Numpy&Pandas 003: Index

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We make two new arrays firstly,

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A = np.arange(0,6)
# array([0, 1, 2, 3, 4, 5])

B = A.reshape((2,3))
#array([[0, 1, 2],
#       [3, 4, 5]])

One-dimensional

It mostly likes list in original Python

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A[0]	#0
A[1:]	#array([1, 2, 3, 4, 5])

Two-dimensional

It is also likely to Python,

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B[0][0]	#0
B[:,1]	#array([1, 4])
B[1,2]	#5
B[0,:]	#array([0, 1, 2])
B[0,1:3]	#array([1, 2])

For array traversal, there is also a normal way

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for row in B:
    print(row)

#[0 1 2]
#[3 4 5]

If we wanna traversal the column of the array, there is not given a function to do that instance, but we can transpose the array firstly,

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for column in B.T:
    print(column)
   
#[0 3]
#[1 4]
#[2 5]

If we wanna change or output every item in the array, we would like to use an inner function called flat, but be careful, it will be a iterator.

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for item in B.flat:
    print(item)


#0
#1
#2
#3
#4
#5

For this function, actually, we can use it to output everything in the array, the key word needed to change flatten and the type of output is an array.

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B.flatten()
#array([0, 1, 2, 3, 4, 5])

Thanks for your reading, I hope it is useful for you.

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