Lesson 19
Mini Python practice lab
Big question
Can we combine loading, summarizing, and graphing in one short workflow?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain mini python practice lab in plain language.
- Use workflow correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
A mini lab ties the starter skills together. Load the data, inspect the first rows, summarize key variables, and make one graph. This is the basic rhythm of many empirical projects.
Key terms
- Workflow
- A sequence of steps used to complete an analysis.
- Inspect
- Look at the data directly to understand its structure.
- Summary
- A compact description of key values.
- Practice lab
- A small exercise that builds confidence with code.
Example
A student loads wage_sample.csv, checks the first rows, summarizes wages, and graphs wage against education.
Practice lab script
1import pandas as pd2import matplotlib.pyplot as plt3 4df = pd.read_csv("wage_sample.csv")5print(df.head())6print(df[["wage", "education", "experience"]].describe())7 8plt.scatter(df["education"], df["wage"])9plt.xlabel("Education")10plt.ylabel("Wage")11plt.title("Practice lab: wage and education")12plt.show()Live notebook
Run this lesson as a notebook
Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.
Checkpoint activity
Pause and explain this lesson's main idea in your own words before moving forward.
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.
Quick quiz
What should usually come before modeling?
Key takeaway
A simple data workflow makes econometrics feel practical instead of abstract.