Session 20 - CSV & JSON

Discussion prompt

Before we open Session 20 - CSV & JSON: without looking back, what was the main idea of Session 19 - Files & Text I/O, and what could you do by the end of it that you could not do before?

Hint: One sentence for the idea, one for the skill. If the second one is blank, that is the part to revisit.

Discussion prompt

When your program ends, every variable disappears. A file on disk stays. Saving means copying your data into a file; loading means reading it back into variables next time.

That is stated as though it always holds. Do one of two things: produce a case where it fails, or say precisely what rules such a case out. "It just does" is not on the menu.

Hint: Hunt at the extremes first — zero, one, negative, empty, equal. If every extreme survives, the reason they survive is the proof.

Discussion prompt

Explain Two standard formats: CSV and JSON by analogy to something with no Python Fundamentals in it at all — a queue, a recipe, a map, a bank balance, whatever fits. Then say where your analogy breaks.

Hint: An analogy that never breaks is not an analogy, it is the same idea wearing a hat. Find the seam — that is the part that is actually new.

Discussion prompt

Explain A spreadsheet vs a labeled box to a student a year behind you. No notation, no jargon they have not met — and it still has to be true.

Hint: If your explanation needs a symbol they have never seen, you are describing the notation rather than the idea.

Fill in the blanks

From Dump a dict to JSON text — one line has had its right-hand side removed. Put it back.

import json

data = json.dumps(data)
s = ___
print(s)
print(type(s))

Why: s is what everything below it consumes, so the wrong expression here fails later and somewhere else. json.dumps walks the dict and builds a JSON string from it.

Comparison matrix

From Dump a dict to JSON text: refill the value column from what you know. The rest of the table is as it appeared.

Predict first

The table runs: type(data) | <class 'dict'> · data["score"] | 90

In Load JSON text into a dict, given the rows so far: what is the next one — the row where expression is data["score"] + 1?

Correct: data["score"] + 1 | 91

Why: The relationship between the columns, not the individual numbers, is what generates the next row. json.loads reads the JSON text and rebuilds a Python dict.

Comparison matrix

From Load JSON text into a dict: every row here is a choice with a cost. Fill the value column, then say which row you would actually pick and what you give up for it.

Put in order

Put the moves of The round trip: dumps then loads into the order they have to happen.

1. dumps flattens the dict to text
2. loads rebuilds an equal dict
3. Read the output

Why: These are the moves of the worked example in the order it makes them, and each one is set up by the one before it. text is now a JSON string version of original.

Annotate

Walk the callouts on The round trip: dumps then loads. Each one is a place this is easy to get subtly wrong.

• text is now a JSON string version of original.
• The value that comes back equals what went in.
• Verified by execution: True, then the rebuilt dict prints.

Step through it

Step through Watch the names change one row at a time. What is driving the change, and what would the row after the last one be?

1. Step 1: Python is True
2. Step 2: Python is False
3. Step 3: Python is None
4. Step 4: Python is ["a", "b"]
5. Step 5: Python is {...}

Sort into buckets

These are the pieces of Session 20 - CSV & JSON, out of order. Put each one back under the part of the lesson it belongs to.

Data That Outlives the Program

A file lets data survive; Two standard formats: CSV and JSON; A spreadsheet vs a labeled box

JSON as a String

json.dumps: value to text; Dump a dict to JSON text; json.loads: text to value

The Type Map

Python types become JSON types; Watch the names change; JSON keys are always strings

s1

Data That Outlives the Program is where Session 20 - CSV & JSON puts A file lets data survive, Two standard formats: CSV and JSON, A spreadsheet vs a labeled box. Knowing which part of the lesson a problem belongs to is most of knowing which method to reach for.

s2

JSON as a String is where Session 20 - CSV & JSON puts json.dumps: value to text, Dump a dict to JSON text, json.loads: text to value. Knowing which part of the lesson a problem belongs to is most of knowing which method to reach for.

s3

The Type Map is where Session 20 - CSV & JSON puts Python types become JSON types, Watch the names change, JSON keys are always strings. Knowing which part of the lesson a problem belongs to is most of knowing which method to reach for.

Fill in the blanks

A JSON array becomes a list, with the scaffolding fading: two lines are gone now — fill both.

import json

s = '[10, 20, 30]'
nums = json.loads(s)
print(nums)
print(sum(nums))
print(type(nums))

Why: Reproducing these unaided, rather than reading them, is what tells you the method has transferred. An array at the top loads into a Python list.

Step through it

Step through A JSON array becomes a list one row at a time. What is driving the change, and what would the row after the last one be?

1. Step 1: expression is nums
2. Step 2: expression is sum(nums)
3. Step 3: expression is type(nums)

Draw it

Draw what Write a dict to a file just did — the shape of it, not the line-by-line working. One picture, labels only where you need them. Then check it against the steps: anything you could not draw is a step you followed rather than understood.

Comparison matrix

From Pretty-print with indent: refill the text column from what you know. The rest of the table is as it appeared.

Predict first

A student writes this, and it looks reasonable:

JSON only allows double quotes. Python's own dict printout uses single quotes - paste that into json.loads and it fails.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: It expects a property name in double quotes and stops at the first single quote.

Use double quotes inside the JSON string. Single-quote the Python string on the outside so the double quotes sit cleanly inside.

Why: It expects a property name in double quotes and stops at the first single quote.

Sort into buckets

Every line below is part of the definition of serialize or of json.decoder.JSONDecodeError — one or the other, never both. Put each where it belongs.

serialize

Turn an in-memory value (a dict, a list) into text you can store in a file or send over a network.; Reading it back is deserializing.

json.decoder.JSONDecodeError

The error json.loads/json.load raises when the text is not valid JSON.; The message names the line, column, and character where parsing failed.

b1

Turn an in-memory value (a dict, a list) into text you can store in a file or send over a network. Reading it back is deserializing.

b2

The error json.loads/json.load raises when the text is not valid JSON. The message names the line, column, and character where parsing failed.

Match the pairs

Match each term to the definition this lesson gave it — not the one you would guess from the word.

• t1. serialize
• t2. json.decoder.JSONDecodeError
• t3. csv.writer / csv.reader

• d1. Turn an in-memory value (a dict, a list) into text you can store in a file or send over a network. Reading it back is deserializing.
• d2. The error json.loads/json.load raises when the text is not valid JSON. The message names the line, column, and character where parsing failed.
• d3. writer.writerow(list) writes one row; looping over a reader yields each row as a list of strings.

Why: These are the working definitions of serialize, json.decoder.JSONDecodeError, csv.writer / csv.reader as Session 20 - CSV & JSON uses them. Pairing them correctly is the test of whether you could state each one with the slide switched off.

Put in order

Put the moves of Write rows with csv.writer into the order they have to happen.

1. Wrap the file in a csv.writer
2. writerow takes a list, one per row
3. The file now holds three lines

Why: These are the moves of the worked example in the order it makes them, and each one is set up by the one before it. The writer knows how to turn a list into a comma-separated line.

Step through it

Step through Write rows with csv.writer one row at a time. What is driving the change, and what would the row after the last one be?

1. Step 1: row is header
2. Step 2: row is 1
3. Step 3: row is 2

Fill in the blanks

From Read rows with csv.reader — one line has had its right-hand side removed. Put it back.

import csv

with open("scores.csv", newline="") as f:
reader = csv.reader(f)
for row in reader:
print(row)

Why: reader is what everything below it consumes, so the wrong expression here fails later and somewhere else. Every pass hands you one row as a list of strings.

Predict first

A student writes this, and it looks reasonable:

You loop the scores and try to total them without converting.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: total is an int and row[1] is a str, so Python refuses to add them.

Convert each value with int() before the math.

Why: total is an int and row[1] is a str, so Python refuses to add them.

Discussion prompt

The rule this trap just fixed:

Verified by execution: 90 + 85 = 175 prints. Rule: convert CSV strings before doing arithmetic.

Now break it on purpose. Build a case that violates it and follow the consequences until something visibly fails. Where does the failure first show up — and would you have noticed it if you had not been looking?

Hint: The dangerous rules are the ones whose violation still produces an answer. If yours fails loudly, try to find one that fails quietly.

Fill in the blanks

Skip the header, then convert, with the scaffolding fading: two lines are gone now — fill both.

import csv

with open("scores.csv", newline="") as f:
reader = csv.reader(f)
next(reader)
total = 0
for row in reader:
total = total + int(row[1])
print(total)

Why: Reproducing these unaided, rather than reading them, is what tells you the method has transferred. The first line is column titles, not data - pull it off before the loop.

Draw it

Draw what Skip the header, then convert just did — the shape of it, not the line-by-line working. One picture, labels only where you need them. Then check it against the steps: anything you could not draw is a step you followed rather than understood.

Fill in the blanks

From Read rows as dicts — one line has had its right-hand side removed. Put it back.

import csv

with open("scores.csv", newline="") as f:
reader = csv.DictReader(f)
for row in reader:
print(row["name"], row["score"])

Why: reader is what everything below it consumes, so the wrong expression here fails later and somewhere else. The first line becomes the keys, so you do not skip it manually.

Annotate

Walk the callouts on Read rows as dicts. Each one is a place this is easy to get subtly wrong.

• The first line becomes the keys, so you do not skip it manually.
• Verified by execution: prints the name and score for each row. Values are still strings.

Comparison matrix

From Write dicts as rows: every row here is a choice with a cost. Fill the text column, then say which row you would actually pick and what you give up for it.

Predict first

A student writes this, and it looks reasonable:

Opening the file without newline='' on Windows.

It is wrong. Say what breaks — and say it before you turn the page.

Correct: The file holds name,score\r\r\nSam,90\r\r\n, so the reader sees an empty row after every real one.

Add newline='' to the open call.

Why: The file holds name,score\r\r\nSam,90\r\r\n, so the reader sees an empty row after every real one.

Sort into buckets

Some of these hold up and some are the exact mistakes this lesson is built to prevent. Sort them.

Holds up

Python ships a module for each: csv and json. You import them; nothing to install.; Picture CSV as a spreadsheet: neat rows, the same columns in every row. It is perfect when every record has the same simple fields.; json.dumps(value) takes a Python dict or list and returns a string of JSON text. The s stands for 'string'.

Breaks

JSON only allows double quotes. Python's own dict printout uses single quotes - paste that into json.loads and it fails.; You loop the scores and try to total them without converting.

sound

These are stated as this lesson states them — each one survives the edge cases Session 20 - CSV & JSON puts it through.

flawed

Each of these is lifted from a trap in this deck: reasonable-sounding, and wrong in a way that only shows up once you rely on it.

Discussion prompt

Explain Nested or varied? Use JSON to a student a year behind you. No notation, no jargon they have not met — and it still has to be true.

Hint: If your explanation needs a symbol they have never seen, you are describing the notation rather than the idea.

Discussion prompt

Explain You can use both together by analogy to something with no Python Fundamentals in it at all — a queue, a recipe, a map, a bank balance, whatever fits. Then say where your analogy breaks.

Hint: An analogy that never breaks is not an analogy, it is the same idea wearing a hat. Find the seam — that is the part that is actually new.

Discussion prompt

Ask: does one record fit in one flat row of cells? If yes, CSV. If a record needs lists or nested groups inside it, JSON.

That is stated as though it always holds. Do one of two things: produce a case where it fails, or say precisely what rules such a case out. "It just does" is not on the menu.

Hint: Hunt at the extremes first — zero, one, negative, empty, equal. If every extreme survives, the reason they survive is the proof.

Predict first

The table runs: name | "Sam" · score | 90

In Nested data is a JSON job, given the rows so far: what is the next one — the row where field is inventory?

Correct: inventory | ["sword", "shield", "potion"]

Why: The relationship between the columns, not the individual numbers, is what generates the next row. inventory is a list - JSON nests it with no trouble; a single CSV cell could not.

Comparison matrix

From Nested data is a JSON job: refill the JSON shape column from what you know. The rest of the table is as it appeared.

Discussion prompt

Outside this lesson: where does Session 20 - CSV & JSON actually turn up? Name one concrete situation — a job, a piece of software someone ships, a decision somebody has to make — and say which part of Save and load CSV is doing the work in it.

Hint: Vague is the failure mode here. "Engineering" is not a situation; "deciding whether this build is fast enough to ship" is.

Check your understanding

What does this print?

• A. <class 'str'> (correct)
• B. <class 'dict'>
• C. <class 'json'>
• D. {"score": 90}

Answer: A

Why: json.dumps serializes the dict to a JSON string, so s is a str and type(s) prints <class 'str'>. Verified by execution.

Why B tempts people

dumps returns text, not a dict. json.loads is the call that returns a dict.

Why C tempts people

There is no json type - the result of dumps is an ordinary string.

Why D tempts people

That is what print(s) would show, but the code prints type(s), which is the class.

Check your understanding

What does this print?

• A. {"note": null} (correct)
• B. {"note": None}
• C. {"note": "None"}
• D. {'note': null}

Answer: A

Why: In the Python-to-JSON map, None becomes the JSON literal null (lowercase), and JSON uses double quotes for keys. Verified by execution.

Why B tempts people

None is Python's spelling; JSON writes null instead, so the output is not None.

Why C tempts people

null is a JSON literal, not the string "None" - there are no quotes around it.

Why D tempts people

JSON keys use double quotes, not single quotes, so 'note' would be invalid JSON.

Check your understanding

What happens?

• A. json.decoder.JSONDecodeError (correct)
• B. It loads fine into {'name': 'Sam'}
• C. It returns the string unchanged
• D. A TypeError

Answer: A

Why: JSON requires double quotes. With single quotes, loads raises json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes. Verified by execution.

Why B tempts people

It would load only if the quotes were double. Single quotes are not valid JSON.

Why C tempts people

loads always tries to parse; it does not hand back the raw string on failure - it raises.

Why D tempts people

The failure is a JSONDecodeError (a parsing error), not a TypeError.

Check your understanding

What does this print?

• A. 9090 (correct)
• B. 180
• C. 90
• D. TypeError

Answer: A

Why: csv.reader returns strings, so row[1] is '90'. Adding two strings concatenates them, giving '9090'. To get 180 you would need int(row[1]) + int(row[1]). Verified by execution.

Why B tempts people

180 needs int() conversion. Without it, + joins the two strings instead of adding numbers.

Why C tempts people

Both row[1] values are added (joined), so you get more than one 90.

Why D tempts people

str + str is valid (it concatenates); adding str to int is what raises TypeError.

Check your understanding

Which call goes in the blank?

• A. json.dump (correct)
• B. json.dumps
• C. json.load
• D. json.write

Answer: A

Why: json.dump(value, file) writes to a file object. The s version, dumps, returns a string instead. Verified by execution.

Why B tempts people

dumps returns a string and ignores the file - it does not write to f.

Why C tempts people

load reads a file back into a value; it does not write.

Why D tempts people

There is no json.write function; the writer is json.dump.

Eliminate the wrong options

What is the likely symptom when you read it back?

3 of these 4 are wrong. Strike them one at a time, and say what rules each one out before you strike the next. The survivor is the answer.

• A. A blank row appears between each real row
• B. The file cannot be opened at all
• C. Every value loses its commas
• D. Numbers come back as ints instead of strings

Survives elimination: A

Why: Without newline='', line endings become \r\r\n on Windows, so csv.reader sees an empty [] row after each real one. Verified by execution.

Check your understanding

What is the likely symptom when you read it back?

• A. A blank row appears between each real row (correct)
• B. The file cannot be opened at all
• C. Every value loses its commas
• D. Numbers come back as ints instead of strings

Answer: A

Why: Without newline='', line endings become \r\r\n on Windows, so csv.reader sees an empty [] row after each real one. Verified by execution.

Why B tempts people

The file opens fine - the problem is extra blank rows, not a failure to open.

Why C tempts people

Commas still separate fields correctly; the doubled newline is the only issue.

Why D tempts people

CSV values are always strings regardless of newline - that is a separate rule.

Draw it

One page, no notation unless you need it: draw how these connect — Data That Outlives the Program · JSON as a String · The Type Map · JSON Files · JSON Traps · CSV Files. Put an arrow wherever one of them is what makes another possible, and label the arrow with why.