How to convert CSV to JSON in Python

Using the csv module: import csv, json; with open('data.csv') as f: reader = csv.DictReader(f); rows = list(reader); with open('data.json', 'w') as f: json.dump(rows, f, indent=2). DictReader automatically uses the first row as keys. For custom delimiters: csv.DictReader(f, delimiter=';'). One-liner with pandas: import pandas as pd; pd.read_csv('data.csv').to_json('data.json', orient='records', indent=2). The orient='records' option produces a JSON array of objects — the most common format for APIs.

# Python — convert CSV to JSON
import csv
import json

# CSV string → JSON
csv_data = """name,age,city
Alice,30,New York
Bob,25,London"""

import io
reader = csv.DictReader(io.StringIO(csv_data))
json_data = json.dumps(list(reader), indent=2)
print(json_data)
# [
#   { "name": "Alice", "age": "30", "city": "New York" },
#   { "name": "Bob", "age": "25", "city": "London" }
# ]

# CSV file → JSON file
with open("data.csv") as f:
    rows = list(csv.DictReader(f))
with open("data.json", "w") as f:
    json.dump(rows, f, indent=2)

Handling CSV encoding and edge cases in Python

Encoding: open('data.csv', encoding='utf-8-sig') handles BOM-prefixed Excel CSVs. For unknown encoding: import chardet; encoding = chardet.detect(open('data.csv', 'rb').read())['encoding']. Missing values: csv.DictReader returns empty strings; pandas returns NaN. Handle nulls: json.dumps(data, default=str) converts non-serializable values to strings. Large files: use chunked reading: for chunk in pd.read_csv('large.csv', chunksize=10000): process(chunk). Type inference: pandas auto-detects integers, floats, and dates; csv module returns everything as strings.

JSON to CSV conversion in Python

Reverse conversion with pandas: pd.read_json('data.json').to_csv('output.csv', index=False). With csv module: import csv, json; data = json.load(open('data.json')); writer = csv.DictWriter(open('output.csv', 'w', newline=''), fieldnames=data[0].keys()); writer.writeheader(); writer.writerows(data). For nested JSON, flatten first: pd.json_normalize(data) handles nested objects by creating dot-separated column names (e.g., 'address.city').