Blogs - Page 58 Of 116

Extract the first and second elements of the rows from an array

In this python numpy program, we will extract the first and second elements of the rows from an array using NumPy.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Extract the first and second elements of the first and second rows from the array using indexing.
Print the output.

				
					import numpy as np
x = np.array([[6, 5, 7],[10, 9, 1],[7, 8, 2]])

print(x[0:2,0:2])

Output :

				
					[[ 6  5]
 [10  9]]

access first two columns of a 3-D array.

Program to access two columns of a NumPy array

In this python numpy program, we will access two columns of a NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Use for loop to iterate over the first two rows of the array.
Access the first two columns of the array using indexing.
Print the output.

				
					import numpy as np
x = np.array([[6, 5, 7],[10, 9, 1],[7, 8, 2]])

for i in range(0,2):
    print(f"{i+1} column: ",x[:,i])

Output :

				
					1 column:  [ 6 10  7]
2 column:  [5 9 8]

convert an array into a CSV file.

extract the first and second elements of the first and second rows from a given (3×3) matrix.

Program to convert a NumPy array into a CSV file

In this python numpy program, we will convert a NumPy array into a CSV file.

Steps to solve the program

Import the numpy library as np.
Import the pandas library as pd
Create an array using np.array().
Convert the array to a matrix form using reshape().
Convert the matrix to a data-frame using pd.DataFrame().
Now save this data-frame as csv file using to_csv().
Read the saved csv file using pd.read_csv().
Print the file.

				
					import pandas as pd
import numpy as np
 
x = np.arange(1,16).reshape(3,5)
 
DF = pd.DataFrame(x)
DF.to_csv("data1.csv")

df=pd.read_csv("data1.csv")
print(df)

Output :

				
					   Unnamed: 0   0   1   2   3   4
0           0   1   2   3   4   5
1           1   6   7   8   9  10
2           2  11  12  13  14  15

calculate the product of an array.

access first two columns of a 3-D array.

Program to calculate the product of a NumPy array

In this python numpy program, we will calculate the product of a NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Calculate the product of the given array using prod().
Print the output.

				
					import numpy as np
x = np.array([3, 5, 1])

print("Product of the elements in the given array: ",x.prod())

Output :

				
					Product of the elements in the given array:  15

check whether an array is empty.

convert an array into a CSV file.

Program to check whether the NumPy array is empty

In this python numpy program, we will check whether the NumPy array is empty.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Get the size of the array (i.e. total number of elements) using size.
Determine whether the given array is empty or not using an if-else statement.
If the size of the array is 0 then the array is empty else the array is not empty.
Print the respective output.

				
					import numpy as np
x = np.array([4,8,0])

if x.size == 0:
    print("Array is empty")
else:
    print("Array is not empty")

Output :

				
					Array is not empty

count the frequency of unique values.

calculate the product of an array.

Get the count the frequency of unique values from the NumPy array

In this python numpy program, we will count the frequency of unique values from the NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Get the unique values and their frequency from the array using np.unique(array,return_counts=True).
Print the output.

				
					import numpy as np
x = np.array([8, 6, 7, 0, 7, 8, 6, 6])

unique, count = np.unique(x, return_counts=True)
print("Frequency of unique values in the given array:")
print(np.asarray((unique, count)))

Output :

				
					Frequency of unique values in the given array:
[[0 6 7 8]
 [1 3 2 2]]

get the magnitude of a vector.

check whether an array is empty.

Program to get the magnitude of a NumPy vector

In this python numpy program, we will get the magnitude of a NumPy vector.

Steps to solve the program

Import the numpy library as np.
Create a vector using np.array().
Get the magnitude of the vector using np.linalg.norm().
Print the output.

				
					import numpy as np
x = np.array([3, 5, 1])

print("Magnitude of the given vector: ",np.linalg.norm(x))

Output :

				
					Magnitude of the given vector:  5.916079783099616

remove all rows in a NumPy array that contain non-numeric values.

count the frequency of unique values.

Remove all rows that contain non-numeric values from an NumPy array

In this python numpy program, we will remove all rows that contain non-numeric values from a NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Remove all rows that contain non-numeric values from the array using array[~np.isnan(array).any(axis=1)].
Print the output.

				
					import numpy as np
x = np.array([[4,8, np.nan],[2,4,6]])
print("Old: \n",x)

print("New: \n",x[~np.isnan(x).any(axis=1)])

Output :

				
					Old: 
 [[ 4.  8. nan]
 [ 2.  4.  6.]]
New: 
 [[2. 4. 6.]]

replace the negative values in an array with 1.

get the magnitude of a vector.

Replace the negative values in an NumPy array

In this python numpy program, we will replace the negative values in an NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Replace the negative values in the given array with 1 using indexing and logic.
Print the output.

				
					import numpy as np
x = np.array([[4,8,-5],[2,-9,6]])
print("Old: \n",x)

x[x<0] = 1
print("New: \n",x)

Output :

				
					Old: 
 [[ 4  8 -5]
 [ 2 -9  6]]
New: 
 [[4 8 1]
 [2 1 6]]

add an extra column to an array.

remove all rows in a NumPy array that contain non-numeric values.

Add an extra column to a NumPy array

In this python numpy program, we will add an extra column to a NumPy array.

Steps to solve the program

Import the numpy library as np.
Create an array using np.array().
Create another array of the same dimension as the given array.
Add the new array as a column to the original array using np.append() and set the axis equal to 1 for the column.
Print the output.

				
					import numpy as np
x = np.array([[4,8,0],[2,0,6]])
y = np.array([[5],[7]])

print(np.append(x, y, axis=1))

Output :

				
					[[4 8 0 5]
 [2 0 6 7]]

convert an array of float values to an array of integer values.

replace the negative values in an array with 1.