Pandas Study Plan

Your brief

First practise table construction and selection until labels and positions are clear. Next validate formats and identity, then combine sources with declared cardinality. Add grouped or time-based reports only when their input grain is stable. Finally package functions, assertions, imports and exports together with environment notes. Keep synthetic examples separate from real personal data, and record limitations rather than inventing a success story from a clean-looking chart.

Run the code

Select Try in playground to load the complete editable source. Nothing executes until you choose Run. The browser uses Pyodide 0.27.7, Pandas 2.2.3 and NumPy 2.0.2. Each ordinary execution starts a fresh isolated runtime. The tables are fictional and do not use personal files.

For local work, use a separate virtual environment and run python study-plan.py. Download the complete source. The reviewed local environment uses Python 3.12.14, Pandas 3.0.6 and NumPy 2.5.4; the browser uses Pyodide 0.27.7 with Pandas 2.2.3 and NumPy 2.0.2. Compatible examples are checked in both. See the environment setup. No Apache or database server is required.

Worked solution

import pandas as pd
import numpy as np

stages = pd.DataFrame({
    "stage":["inspect", "validate", "combine", "report"],
    "check":["schema", "identity", "cardinality", "reconciliation"],
})
print(stages["stage"].tolist())
print(stages.loc[stages["stage"].eq("combine"), "check"].item())

['inspect', 'validate', 'combine', 'report']
cardinality

Extend and test

Deliver one script, a small fictional CSV, the accepted/rejected counts and a short explanation of the report limits. No account or certificate is required.