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Python — NHS Quickstart

🐍 Data wrangling · SQL Server · Dash dashboards · Automation

Why Python matters in the NHS

Python supercharges data extraction, cleaning, analytics, and automation. It plugs neatly into SQL Server, powers interactive dashboards (Dash/Plotly), and lets you productionise small services quickly.

Common NHS uses

• Pull data from SQL Server or public datasets (CSV/Parquet/APIs).
• Transform with pandas; model with scikit-learn.
• Publish dashboards with Dash or scripts as small services.
• Automate recurring reporting and validation checks.

---

⚙️ 10-minute install

Windows (recommended)

# 1) Install Python 3.11+ from python.org and tick "Add to PATH"
# 2) Create a virtual environment in your project folder
py -3.11 -m venv .venv
.\.venv\Scriptsctivate

# 3) Verify
python --version
pip --version

macOS / Linux

# 1) Install Python 3.11+ (brew, apt, etc.)
python3 -m venv .venv && source .venv/bin/activate

# 2) Verify
python --version
pip --version

---

🚀 “Hello NHS” (pandas)

Create hello_nhs.py:

hello_nhs.py

import pandas as pd

# Fake daily admissions (3 days)
df = pd.DataFrame({
    "date": pd.date_range("2025-01-01", periods=3, freq="D"),
    "patients": [120, 135, 128]
})

print("Admissions summary:")
print(df.describe(include="all"))

Run:

python hello_nhs.py

---

🔗 Connect to SQL Server (safe pattern)

Install deps:

pip install pandas sqlalchemy pyodbc python-dotenv

Add .env (do not commit this file):

.env

SQLSERVER_SERVER=YOURSERVER
SQLSERVER_DATABASE=NHS_Analytics

Create query_sqlserver.py:

query_sqlserver.py

import os, urllib.parse, pandas as pd
from sqlalchemy import create_engine, text
from dotenv import load_dotenv

load_dotenv()

params = urllib.parse.quote_plus(
    "DRIVER={ODBC Driver 18 for SQL Server};"
    f"SERVER={os.getenv('SQLSERVER_SERVER')};"
    f"DATABASE={os.getenv('SQLSERVER_DATABASE')};"
    "Trusted_Connection=Yes;Encrypt=Yes;"
)
engine = create_engine(f"mssql+pyodbc:///?odbc_connect={params}")

sql = text("""
SELECT TOP (100)
  practice_id, total_appointments, attendance_rate
FROM dbo.vw_PracticeKPI
ORDER BY total_appointments DESC
""")
df = pd.read_sql(sql, engine)
print(df.head())

Run:

python query_sqlserver.py

---

📊 First chart (Plotly Express)

pip install plotly

Create chart.py:

chart.py

import pandas as pd, plotly.express as px

df = pd.DataFrame({
  "practice_id": ["A","B","C"],
  "appointments": [140,120,95]
})
fig = px.bar(df, x="practice_id", y="appointments",
             title="Appointments by Practice")
fig.show()

---

🧪 Optional: tiny Dash dashboard (15 min)

pip install dash

Create app.py:

app.py

import dash
from dash import html, dcc
import plotly.express as px, pandas as pd

df = pd.DataFrame({
  "practice_id": ["A","B","C"],
  "appointments": [140,120,95]
})
fig = px.bar(df, x="practice_id", y="appointments", title="Appointments by Practice")

app = dash.Dash(__name__); app.title = "NHS KPI (Demo)"
app.layout = html.Div([html.H2("NHS KPI Dashboard"), dcc.Graph(figure=fig)])

if __name__ == "__main__":
    app.run_server(debug=True)

Run and open http://127.0.0.1:8050

---

🧰 NHS-ready packages

Package	Use case
pandas	data cleaning & aggregation
plotly / matplotlib	charts & exploratory viz
sqlalchemy + pyodbc	connect to SQL Server
python-dotenv	load secrets from .env (local only)
dash	interactive dashboards
openpyxl	Excel automation (read/write)
pyarrow	fast Parquet/Arrow IO

---

🖥 IDE setup (VS Code)

1. Install VS Code.
2. Add extensions: Python (Microsoft), Jupyter, Pylance.
3. Enable the interpreter from your project’s .venv.
4. Turn on Format on Save and add a linter/formatter (e.g., ruff, black).

Optional requirements.txt:

requirements.txt

pandas
sqlalchemy
pyodbc
python-dotenv
plotly
dash

Install with:

pip install -r requirements.txt

---

🔒 IG & safety checklist

• Use synthetic/de-identified data in examples.
• Keep secrets out of code; load from env vars; rotate in production via a secret store.
• Avoid free-text PHI in logs/notebooks; aggregate before export; apply small-number suppression.
• Document sources, owners, and update cadence in the repo README.

---

📏 Measuring impact

• Velocity: time from data pull → first chart.
• Reliability: % successful script runs / dashboard refreshes.
• Quality: validation checks; peer review via PRs.
• Reusability: runs from a clean clone without manual tweaks.

---

🔗 See also

See also: SQL Server · Dash · VS Code · Git · GitHub · Docker · Secrets & .env

What’s next?

You’ve completed the Learn — Python stage. Keep momentum:

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