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Synthesis — group CSV by month into JSON — Python Patterns

Day 21 · ~12 min●

Twenty lessons of primitives. Today: compose six of them on a small generic input — no new concepts, just careful arrangement.

Given a CSV file with columns id,date,value:

id,date,value
a1,2024-01-15,10
b2,2024-01-22,15
c3,2025-06-01,20
d4,2024-12-31,5

Produce a JSON file mapping "YYYY-MM" → list of values, in insertion order:

python

{
  "2024-01": [10, 15],
  "2025-06": [20],
  "2024-12": [5]
}

Read CSV, parse the date, format as YYYY-MM, accumulate values into a dict keyed by that month-string, write JSON.

Right. Six steps, six primitives:

Step	Primitive	Lesson
1. Write the CSV (so we have a fixture)	file write + `"w"` mode	L2, L3
2. Read CSV rows as dicts	`csv.DictReader`	L15
3. Parse each date	`datetime.fromisoformat`	L19
4. Format as `YYYY-MM`	`.strftime("%Y-%m")`	L19
5. Accumulate per-key values	dict + `setdefault` (or `if key in result`)	L11
6. Write JSON	`json.dump`	L16

And the value-list per month — it's a list, so I need to append not overwrite.

Right. The shape result.setdefault(key, []).append(value) does both things at once: ensures the key has a list (creating an empty one if missing), then appends to it. We didn't introduce setdefault directly — it's the dict equivalent of "if key in d: ... else: d[key] = []; d[key].append(...)".

And the value int(row["value"]) — because csv returns strings.

That's the only conversion. After the loop, json.dump(result, f) writes the final dict.

What this exercise composes

Primitive	From	Used for
`open(..., "w")` and `f.write(...)`	L2, L3	creating the fixture CSV file
`with open(...) as f:`	L4	guaranteed close
`csv.DictReader(f)`	L15	iterating CSV rows as dicts
`datetime.fromisoformat(s)`	L19	turning the date string into a datetime
`d.strftime("%Y-%m")`	L19	formatting back to a `YYYY-MM` group key
`result.setdefault(key, [])`	new — see below	ensure key has a list before appending
`int(...)`	foundations	str → int for the numeric value
`json.dump(result, f)`	L16	writing the result file

`dict.setdefault(key, default)` — get or create

A shorthand for the "if key in d, use d[key], else create it" pattern:

python

result = {}
result.setdefault("a", [])    # 'a' wasn't there → set d['a'] = [] and return it
result.setdefault("a", [])    # 'a' is there → return d['a'] (the existing list)

Returns the value at the key (existing or freshly created). The classic pattern for grouping:

python

result = {}
for key, value in pairs:
    result.setdefault(key, []).append(value)

Reads: "Make sure result[key] is a list, then append value to it."

(You'll see an even cleaner form in week 4 with defaultdict. Today: explicit setdefault.)

Building it up step by step

python

import csv, json
from datetime import datetime

# Step 1: write fixture
with open("events.csv", "w") as f:
    f.write("id,date,value\n")
    f.write("a1,2024-01-15,10\n")
    f.write("b2,2024-01-22,15\n")
    f.write("c3,2025-06-01,20\n")
    f.write("d4,2024-12-31,5\n")

# Step 2-5: read, parse, group
result = {}
with open("events.csv") as f:
    for row in csv.DictReader(f):
        d = datetime.fromisoformat(row["date"])
        key = d.strftime("%Y-%m")
        result.setdefault(key, []).append(int(row["value"]))

# Step 6: write JSON
with open("out.json", "w") as f:
    json.dump(result, f)

What you don't have to do

Sort the result. The exercise asks for insertion order — Python 3.7+ dicts preserve it. The key is whatever order the source CSV rows appear.
Handle missing or malformed dates. The fixture is well-formed; defensive code can come later.

The pattern: "group rows by key"

This shape — read rows, derive a key per row, accumulate values into a per-key list — comes up constantly in real Python. CSV → grouped JSON, JSON logs → daily summaries, lines of input → bucket-by-something. Today's lesson is one variant; you'll write the same shape with different keys and accumulators a hundred times.

Day 21 · ~12 min●

Twenty lessons of primitives. Today: compose six of them on a small generic input — no new concepts, just careful arrangement.

Given a CSV file with columns id,date,value:

id,date,value
a1,2024-01-15,10
b2,2024-01-22,15
c3,2025-06-01,20
d4,2024-12-31,5

Produce a JSON file mapping "YYYY-MM" → list of values, in insertion order:

python

{
  "2024-01": [10, 15],
  "2025-06": [20],
  "2024-12": [5]
}

Read CSV, parse the date, format as YYYY-MM, accumulate values into a dict keyed by that month-string, write JSON.

Right. Six steps, six primitives:

Step	Primitive	Lesson
1. Write the CSV (so we have a fixture)	file write + `"w"` mode	L2, L3
2. Read CSV rows as dicts	`csv.DictReader`	L15
3. Parse each date	`datetime.fromisoformat`	L19
4. Format as `YYYY-MM`	`.strftime("%Y-%m")`	L19
5. Accumulate per-key values	dict + `setdefault` (or `if key in result`)	L11
6. Write JSON	`json.dump`	L16

And the value-list per month — it's a list, so I need to append not overwrite.

And the value int(row["value"]) — because csv returns strings.

That's the only conversion. After the loop, json.dump(result, f) writes the final dict.

What this exercise composes

Primitive	From	Used for
`open(..., "w")` and `f.write(...)`	L2, L3	creating the fixture CSV file
`with open(...) as f:`	L4	guaranteed close
`csv.DictReader(f)`	L15	iterating CSV rows as dicts
`datetime.fromisoformat(s)`	L19	turning the date string into a datetime
`d.strftime("%Y-%m")`	L19	formatting back to a `YYYY-MM` group key
`result.setdefault(key, [])`	new — see below	ensure key has a list before appending
`int(...)`	foundations	str → int for the numeric value
`json.dump(result, f)`	L16	writing the result file

`dict.setdefault(key, default)` — get or create

A shorthand for the "if key in d, use d[key], else create it" pattern:

python

result = {}
result.setdefault("a", [])    # 'a' wasn't there → set d['a'] = [] and return it
result.setdefault("a", [])    # 'a' is there → return d['a'] (the existing list)

Returns the value at the key (existing or freshly created). The classic pattern for grouping:

python

result = {}
for key, value in pairs:
    result.setdefault(key, []).append(value)

Reads: "Make sure result[key] is a list, then append value to it."

(You'll see an even cleaner form in week 4 with defaultdict. Today: explicit setdefault.)

Building it up step by step

python

import csv, json
from datetime import datetime

# Step 1: write fixture
with open("events.csv", "w") as f:
    f.write("id,date,value\n")
    f.write("a1,2024-01-15,10\n")
    f.write("b2,2024-01-22,15\n")
    f.write("c3,2025-06-01,20\n")
    f.write("d4,2024-12-31,5\n")

# Step 2-5: read, parse, group
result = {}
with open("events.csv") as f:
    for row in csv.DictReader(f):
        d = datetime.fromisoformat(row["date"])
        key = d.strftime("%Y-%m")
        result.setdefault(key, []).append(int(row["value"]))

# Step 6: write JSON
with open("out.json", "w") as f:
    json.dump(result, f)

What you don't have to do

Sort the result. The exercise asks for insertion order — Python 3.7+ dicts preserve it. The key is whatever order the source CSV rows appear.
Handle missing or malformed dates. The fixture is well-formed; defensive code can come later.

What this exercise composes

`dict.setdefault(key, default)` — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

What this exercise composes

`dict.setdefault(key, default)` — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

What this exercise composes

`dict.setdefault(key, default)` — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

Sign up to practice

What this exercise composes

`dict.setdefault(key, default)` — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

Sign up to practice

What this exercise composes

dict.setdefault(key, default) — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

What this exercise composes

dict.setdefault(key, default) — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

What this exercise composes

dict.setdefault(key, default) — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

Sign up to practice

What this exercise composes

dict.setdefault(key, default) — get or create

Building it up step by step

What you don't have to do

The pattern: "group rows by key"

Sign up to practice

`dict.setdefault(key, default)` — get or create

`dict.setdefault(key, default)` — get or create

`dict.setdefault(key, default)` — get or create

`dict.setdefault(key, default)` — get or create