NumPy applied: broadcasting, statistics and safe division

What you will learn

Calculate revenue, weighted prices and normalized features with explicit axis and zero-division checks.

Before you start

You need basic variables, lists and function calls, not advanced Python algorithms. If these are new, visit Python foundations first.

1. Create a local environment: python -m venv .venv. Activate it for your OS and install python -m pip install jupyterlab numpy pandas matplotlib seaborn plotly.
2. Download the 12-row sales CSV into your notebook folder. Keep its filename unchanged.
3. Start Jupyter with jupyter lab. Run the setup below in a fresh notebook before every workshop.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
raw = pd.read_csv('data-science-sales.csv', parse_dates=['date'])
df = raw.copy()
df['revenue'] = df['units'] * df['unit_price']
net = df.loc[df['returned'].eq(0)].copy()

Data dictionary: each row is one order for one product; units is a positive count; unit_price is INR per unit; returned is 0 or 1. Revenue is units × unit_price. For this workshop, net sales exclude the entire returned order (not partial returns). The CSV has 12 rows, 4950 gross revenue, 4450 net revenue and 35 net units. This small fictional sample teaches technique; it does not justify population or causal claims.

Save your notebook and written interpretation. There is no browser code judge and no automatic grading of visual design.

Official NumPy documentation

Learn the idea

Broadcasting compares shapes from the trailing axis: dimensions must match or one must be 1. Multiply aligned units and prices elementwise, not with a dot product unless you want a scalar. A simple average of product prices ignores quantities; weighted average selling price is revenue divided by units. Standardisation divides by a feature's standard deviation; constant features need a zero guard. Check vectorised results with a small hand calculation.

Follow a worked example

units = net['units'].to_numpy()
prices = net['unit_price'].to_numpy()
revenue = units * prices
print(revenue.sum())

Expected result

4450. All examples use non-returned rows.

Skill-development workshop

These are practical assignments, not MCQs or code-judge puzzles. Work in your own notebook or MySQL client. Complete the levels in order; if a task feels difficult, follow the hint and compare with the reference only after making an attempt. Keep your result and a short explanation of what it means.

Easy: Calculate a 10% revenue reduction without a loop.

Success criteria: 4005 total, using net sales only.

revenue = net['units'].to_numpy() * net['unit_price'].to_numpy()
assert np.isclose((revenue * 0.9).sum(),4005)

Medium: Evaluate 5%, 10% and 15% discounts in one matrix.

Success criteria: Shape (11,3); totals [4227.5,4005,3782.5].

revenue = net['revenue'].to_numpy()
scenarios = revenue[:,None] * (1-np.array([0.05,0.10,0.15]))
print(scenarios.shape, scenarios.sum(axis=0))

Hard: Compute the quantity-weighted selling price.

Success criteria: Net revenue divided by net units; reconcile both denominators to the data rather than averaging price rows.

weighted = np.average(net['unit_price'], weights=net['units'])
assert np.isclose(weighted, net['revenue'].sum()/net['units'].sum())
print(weighted)

Professional: Standardise units and prices without NaNs for constant columns.

Success criteria: Shape (11,2), both means approximately 0 and std 1 (ddof=0). Explain ddof and avoid an unmeasured speed claim.

x=net[['units','unit_price']].to_numpy(dtype=float)
mean=x.mean(axis=0); std=x.std(axis=0)
z=np.divide(x-mean,std,out=np.zeros_like(x),where=std!=0)
assert np.allclose(z.mean(axis=0),0)
assert np.allclose(z.std(axis=0),1)

Reflection and submission

Save your queries/notebook and outputs. Write three sentences: what you changed, how you checked it, and one limitation. A correct chart or query without a clear interpretation is not a completed professional assignment.