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# 12- Scientific computation using NumPy library

·Jun 27, 2021·

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

``````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
``````

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