The short answer: open PyCharm, position your cursor in a docstring or notebook cell, press ⌃+⌥+R (Mac) or Ctrl+Alt+R (Windows/Linux), speak your documentation for 60-120 seconds, and AICHE inserts detailed text.

PyCharm users working with data science and machine learning need detailed explanations of algorithms, but typing mathematical formulas and statistical concepts takes 12+ minutes. Voice captures complex technical explanations faster than keyboard mechanics.

Open PyCharm with your Python project.
Position your cursor in a docstring, comment, or Jupyter notebook cell.
Press your AICHE hotkey to start recording.
Speak your complete explanation.
Press the hotkey again-AICHE transcribes and inserts the text.
Add Python docstring formatting (""") and type hints manually.

Function Documentation

Machine Learning Pipeline Docstrings

Position cursor in function definition and dictate comprehensive docstrings. Example: "Document this machine learning pipeline function. Function preprocesses raw customer data for churn prediction model. Accepts pandas DataFrame with transaction history. First step: handles missing values using forward fill for time series data and mean imputation for numeric features. Second step: encodes categorical variables including customer tier, product category, and region using one-hot encoding. Third step: scales numeric features using StandardScaler to normalize spending amounts and transaction counts. Fourth step: creates engineered features including average transaction value, days since last purchase, and purchase frequency over rolling 90 day window. Fifth step: splits data into training and test sets using 80-20 ratio with stratification on churn label. Returns tuple of X train, X test, y train, y test arrays. Include notes on handling class imbalance and feature importance considerations."

The complete pipeline documentation flows naturally when spoken, capturing every preprocessing step.

Statistical Analysis Documentation

For data analysis functions, dictate the statistical approach. Example: "Explain this hypothesis testing function. Function performs A/B test analysis comparing conversion rates between control and treatment groups. Accepts two pandas Series containing binary conversion outcomes. First calculates sample proportions and standard errors for both groups. Second computes z-statistic using difference in proportions divided by pooled standard error. Third calculates two-tailed p-value using normal distribution. Fourth computes 95% confidence interval for difference in conversion rates using normal approximation. Returns test result object with p-value, confidence interval, and recommendation whether to reject null hypothesis at 0.05 significance level. Assumptions: samples are independent, sample sizes sufficient for normal approximation with np greater than 10 for both groups, and conversions follow binomial distribution. Function validates assumptions and raises ValueError if violated."

Speaking statistical methodology is clearer than typing mathematical notation.

Jupyter Notebook Documentation

Analysis Narrative Cells

In Jupyter notebooks, dictate markdown cells explaining analysis steps. Example: "Add markdown cell explaining exploratory data analysis findings. Dataset contains 50,000 customer records with 15 features including demographic info, purchase history, and engagement metrics. Initial observations: 23% missing values in income field requiring imputation strategy, strong positive correlation 0.78 between total spend and days as customer suggesting tenure drives value, bimodal distribution in purchase frequency with casual buyers averaging 2 orders per year and power users averaging 45 orders, and churn rate of 18% concentrated in first 90 days indicating onboarding challenges. Data quality issues: 340 records with negative transaction amounts requiring investigation, 12 duplicate customer IDs needing deduplication, and inconsistent date formats across purchase_date and registration_date fields. Next steps: clean negative transactions by treating as refunds, merge duplicate records using most recent data, and standardize date parsing using pandas to_datetime with multiple format attempts."

Voice captures analysis narrative that documents findings for team review.

Model Evaluation Cells

Document model performance in notebook cells. Example: "Explain model evaluation results for churn prediction classifier. Trained random forest model with 100 estimators and max depth 10 to prevent overfitting. Training set performance: accuracy 92%, precision 88%, recall 85%, F1 score 0.86. Test set performance: accuracy 87%, precision 81%, recall 78%, F1 score 0.79 showing slight overfitting. Confusion matrix reveals 156 false positives predicting churn when customer retained and 189 false negatives missing actual churners. Feature importance analysis shows days since last purchase ranked highest at 0.34 importance, total spend second at 0.21, and customer support interactions third at 0.18. ROC AUC of 0.91 indicates strong discriminatory power. Recommendations: tune decision threshold to prioritize recall over precision for retention campaigns, add temporal features like day of week and seasonality patterns, and consider ensemble with gradient boosting for 2-3% accuracy improvement observed in similar problems."

Comprehensive model evaluation documentation becomes standard because speaking is effortless.

Code Review and Refactoring

Algorithmic Complexity Analysis

When reviewing algorithms, dictate performance analysis. Example: "Add comment explaining time complexity trade-offs in this recommendation algorithm. Current implementation uses nested loops iterating over all users and all items calculating similarity scores with time complexity O of n squared where n is number of users. For dataset with 10,000 users this requires 100 million comparisons taking 45 seconds. Optimization approach: precompute user embeddings using matrix factorization reducing online computation to dot product with complexity O of n times d where d is embedding dimension typically 50. Alternative: use approximate nearest neighbors with locality sensitive hashing reducing complexity to O of n log n with 98% accuracy. Memory trade-off: precomputed embeddings require 50 MB storage versus online computation with zero memory overhead. Recommendation: implement embedding approach for production given response time requirement under 100 milliseconds, acceptable memory cost, and offline training running nightly."

Voice captures algorithmic reasoning that guides optimization decisions.

Result: Python docstrings with statistical methodology, Jupyter notebook analysis narratives, and algorithmic complexity explanations that took 20 minutes to type now take 5 minutes to dictate, and data science workflows become more documented because speaking technical concepts is faster than typing mathematical notation.

Do this now: open PyCharm with a Jupyter notebook, create a new markdown cell, press your hotkey, and spend 2 minutes explaining your most recent data analysis as if presenting findings to stakeholders who need context beyond the code.

AICHE +PPycharm Integration