope. We rebuild it to reproduce from a blank Kernel, Restart, and Run All, which is exactly how Otter and Gradescope execute it, so local and graded results match.
Weekly problem sets
Python Homework Help for Problem Sets and Notebooks
Homework is the recurring weekly work: a CS50P-style .py pset with its OOP classes unit or a re regex parser, or a short Otter-graded notebook for a data-science course. These need a passing solution and a fast turnaround, not a milestone repo. Send the brief, get a fixed quote, and get back code that passes the public tests and reads cleanly against PEP 8.
Get Help With Python Homework Tonight
When the deadline is hours away and the autograder is still red, the fastest path is the common one: a notebook that runs for you but fails on Gradescope. We rebuild it to reproduce from a blank Restart and Run All, the way Otter executes it, and turn it around inside the window so it passes where it is scored.
Larger graded projects
Do My Python Assignment (Projects and Notebooks)
An assignment here means the bigger graded project, not the weekly drill. The CS50W Wiki reimplementation, a scikit-learn pipeline scored on a held-out test set, or a milestone Data 100 notebook all land as one working deliverable. Web projects carry a GitHub commit history that grows in staggered commits across the project window, the way a grader expects a milestone build to come together rather than one end-of-night push.
Notebooks reproduce from a clean kernel, scripts run inside the pinned venv, and the evaluation is the one the rubric names. A leakage-free Pipeline keeps the test fold unseen so the reported score is the score the autograder will compute, not an inflated local number.
Do My Python Homework
If you simply want the work done and handed back, send the brief and the rubric. You get a fixed price, code written to your version and pre-checked against the named grader, and the 50% milestone split, so you pay the balance only after the suite is green on your machine.
Walkthrough on the brief
Help With a Python Assignment You Can Defend
Some students want the work built and explained, not just delivered. Every project comes with a short write-up of the approach, why this model or this Pipeline, and how each section of the notebook earns its cell. Two or three viva-defense questions and answers ship with it, the kind a TA asks when they want you to account for your own design.
Get Help With a Single Python Problem
Stuck on one piece rather than the whole brief? Send the part that is failing: a SettingWithCopyWarning you cannot clear, a single hidden test that stays red, a Django migration that will not apply. We fix that slice, explain the cause, and leave the rest of your code as your own.
# before: public tests pass, hidden tests red on Gradescope
# cause: notebook ran on a stale kernel, cells out of order,
# and a chained .loc assignment wrote to a copy
# after: Restart and Run All from a blank kernel reproduces it,
# df.loc[mask, "col"] = value lands on the frame,
# requirements.txt pinned, public + hidden tests green
# "I could walk the TA through a clean Restart and Run All."
# - data science student, CPython 3.11, 2026How it works
From brief to green tests
Send the brief and your Python version
Upload the spec, the rubric, your CPython line (3.9 through 3.13), and whether it is a .py file or a .ipynb notebook. Name the autograder if you know it: check50, Otter on Gradescope, a Pensive format.
Get a fixed quote in 15 minutes
A working Python developer reads the spec and sends one price. No hourly meter, no surprise fees.
Pay half, code written and tested
You pay 50% upfront. The code is written to your version and requirements.txt, the notebook is reproduced from a blank Restart and Run All, and the public and hidden tests are checked before anything reaches you.
Pay the rest after it runs
Run pytest, check50, or the notebook on your machine. Pay the other 50% only once the tests are green. Revisions stay free for 7 days.
Want the full process first? Read how it works.
Pricing
One fixed price per Python assignment, from $20
A single-file pset or a short notebook sits at the Standard tier. A multi-part notebook or an OOP project moves up. A Django app or a full scikit-learn pipeline lands at Advanced. You see the full number before you pay, you pay half to start, and there are no rush fees.
Python homework help
Questions, answered
The Python-specific questions students ask before they send a brief: versions, autograders, pandas, notebooks, and the style gate.
Will it pass check50 or the Gradescope autograder? +
Yes. The submission is pre-run against the autograder format your course uses: exact-output matching for check50, including trailing newlines, and public plus hidden tests for Otter and Gradescope, so the checks are green before you upload it.
My notebook runs for me but fails the autograder. Can you fix it? +
Yes. That is almost always an out-of-order-cells or stale-kernel problem. The notebook is rebuilt to reproduce from a blank Kernel, Restart, and Run All, exactly how the grader runs it, so it passes where it is scored and not only on your machine.
Can you match my Python version and requirements.txt? +
Yes. The code is written to your CPython line, anywhere from 3.9 to 3.13, and to the exact pandas, NumPy, and scikit-learn versions your requirements.txt pins, so nothing breaks because the grader installs a different release than your laptop.
My professor grades style too. Will it be PEP 8 clean? +
Yes. The code ships black and flake8 clean, with docstrings and type hints where the rubric asks for them. On CS50P, where style is a quarter of the grade, that style portion is handled alongside correctness, not left to chance.
Can you fix the SettingWithCopyWarning in my pandas code? +
Yes. Chained-indexing assignments are rewritten as single .loc calls so the value lands on the real frame and the warning clears, and the rest of the reshape, the groupby, merge, and NaN handling, is checked against the expected output.
Can you build a Django or Flask web app, not just a script? +
Yes. Models and migrations, views or routes, Jinja2 or Django templates, the ORM, and user auth come together as one working project: the CS50W Wiki-style build, a CRUD app, or a small REST API, with a milestone commit history behind it.
Can you do a scikit-learn pipeline with a train and test split? +
Yes. A train_test_split, a leakage-free Pipeline that fits preprocessing on the training fold only, fit and predict, and the evaluation metrics the rubric scores, accuracy, RMSE, or precision and recall, are all in scope as a notebook or a script.
Can you comment it so I can explain it to my grader? +
Yes. Each section carries plain-English comments, and two or three viva-defense questions and answers ship with the work, so you can walk a TA through the logic, the model choice, and a clean Restart and Run All without being caught out.
Related pages
Pages students pair with Python
R for the statistics-first alternative to pandas, SQL for the database half of a data pipeline, JavaScript for the front end of a Django or Flask backend, and the machine-learning hub where the scikit-learn coursework lives.
Send your Python brief now
Name your Python version, your grader, and your deadline. The first reply is free, and you pay nothing until you approve the price.