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Basic Python (AI-powered) · Lesson 1 of 3

Python Variables, Types, Conditions, and Loops for Analysis

Use Python values and control flow to make simple data transformations explicit.

Lesson

1. Introduction

Data analysis code manipulates values repeatedly, so basic Python rules matter even when a library does most of the work. Knowing the type and shape of a value helps diagnose whether a surprising result comes from the data, a conversion, or the analysis logic.

2. Understand the concept

Strings represent text, integers and floats represent numbers, booleans represent true/false conditions, and None represents an absent value. A for loop visits each item in an iterable; conditions select which operations apply. Convert external text deliberately before arithmetic.

3. Syntax and interpretation

amounts = ['12.50', '8.00', 'invalid']
valid_amounts = []
for raw in amounts:
    try:
        valid_amounts.append(float(raw))
    except ValueError:
        print(f'Skipping invalid amount: {raw}')

The loop converts valid text values and reports the invalid record instead of hiding it. Production work should retain rejected-row details for review.

4. Apply it to an analysis

Use Python values and control flow to make simple data transformations explicit. Start with a small, known example and state what each row or value represents. After the transformation, check row counts, types, keys, and units against the original data. If an assumption fails, stop and investigate instead of presenting the output as a valid result.

5. Common mistakes

  • Assuming input text or CSV fields are already numeric.
  • Using a broad exception handler that hides unrelated programming errors.

6. Practice task

From [0, -2, 5, 8], create a new list with values greater than zero.

7. One possible solution

positive = [value for value in [0, -2, 5, 8] if value > 0] gives [5, 8].

8. Summary and next step

Use Python values and control flow to make simple data transformations explicit. Use the next lesson in this module to extend the idea with another technique and a more realistic analysis case.

Visual guide

Working through Python Variables, Types, Conditions, and Loops for Analysis

  1. Step 01 Define

    State the question, data grain, and expected output.

  2. Step 02 Apply

    Use the method and check its assumptions on a small example.

  3. Step 03 Verify

    Reconcile the result and communicate limitations.

Code examples

Filter values with a comprehension
PYTHON 
scores = [72, 48, 91, 65]
passing = [score for score in scores if score >= 60]
print(passing)
Expected output
[72, 91, 65]
Count labels with a dictionary
PYTHON 
regions = ['North', 'South', 'North']
counts = {region: regions.count(region) for region in set(regions)}
print(counts['North'])
Expected output
2

Key points

  • Use Python values and control flow to make simple data transformations explicit.
  • Check the result against the stated data definition and a representative example.
  • Record assumptions and report data-quality limitations rather than hiding them.

Check your understanding

Practice

Which approach best applies the ideas in Python Variables, Types, Conditions, and Loops for Analysis?

Your learning progress

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