Workflow and Loading Data

data-science

Move from question to dataset and load tabular data with pandas.

Level: Beginner to intermediate
Estimated time: 35–50 minutes
You will learn: Move from question to dataset and load tabular data with pandas.
Practice in: Jupyter or Google Colab

Open this lesson in Google Colab

Questions

What problem does Workflow and Loading Data help us solve in a small Python program?
What should we predict before running the example?
What value, output, or error should we inspect after changing one line?

Objectives

Run a complete example for data science workflow and loading data in Colab.
Explain the example line by line using plain language.
Change one part of the code and predict the result before running it.
Recognize one common mistake and use the error message as evidence.

Hands-on episode: Workflow and Loading Data

A data workflow moves from question to data loading, inspection, cleaning, analysis, visualization, and communication. In pandas, rows are observations and columns are variables.

We will learn this by running code, not by memorizing a definition first. Open the Colab notebook from the button above, find this section, and run each cell in order. Keep a small note beside the notebook with three columns: prediction, actual result, and what changed.

Example 1.1

Predict the shape and column names after loading this tiny table.

import pandas as pd

data = pd.DataFrame({
"name": ["Ada", "Grace"],
"score": [9, 8],
})
print(data.shape)
print(data.head())

Run the cell once without editing it. If the result is different from your prediction, leave the prediction visible and write one sentence about the difference. That sentence is more useful than a perfect first guess.

Explain Example 1.1

The DataFrame has two rows because there are two learners.
It has two columns: name and score.
.shape reports row count and column count.
.head() displays the first rows so you can inspect before analyzing.

Now explain the example out loud or in a Markdown cell. Use short sentences: “this line creates…”, “this name stores…”, “this output appears because…”. If you cannot explain a line yet, run only the lines above it and inspect the values that exist at that moment.

Challenge 1.1

Challenge

Add a third learner and run .shape again. The first number changes because the number of observations changed.

Show a safe way to approach the challenge

Copy Example 1.1 into a new Colab cell.
Change exactly one value, name, condition, or line.
Write the expected output before running the cell.
Run the cell and compare the actual result with your prediction.
If the result surprises you, undo the change and try a smaller one.

Suggested first move: Add a third learner and run .shape again.

Debugging checkpoint 1.1

Debugging checkpoint

Do not skip inspection after loading. File paths, headers, missing values, and data types often differ from what you expected. Start every dataset by checking shape, columns, head, and types.

Do not debug by rewriting the whole example. Read the error type or surprising output, inspect the closest value with print(...) or type(...), then change one thing. This is the same routine you will use in larger projects.

Apply it

Load or create a small DataFrame in Colab. Write one question about it, inspect it with .shape, .head(), and .dtypes, then say whether the data can answer your question.

Finish by adding a Markdown cell that answers: What did this example teach me that I can reuse in a project?

Key points

Learn the concept by running a complete, small example first.
Predict before execution so your thinking becomes visible.
Change one thing at a time so cause and effect stay clear.
Treat errors as clues about the exact line or value Python could not handle.

Why this matters

Move from question to dataset and load tabular data with pandas.

This lesson combines related subtopics that belong together in one learning conversation. You will still pause for a quiz after each section, but you do not need to jump between separate pages while building one clear explanation.

Guiding questions

By the end of this lesson, you should be able to answer:

How do the sections in Workflow and Loading Data fit together?
Which small example demonstrates each section?
Which debugging clue should I check first for each section?

Learning objectives

You will practice how to:

explain the shared concept for this lesson;
use each section as one step in a larger workflow;
complete 2 short section quizzes before moving on;
connect examples, mistakes, and debugging routines.

Lesson map

1. Data Science Workflow — Follow a repeatable path from question to communication.
2. Loading Data with pandas — Load CSV and similar tabular data into DataFrames.

The data science workflow at a glance

Data science is an iterative workflow. New questions often appear after you inspect or clean the data.

flowchart LR
  question["Ask question"] --> load["Load data"]
  load --> inspect["Inspect structure"]
  inspect --> clean["Clean data"]
  clean --> analyze["Analyze"]
  analyze --> communicate["Communicate result"]
  communicate -. revise .-> question

Do not skip the question and inspection steps; they shape every later choice.

1. Data Science Workflow

Follow a repeatable path from question to communication.

Analogy

A workflow is a recipe card for inquiry: ingredients, steps, checks, and serving notes.

What this means

A workflow keeps analysis organized and honest from initial question to final claim.

Example 1

Predict what will happen before you run the code.

question = "Which category has the highest average score?"
steps = ["load", "inspect", "clean", "analyze", "communicate"]
print(question, steps)

Step-by-step explanation

question = "Which category has the highest average score?" — pause here and say what this line reads, creates, changes, or displays.
steps = ["load", "inspect", "clean", "analyze", "communicate"] — pause here and say what this line reads, creates, changes, or displays.
print(question, steps) — pause here and say what this line reads, creates, changes, or displays.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

Practice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

Copy Example 1 into Colab, Jupyter, or a .py file.
Mark the line you plan to change.
Write a one-sentence prediction.
Run the changed code.
If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

Common mistake

Starting with a chart before defining the question can lead to attractive but unfocused analysis.

When you get stuck, use this debugging routine:

Read the last line of the error message or inspect the unexpected output.
Find the smallest line of code that could be responsible.
Print or inspect the value and type at that point.
Change one thing.
Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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2. Loading Data with pandas

Load CSV and similar tabular data into DataFrames.

Analogy

A DataFrame is like a spreadsheet with Python superpowers.

What this means

A DataFrame is a labeled table with rows and columns.

Example 2

Predict what will happen before you run the code.

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())

Step-by-step explanation

import pandas as pd — pause here and say what this line reads, creates, changes, or displays.
df = pd.read_csv("data.csv") — pause here and say what this line reads, creates, changes, or displays.
print(df.head()) — pause here and say what this line reads, creates, changes, or displays.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

Practice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

Copy Example 1 into Colab, Jupyter, or a .py file.
Mark the line you plan to change.
Write a one-sentence prediction.
Run the changed code.
If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

Common mistake

File paths and column names are common first failures. Print the path and df.columns when loading data.

When you get stuck, use this debugging routine:

Read the last line of the error message or inspect the unexpected output.
Find the smallest line of code that could be responsible.
Print or inspect the value and type at that point.
Change one thing.
Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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Notebook and Colab practice

Open a blank notebook at https://colab.new. Use one section at a time: copy the Example 1, predict the result, run it, answer the section quiz, and then move to the next section. This is better than copying the entire page at once.

Instructor note

Teaching notes

Treat each section as a short teaching episode.
Pause for the section quiz before introducing the next section.
Ask learners to compare sections: what stayed the same, and what changed?
If time is short, teach the first two sections live and assign the rest as practice.

Key points

Data Science Workflow: A workflow keeps analysis organized and honest from initial question to final claim.
Loading Data with pandas: A DataFrame is a labeled table with rows and columns.
Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

pandas documentation: https://pandas.pydata.org/docs/
seaborn documentation: https://seaborn.pydata.org/
Matplotlib documentation: https://matplotlib.org/stable/
Python Tutorial: https://docs.python.org/3/tutorial/
Quarto OJS documentation: https://quarto.org/docs/interactive/ojs/
ipywidgets documentation: https://ipywidgets.readthedocs.io/en/stable/