Module 3 · Foundations
Foundation bridge for Module 3
You can use this support during the lesson. It is not an entrance requirement.
Files and folders
The input file is raw evidence; generated outputs belong in runs/. A relative path starts from the folder where your command runs. A file-not-found error often means the working folder or filename differs from the instruction.
Dataframes and types
A dataframe is a table with named columns. Numeric-looking text still needs interpretation: the string "12" can be converted to a number, while "unknown" cannot. A missing measurement and a measured zero carry different meanings.
import pandas as pd
events = pd.read_csv("data/samples/network-events/events.csv")
print("Column names:", list(events.columns))
print("Number of records:", len(events))
print(events[["event_id", "source"]].head(3).to_string(index=False))
This code is an exploration example. The shared preparation function initially reads fields as strings so its conversion and validation policy is explicit.
Functions and results
from practical_ml_ai.data.events import prepare_file
result = prepare_file("data/samples/network-events/events.csv", "runs/my-first-run")
print(result.report["rows_accepted"])
prepare_file is a function with input and output paths. It returns a result with cleaned records, rejected records, and a report. The report is a dictionary: square brackets select a named value.
Where to ask for help
Use the help channel chosen by your instructor, or a project issue for a nonsensitive reproducible problem. Include the command, expected result, actual result/error, and one attempted fix. A plain-text error is more useful than a screenshot alone. Keep sensitive files and credentials private.
Continue the learning route
Continue with concepts and a small trace before the full workflow.