import numpy as np
import json

arr = np.array([1, 2, 3], dtype=np.int64)
json.dumps(arr)  # ❌ TypeError: Object of type int64 is not JSON serializable

import numpy as np
import numpy2 as np2

arr = np.array([1, 2, 3], dtype=np.int64)
json_str = np2.to_json(arr)  # ✅ '[1, 2, 3]'

# ❌ Manual (error-prone, 10+ lines)
import json
result = {}
for key, val in data.items():
    if isinstance(val, np.int64):
        result[key] = int(val)
    elif isinstance(val, np.float64):
        result[key] = float(val)
    elif isinstance(val, np.ndarray):
        result[key] = val.tolist()
    # ... 10 more cases ...

# ✅ numpy2 (1 line)
result = np2.serialize(data)

from fastapi import FastAPI
import numpy as np

app = FastAPI()

@app.get("/compute")
def compute():
    result = np.array([1, 2, 3])
    return result  # ❌ TypeError: Object of type ndarray is not JSON serializable

from fastapi import FastAPI
from fastapi.responses import JSONResponse
import numpy as np
import numpy2 as np2

app = FastAPI()

@app.get("/compute")
def compute():
    result = np.array([1, 2, 3])
    return JSONResponse(np2.serialize(result))  # ✅ Works!

import pandas as pd
import json

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4.5, 5.5, 6.5]})
json.dumps(df)  # ❌ TypeError: Object of type DataFrame is not JSON serializable

import pandas as pd
import numpy2 as np2

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4.5, 5.5, 6.5]})
json_data = np2.serialize(df)  # ✅ Returns JSON-safe dict

import numpy as np
import json

arr = np.array([1, 2, 3], dtype=np.int64)
# Developer doesn't notice int64 → Python int conversion
# Data integrity silently lost in high-precision calculations

import numpy as np
import numpy2 as np2

arr = np.array([1, 2, 3], dtype=np.int64)
# Metadata preserved if needed
serialized = np2.serialize(arr, include_metadata=True)
# {'data': [1, 2, 3], 'dtype': 'int64', 'shape': [3]}

import numpy as np
import numpy2 as np2

# Create NumPy array
arr = np.array([1, 2, 3], dtype=np.int64)

# Convert to JSON string
json_str = np2.to_json(arr)
print(json_str)  # '[1, 2, 3]'

# Convert back
arr_restored = np2.from_json(json_str, to_numpy=True, dtype='int64')
print(arr_restored)  # array([1, 2, 3])

from fastapi import FastAPI
from fastapi.responses import JSONResponse
import numpy as np
import numpy2 as np2

app = FastAPI()

@app.get("/api/compute")
def compute_endpoint():
    # Your NumPy computation
    result = np.array([[1, 2], [3, 4]], dtype=np.int32)
    
    # Serialize and return
    return JSONResponse(content=np2.serialize(result))

from flask import Flask, jsonify
import numpy as np
import numpy2 as np2

app = Flask(__name__)

@app.route('/api/data')
def get_data():
    data = np.array([1.5, 2.5, 3.5], dtype=np.float32)
    return jsonify(np2.serialize(data))

import numpy2 as np2

# Infer appropriate dtype
dtype = np2.infer_dtype([1, 2, 3])
print(dtype)  # 'int64'

# Safe type casting
value = np2.safe_cast("123", 'int32')
print(value)  # 123 (int)

# Batch conversion with type mapping
data = [
    {'id': 1, 'price': 9.99},
    {'id': 2, 'price': 19.99},
]
converted = np2.batch_convert(
    data,
    dtype_map={'id': 'int32', 'price': 'float32'}
)

import pandas as pd
import numpy2 as np2

# Create DataFrame with NumPy dtypes
df = pd.DataFrame({
    'id': np.array([1, 2, 3], dtype=np.int64),
    'value': np.array([1.1, 2.2, 3.3], dtype=np.float32)
})

# Convert to JSON-safe dict
json_data = np2.pandas_to_json(df)
print(json_data)
# [{'id': 1, 'value': 1.1}, {'id': 2, 'value': 2.2}, ...]

import numpy as np
import numpy2 as np2

arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float64)

# Include array metadata in serialization
serialized = np2.serialize(arr, include_metadata=True)
print(serialized)
# {
#     'data': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
#     'shape': [2, 3],
#     'dtype': 'float64',
#     'size': 6,
#     'ndim': 2
# }