Playwright Skills for Data Analyst Jobs

Common analyst scenarios:

1. "This site only has data in a JS-loaded table."
   → Playwright can wait for the data and extract it.

2. "I need to log in and download 20 reports daily."
   → Playwright automates login + download.

3. "The API changed and broke our pipeline."
   → Playwright can act as a reliable fallback.

4. "I need competitor prices weekly."
   → Playwright + scheduler = automatic price snapshots.

5. "I need to prove data appeared on a client dashboard."
   → Playwright can screenshot + verify.

Playwright turns manual grunt work
into scheduled, reliable automations.

Where Playwright fits in an analyst workflow:

Manual data:
- Copy-paste from websites
- Downloading reports by hand
- Screenshotting dashboards
- "Can you check this page?" tasks

Playwright-automated:
- Web scraping from JS-heavy sites
- Scheduled report downloads
- Auto-refresh of dashboards
- Automated data QA checks
- Competitor / market snapshots

What stays manual:
- Judgement calls
- Business interpretation
- Stakeholder communication

Automation doesn't replace analysts —
it frees them from grunt work.

# Minimal Playwright script — analyst version

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()

    # Navigate
    page.goto("https://example.com/products")

    # Wait for dynamic content
    page.wait_for_selector(".product-card")

    # Extract data
    products = page.query_selector_all(".product-card")
    rows = []
    for prod in products:
        name = prod.query_selector(".name").inner_text()
        price = prod.query_selector(".price").inner_text()
        rows.append({"name": name, "price": price})

    browser.close()

# Save to CSV and analyse in Pandas
import pandas as pd
pd.DataFrame(rows).to_csv("products.csv", index=False)

Essentials for analysts:

1. Launching a browser
2. Navigating to a URL
3. Waiting for elements
4. Selecting elements (CSS / text / role)
5. Extracting text / attributes
6. Handling multiple elements (loops)
7. Saving results to CSV / Pandas

That's enough for:
- Scraping data
- Filling forms
- Downloading reports
- Validating dashboards

What you can skip:
- Testing frameworks (pytest, expect)
- CI/CD integration (unless needed)
- Advanced debugging

Focus on extraction and automation,
not test frameworks.

# Pattern 1 — Scrape a table from a dynamic site

from playwright.sync_api import sync_playwright
import pandas as pd

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://example.com/data")
    page.wait_for_selector("table.data")

    rows = page.query_selector_all("table.data tbody tr")
    data = []
    for row in rows:
        cells = row.query_selector_all("td")
        data.append([c.inner_text() for c in cells])

    browser.close()

df = pd.DataFrame(data, columns=["date", "product", "sales"])
df.to_csv("sales_data.csv", index=False)

# Pattern 2 — Login + download a report

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://app.example.com/login")

    page.fill("#email", "analyst@example.com")
    page.fill("#password", "secret")
    page.click("button[type=submit]")

    # Wait for dashboard
    page.wait_for_url("**/dashboard")

    # Trigger a download
    with page.expect_download() as dl_info:
        page.click("text=Download Report")

    download = dl_info.value
    download.save_as("daily_report.xlsx")

    browser.close()

# Then read it in Pandas
import pandas as pd
df = pd.read_excel("daily_report.xlsx")

# Pattern 3 — Extract from paginated results

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://example.com/listings")

    all_rows = []
    while True:
        page.wait_for_selector(".listing")

        cards = page.query_selector_all(".listing")
        for c in cards:
            all_rows.append({
                "title": c.query_selector(".title").inner_text(),
                "price": c.query_selector(".price").inner_text(),
            })

        # Try next page
        next_btn = page.query_selector("button.next")
        if not next_btn or not next_btn.is_enabled():
            break
        next_btn.click()
        page.wait_for_load_state("networkidle")

    browser.close()
    print(f"Total rows: {len(all_rows)}")

# Daily report automation (pseudo-flow)

from playwright.sync_api import sync_playwright
import datetime

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()

    page.goto("https://reports.example.com/login")
    page.fill("#user", "analyst")
    page.fill("#pwd", "secret")
    page.click("text=Sign In")
    page.wait_for_url("**/home")

    # Download daily CSV
    today = datetime.date.today().isoformat()
    with page.expect_download() as dl:
        page.click(f"text=Download {today}")

    dl.value.save_as(f"reports/daily_{today}.csv")
    browser.close()

# Then load into warehouse / Pandas
# Schedule via cron / Windows Task Scheduler

# Dashboard screenshots for weekly summaries

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page(
        viewport={"width": 1440, "height": 900}
    )

    page.goto("https://dashboard.example.com")
    page.wait_for_selector(".chart-loaded")

    page.screenshot(
        path="reports/dashboard_weekly.png",
        full_page=True,
    )

    browser.close()

# Use screenshots in:
# - Weekly email updates
# - Slack posts
# - Presentation decks

# Automated quality checks

from playwright.sync_api import sync_playwright

expected_widgets = [
    "revenue_chart",
    "orders_table",
    "top_products",
]

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    page.goto("https://dashboard.example.com")
    page.wait_for_selector(".dashboard-ready")

    missing = []
    for w in expected_widgets:
        if not page.query_selector(f"#{w}"):
            missing.append(w)

    if missing:
        # Trigger alert (email, Slack, etc.)
        print(f"ALERT: Missing widgets {missing}")

    browser.close()

Project 1 — Price tracker

What to build:
□ Scrape 3-5 competitor product pages
□ Extract name, price, availability
□ Save to CSV / SQLite daily
□ Automate with cron / GitHub Actions
□ Build a small Streamlit dashboard
□ Compare prices over time

Skills demonstrated:
- Scraping + extraction
- Scheduling
- Data storage
- Small app (dashboard)

Analyst relevance:
- Market research
- Pricing analysis
- Competitive intelligence

Project 2 — Automated daily report

What to build:
□ Log in to a demo report site
□ Download daily CSV
□ Load into Pandas
□ Produce a summary (PDF or email)
□ Schedule with GitHub Actions
□ Store outputs in a repo

Skills demonstrated:
- Login automation
- File downloads
- Data pipelines
- Reporting
- Automation

Analyst relevance:
- Fully automated recurring reports
- Time saved for real analysis
- Reliable daily pipelines

Portfolio checklist (per project):

□ Public GitHub repo
□ Clear README (problem + setup)
□ Working code (not a gist)
□ Requirements file
□ Example output
□ Screenshot of result
□ Notes on scheduling
□ What you'd improve next

Two strong automation projects
beat five small scripts.
Every time.