By language · Python

Do my Python homework. Readable, tested, explained.

From intro scripts to pandas pipelines and Django apps. PEP 8 code with tests and a written explanation of how it works.

Python 3.8–3.12pandas & NumPyJupyterDjango / FlaskpytestMatplotlib

Upload

Questions

Budget

Send

Get your predicted budget

Free

Upload your assignment files or browsePrompt, rubric, starter code · PDF, DOCX, ZIP, code · up to 25 MB in total

Anything your files don’t say? (optional)

Files stay private under our NDA. No email needed to see your budget.

Price cappedFrom $45Full refund if we’re late800+ students

See an example order

What we cover

Every part of Python your course covers.

Fundamentals

Functions, loops, recursion
Classes and dunder methods
File I/O, CSV and JSON
Exceptions and testing

Data

pandas and NumPy
Data cleaning and EDA
Matplotlib and seaborn
Jupyter notebooks

Web & automation

Django and Flask apps
REST APIs with requests
Web scraping
Automation scripts

CS courses

Data structures in Python
Algorithms and complexity
Intro ML with scikit-learn
Simulations

Example order

One Python order, start to finish.

What the student uploaded, what came back, and how the price was set.

01The brief

Data ProgrammingUndergradDue in 5 days

Clean and analyze a bike-share dataset

Load a year of trip records, remove invalid trips, compute average trip duration by station, and list the 10 busiest stations. Every function needs a pytest test.

Requirements

Load and validate a 400k-row CSV
Drop trips under 60 seconds or over 24 hours
Top 10 stations by trip count
pytest tests for each function

02The solution

analysis.py15 lines

1import pandas as pd

3def load_trips(path: str) -> pd.DataFrame:

4 """Read trips and compute duration in seconds."""

5 df = pd.read_csv(path, parse_dates=["start", "end"])

6 df["duration_s"] = (df["end"] - df["start"]).dt.total_seconds()

7 return df

9def clean(df: pd.DataFrame) -> pd.DataFrame:

10 # keep trips between 1 minute and 24 hours

11 ok = df["duration_s"].between(60, 24 * 3600)

12 return df[ok].dropna(subset=["station_id"])

14def busiest(df: pd.DataFrame, n: int = 10) -> pd.Series:

15 return df["station_id"].value_counts().head(n)

9 tests passing · Python 3.11 · flake8 clean

03The explanation doc

explanation.pdf

Parsing dates on load

parse_dates turns the columns into timestamps once, so every later step is vectorized.

The cleaning thresholds

Trips under a minute are dock errors; over 24 hours are lost bikes. Both skew the averages.

Why value_counts()

It counts and sorts in one call, which is faster and clearer than a manual groupby here.

04The price

AI predicted budget

$60–$110

Level · UndergradIncluded

Deadline · 5 daysStandard

Shown before any contact details were shared.

Final price from our team

$85

Delivered 5h early

How it works

From upload to predicted budget in under a minute.

The AI does one job: it predicts your price. A senior engineer reviews your files and sets the final number.

01
Step 1 of 4
Upload your files
Prompt, rubric, starter code, or a screenshot of the task. PDF, DOCX, ZIP, code or images, in any mix.
02
Step 2 of 4
The AI asks what’s missing
It confirms your due date, then asks up to 3 questions your files don’t answer. Each one has an “I don’t know” option.
03
Step 3 of 4
See your predicted budget
A price range appears right away. No email, no sign-up.
04
Step 4 of 4
A senior engineer confirms your price
Leave your email. Within 5 minutes you get one fixed price. It never goes above the top of your range.

No payment to get a price. You pay only after you accept the fixed price. If the AI can’t price your files, an engineer emails you a price instead.

Typical orders

Common Python assignments and typical budgets.

Intro scripts and functions

$40–$90

Usually 1–2 days

typical range

pandas data analysis notebook

$80–$180

Usually 2–4 days

typical range

Django or Flask web app

$180–$420

Usually 5–7 days

typical range

Scraper or automation script

$70–$160

Usually 2–3 days

typical range

Your budget depends on your files, deadline and level. Upload them for your own prediction.

Guarantees

Every order is covered. In writing.

Full refund, in three cases

We miss your deadline. Your code still doesn’t meet your original brief after revisions. Or you cancel before an engineer starts.

Partial refunds, worked out in the open

Cancel after work starts, or keep part of the work: we refund the rest and show you how we worked it out first.

Refunds that move fast

We reply within 24 hours. Approved refunds go back to your card through Stripe within 2 business days.

Unlimited revisions for 14 days

We revise until it matches your original instructions. No fees.

Your price is capped

Your fixed price never goes above the top of your range.

How the cap works

NDA and privacy

Your name, school and files are never shared, sold or reused. Engineers never see your name or email.

Secure payment

Processed by Stripe. We never see or store your card details.

Read the full refund policyFree. No email needed.

FAQ

Questions students ask first.

Still have a question?

Email us any time. Support runs 24/7 and a real person replies.

team@domyprogramminghomework.io

Do you deliver Jupyter notebooks?

Yes, with outputs run or cleared, your choice, plus a requirements.txt so it runs the same on your machine.

Which libraries are allowed?

Only what your assignment allows. If the prompt says standard library only, that’s what we use.

Will the code follow PEP 8?

Yes, with type hints and docstrings where they help. We match your course style guide if there is one.

Is the predicted budget the final price?

Not exactly, but it’s capped. Our team reviews your files and sends one fixed price before you pay anything, and it never goes above the top of your range.

What if you miss my deadline?

You get a full refund, back to your card through Stripe. It’s in our refund policy, in writing.

Related services