12- Scientific computation using NumPy library

12- Scientific computation using NumPy library

In Python, NumPy (Numerical Python) is the essential package for scientific computation. It is used for working with arrays. An array in NumPy is very fast compared to traditional Python lists. It can also be used for computing Pearson’s correlation coefficient and generating random numbers.

Installation of NumPy:

pip install numpy

Import NumPy

import numpy as np

Arrays in NumPy

0D array

da = np.array(1977)
print(da)
# 1977

1D array

da = np.array([3, 5, 7, 9, 12])
type(da)
# numpy.ndarray
da.max() # calculate max of array
# 12
da.min() # calculate min of array
# 3
da.mean() # calculate mean of array
#  7.2
da.sum() # calculate sum of array
# 36
np.median(da)
# 7.0

2D array

da = np.array([[22, 15, 33], [24, 25, 16]])
da
# array([[22, 15, 33],
#           [24, 25, 16]])
```

3D array

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

#          [[1, 3, 5],
#          [2, 4, 6]]])
print('shape of array:', da.shape)
# shape of array: (3, 3)

Data type of array

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

print(da.dtype)
# int32

Accessing and Slicing Arrays

da = np.array([5, 9, 7, 11])
da[0] # first number
# 5
da[2] # third number
# 7
da = np.arange(50)
da[1:10]
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])

Operations

da = np.array([1, 2, 3, 4])
da + 1
# array([2, 3, 4, 5])
da * da
# array([ 1,  4,  9, 16])

Array manipulations

da = np.arange(20).reshape(4, 5)
da
# array([[ 0,  1,  2,  3,  4],
#           [ 5,  6,  7,  8,  9],
#           [10, 11, 12, 13, 14],
#           [15, 16, 17, 18, 19]])

Random numbers in NumPy

Random means that numbers cannot be anticipated logically.

from numpy import random
rn = random.randint(100)
print(rn)
# 56
rn = random.randint(100)
print(rn)
# 43
rn = random.randint(100)
print(rn)
# 85
rn = random.rand(3) # float
print(rn)
# [0.75700426 0.97003262 0.16064961]

NaN values

NaN means "Not a Number". If we multiply a NaN value by another value, we get NaN.

To calculate the sum, we can use np.nansum instead of np.sum in order to find the sum and avoid NaN:

x = np.array([12,np.nan,31,56, 88, np.nan])
x
# array([12., nan, 31., 56., 88., nan])
np.nansum(x)
# 187.0
np.nanmean(x)
# 46.75
np.nanmax(x)
# 88.0
np.nanmin(x)
# 12.0

Mask in NumPy

Download the data here

t=np.loadtxt('D:\Python\Python_for_Researchers\munich_temp_with_bad_data.txt')
np.min(t)
# -99.0
keep = (t > -30) & (t < 50) # Mask with conditions temperature should lower than 50 and higher than -30
t1 = t[keep]
np.max(t1)
# 27.6667
np.mean(t1)
# 8.933222104668378
np.min(t1)
# -16.7778

Calculate correlation coefficient in NumPy

x = np.array([2, 4, 2, 8])
y = np.array([2, 3, 1, 8])
np.corrcoef(x, y)
# array([[1.        , 0.98552746],
#     [0.98552746, 1.        ]])

r = np.corrcoef(x, y)
r
r[0, 1]
# 0.9855274566525744
r[1, 0]
# 0.9855274566525744
r[0, 0]
# 1
r[1, 1]
# 1

If you find this content helpful, please consider SUBSCRIBING to my channel for future updates.

If you would like to get the full video tutorial and a certificate, you can enroll in the course by following this link: https://www.udemy.com/course/python-for-researchers/?referralCode=886CCF5C552567F1C4E7

Did you find this article valuable?

Support Azad Rasul by becoming a sponsor. Any amount is appreciated!