Analyze data with Python and pandas, built for Excel and SQL users: loading, cleaning, reshaping, aggregating, visualizing, and automating reports on real datasets.
Getting your Python environment wrong is the fastest way to kill your momentum before you write a single line of analysis code. This lesson walks you through installing Python, VS Code, and Jupyter correctly — and shows you why virtual environments are the professional habit that saves hours of future frustration.
Already know Excel? You're closer to Python than you think. This hands-on lesson translates your spreadsheet intuition into Python fundamentals — variables, lists, dictionaries, and loops — using realistic data scenarios you'll actually encounter on the job.
Loading data into pandas is more nuanced than calling pd.read_csv() and hoping for the best. This lesson teaches you exactly how to load CSV and Excel files with precision — handling messy headers, wrong dtypes, and missing values — then gives you a professional first-look exploration workflow to audit any dataset before you touch it.
Learn the three essential tools for pulling exactly the data you need from a pandas DataFrame: loc for label-based access, iloc for position-based access, and boolean masks for filtering by condition. By the end, you'll be combining multiple conditions and selecting specific rows and columns with confidence.
Real-world data is broken — currency strings, mixed date formats, duplicated rows, and missing values in five different forms. This hands-on lesson walks you through building a systematic, reusable cleaning pipeline in pandas that you can apply to any messy dataset.
If you know Excel's PivotTable, you already understand what pandas groupby does — you just need to learn the mechanics. This deep-dive lesson covers split-apply-combine, agg(), transform(), apply(), multi-key grouping, reshaping results, and performance optimization on real datasets.
Learn how to combine DataFrames in pandas using merge() — covering inner, left, right, and outer joins, merging on different column names, and debugging the duplicate key problem that silently corrupts results. Built for Excel and SQL users who want the full picture.
Learn how to transform data between wide and long formats using pandas' pivot_table, melt, and stack functions. This hands-on lesson builds real analyst fluency — with realistic datasets, composable workflows, and production-ready techniques.
Dates are where real-world data gets messy and where pandas gets powerful. Learn how to parse tricky date formats, resample transactions to any frequency, and compute rolling averages and anomaly-detection bands — with production-ready patterns throughout.
Dirty text data is one of the most common blockers in real analysis work. This lesson teaches you how to use pandas `.str` methods and regular expressions to normalize, extract, and validate text at scale — turning chaotic string columns into clean, queryable data with reusable pipeline functions.
Learn to build bar charts, line charts, scatter plots, heatmaps, and multi-panel dashboards with matplotlib and seaborn. Covers the Figure/Axes model, seaborn's statistical charts, professional styling, and annotation — all with realistic sales data you can run immediately.
Stop exporting CSVs manually. Learn how to build a direct, production-grade bridge from any SQL database into a pandas DataFrame using SQLAlchemy — with safe parameterization, connection pooling, chunked reads for large datasets, and secure credential management.
Stop spending Monday mornings manually formatting spreadsheets. Learn how to build fully formatted, multi-sheet Excel workbooks from pandas DataFrames using openpyxl — with conditional formatting, embedded charts, and reusable code you can schedule to run itself.
If your pandas scripts are slow, the culprit is almost always apply and for loops — and the fix is vectorization. This deep-dive lesson explains why the performance gap is so extreme, then gives you a complete hierarchy of fast alternatives including np.where, np.select, and categorical dtypes with real benchmarks throughout.
Most data analysis notebooks are built to run once and rebuilt every time requirements change. This lesson shows you how to structure a Python project with a proper module layer, testable functions, purposeful notebooks, and a schedulable script — so your work adapts instead of breaks.
When your dataset won't fit in memory, the tools that worked on small data start to fail. This expert-level lesson teaches you chunked reading, dtype optimization, Parquet conversion, and Polars — a complete toolkit for handling files that break normal pandas workflows.
Learn how to export pandas DataFrames to CSV, Excel, and JSON files with professional-grade control over formatting, encoding, and structure. Covers multi-sheet workbooks, JSON orientations, datetime handling, and the common mistakes that corrupt or mangle your output.
Learn how to turn a pandas analysis into a fully automated report that runs on a schedule, handles errors gracefully, and notifies you when something goes wrong — all without you lifting a finger. This lesson covers script structure, logging, email alerts, and both Windows and Mac/Linux scheduling.
Stop copying and pasting data between spreadsheets. Learn how to automatically find, read, and combine dozens of CSV or Excel files into a single pandas DataFrame using glob, pathlib, and pd.concat — with source tracking built in.
Jumping straight into analysis on a new dataset is one of the most common — and costly — mistakes in data work. This lesson teaches you a systematic profiling workflow using pandas: checking shape, completeness, duplicates, distributions, and outliers before a single aggregation runs.
Learn how to build running totals, cumulative averages, and within-group rankings in pandas using cumsum, expanding, and rank. Goes beyond the basics to cover groupby integration, tie-breaking strategies, NaN handling, and a complete sales dashboard project.
Learn how to derive new columns in pandas using three essential tools: np.where for conditional logic, pd.cut for numeric binning, and .map() for lookup-style translation. By the end, you'll be able to transform raw transactional data into analysis-ready features without writing a single loop.
MultiIndex DataFrames from groupby and pivot_table are powerful but consistently confusing — until you understand the structure. This lesson teaches you to select, flatten, stack, and unstack multi-level data through a complete sales analysis project.
Learn to build a production-grade ETL pipeline in pandas that extracts data from CSV files, Excel workbooks, and SQL databases; applies a layered transformation strategy; and loads results to multiple output formats. This lesson covers architecture, error handling, validation, and performance — the full picture for data professionals who need pipelines that actually hold up.
Learn how to summarize categorical columns in pandas using value_counts() and build two-way contingency tables with pd.crosstab(). This lesson covers normalization, margins, and percentage breakdowns — the tools you need to answer "how is X distributed across Y?" questions quickly and correctly.
Learn to compute revenue-weighted averages, percent-of-total share metrics, and fully custom aggregations in pandas groupby. This lesson builds a complete, production-ready sales analytics summary using apply, agg, and transform together.
Silent join bugs — row multiplication, unmatched keys, type mismatches — are the most dangerous errors in data analysis because pandas won't warn you. This lesson gives you a complete diagnostic toolkit: pre-merge key profiling, cardinality analysis, post-merge row count auditing, and defensive merge wrappers that catch problems before they corrupt your analysis.
Wide data is easy to read but hard to analyze. Long data is easy to analyze but hard to present. This lesson teaches you exactly when to use melt, pivot, and unstack in pandas — with realistic examples and a clear workflow for converting between formats.
Learn how to create new columns in pandas using np.where for conditional flags, pd.cut for numeric bucketing, and .map() for code-to-label substitution. Three essential tools that replace slow loops and Excel workarounds with fast, readable Python.
Learn how to build production-quality period comparison analysis in pandas using shift and pct_change. This lesson covers MoM, YoY, and rolling metrics for single and multi-segment time series, with realistic business data and a reusable report function you can ship immediately.
When your ERP, CRM, and finance spreadsheet all show different numbers, you need a systematic way to find out who's wrong and by how much. This expert-level lesson teaches you to build a complete data reconciliation pipeline in pandas — from normalizing heterogeneous sources, through row-level mismatch detection, to a formatted multi-sheet Excel report stakeholders can actually use.
Learn how to turn raw transaction data into a professional, multi-sheet Excel workbook using pandas pivot_table and openpyxl. This lesson covers building multiple pivot summaries, applying header styles and number formats, adding a cover sheet, and structuring the whole pipeline as a reusable script.
Learn how to build a production-quality reconciliation pipeline in pandas that merges actuals, targets, and adjustments from multiple sources, applies tiered variance flag logic, and exports a formatted multi-sheet Excel workbook — complete with conditional formatting, frozen panes, and an executive scorecard. This is the report your CFO actually wants.
Learn how to build production-grade leaderboards, percentile tiers, and running totals in pandas using rank(), cumsum(), and rolling window functions. Covers groupby-scoped ranking, month-over-month growth, and a complete sales intelligence report you can export to Excel.
Learn how to compute running totals, cumulative averages, rolling windows, and within-group rankings in pandas using groupby and transform. Practical examples using realistic sales data, with full copy-paste code and a complete weekly performance report you can adapt immediately.
Learn how to calculate MoM and YoY changes in pandas using pct_change() and shift() — the same logic as Excel formulas, but cleaner, faster, and scalable across multiple product lines or regions. Includes a complete business reporting example with formatted output.
Real-world APIs return deeply nested JSON that breaks naive DataFrame loading. Learn how to use json_normalize, explode, and defensive sanitization to flatten any nested structure—including lists of sub-objects and missing keys—into a clean, analysis-ready DataFrame. Includes a complete, production-ready pipeline function.
Stop rewriting your analysis every time someone asks for a different region or date range. This expert-level lesson walks you through building a fully parameterized pandas report pipeline that filters, aggregates, and exports formatted Excel workbooks from a single, reusable codebase — with batch generation across any parameter grid.
Learn how to build executive-quality summary tables in pandas — complete with running totals, group subtotals, and grand totals. This step-by-step lesson teaches you the exact patterns that turn raw data into boardroom-ready reports, using a realistic retail sales dataset you can run immediately.
Learn how to turn a raw transaction log into a complete cohort analysis in pandas — including a retention matrix, period-over-period churn rates, and cumulative customer lifetime value broken down by acquisition cohort. Covers everything from cohort assignment to a reusable production function.
Real exports are never clean — dates in four formats, currency fields as strings, category columns with a dozen spellings of the same value. This lesson builds a complete, production-grade cleaning pipeline that handles all three mess types together, with validation built in.
Learn how to decompose business time series into trend, seasonal, and residual components using pandas and statsmodels. Go from raw monthly data to formatted seasonal forecasts and stakeholder-ready Excel reports.
Before you analyze anything, you need to orient yourself in your data. This lesson teaches you the five pandas methods every analyst uses in the first two minutes with a new dataset — and how to combine them into a fast, reliable exploration workflow.
Bad data doesn't announce itself — it hides in nulls, inconsistent categories, out-of-range values, and pseudo-null strings until it breaks your analysis. This lesson walks you through building a complete, reusable data quality scorecard in pandas that interrogates every column before a single analysis runs, scores failures by severity, and exports a stakeholder-ready Excel report.
Learn how to turn raw transaction data into real business KPIs using pandas. This hands-on lesson walks through computing revenue, gross margin, conversion rate, average order value, and customer-level metrics — with the common pitfalls explained so you get the numbers right.
Learn how to build a production-grade data cleaning pipeline in pandas that validates rows against custom business rules, accumulates a structured error log, applies traceable corrections, and exports a clean output file alongside a stakeholder-ready validation report. This is the system that replaces three hours of manual Excel work with a single script.
Learn how to build a professional Excel sales dashboard that regenerates itself from a live data source using pandas and openpyxl. Covers automated chart generation, four types of conditional formatting, named ranges, and Excel Tables — all from a clean, schedulable Python script.
Learn how to transform raw transaction data into a fully scored, segmented customer table using RFM analysis in pandas. This lesson covers every step — from groupby aggregation to quantile scoring to production-ready pipeline design — with realistic code and edge case handling that most tutorials skip entirely.