import numpy as np
# Now we can use 'np' to work with NumPy arrays!

numbers = np.array([10, 20, 30, 40, 50])
print(numbers)

# This is a basic NumPy array!

first_value = numbers[0]
print('The first value is:', first_value)

third_value = numbers[2]
print('The third value is:', third_value)

# Remember: counting starts at zero!

last_value = numbers[-1]
print('The last value is:', last_value)

# Negative indexes count from the end.

print(numbers[1:4])  # This means spots 1 up to, but not including, 4.

# This is called a slice!

every_other = numbers[::2]
print('Every other value:', every_other)

# The double colon lets us skip values.

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

# This is a '2D array' or a 'matrix'.

print(grid[1, 2])

# This grabs row 1, column 2.

print(grid[:, 0])

# Colon means all rows of column zero.

# Let us try changing a value in the array.
grid[0, 1] = 42
print(grid)

# NumPy arrays let us update spots directly!

# Indexing works with input too.
chosen_row = int(input('Pick a row number (0-2): '))
chosen_column = int(input('Pick a column number (0-2): '))
value = grid[chosen_row, chosen_column]
print('You selected:', value)

big_numbers = numbers[numbers > 25]
print('Numbers bigger than 25:', big_numbers)

# This builds a new array with only values that match.

print(grid[grid % 2 == 0])

# This finds all even numbers in our grid.

special = numbers[[1, 3, 4]]
print('Picked spots:', special)

# We selected the second, fourth, and fifth values.

# You can also combine fancy and boolean indexing.
odd_rows = grid[[0, 2], :]  # Pick rows 0 and 2
print('Odd rows only:', odd_rows)

# Let us sort an array with NumPy.
shuffled = np.array([5, 2, 7, 1, 3])
sorted_array = np.sort(shuffled)
print('Sorted:', sorted_array)
# Sorting helps us organize data.

# Filtering for values greater than three in a 2D array.
greater_than_three = grid[grid > 3]
print('Values over three:', greater_than_three)

# Combining data using NumPy.
extra = np.array([100, 200, 300, 400, 500])
combined = np.concatenate((numbers, extra))
print('Combined arrays:', combined)

scores = np.array([[88, 92, 85, 90],
                 [76, 95, 89, 91],
                 [84, 87, 93, 89]])
print(scores)

# Each row is one student. Each column is one test.

# Let us find the highest score for each student.
best_scores = np.max(scores, axis=1)
print('Best score per student:', best_scores)

# Challenge: Get all test scores above ninety.
above_ninety = scores[scores > 90]
print('All scores above ninety:', above_ninety)

# Common mistake: Out of range index.
try:
    bad_value = numbers[12]
except IndexError as e:
    print('Oops:', e)