Atribut dan Tipe Data dengan NumPy

import numpy as np# Contoh tipe data dasarbool_array = np.array([True, False, True], dtype=bool)print("Boolean array:", bool_array)  # Output: Boolean array: [ True False  True]print("Dtype:", bool_array.dtype)  # Output: Dtype: boolint_array = np.array([1, 2, 3], dtype=int)print("Integer array:", int_array)  # Output: Integer array: [1 2 3]print("Dtype:", int_array.dtype)  # Output: Dtype: int64float_array = np.array([1.0, 2.5, 3.7], dtype=float)print("Float array:", float_array)  # Output: Float array: [1.  2.5 3.7]print("Dtype:", float_array.dtype)  # Output: Dtype: float64

import numpy as np# Menentukan tipe data saat pembuatan arraya = np.array([0, 1, 2], dtype=float)print("Array dengan dtype float:", a)  # Output: Array dengan dtype float: [0. 1. 2.]print("Dtype:", a.dtype)  # Output: Dtype: float64# Default adalah float untuk fungsi onesa = np.ones((3, 3))print("Default ones dtype:", a.dtype)  # Output: Default ones dtype: float64# Mengubah ke integera = np.ones((3, 3), dtype=int)print("Ones dengan dtype int:", a.dtype)  # Output: Ones dengan dtype int: int64print("Array:")print(a)# Output:# [[1 1 1]#  [1 1 1]#  [1 1 1]]

import numpy as np# Semua integera = np.array([0, 1, 2])print("Semua int - dtype:", a.dtype)  # Output: Semua int - dtype: int64# Semua floata = np.array([0., 1., 2.])print("Semua float - dtype:", a.dtype)  # Output: Semua float - dtype: float64# Campuran int dan floata = np.array([0, 1, 2.])print("Campuran - dtype:", a.dtype)  # Output: Campuran - dtype: float64print("Array result:", a)  # Output: Array result: [0. 1. 2.]

import numpy as np# Membuat array 2D sebagai contoha = np.array([[0, 1, 2], [3, 4, 5]])print("Array:")print(a)# Output:# [[0 1 2]#  [3 4 5]]print("Shape (bentuk):", a.shape)  # Output: Shape (bentuk): (2, 3)print("Ndim (dimensi):", a.ndim)  # Output: Ndim (dimensi): 2print("Size (total elemen):", a.size)  # Output: Size (total elemen): 6print("Dtype (tipe data):", a.dtype)  # Output: Dtype (tipe data): int64print("Data pointer:", a.data)  # Output: Data pointer: <memory at 0x...>

import numpy as np# Semua integera = np.array([0, 1, 2])print("All int - dtype:", a.dtype)  # Output: All int - dtype: int64# Semua float  a = np.array([0., 1., 2.])print("All float - dtype:", a.dtype)  # Output: All float - dtype: float64# Campuran integer dan float (otomatis menjadi float)a = np.array([0, 1, 2.])print("Mixed - dtype:", a.dtype)  # Output: Mixed - dtype: float64print("Mixed result:", a)  # Output: Mixed result: [0. 1. 2.]

import numpy as np# Array float originaloriginal = np.array([1.1, 2.7, 3.9])print("Original array:", original)  # Output: Original array: [1.1 2.7 3.9]print("Original dtype:", original.dtype)  # Output: Original dtype: float64# Konversi ke integer menggunakan astypeconverted = original.astype(int)print("Converted to int:", converted)  # Output: Converted to int: [1 2 3]print("Converted dtype:", converted.dtype)  # Output: Converted dtype: int64# Konversi ke tipe data spesifikfloat32_array = original.astype(np.float32)print("Float32 dtype:", float32_array.dtype)  # Output: Float32 dtype: float32# Konversi string ke integerstring_array = np.array(['1', '2', '3'])int_from_string = string_array.astype(int)print("From string:", int_from_string)  # Output: From string: [1 2 3]print("String to int dtype:", int_from_string.dtype)  # Output: String to int dtype: int64

import numpy as np# Array dengan tipe data default (int64)large_array_int64 = np.arange(1000000)print("Int64 itemsize:", large_array_int64.itemsize, "bytes")  # Output: Int64 itemsize: 8 bytesprint("Int64 total memory:", large_array_int64.nbytes, "bytes")  # Output: Int64 total memory: 8000000 bytes# Array dengan tipe data lebih kecil (int32)large_array_int32 = np.arange(1000000, dtype=np.int32)print("Int32 itemsize:", large_array_int32.itemsize, "bytes")  # Output: Int32 itemsize: 4 bytesprint("Int32 total memory:", large_array_int32.nbytes, "bytes")  # Output: Int32 total memory: 4000000 bytes# Penghematan memorimemory_saved = large_array_int64.nbytes - large_array_int32.nbytesprint("Memory saved:", memory_saved, "bytes")  # Output: Memory saved: 4000000 bytesprint("Memory saved percentage:", (memory_saved / large_array_int64.nbytes) * 100, "%")  # Output: Memory saved percentage: 50.0 %

import numpy as np# Membuat array dengan berbagai tipe dataarrays = {  'int8': np.array([1, 2, 3], dtype=np.int8),  'int32': np.array([1, 2, 3], dtype=np.int32),  'int64': np.array([1, 2, 3], dtype=np.int64),  'float32': np.array([1.0, 2.0, 3.0], dtype=np.float32),  'float64': np.array([1.0, 2.0, 3.0], dtype=np.float64)}print("Informasi Tipe Data:")print("=" * 50)for name, arr in arrays.items():  print(f"{name:8} - itemsize: {arr.itemsize:2} bytes, dtype: {arr.dtype}")# Output:# Informasi Tipe Data:# ==================================================# int8     - itemsize:  1 bytes, dtype: int8# int32    - itemsize:  4 bytes, dtype: int32# int64    - itemsize:  8 bytes, dtype: int64# float32  - itemsize:  4 bytes, dtype: float32# float64  - itemsize:  8 bytes, dtype: float64

import numpy as npdef analyze_array(arr, name="Array"):  """Fungsi untuk menganalisis struktur array"""  print(f"\n=== Analisis {name} ===")  print(f"Shape: {arr.shape}")  print(f"Dimensions: {arr.ndim}")  print(f"Size: {arr.size}")  print(f"Data type: {arr.dtype}")  print(f"Item size: {arr.itemsize} bytes")  print(f"Total memory: {arr.nbytes} bytes")    if arr.ndim <= 2:      print(f"Array content:\n{arr}")# Contoh analisis berbagai arrayarray_1d = np.array([1, 2, 3, 4, 5])array_2d = np.array([[1, 2, 3], [4, 5, 6]])array_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])analyze_array(array_1d, "1D")analyze_array(array_2d, "2D") analyze_array(array_3d, "3D")# Output:# === Analisis 1D ===# Shape: (5,)# Dimensions: 1# Size: 5# Data type: int64# Item size: 8 bytes# Total memory: 40 bytes# Array content:# [1 2 3 4 5]# # === Analisis 2D ===# Shape: (2, 3)# Dimensions: 2# Size: 6# Data type: int64# Item size: 8 bytes# Total memory: 48 bytes# Array content:# [[1 2 3]#  [4 5 6]]# # === Analisis 3D ===# Shape: (2, 2, 2)# Dimensions: 3# Size: 8# Data type: int64# Item size: 8 bytes# Total memory: 64 bytes

import numpy as npdef validate_array_for_ml(arr):  """Validasi array untuk machine learning"""  print("=== Validasi Array untuk ML ===")    # Cek dimensi  if arr.ndim != 2:      print(f"WARNING: Array bukan 2D (current: {arr.ndim}D)")  else:      print(f"✓ Array 2D dengan shape: {arr.shape}")    # Cek tipe data  if arr.dtype in [np.float32, np.float64]:      print(f"✓ Tipe data sesuai: {arr.dtype}")  else:      print(f"WARNING: Tipe data mungkin perlu konversi: {arr.dtype}")    # Cek missing values (NaN)  if np.isnan(arr).any():      nan_count = np.isnan(arr).sum()      print(f"WARNING: Terdapat {nan_count} nilai NaN")  else:      print("✓ Tidak ada nilai NaN")    # Informasi memori  memory_mb = arr.nbytes / (1024 * 1024)  print(f"Penggunaan memori: {memory_mb:.2f} MB")# Test dengan berbagai arraytest_arrays = [  np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float64),  np.array([1, 2, 3, 4, 5]),  np.array([[1, 2, np.nan], [4, 5, 6]], dtype=np.float64)]for i, arr in enumerate(test_arrays):  print(f"\n--- Test Array {i+1} ---")  validate_array_for_ml(arr)# Output:# --- Test Array 1 ---# === Validasi Array untuk ML ===# ✓ Array 2D dengan shape: (2, 3)# ✓ Tipe data sesuai: float64# ✓ Tidak ada nilai NaN# Penggunaan memori: 0.00 MB# # --- Test Array 2 ---# === Validasi Array untuk ML ===# WARNING: Array bukan 2D (current: 1D)# WARNING: Tipe data mungkin perlu konversi: int64# ✓ Tidak ada nilai NaN# Penggunaan memori: 0.00 MB# # --- Test Array 3 ---# === Validasi Array untuk ML ===# ✓ Array 2D dengan shape: (2, 3)# ✓ Tipe data sesuai: float64# WARNING: Terdapat 1 nilai NaN# Penggunaan memori: 0.00 MB