import numpy as np

# Array manipulation basics
data = np.array([[1, 2, 3, 4],
                 [5, 6, 7, 8]])

print(f"Original: \n{data}")

# Reshape to different dimensions
reshaped = data.reshape(4, 2)
print(f"Reshaped: \n{reshaped}")

# Split into parts
parts = np.hsplit(data, 2)
print(f"Split: {parts[0]} and {parts[1]}")

import numpy as np

# Time series data - daily sales for 4 weeks
daily_sales = np.array([100, 120, 110, 95, 105, 130, 125,
                       115, 140, 135, 90, 100, 145, 150])

# Reshape to weekly view
weekly = daily_sales.reshape(2, 7)
print(f"Weekly sales: \n{weekly}")
print(f"Week averages: {weekly.mean(axis=1)}")

import numpy as np

# Split dataset for train/test
data = np.random.rand(100, 5)
train, test = np.split(data, [80])

print(f"Training set: {train.shape}")
print(f"Test set: {test.shape}")

import numpy as np

# Combine results from different sources
results_a = np.array([85, 90, 88])
results_b = np.array([82, 87, 91])

combined = np.vstack([results_a, results_b])
print(f"Combined results: \n{combined}")
print(f"Overall average: {combined.mean():.1f}")

import numpy as np

original = np.array([1, 2, 3, 4, 5])

# View (shares memory)
view = original[1:4]
print(f"View shares memory: {np.shares_memory(original, view)}")

# Copy (independent)
copy = original.copy()
print(f"Copy shares memory: {np.shares_memory(original, copy)}")

# Modify view affects original
view[0] = 999
print(f"Original after view change: {original}")