Bab 16: CSV & JSON¶

Dua format pertukaran data paling populer di dunia. Wajib mahir.

CSV = format data tabular paling sederhana. JSON = format pertukaran data API & web. Setelah Bab 16, kamu akan bisa proses keduanya dengan lancar.

16.1. CSV¶

CSV (Comma-Separated Values) = teks plain dengan koma sebagai pemisah kolom.

nama,umur,kota
Andi,25,Jakarta
Sari,28,Bandung
Budi,30,Surabaya

flowchart LR
    CSV["📄 CSV<br/>tabular,<br/>flat"]
    JSON["📋 JSON<br/>nested,<br/>typed"]

    CSV -- "DictReader" --> Pylist["Python:<br/>list of dict"]
    JSON -- "json.load" --> Pyobj["Python:<br/>dict / list"]
    Pylist -- "DictWriter" --> CSV
    Pyobj -- "json.dump" --> JSON

    style CSV fill:#1a1a1a,stroke:#6366f1,color:#fafafa
    style JSON fill:#1a1a1a,stroke:#ec4899,color:#fafafa
    style Pylist fill:#1a1a1a,stroke:#10b981,color:#fafafa
    style Pyobj fill:#1a1a1a,stroke:#10b981,color:#fafafa

Cara baca diagram

Diagram ini membandingkan CSV vs JSON dan kapan pakai yang mana.

CSV (indigo):

Format tabular — seperti spreadsheet, baris dan kolom.
Flat — tidak bisa nested. Cocok untuk data simple yang seragam.
Tidak punya tipe — semua nilai dibaca sebagai string.
Lebih kecil ukurannya, mudah dibuka di Excel.

JSON (pink):

Format nested — bisa berisi list dalam dict, dict dalam list, dst.
Typed — tahu apa itu string, integer, boolean, null, list, dict.
Standar di API web modern.
Lebih ekspresif untuk data kompleks.

Pilihan praktis:

Data tabular sederhana (export dari database, log) → CSV
Data dengan struktur (config, API response, profil dengan list/nested) → JSON
Yang akan dibuka manusia di Excel → CSV
Yang akan dikirim antar service → JSON

Konversi keduanya 2 arah seperti diagram — Python jadi bahasa perantara. Pakai DictReader/DictWriter untuk CSV, json.load/json.dump untuk JSON.

Baca CSV¶

import csv

with open("data.csv", "r", encoding="utf-8") as f:
    reader = csv.reader(f)
    for baris in reader:
        print(baris)        # ['Andi', '25', 'Jakarta']

Baca dengan Header (Lebih Recommended)¶

with open("data.csv", "r", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    for baris in reader:
        print(baris)        # {'nama': 'Andi', 'umur': '25', 'kota': 'Jakarta'}
        print(baris["nama"])

DictReader jauh lebih nyaman — akses by column name, bukan index.

Tulis CSV¶

import csv

data = [
    ["nama", "umur", "kota"],
    ["Andi", 25, "Jakarta"],
    ["Sari", 28, "Bandung"],
]

with open("output.csv", "w", encoding="utf-8", newline="") as f:
    writer = csv.writer(f)
    writer.writerows(data)

newline="" penting di Windows untuk hindari double newline.

Tulis dengan DictWriter¶

data = [
    {"nama": "Andi", "umur": 25, "kota": "Jakarta"},
    {"nama": "Sari", "umur": 28, "kota": "Bandung"},
]

with open("output.csv", "w", encoding="utf-8", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["nama", "umur", "kota"])
    writer.writeheader()
    writer.writerows(data)

Pemisah Lain¶

CSV kadang pakai semicolon ; (umum di Eropa) atau tab. Konfigurasi dengan parameter delimiter:

reader = csv.reader(f, delimiter=";")

Pandas — Untuk Data Besar¶

Untuk file CSV besar (>10MB) atau data analysis serius, pakai pandas:

import pandas as pd

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

# Filter
df_jakarta = df[df["kota"] == "Jakarta"]

# Group
df.groupby("kota")["umur"].mean()

# Save
df.to_csv("output.csv", index=False)

16.2. JSON¶

JSON = JavaScript Object Notation. Format paling populer untuk API web & config file.

{
  "nama": "Andi",
  "umur": 25,
  "hobi": ["baca", "musik"],
  "alamat": {
    "kota": "Jakarta",
    "kode_pos": "12345"
  }
}

JSON dan Python dictionary mirip banget — perbedaannya kecil.

Baca JSON¶

import json

# Dari file
with open("data.json", "r", encoding="utf-8") as f:
    data = json.load(f)

print(data["nama"])
print(data["alamat"]["kota"])

# Dari string
text = '{"nama": "Sari", "umur": 28}'
data = json.loads(text)

Tulis JSON¶

import json

data = {
    "nama": "Budi",
    "skills": ["Python", "Excel", "SQL"],
    "active": True,
}

# Ke file (dengan indentation untuk readability)
with open("data.json", "w", encoding="utf-8") as f:
    json.dump(data, f, indent=2, ensure_ascii=False)

# Ke string
text = json.dumps(data, indent=2, ensure_ascii=False)
print(text)

ensure_ascii=False penting biar karakter non-ASCII (Indonesian!) tidak di-escape jadi \u....

JSON dari API¶

import requests

response = requests.get("https://api.exchangerate-api.com/v4/latest/IDR")
data = response.json()       # langsung parse JSON
print(data["rates"]["USD"])

response.json() adalah shortcut untuk json.loads(response.text).

16.3. Konversi CSV ↔ JSON¶

CSV → JSON¶

import csv
import json

with open("data.csv", "r", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    data = list(reader)

with open("data.json", "w", encoding="utf-8") as f:
    json.dump(data, f, indent=2, ensure_ascii=False)

JSON → CSV¶

import csv
import json

with open("data.json", "r", encoding="utf-8") as f:
    data = json.load(f)

with open("data.csv", "w", encoding="utf-8", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=data[0].keys())
    writer.writeheader()
    writer.writerows(data)

16.4. Project: Konversi & Filter Data¶

import csv
import json
from pathlib import Path

def csv_to_json_filter(csv_file, output_json, filter_fn=None):
    """Convert CSV ke JSON, dengan opsi filter."""
    with open(csv_file, "r", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        data = list(reader)

    # Konversi tipe (CSV semua string)
    for row in data:
        for key, value in row.items():
            if value.isdigit():
                row[key] = int(value)
            else:
                try:
                    row[key] = float(value)
                except ValueError:
                    pass  # tetap string

    # Filter kalau ada
    if filter_fn:
        data = [row for row in data if filter_fn(row)]

    with open(output_json, "w", encoding="utf-8") as f:
        json.dump(data, f, indent=2, ensure_ascii=False)

    print(f"✓ {len(data)} rows → {output_json}")

# Convert + filter umur >= 25
csv_to_json_filter(
    "siswa.csv",
    "siswa_dewasa.json",
    filter_fn=lambda row: row.get("umur", 0) >= 25,
)

16.5. Tips¶

Encoding

Selalu eksplisit encoding="utf-8" saat read/write file teks. Tanpa ini, di Windows bisa baca dengan encoding yang berbeda dan kacau untuk karakter Indonesia/Unicode.

JSON tidak punya tanggal native

Format date bukan tipe JSON. Konvensi:

Save sebagai string ISO 8601: "2026-05-15T10:30:00"
Pakai datetime.fromisoformat(s) untuk parse

16.6. Ringkasan¶

CSV: pakai csv.DictReader dan csv.DictWriter (lebih readable dari plain reader/writer)
newline="" di Windows
JSON: json.load() / json.dump() (file), json.loads() / json.dumps() (string)
indent=2, ensure_ascii=False untuk JSON yang readable
response.json() untuk API
pandas kalau file besar atau analysis kompleks

16.7. Latihan¶

16.1 — Stats from CSV¶

Baca file penjualan.csv (kolom: tanggal, produk, harga). Cetak total per produk.

16.2 — JSON Pretty Printer¶

Tulis script yang baca JSON dari clipboard, format dengan indent, kembalikan ke clipboard.

16.3 — API to CSV¶

Ambil data dari https://jsonplaceholder.typicode.com/users, simpan ke CSV.

16.4 — Tantangan: Multi-Format Converter¶

Tulis program yang bisa convert: CSV ↔ JSON ↔ Excel.

CSV — Baca¶

import csv

with open("data.csv", encoding="utf-8") as f:
    reader = csv.reader(f)
    for row in reader: ...

# Lebih baik: DictReader
with open("data.csv", encoding="utf-8") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["nama"])

CSV — Tulis¶

with open("out.csv", "w", encoding="utf-8", newline="") as f:
    writer = csv.writer(f)
    writer.writerows([
        ["nama", "umur"],
        ["Andi", 25],
    ])

# Dengan dict
with open("out.csv", "w", encoding="utf-8", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["nama", "umur"])
    writer.writeheader()
    writer.writerows(data)

JSON — Baca¶

import json

with open("data.json", encoding="utf-8") as f:
    data = json.load(f)         # dari file

data = json.loads(string)        # dari string

JSON — Tulis¶

with open("data.json", "w", encoding="utf-8") as f:
    json.dump(data, f, indent=2, ensure_ascii=False)

text = json.dumps(data, indent=2, ensure_ascii=False)

JSON dari API¶

import requests
data = requests.get(url).json()

Aturan Wajib¶

Selalu encoding="utf-8"
CSV: newline="" di Windows
JSON: ensure_ascii=False untuk Indonesian

Pandas (Data Besar)¶

import pandas as pd

df = pd.read_csv("data.csv")
df = pd.read_json("data.json")
df = pd.read_excel("data.xlsx")

df.to_csv("out.csv", index=False)
df.to_json("out.json", orient="records")

← Bab 15 Lanjut Bab 17 →

Diadaptasi dari Chapter 16: Working with CSV Files and JSON Data, "Automate the Boring Stuff with Python" karya Al Sweigart. Dilisensikan CC BY-NC-SA 4.0.