Open-source Practice Issues with Data Analytics with Python: A 2027 Project Guide

The starting point for this Data Analytics with Python exercise is a student contributor working with issue records. Their question is specific: How much review effort belongs to reviewed practice issues? Building a reproducible analysis report around that question gives the project a manageable scope and makes the result possible to verify.

This is a 2027 study-edition project brief. All example records and operational situations are fictional. Start with basic Data Analytics with Python knowledge, the ability to inspect structured data and a local practice environment. The complete first version should answer the stated question before adding accounts, real integrations or additional screens.

Define the project rule

Reviewed issue minutes record practice review effort. A closed-unreviewed issue is not equivalent to a reviewed one, and the fixture must not imply contributions to a real external project. For this exercise, one row represents one issue. The stable key is record_id; repository_area is the grouping or filtering label. The numeric review_minutes field uses minutes. Accepted states are opened, reviewed, closed-unreviewed.

The summary selects status reviewed and applies sum to the selected numeric values. Keep this definition visible in the interface, response or report so another learner can reproduce it. Use positive finite values in the starter fixture and define any future zero or missing-value policy explicitly.

Choose the Data Analytics with Python approach

Write the business question before loading data. Inspect types, uniqueness and missing values, then calculate the metric from a clearly filtered table. Keep a raw fixture unchanged and record cleaning decisions separately. Reconcile a hand-calculated sample before making charts; a polished visual is not evidence that the aggregation is correct.

The three design decisions to explain in your project notes are:

• Data grain and quality checks. Apply this choice to issue records and explain its effect on review_minutes.
• Reproducible transformations. Apply this choice to issue records and explain its effect on review_minutes.
• Metric reconciliation. Apply this choice to issue records and explain its effect on review_minutes.

Work through a small sample

The three records below are deliberately small enough to inspect by hand. They are a compact specification for the data shape and expected result, and can later be expanded into a larger synthetic fixture.

[
  {
    "record_id": "open-source-issues-01",
    "repository_area": "Documentation",
    "review_minutes": 25,
    "status": "reviewed",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "open-source-issues-02",
    "repository_area": "Examples",
    "review_minutes": 40,
    "status": "opened",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "open-source-issues-03",
    "repository_area": "Tests",
    "review_minutes": 15,
    "status": "reviewed",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match reviewed. Their review_minutes values are 25 and 15; the full-fixture result is 40 minutes, with 2 matching records. Selecting repository_area = Documentation leaves the first record, so the result becomes 25 minutes, with 1 match. This provides two independent checks for the filter and aggregation.

The full-fixture output contract is:

{
  "matching_count": 2,
  "operation": "sum",
  "value": 40,
  "unit": "minutes"
}

Check the result by repository_area

The overall figure answers the all-records version of the question. Grouping by repository_area produces the following separate expectations, using the same reviewed rule. The middle group has a record in the source fixture but no qualifying observation.

Use this breakdown when answering the group-level question. Compare like-for-like units and keep the matching count visible, especially for averages. A different filter changes which records enter the calculation; it does not change the meaning of the metric.

The code blocks describe sample data and an expected result. Implement the surrounding feature using the installed version of your chosen tools, then save the actual run evidence separately from these expectations.

Build the feature in three passes

1. Inspect the data before charting

Check record_id uniqueness, repository_area labels, review_minutes types and status values. Keep the supplied data as a raw input and create a separate cleaned representation. Record exclusions so the student contributor can understand what entered the report.

2. Calculate a transparent metric

Restrict the analysis to reviewed entries and calculate the sum of review_minutes. Show both the result in minutes and the matching-record count. Reconcile the small fixture by hand before applying a chart, pivot or dashboard measure.

3. Present a reproducible answer

Use a repository_area filter and label every unit. Show the empty-selection behaviour and explain the difference between this metric and a wider operational claim. Include the input version and transformation notes so another learner can rebuild the result.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: Every closed issue is counted as reviewed work. First reproduce it against the fixture, then identify the smallest rule or state transition that explains the mismatch. Fix that boundary and rerun the full-fixture and selected-label examples. This creates a stronger explanation than changing several unrelated parts until the screen looks right.

The scenario-specific acceptance check is: Close an issue without review and prove that reviewed effort stays unchanged. Preserve before-and-after evidence. Add a missing repository_area, a negative review_minutes, an unrecognised status and a duplicate record_id as separate invalid cases. Define the rejection or duplicate-handling policy before testing, and verify that invalid input does not silently change valid existing records.

Check the Data Analytics with Python concerns as well:

• A chart total must match the filtered table.
• Invalid numeric strings must be reported.
• The raw fixture must remain available.

For an empty filter match, the expected sum value is 0. Keep matching_count at zero. This distinction prevents an empty dataset from being interpreted as a measured observation.

Explain the finished work in a portfolio

Show the student contributor question, the record definition and the expected calculation first. Then demonstrate a repository_area filter and the acceptance check. Include the fixture, setup instructions, chosen tool versions, actual test output and one remaining limitation in the repository. If you show a screenshot, pair it with the input that produced it. Another learner should be able to repeat the same result from a clean checkout.

For a focused extension, add a second repository_area selection and compare the two groups using the same rule. Specify how missing groups appear and keep the original fixture as a regression case. Expand scope only after the current calculation, state handling and recovery path remain understandable.

Questions learners often ask

Which issue records belong in the result?

Select records whose status is exactly reviewed and that satisfy the chosen repository_area filter. Other states remain in the source data and can have separate summaries. Follow the domain rule above when a record changes state.

What should happen when no records match?

Return a zero total with a matching count of zero and a clear empty-result message. Keep the applied filter visible so the user can change it. Avoid using a loading indicator for a completed request that simply has no matching data.

How can I prove the review_minutes result is correct?

Reconcile the first and third sample records, then repeat the calculation for Documentation alone. Save both expected and actual outputs, and rerun the scenario-specific acceptance check after making a change.

Continue learning with Groot Academy

Use this exercise as a concrete discussion point when choosing a learning path. Explore the related Groot Academy course and ask about the current syllabus, learning mode and project practice. Course availability and batch details should be confirmed directly with the academy.

For implementation details, consult the official Data Analytics with Python documentation matching the tools you install. This project defines an original practice brief; it does not depend on a forecast about 2027 software releases.

Continue with Exam Revision Planner using Data Analytics with Python, or compare Open-source Practice Issues using Data Science. These companion practice guides are part of the 2027 learning collection.