AITT.TECHCA.APP

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# Indexing & Slicing (Lists)

my_list = [1, 2, 3, 4, 5]

# Access first element (index starts at 0)
first_element = my_list[0]
print("first_element:", first_element)  # 1

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# Negative index: last element
last_element = my_list[-1]
print("last_element:", last_element)  # 5

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# Slice: elements from index 1 up to (but not including) index 4
sub_list = my_list[1:4]
print("sub_list 1:4 ->", sub_list)  # [2, 3, 4]

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# Slice with step of 2
step_list = my_list[0:5:2]
print("step_list 0:5:2 ->", step_list)  # [1, 3, 5]

# From beginning to index 3 (exclusive)
partial_list_start = my_list[:3]
print("partial_list_start :", partial_list_start)  # [1, 2, 3]

# From index 2 to the end
partial_list_end = my_list[2:]
print("partial_list_end   :", partial_list_end)  # [3, 4, 5]

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# Aggregation with pure Python

exam_scores = [85, 92, 88, 78, 95, 89, 92, 87, 91, 84]

total = sum(exam_scores)
mean = total / len(exam_scores)
minimum = min(exam_scores)
maximum = max(exam_scores)

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# Correct median for even-length lists: average of the two middle values
sorted_scores = sorted(exam_scores)
mid = len(sorted_scores) // 2
median = (sorted_scores[mid - 1] + sorted_scores[mid]) / 2

print("total  :", total)     # 881
print("mean   :", mean)      # 88.1
print("min    :", minimum)   # 78
print("max    :", maximum)   # 95
print("median :", median)    # 88.5

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# Using the statistics module

import statistics as stats

exam_scores = [85, 92, 88, 78, 95, 89, 92, 87, 91, 84]

print("mean       :", stats.mean(exam_scores))      # 88.1
print("median     :", stats.median(exam_scores))    # 88.5
print("variance   :", stats.variance(exam_scores))  # sample variance
print("stdev      :", stats.stdev(exam_scores))     # sample standard deviation

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# Reshaping & Transposing with NumPy

import numpy as np

matrix = np.array([[1, 2, 3],
                   [4, 5, 6],
                   [7, 8, 9]])

# Reshape (stays 3x3 here; pattern shown)
reshaped = matrix.reshape((3, 3))
print("reshaped:\n", reshaped)

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# Flatten to 1D
flattened = matrix.flatten()
print("flattened:", flattened)

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# Transpose
transposed = np.transpose(matrix)  # or matrix.T
print("transposed:\n", transposed)

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# Universal functions (ufuncs)

import numpy as np

arr = np.array([1, 2, 3])

exp_arr = np.exp(arr)   # e^x for each element
sin_arr = np.sin(arr)   # sine of each element (radians)
log_arr = np.log(arr)   # natural log of each element

print("exp :", exp_arr)
print("sin :", sin_arr)
print("log :", log_arr)

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# Random number generation

import numpy as np

random_array = np.random.rand(3, 3)
print("random 3x3 array:\n", random_array)

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# Broadcasting

import numpy as np

arr = np.array([1, 2, 3])
result = arr + 5  # adds 5 to each element
print("result:", result)  # [6 7 8]

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# Reshape a 1D array into 2x3

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = np.reshape(arr, (2, 3))
print("reshaped 2x3:\n", reshaped)