Numpy and Pandas are the most frequently used during data analysis. In this blog, let's see how NumPy is installed, imported, and is used for arrays creation.
Install the numpy package
NumPy is the fundamental package for scientific computing in Python. It’s a python package used for doing math more advanced than basic additions and subtractions, etc…
Numpy package includes functions like cosine, sqrt. Numpy can be used for vectors, matrices, and tensors, random simulation.
To install numpy package, use either
Conda install numpy
Or
Pip install numpy
Before accessing numpy and its functions, we need to import first in the python code as
Import numpy as np
array function
Numpy is used for 1-Dimensional , 2-Dimensional and 3-Dimensional arrays. Below are the examples of creating arrays by converting lists using array() function.
oneDarray = np.array([1,2,3,4])
twoDarray = np.array([[1,2],[3,4]])
threeDarray = np.array([[[1,2],[3,4]],[[5,6],[7,8]]])
arange function
Another way of creating an 1- D array or vector is by using an arange function.
numpy.arange([start, ]stop, [step, ]dtype=None, *, like=None)
It returns evenly spaced values within a given interval.
array = np.arange(1,10)
This is similar to range() in python.This creates an 1-D array or vector as
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
1 is inclusive and 10 is exclusive and as the 3rd perimeter is not mentioned, it is incremented by 1.
vec = np.arange(1,21,5)
[out]: [ 1 6 11 16]
In this example, it increments the number by 5. 1 is inclusive and 21 is exclusive.
linspace function
numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)
Linspace returns equally spaced values within an interval with both starting and ending point inclusive. linspace command must have 3 arguments.
vec= np.linspace(0,5,10)
array([0. , 0.55555556, 1.11111111, 1.66666667, 2.22222222,
2.77777778, 3.33333333, 3.88888889, 4.44444444, 5. ])
zeros function
numpy.zeros(shape, dtype=float, order=’C’, *, like=None)
zeros() return a new array of given shape and type, filled with zeros.
zeroarray = np.zeros([5,2])
Out:
array([[0., 0.],
[0., 0.],
[0., 0.],
[0., 0.],
[0., 0.]])
ones function
numpy.ones(shape, dtype=None, order=’C’, *, like=None)
ones() returns a new array of given shape and type, filled with ones.
onearray = np.ones((3,5))
out:
array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])
reshape function
numpy.reshape(a, newshape, order=’C’)
numpy.reshape() gives a new shape to an array without changing its data.
array = np.array([1,2,3,4,5,6,7,8,9,10])
reshapedarray = array.reshape(5,2)
out:
array([[ 1, 2],
[ 3, 4],
[ 5, 6],
[ 7, 8],
[ 9, 10]])
