NumPy Introduction
-Stands for Numerical Python
provides an array object that is faster than the common python array.
To install
pip install numpyAfter installing open your jupiter notebook or python file and type `import numpy`
Example 1:
convert and array to numpy array
import numpy as np
arr = [1,2,4,5]
arr = numpy.array(arr)
print(arr)
Example 2: Creating an array from tuple
import numpy as np
tuple1 = (1,3,5,6)
nparr_from_tupple = np.array(tuple1)
Example 3: 0-D Arrays
When an array has single elements like `42` we call it a one dimesional array
import numpy as np
oned = np.array(42)
print(oned)
Example 4: 1D-Arrays
If the array consists of multiple elements like [1, 4, 5, 6, 7, 8] in nupmy it it will be: array([1, 4, 5, 6, 7, 8]) ,then the array is 1D
import numpy as np
nparr = np.array([1,4,5,6,7])
print(nparr)
Example 5: 2-D Arrays
If the array elements has other arrays as its elemets which are 1-D, then its is 2D
import numpy as np
arr= [[1,2],[3,4]]
nparr = np.array(arr)
Example 6: Summary
one_D = np.array(42) #one D
two_D = np.array([1, 2, 3, 4, 5]) #2D
three_D = np.array([[1, 2, 3], [4, 5, 6]]) #3D
four_D = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]) #4D
Example 7: Defining dimensions with ndmin
To do that we use ndmin argument
import numpy as np
arr = np.array([1,2,4], ndim=5)
arr = np.array([1,2,4], ndmin=2)
print(arr)
Example 8: Accessing Numpy array Elements
You can do so by using an index eg
import numpy as np
arr = [1,2,3]
print(arr[0])
print(arr[1])
print(arr[2])
Example 9: Access 2-D Arrays
-use comma separated integers. Since its like rows and colums it will ideally be like [row,column]
The same also applies to 3D arrays you can access them like arr[1,1,0] or arr[0,0,1]
import numpy as np
arr2 = np.array([[1,2,3,4,5,6,7], [1,2,3,4,5,6,7]])
arr[0,0] # 1
arr[1,0] #1
Example 10: Negative Indexing
To access array from the end, use negative indexes
import numpy as np
arr2 = np.array([[1,2,3,4,5,6,7], [1,2,3,4,5,6,10]])
arr[0,-1] # 7
arr[1,-2] #10
Example 11: Slicing Arrays
# syntax [start:end] or [start:end:step]
#NOTE:result includes the start index, but excludes the end index.
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5]) # prints form 1-4, [2,3,4,5]
arr[1:] # from index 1 to the end
arr[:3] # START FROM 0 to index 2 (dont include 3)
arr[-4:-1] #[4,5,6] will not inlude the end index value
Example 12: Slicing 2-D Arrays
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
arr[1, 1:4] # in row 1 return 1 to 3 [6,7,8]
arr[0:, 2] # return second element in each row
Example 13: Iterating using nditer()
import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
print(x) # 1,2,3,4,5,6,7,8
Well, that the commonly used numpy concept for now. Thank you and see you soon.