How much does zuzu.codes cost?

The starter track is free — read all lessons and practice for free. Full access to every track (current and future) is $14.99/month. Cancel anytime.

How long does each track take?

Each track is designed as a 30-day challenge — one lesson per day, about 15 minutes each. Go at your own pace, but the structure is built around daily consistency.

What's the lesson format?

Each lesson is a student-teacher dialogue with code examples, followed by a hands-on code challenge in an in-browser editor. You read, you understand, then you write real code.

Do I need prior coding experience?

Our beginner track starts from absolute zero — no prior experience needed. Advanced tracks build on earlier ones, and the platform tells you exactly where to start.

How is zuzu.codes different from freeCodeCamp or Codecademy?

zuzu.codes uses a structured 30-day track format with dialogue-based teaching, an in-browser code editor, and gamification (XP, streaks, progress tracking). The format builds genuine understanding through daily practice.

csv.DictReader — Python Patterns

Day 16 · ~12 min●

A CSV file is a list of rows separated by newlines, with values in each row separated by commas. The first line is usually the header — column names. Python's csv module knows the rules.

python

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

What's row?

A dict — column name to cell value. If the CSV looks like:

python

key,value
a,10
b,20

Then the loop iterates two rows: {"key": "a", "value": "10"} and {"key": "b", "value": "20"}. Note both values are strings — csv doesn't convert types. "10" not 10.

What's the difference between csv.reader and csv.DictReader?

csv.reader gives you a list per row (["a", "10"]). csv.DictReader reads the first line as headers and returns dicts. Use DictReader when columns have meaning and you'll reference cells by name. Use reader when columns are positional and the file has no header.

And it handles edge cases — quoted values with commas inside, escaped quotes?

Yes. "a,b",10 parses to one cell "a,b" and one cell "10". Don't roll your own CSV parser with .split(",") — the rules are subtle and the module handles them.

`csv.DictReader` — read CSV as a stream of dicts

python

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["key"], row["value"])

What you get

The first line of the file is treated as the header row — those become the dict keys.
Every subsequent line yields a dict mapping header → cell_value.
All values are strings. To get an int or float, convert: int(row["value"]).

Example

File data.csv:

python

key,value
a,10
b,20
c,30

Loop:

python

import csv
with open("data.csv") as f:
    for row in csv.DictReader(f):
        print(row)
# {'key': 'a', 'value': '10'}
# {'key': 'b', 'value': '20'}
# {'key': 'c', 'value': '30'}

Pick a row by header value

python

import csv
with open("data.csv") as f:
    for row in csv.DictReader(f):
        if row["key"] == "c":
            print(row["value"])
            break
# 30

Custom delimiter

For TSV (tab-separated):

python

csv.DictReader(f, delimiter="\t")

Writing — preview

csv.DictWriter is the symmetric tool for writing rows from dicts. Same shape, write side. Lesson 17 sticks with JSON for now; you'll come back to DictWriter when you need it.

Why not just `.split(",")`?

A cell value can contain commas if it's quoted: "hello, world",42 — that's one cell hello, world and a second cell 42. A naive split(",") produces three cells. The csv module follows RFC 4180 — quoting, escaping, line endings. Use it.

Common gotchas

Strings, not types. row["value"] is "10", not 10. Convert before arithmetic.
First row is header. If your file has no header and you use DictReader, the first data row gets eaten as headers. Either fix the file or use csv.reader.
open(..., newline="") is the recommended way on some platforms — Pyodide-on-the-web is forgiving here, but it's good practice.

Day 16 · ~12 min●

A CSV file is a list of rows separated by newlines, with values in each row separated by commas. The first line is usually the header — column names. Python's csv module knows the rules.

python

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

What's row?

A dict — column name to cell value. If the CSV looks like:

python

key,value
a,10
b,20

Then the loop iterates two rows: {"key": "a", "value": "10"} and {"key": "b", "value": "20"}. Note both values are strings — csv doesn't convert types. "10" not 10.

What's the difference between csv.reader and csv.DictReader?

And it handles edge cases — quoted values with commas inside, escaped quotes?

Yes. "a,b",10 parses to one cell "a,b" and one cell "10". Don't roll your own CSV parser with .split(",") — the rules are subtle and the module handles them.

`csv.DictReader` — read CSV as a stream of dicts

python

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["key"], row["value"])

What you get

The first line of the file is treated as the header row — those become the dict keys.
Every subsequent line yields a dict mapping header → cell_value.
All values are strings. To get an int or float, convert: int(row["value"]).

Example

File data.csv:

python

key,value
a,10
b,20
c,30

Loop:

python

import csv
with open("data.csv") as f:
    for row in csv.DictReader(f):
        print(row)
# {'key': 'a', 'value': '10'}
# {'key': 'b', 'value': '20'}
# {'key': 'c', 'value': '30'}

Pick a row by header value

python

import csv
with open("data.csv") as f:
    for row in csv.DictReader(f):
        if row["key"] == "c":
            print(row["value"])
            break
# 30

Custom delimiter

For TSV (tab-separated):

python

csv.DictReader(f, delimiter="\t")

Writing — preview

csv.DictWriter is the symmetric tool for writing rows from dicts. Same shape, write side. Lesson 17 sticks with JSON for now; you'll come back to DictWriter when you need it.

Why not just `.split(",")`?

Common gotchas

Strings, not types. row["value"] is "10", not 10. Convert before arithmetic.
First row is header. If your file has no header and you use DictReader, the first data row gets eaten as headers. Either fix the file or use csv.reader.
open(..., newline="") is the recommended way on some platforms — Pyodide-on-the-web is forgiving here, but it's good practice.

`csv.DictReader` — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just `.split(",")`?

Common gotchas

`csv.DictReader` — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just `.split(",")`?

Common gotchas

`csv.DictReader` — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just `.split(",")`?

Common gotchas

Sign up to practice

`csv.DictReader` — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just `.split(",")`?

Common gotchas

Sign up to practice

csv.DictReader — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just .split(",")?

Common gotchas

csv.DictReader — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just .split(",")?

Common gotchas

csv.DictReader — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just .split(",")?

Common gotchas

Sign up to practice

csv.DictReader — read CSV as a stream of dicts

What you get

Example

Pick a row by header value

Custom delimiter

Writing — preview

Why not just .split(",")?

Common gotchas

Sign up to practice

`csv.DictReader` — read CSV as a stream of dicts

Why not just `.split(",")`?

`csv.DictReader` — read CSV as a stream of dicts

Why not just `.split(",")`?

`csv.DictReader` — read CSV as a stream of dicts

Why not just `.split(",")`?

`csv.DictReader` — read CSV as a stream of dicts

Why not just `.split(",")`?