Web Scraping

Key Libraries

Web automation & scraping

selenium — browser automation, DOM navigation, waiting for dynamic elements, XPath extraction, and interaction with site consent banners, as implemented in Scraping_Calendar_Economics.py.
selenium.webdriver.support (EC, WebDriverWait) — synchronization for dynamic page loads and element visibility.
selenium.common.exceptions — robust handling of timeouts, missing elements, and blocked interactions.

Data manipulation & cleaning

pandas — central library for reading the input indicator list, building and filtering DataFrames, column transformations, and exporting final data.
re — regex matching to identify relevant indicators in scraped text and aggressive text cleaning before export.

File output & Excel integration

openpyxl — Excel writing through ExcelWriter, creating the final structured output file.

➡️ Manual collection of macroeconomic data was inefficient

Weekly internal macro updates (PMIs, CPIs, labor data, sentiment indices, etc.) were compiled manually from free online sources.
The indicators were predefined and recurring, forcing analysts to repeat the same tasks every week.

➡️ Non-automated consolidation process

Each indicator had to be searched manually, copied, and formatted into the internal reporting template.
Ensuring that all predefined indicators were included was time-consuming and prone to human error.

➡️ Need for an internal weekly report

The final dataset had to be compiled and distributed internally, requiring precision, consistency, and stability.

To sum up:

➡️ Automated Web Scraping via Selenium

Developed a Selenium workflow that navigates public economic calendars, applies filters, and extracts relevant macro indicators.
Scraped values are parsed into pandas DataFrames and validated through custom filtering logic.

➡️ Input-driven and scalable architecture

The process is fully governed by a single input Excel file listing:
- the indicator name
- the geographic area
- the source website
Adding/removing indicators requires no modification to the code — only adjustments to the input list.

➡️ Excel Output for Internal Weekly Reporting

The consolidated dataset is automatically exported to an Excel file, with one row per indicator and enriched metadata (translation, category, documentation links).
Values are then placed into a fixed internal template for weekly circulation

Operational efficiency: reduced weekly extraction time from ~1 hour to ~15 minutes (only final formatting checks remain).
Accuracy & consistency: fixed indicator list ensures completeness and standardization.
Reliability: scraping logic filters out stale data and captures only newly published indicators.
Quality of internal reporting: faster turnaround and structurally consistent weekly macro updates.

Irregular publication schedules
- Many indicators are not released weekly.
- Selenium applies date filters available on the website; if no new release exists, the script skips the indicator to avoid importing stale data.
Heterogeneous website structures
- Economic calendars use different HTML structures, requiring custom XPath logic and dynamic waits for each source.
Matching accuracy
- Some indicators have similar names (“inflation”, “core inflation”, “MoM inflation”), requiring increasingly strict matching rules in the filtering logic.
Resilience of scraping
- Cookie banners, page delays, or blocked buttons required exception handling and fallback logic.

Full automation of the internal report
Generate a final ready-to-send weekly PDF or formatted Excel without manual adjustments.

Alerting & monitoring
Notify the team when:

Migration to API sources
Replace scraping with free APIs (when available) to increase reliability and reduce runtime.