Mission 04 / ANALYSIS / REPORTING

Pandas and Data Analytics

Load synthetic CSV data, remove incomplete measurements, group observations, and report the result with Pandas.

Starter lesson26 min

Briefing

Clean and group synthetic telemetry

A DataFrame is a labeled table of rows and columns. In this lab, Pyodide loads Pandas in a browser worker and supplies a tiny synthetic CSV file without uploading it.

Load and inspect

`pandas.read_csv` turns CSV text into a DataFrame. Check column names and missing values before making assumptions. A row with an absent temperature is not a valid temperature sample, though it may still contain other useful fields.

Clean with a stated rule

`dropna(subset=[...])` removes rows missing a required measurement. Record the rule in the report so readers know why the row count changed. Avoid silently converting missing values to zero: that invents a measurement.

Group and summarize

`groupby` partitions rows by a key; `size` counts observations in each group. This exercise reports cleaned sample counts by zone. Visualization and notebook export are planned modules; the executable starter uses a compact text summary.

Practical exercise

Run the mission

Load the provided CSV, remove rows without `temperature_c`, group by zone, and print `north:3,south:1`.

Progressive hint

Use `pd.read_csv(StringIO(csv_data))`, `dropna(subset=['temperature_c'])`, and `groupby('zone').size()`.

Need another hint?

Sort the grouped counts by zone before formatting so the output order is stable.

Sample data · synthetic CSV

flight_id,zone,temperature_c,status
A-01,north,28,nominal
A-02,north,31,review
A-03,south,,nominal
A-04,north,26,nominal
A-05,south,30,review

import pandas as pd
from io import StringIO

data = pd.read_csv(StringIO(csv_data))
clean = data.dropna(subset=["temperature_c"])
counts = clean.groupby("zone").size().sort_index()
print(",".join(f"{zone}:{count}" for zone, count in counts.items()))

import pandas as pd
from io import StringIO

data = pd.read_csv(StringIO(csv_data))
clean = data.dropna(subset=["temperature_c"])
counts = clean.groupby("zone").size().sort_index()
print(",".join(f"{zone}:{count}" for zone, count in counts.items()))

Output

Run the starter to see observable output. Code is saved in this browser.

Pyodide · Python in WebAssembly · isolated short-lived browser worker

Passing work is graded by the academy and saved to your private portfolio when you submit it.

Knowledge check

Which Pandas method removes rows missing a required column?

Pass both checks to save progress and earn 175 XP.