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Why Python for
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Why are major companies like Amazon, Microsoft, Goldman Sachs, JP Morgan, Apollo,
Blackrock, etc., increasingly preferring or requiring Python for their finance employees?
Python outperforms Excel in three key areas.
Automations
Visualization
Predictive Analysis
Data Extraction & Transformation
Excel:
- Manually download financial data from external sources (e.g., Bloomberg, ERP systems).
- Use Excel to clean, transform, and organize data (e.g., currency conversions, date reformatting).
- Save processed files into standardized templates for further analysis.
Estimated Time: 6-10 hours per dataset.
Python:
- Use APIs (e.g., Bloomberg Terminal API, Alpha Vantage) or web scraping (e.g., "BeautifulSoup") to automate data extraction.
- Process data using "Pandas" for transformations, such as currency normalization and date conversions, programmatically.
- Export cleaned and transformed datasets directly to desired formats (e.g., CSV, SQL database).
Estimated Time: 1-2 hours for setup; ongoing runs take seconds.
Efficiency Gains:
- Reduces data extraction and transformation time by 80-90%.
- Ensured standardized outputs for all datasets greatly reducing the opportunity for human caused error.
- Handles lager datasets with ease, accommodating diverse data sources.
Financial Reporting & Dashboards
Excel:
- Compile data manually from various systems (e.g., accounting software, ERP).
- Build static financial reports (e.g., P&L, balance sheet) using formulas and pivot tables.
- Update reports periodically, requiring manual recalculations and formatting.
Estimated Time: 8-12 hours per reporting cycle.
Python:
- Automate data aggregating using Python scripts to pull from APIs or databases.
- Generate financial reports programmatically with libraries like "Matplotlib," "Plotly," or "OpenPyXL" for styled Excel outputs.
- Build interactive dashboards using "Dash" or "Streamlit" for real-time financial insights.
Estimated Time: 2-3 hours to set up; updates occur in real time.
Efficiency Gains:
- Reduces reporting time by 75-85%
- Provides live dashboards with automatically refreshed data.
- Delivers consistent and branded report formats.
Risk Management
Excel:
- Manually collect data for risk metrics (e.g., portfolio exposures, market data) and enter into Excel.
- Calculate key risk metrics (e.g., beta, standard deviation, VAR) using custom formulas.
- Compile static risk reports and update chats for board presentations.
Estimated Time: 10-15 hours per risk assessment.
Python:
- Automate data ingestion for market and portfolio data via APIs or database queries.
- Calculate risk metrics programmatically using libraries like "scipy" and "NumPy."
- Generate dynamic, interactive risk dashboards with "Plotly" or "Dash," including drill-down capabilities.
Estimated Time: 2-4 hours for setup; ongoing tasks occur in real time.
Efficiency Gains:
- Reduces risk analysis and reporting time by 75-85%.
- Automates complex calculations, minimizing errors.
- Allows for more granular insights and real-time tracking.
Report Consolidation
Excel:
- Manually collect individual reports from various teams or departments.
- Copy data into a single workbook, adjusting formats and aligning metrics.
- Use pivot tables or formulas to create a unified summary.
Estimated Time: 6-10 hours per reporting cycle.
Python:
- Use "Pandas" to merge and align departmental data programmatically.
- Automate formatting and metric alignment to ensure consistency.
- Generate a consolidated report in Excel, PDF, or dashboard format directly using libraries like "XlsxWriter" or "Plotly."
Estimated Time: 2-3 hours for initial setup; updates occur in minutes.
Efficiency Gains:
- Reduces reporting time by up to 70%
- Ensures unified formatting and structure across all reports.
- Handles more data sources and lager datasets efficiently.
Data Exploration and Trends
Excel:
- Import data into Excel and create static charts (e.g., line charts, bar charts) to visualize historical trends, such as revenue growth or portfolio returns.
- Use filters and slicers to explore subsets of data manually.
- Customize chart formatting for reports, adjusting axes, labels and colors.
Estimated Time: 4-6 hours for initial setup and formatting; additional time needed for updates.
Python:
- Use "Matplotlib" or "Seaborn" to programmatically generate detailed trend chats (e.g., revenue trends, ROI over time).
- Enable exploration by building interactive widgets with "Plotly" or "Altair" for filtering and dynamic views.
- Automate chat updates directly from a live data sources, eliminating the need for manual adjustments.
Estimated Time: 1-2 hours for initial setup; updates occur automatically.
Efficiency Gains:
- Reduces chart creation and updates by 75-85%
- Provides tools for real-time data slicing and exploration, enhancing insight generation.
- Maintains professional, uniform visuals across datasets and reports.
Interactive Dashboards
Excel:
- Use pivot tables and slicers to create static dashboards for financial metrics (e.g., revenue breakdowns or cost trends).
- Manually adjust charts and layouts for each reporting period or aduience.
- Refresh data manually, risking errors in linked formulas or connections.
Estimated Time: 6-10 hours per dashboard.
Python:
- Build interactive dashboards using "Dash" or "Streamlit," allowing real-time filtering, chart updates, and user inputs.
- Integrate dashboards riectly with live data sources (e.g., APIs, databases) to ensure real-time accuracy.
- Include advanced features like drill-downs, trend forecasts, and customizable views for specific audiences.
Estimated Time: 3-5 hours for setup; updates occur in real time.
Efficiency Gains
- Reduces dashboard creation time by up to 50%, with no ongoing update requirements.
- Enables dynamic exploration and customization, far surpassing Excel's capabilities.
- Connects to live data, ensuring all metrics are current and error-free.
Portfolio and Risk Visualizations
Excel:
- Create static charts (e.g., pie charts for portfolio allocation, scatter plots for risk vs. return) manually, adjusting ranges and formatting repeatedly.
- Use formulas and conditional formatting to highlight outliers or key risk areas.
- Update charts and calculations manually as portfolio data changes.
Estimates Time: 5-8 hours per visualization set.
Python:
- Use "Plotly" or "Matplotlib" to generate interactive visuals, such as dynamic scatter plots of risk-return profiles or heatmaps of portfolio diversification.
- Automate visual updates directly from portfolio management systems or databases.
- Add interactive features like tooltips, zooming, and filtering for deeper analysis.
Estimated Time: 2-3 hours for setup; updates occur automatically.
Efficiency Gains
- Reduces manual effort by 60-70%, freeing time for deeper analysis.
- Offers richer visual detail with interactivity, enabling better portfolio decisions.
- Maintains up-to-date visuals as portfolio data evolves.
Waterfall Charts
Excel:
- Manually calculate step-by-step changes in values (e.g., revenue to net income) and input these into chart data ranges.
- Use Excel's waterfall chart feature, requiring manual adjustments for formatting and colors.
- Repeat the process for each new dataset, rebuilding charts from scratch.
Estimated Time: 6-8 hours per chat cycle.
Python:
- Use "Matplotlib" or "Plotly" to programmatically generate waterfall charts from data.
- Automate the calculation of cumulative changes and visual formatting for presentation-ready outputs.
- Build reusable scripts to create charts for multiple datasets or reporting periods without manual intervention.
Estimated Time: 2-3 hours to set up; new charts update in minutes.
Efficiency Gains:
- Cuts chart creation time by 70-80%.
- Reusable scripts eliminate repetitive work for future cycles.
- Allows precise control over aesthetics and data handling.
Predictive Modeling
Excel:
- Manually prepare datasets by importing data and cleaning it with formulas (e.g., "IF," "VLOOKUP," and "FILTER."
- Build regression models using Excel's built-in tools like "Data Analysis ToolPak" or "LINEST,: often requiring multiple iterations to refine.
- Validate models by creating separate sheets for testing and comparing predictions against actual results.
Estimated Time: 8-12 hours per model, including preparation, building, and testing.
Python:
- Use "Pandas" to clean and prepare datasets programmatically.
- Leverage libraries like "scikit-learn" to build and refine regression, classification, or clustering models efficiently.
- Automate model validation with cross-validation techniques and metrics like "RMSE" or accuracy scores.
Estimated Time: 2-4 hours per model, depending on complexity.
Efficiency Gains:
- Reduces time spent by 60-70%.
- Handles lager datasets and more complex models with ease.
- Automates testing and validation for higher reliability.
Risk Analysis and Credit Scoring
Excel:
- Import and preprocess borrower data manually using formulas to alculate credit metrics like DSCR (Debt-Service Coverage Ratio) or credit utilization rates.
- Develop scoring models using weighted averages or ranking methods.
- Manually update credit scores when new data becomes available.
Estimated Time: 6-10 hours per analysis, including updates.
Python:
- Automate data preprocessing with "Pandas," applying transformations and aggregations for key metrics.
- Use machine learning models (e.g., decision trees or logistic regression in "scikit-learn") to predict default probabilities or assign credit scores dynamically.
- Integrate scripts with real-time data feeds to update credit scoring automatically.
Estimated Time: 3-5 hours for setup; updates occur automatically.
Efficiency Gains:
- Reduces analysis time by up to 50%.
- Produces more robust scoring models through machine learning.
- Eliminates manual recalculations for data updates greatly reducing the opportunity for human-error.
Debt Sustainability
Excel:
- Create a spreadsheet for debt metrics, such as interest coverage ratio and leverage ratio, and manually input assumptions for scenarios.
- Use Excel formulas and goal-seek tools to test sustainability under different conditions (e.g., interest rate hikes or revenue drops).
- Manually adjust and record results for different scenarios, risking errors in formula and references.
Estimated Time: 8-12 hours per analysis.
Python:
- Build automated models using "NumPy" or "Pandas" to calculate debt metrics and simulate sustainability scenarios programmatically.
- Use Monte Carlo simulations with libraries like "SciPy" to assess risk under various assumptions.
- Output results into structured reports or interactive dashboards for better decision-making.
Estimated Time: 2-4 hours for setup; scalability for additional scenarios is instantaneous.
Efficiency Gains:
- Reduces effort by 70-80%
- Tests thousands of scenarios quickly, enhancing depth of analysis.
- Minimizes errors and ensures consistent results.
Revenue Projections
Excel:
- Use historical data to calculate growth rates manually and build projection models with basic formulas (CAGR, TREND).
- Apply static growth scenarios and manually adjust forecasts for variables like seasonality or market conditions.
- Test sensitivity by duplicating sheets and running "what-if" scenarios manually.
Estimated Time: 10-15 hours for detailed projections with sensitivity analysis.
Python:
- Automate data preprocessing with "Pandas" to clean and organize historical revenue data.
- Apply advanced predictive techniques like "time-series forecasting" (e.g., ARIMA, Prophet) to generate accurate projections.
- Conduct sensitivity analysis programmatically, varying multiple inputs and visualizing impacts dynamically.
Estimated Time: 3-5 hours for setup and analysis.
Efficiency Gains:
- Reduces effort by 70-80%
- Improves forecasts with advanced models like time-series analysis.
- Simplifies running multiple scenarios without added manual effort.
Unsure if Python, or PyFi is right for you?
Hear from PyFi students. Be Certain in your decision.
Meet Zach Washam
EX Wells Fargo
PyFi Founder & Head of Instruction
While learning Python as an investment banker, Zach made an interesting observation: Python programming had a lot in common with the Excel models he made at work. By thinking of Python like Excel, Zach quickly learned the coding language and invented Wells Fargo Securities' first machine learning algorithm for investment banking and capital markets.
After submitting two algorithms for patent protection and winning Wells Fargo's 2018 "Local Sphere Innovation Award," Zach left investment banking to launch PyFi.
Now, Zach’s courses have been delivered to thousands of finance students and professionals around the world.
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Learn the same algorithms that were used to advise the following companies:
*Slight changes to the code made to protect IP
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