Follow the transformation
State the small task and expected result before coding.
Use the concepts from this module rather than introducing unnecessary new machinery.
Inspect intermediate values on the tiny example.
This mini lab combines distribution and relationship views in one deliberately designed figure so each subplot answers a different exploratory question.
This mini lab combines distribution and relationship views in one deliberately designed figure so each subplot answers a different exploratory question.
Learning goal: explain why Mini Lab Build an EDA Figure behaves this way, apply it to a small example, and verify the result independently. Begin by being able to justify this first step: State the small task and expected result before coding.
Treat this as a sequence of observable decisions rather than one opaque command. Stage 1: State the small task and expected result before coding. Stage 2: Use the concepts from this module rather than introducing unnecessary new machinery. Stage 3: Inspect intermediate values on the tiny example. Final checkpoint: Explain why the final result follows from the code.
State the small task and expected result before coding.
Use the concepts from this module rather than introducing unnecessary new machinery.
Inspect intermediate values on the tiny example.
State the small task and expected result before coding. For Mini Lab Build an EDA Figure, identify the exact state before this stage, the operation or rule applied here, and the observable state afterwards so the mechanism remains inspectable.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import matplotlib.pyplot as plt
# Step 2 — Encode the selected variables into a visual layer that can be inspected for pattern and anomalies.
fig, ax = plt.subplots(1, 2, figsize=(7,3))
# Step 3 — Execute this statement and inspect how it changes the current value, object or program state.
ax[0].hist([1,2,2,3,5])
# Step 4 — Execute this statement and inspect how it changes the current value, object or program state.
ax[0].set_title("Distribution")
# Step 5 — Execute this statement and inspect how it changes the current value, object or program state.
ax[1].scatter([1,2,3],[2,4,5])
# Step 6 — Execute this statement and inspect how it changes the current value, object or program state.
ax[1].set_title("Relationship")
# Step 7 — Encode the selected variables into a visual layer that can be inspected for pattern and anomalies.
plt.tight_layout()The first axis shows shape/frequency; the second shows a two-variable relationship.
For Mini Lab Build an EDA Figure, connect every important mark, axis position or summary to its source values; check how scale, ordering, aggregation or binning affects what a reader sees.
InputObjects/values supplied to the operation.StateNames or mutable objects that may change during execution.OutputReturned value, side effect, file, plot or exception to inspect.Use Mini Lab Build an EDA Figure when it answers a defined question in Matplotlib & Visualisation and its inputs/assumptions match the current data or program state.
Reconsider Mini Lab Build an EDA Figure when the required information is unavailable, the operation would violate a validation/data boundary, or a simpler operation answers the question more transparently.
Construct a tiny example of Mini Lab Build an EDA Figure. First state the small task and expected result before coding. Then use the concepts from this module rather than introducing unnecessary new machinery. Predict the result before execution and explain one boundary or failure case.
Which approach best demonstrates understanding of Mini Lab Build an EDA Figure?
Step 1State the small task and expected result before coding.Step 2Use the concepts from this module rather than introducing unnecessary new machinery.Step 3Inspect intermediate values on the tiny example.Step 4Check at least one edge case.