Numpy

📋 Copia tutto Comandi NumPy_HTML_TAG_157__ __HTML_TAG_158_📄 Generare NumPy PDF Guida_HTML_TAG_159__ __HTML_TAG_160_ # NumPy Cheatsheet # ## Installazione _Tabella_162__ # Comandi di base ## Array Creation # _Tabella_163__ ### Array Properties _Tabella_164__ ### Array Indexing & Slicing _Tabella_165_ # Operazioni matematiche di base _Tabella_166_ ## Array Reshaping # _Tabella_167__ ### Array Concatenation & Split _Tabella_168__ ## Uso avanzato ## Algebra lineare _Tabella_169_ ### Funzioni statistiche _Tabella_170__ ### Broadcasting & Advanced Indexing TABELLA 171_ ### Funzioni universali (ufuncs) _Tabella_172__ # # Random Number Generation TABELLA 173_ ### Memory & Performance _Tabella_174__ ## Configurazione # Tipi di dati NumPy supporta vari tipi di dati per l'ottimizzazione della memoria:

# Integer types
np.int8      # -128 to 127
np.int16     # -32,768 to 32,767
np.int32     # -2^31 to 2^31-1
np.int64     # -2^63 to 2^63-1
np.uint8     # 0 to 255
np.uint16    # 0 to 65,535

# Float types
np.float16   # Half precision
np.float32   # Single precision
np.float64   # Double precision (default)

# Complex types
np.complex64  # Two 32-bit floats
np.complex128 # Two 64-bit floats

# Boolean
np.bool_     # True or False

# String types
np.str_      # Unicode string
np.bytes_    # Byte string

# Create array with specific dtype
arr = np.array([1, 2, 3], dtype=np.float32)

### Opzioni di stampa Configurare come vengono visualizzati gli array:

# Set print options
np.set_printoptions(
    precision=3,        # Decimal places
    suppress=True,      # Suppress scientific notation
    threshold=1000,     # Max elements before summarizing
    edgeitems=3,        # Items at start/end when summarizing
    linewidth=120       # Characters per line
)

# Example: suppress scientific notation
np.set_printoptions(suppress=True)
print(np.array([1e-10, 1e10]))  # [0. 10000000000.]

# Reset to defaults
np.set_printoptions()

### Gestione degli errori Configurare come NumPy gestisce errori numerici:

# Set error handling
np.seterr(
    divide='warn',    # Division by zero: 'ignore', 'warn', 'raise', 'call'
    over='warn',      # Overflow: 'ignore', 'warn', 'raise', 'call'
    under='ignore',   # Underflow
    invalid='warn'    # Invalid operation (e.g., sqrt(-1))
)

# Context manager for temporary settings
with np.errstate(divide='ignore'):
    result = np.array([1, 2]) / 0  # No warning in this block

# Check for errors after operations
np.seterr(all='ignore')
result = np.array([1]) / 0
if np.isinf(result).any():
    print("Infinity detected")

## Configurazione del generatore di numeri casuali

# Legacy API (older code)
np.random.seed(42)

# Modern API (recommended)
rng = np.random.default_rng(seed=42)

# Use different algorithms
from numpy.random import PCG64, Philox, MT19937
rng = np.random.Generator(PCG64(seed=42))  # Default, fastest
rng = np.random.Generator(Philox(seed=42))  # Parallel streams
rng = np.random.Generator(MT19937(seed=42)) # Legacy compatibility

## Common Use Cases ### Use Case 1: Normalizzazione dei dati Normalizzare i dati a zero media e variazione unità (standardizzazione):

import numpy as np

# Sample data
data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=float)

# Standardize: (x - mean) / std
mean = np.mean(data, axis=0)
std = np.std(data, axis=0)
normalized = (data - mean) / std

print(normalized)
# [[-1.22474487 -1.22474487 -1.22474487]
#  [ 0.          0.          0.        ]
#  [ 1.22474487  1.22474487  1.22474487]]

# Min-Max normalization: (x - min) / (max - min)
min_val = np.min(data, axis=0)
max_val = np.max(data, axis=0)
normalized_minmax = (data - min_val) / (max_val - min_val)

### Use Case 2: Image Processing Manipolare immagini come array NumPy:

import numpy as np
from PIL import Image

# Load image as array
img = np.array(Image.open('photo.jpg'))
print(f"Shape: {img.shape}")  # (height, width, channels)

# Convert to grayscale
gray = np.mean(img, axis=2).astype(np.uint8)

# Crop image (top-left 100x100 pixels)
cropped = img[:100, :100]

# Flip image vertically
flipped = np.flipud(img)

# Rotate 90 degrees
rotated = np.rot90(img)

# Adjust brightness (add value to all pixels)
brighter = np.clip(img + 50, 0, 255).astype(np.uint8)

# Apply threshold
threshold = 128
binary = np.where(gray > threshold, 255, 0).astype(np.uint8)

# Save result
Image.fromarray(binary).save('processed.jpg')

### Use Case 3: Time Series Analysis Calcola le medie e le statistiche in movimento:

import numpy as np

# Sample time series data
prices = np.array([100, 102, 101, 105, 107, 106, 108, 110, 109, 111])

# Simple moving average (window size 3)
window = 3
moving_avg = np.convolve(prices, np.ones(window)/window, mode='valid')
print(f"Moving average: {moving_avg}")

# Calculate returns (percentage change)
returns = np.diff(prices) / prices[:-1] * 100
print(f"Returns: {returns}")

# Cumulative returns
cumulative_returns = np.cumprod(1 + returns/100) - 1
print(f"Cumulative returns: {cumulative_returns}")

# Volatility (rolling standard deviation)
def rolling_std(arr, window):
    return np.array([np.std(arr[i:i+window]) for i in range(len(arr)-window+1)])

volatility = rolling_std(returns, window=3)
print(f"Volatility: {volatility}")

# Detect outliers (values > 2 std from mean)
mean = np.mean(prices)
std = np.std(prices)
outliers = np.abs(prices - mean) > 2 * std
print(f"Outliers at indices: {np.where(outliers)[0]}")

### Use Case 4: Matrix Operations for Machine Learning Attuazione delle operazioni di rete neurale di base:

import numpy as np

# Initialize weights and biases
np.random.seed(42)
input_size, hidden_size, output_size = 4, 5, 3
W1 = np.random.randn(input_size, hidden_size) * 0.01
b1 = np.zeros((1, hidden_size))
W2 = np.random.randn(hidden_size, output_size) * 0.01
b2 = np.zeros((1, output_size))

# Sample input (batch of 10 samples)
X = np.random.randn(10, input_size)

# Forward pass
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def softmax(x):
    exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))
    return exp_x / np.sum(exp_x, axis=1, keepdims=True)

# Hidden layer
z1 = np.dot(X, W1) + b1
a1 = sigmoid(z1)

# Output layer
z2 = np.dot(a1, W2) + b2
a2 = softmax(z2)

print(f"Output shape: {a2.shape}")  # (10, 3)
print(f"Output probabilities sum to 1: {np.allclose(np.sum(a2, axis=1), 1)}")

# Calculate loss (cross-entropy)
y_true = np.array([0, 1, 2, 0, 1, 2, 0, 1, 2, 0])  # True labels
y_one_hot = np.eye(output_size)[y_true]
loss = -np.mean(np.sum(y_one_hot * np.log(a2 + 1e-8), axis=1))
print(f"Loss: {loss}")

### Use Case 5: Analisi statistica Eseguire test di ipotesi e calcoli statistici: ``python' importazione numpy come np # Dati campione: punteggi di prova da due gruppi [85, 88, 90, 92, 87, 89, 91, 86, 88, 90]) gruppo_b = np.array([78, 82, 80, 85, 83, 81, 84, 79, 82, 80]) Statistiche descrittive stampa (f"Gruppo A - Mean: {np.mean(group_a):.2f}, Std: {np.std(group_a):.2f}") stampa(f"Gruppo B - Mean: {np.mean(group_b):.2f}, Std: {np.std(group_b):.2f}") # T-test (calcolo manuale) n_a, n_b = len(group_a), len(group_b) mean_a, mean_b = np.mean(group_a), np.mean(group_b) var_a, var_b = np.var(group_a, ddof=1), np.var(group_b, ddof=1) # Deviazione standard in piscina (n_a - 1) * var_a + (n_b - 1) * var_b - 1) * var_b) / (n_a + n_b - 2) t_statistic = (mean_a - mean_b) / (pooled_std * np.sqrt(1/n_a + 1/n_b) stampa (f"T-statistica: {t_statistic:.3f}") # Analisi della correlazione correlazione = np.corrcoef(group_a, group_b)[0, 1] stampa(f"Correlazione: {correlazione:.3f}") # Intervallo di fiducia Bootstrap N_bootstrap = 10000 bootstrap_means = np.array([