Standard pandas read_excel loads the entire file into memory. For large files (100MB+, 500K+ rows), this causes memory errors or extreme slowness. Efficient approaches: openpyxl with read_only mode — iterates row by row without loading the entire file: wb = openpyxl.load_workbook("large.xlsx", read_only=True). Process in chunks. pandas with chunk processing — read in batches: for chunk in pd.read_csv("large.csv", chunksize=10000). Process each chunk and aggregate results. Convert Excel to CSV first — CSV processing is significantly faster than Excel. For very large files, convert once and work with CSV. Use calamine (fast Excel reader): pip install python-calamine gives 5-10x speed improvement over openpyxl for reading. Specific techniques for MIS: if you only need certain columns, read only those: pd.read_excel("file.xlsx", usecols=["Date","Region","Revenue"]). If reading the same file repeatedly, cache it as parquet: df.to_parquet("cache.parquet") — parquet reads 10-100x faster than Excel. For monthly MIS processing of large Excel exports, the typical workflow is: convert to parquet on first load, run all analysis on parquet, save results to a smaller Excel summary file.

Automated MIS report pipeline in Python. Step 1: data collection — connect to database with SQLAlchemy or pyodbc, run SQL queries to extract KPIs, or read from Excel/CSV sources. Parameterise by date (always calculate relative to today: datetime.now() - timedelta(days=1)). Step 2: data processing with pandas — calculate KPIs, create summary tables, flag anomalies (values outside normal range). Step 3: report generation — use openpyxl to create formatted Excel with charts, conditional formatting (red/green cells), and multiple sheets. Or use matplotlib/plotly to create charts. Step 4: email delivery with smtplib: create MIMEMultipart email, attach the Excel/PDF, send to distribution list. Use environment variables for SMTP credentials — never hardcode. Step 5: scheduling — on Linux: cron job (0 7 * * 1-5 python3 /reports/daily_mis.py runs at 7am weekdays). On Windows: Task Scheduler. For cloud: AWS Lambda + EventBridge schedule, or Azure Functions with timer trigger. Step 6: error handling — wrap the entire pipeline in try/except, send an alert email if it fails, log all errors to a file. The complete solution: one Python script, runs daily at 7am, generates formatted Excel, emails to 10 managers, logs to file, alerts IT if it fails. No manual intervention needed.

Real-time MIS dashboard using Streamlit or Dash. Streamlit (simpler, rapid development): import streamlit as st. Connect to database, query data, use st.metric() for KPI cards, st.line_chart() for trends, st.dataframe() for tables. Add st.selectbox() for filters (region, date range). Run with streamlit run app.py. Auto-refreshes with st.experimental_rerun(). Deploy on Streamlit Cloud (free) or internal server. Dash (more control, production-grade): define layout with HTML components and dcc (Dash Core Components). Use @app.callback decorators to make charts interactive — when user changes a dropdown, the chart updates automatically. Dash apps look like professional web applications. Data refresh strategies: polling (query database every 30-60 seconds using dcc.Interval component), caching (use @cache decorator so the database is only queried once per minute, not once per user request), incremental updates (only fetch new records since last refresh using a timestamp). For a sales MIS dashboard: top row shows today's revenue, orders, conversion rate as metric cards. Middle section shows hourly revenue trend chart. Bottom section shows product and region breakdown tables. All filters (date range, region selector) are interactive dropdowns that update all charts simultaneously.