Python for Data Work

• Understand where Python fits relative to SQL and Excel
• Run Python scripts and Jupyter notebooks
• Structure an analysis as small, testable scripts

• Excel caps out at a million rows; Python handles billions across memory-mapped tools.
• Reproducibility: a script replays an analysis exactly; a workbook hides the steps.
• The same language powers notebooks, pipelines, dashboards and ML — one skill compounds.
• SQL fetches data; Python reshapes, joins, aggregates and models it without losing context.

Excel is the one trusty pot; SQL is the pantry order. Python is the full kit — each library a knife for a specific cut, all in one drawer you reach for without switching kitchens.

• You write instructions in .py files or .ipynb notebooks that the interpreter executes step by step.
• Libraries are imported once and then callable: pandas, numpy, os, sys, json.
• In a notebook, cells run interactively — ideal for exploration while charts and notes sit beside the code.
• Raw data is pulled in (CSV, SQL, API), shaped via DataFrames, analyzed, then written out or plotted.
• A finished analysis is a sequence of scripts with a clear flow: ingest → clean → analyze → report.

• A growth analyst pulls 12M events from the warehouse with Python and builds a cohort matrix that SQL alone couldn't render.
• An ops analyst automates the Monday morning KPI digest with a 40-line script run by cron, saving four hours weekly.
• A data scientist's notebook becomes a scheduled pipeline that re-trains a churn model every night.

• Keep the ingest/clean/analyze/report steps in separate functions so each is testable.
• Prefer explicit prints and asserts over silent failures in early exploration.
• Set a random seed wherever sampling or modeling is involved.
• Pin library versions in a requirements.txt so the analysis replays on any machine.

• Doing everything in one giant notebook cell, then losing track of which transform ran first.
• Installing packages ad hoc and wondering why the notebook breaks after a version bump.
• Hard-coding a file path that only exists on one laptop.
• Ignoring the seed, so 'the same analysis' gives different numbers every run.

• Print df.shape and df.head() right after every load to confirm the data arrived whole.
• When a script fails, read the last error line first — it names the file, line and exception.
• Use df.info() to spot unexpected dtypes or all-NaN columns before analysis.
• Run the smallest possible cell to isolate whether the bug is ingest, transform or output.