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Data Science

Numpy function

Indexing and slicing

  • You can index and slice NumPy arrays in the same ways you can slice Python lists.

Indexing

  • Indexing mean any secific position to given element are get is Indexing.
data = np.array([1, 2, 3])
data[1]
data[0:2]
data[1:]
data[-2:]
  • You can visualize This array

np_index

  • In the data[1] meaning array index starting is 0 and this is access 1 index . In this index position values is 2.

  • You can access Multi dimension array in the indexing in below EX:

arr = np.array([
    [1,2,3],[4,5,6]
])


print(arr[0,2])

  • In a[0,2] fist 0 is which array are you access and 2 is which element are access in the array.

  • This case 0 meaning your are access this array [1,2,3] in second is 2 . This array [1,2,3] are second index value are the access. then finally 3 is return.

  • You can Try the 3D and 4D array Just practice

slicing

  • Slicing in python means taking elements from one given index to another given index.

  • We are pass the slicing index this [start:end:step]

arr = np.array([1,2,3,4,5,6])


print(arr[2:4])
  • In arr[2:4] Its meaning start 2 index and end 4 index. Ans : 3,4
  • Can you Ans this Quation : a[1:5:2] .
  • Slicing support negative Index.

Math and Statstici function

sum

  • This function is use to the sum of the array
arr = np.array([1,2,4,3,3])


print(np.sum(arr))

function

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


print(np.abs(arr)) # Find the absolute values


print(np.log(arr)) # Natural logarithm, element-wise


print(np.average(arr)) # find the avg


print(np.mean(arr)) # find the mean values


print(np.median(arr)) # find the median values


print(np.std(arr)) # find the Standard deviation


print(np.var(arr)) # find the Compute the variance along the specified axis.

View code file

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