Question 48: Write a NumPy program to generate six random integers between 10 and 30. Download hole Program / Project code, by clicking following link: Why should we use Numpy rather than Matlab, Octave or Yorick ?NumPy is a popular library for numerical and scientific computing in Python, while MATLAB, Octave, and Yorick are also used for similar purposes. Each of these tools has its own strengths and areas where it excels. The choice of which tool to use depends on your specific requirements and preferences. Here are some reasons why you might choose NumPy over the others:
Here are some key characteristics and features of NumPy arrays: - Integration with Python Ecosystem: NumPy seamlessly integrates with the broader Python ecosystem, which includes a wide range of libraries for data analysis, machine learning, visualization, and more.
Python is a versatile and general-purpose programming language, making it a good choice for various tasks beyond numerical computing. - Open Source and Free: NumPy is open-source and free to use. It's a part of the SciPy ecosystem, which includes many other scientific libraries and tools.
- Community and Documentation: NumPy has a large and active user community, which means there are extensive online resources, tutorials, and documentation available. It's relatively easy to find solutions to problems and get help from the community.
- Versatility: NumPy is not limited to numerical computing. It's used in various domains, including data science, machine learning, scientific research, and engineering. You can use NumPy for tasks beyond numerical computing, such as working with images, text data, and more.
- Flexibility and Customization: NumPy allows for fine-grained control and customization. You can create custom data types, work with complex data structures, and implement specialized algorithms. You can also interface with other libraries and tools, including C/C++ code, through NumPy.
- Performance: NumPy is highly optimized for performance, especially for large datasets. It provides efficient array operations and can leverage multi-core processors.
- Machine Learning Ecosystem: NumPy is the foundation for many machine learning libraries in Python, including scikit-learn and TensorFlow. If you plan to work on machine learning projects, NumPy is a natural choice.
- Interactivity: Python, which uses NumPy, allows for interactive data exploration and analysis. Jupyter notebooks and interactive development environments are commonly used with NumPy for this purpose.
That said, MATLAB, Octave, and Yorick also have their strengths, and your choice should be based on your specific needs and preferences. For example, MATLAB is known for its extensive toolboxes and its popularity in academia, while Octave and Yorick are open-source alternatives with similar functionality. The right choice depends on factors like your specific application, available resources, and familiarity with the tools. Programming Code: Following code write in: BP_P48.py # Python Program
# generate six random integers between 10 and 30
# import lib.
import numpy as np
size_num = int(input("Enter size of random integers number between 10 to 30: "))
num = np.random.randint(low= 10, high= 30, size= size_num)
# size is output shape
# low is start value
# high is end value
# number of values show based on size, let's see
print(f" {size_num} Random integers between 10 and 30: ")
print(num)
# Thanks for Reading.
Output:
- Integration with Python Ecosystem: NumPy seamlessly integrates with the broader Python ecosystem, which includes a wide range of libraries for data analysis, machine learning, visualization, and more.
Python is a versatile and general-purpose programming language, making it a good choice for various tasks beyond numerical computing. - Open Source and Free: NumPy is open-source and free to use. It's a part of the SciPy ecosystem, which includes many other scientific libraries and tools.
- Community and Documentation: NumPy has a large and active user community, which means there are extensive online resources, tutorials, and documentation available. It's relatively easy to find solutions to problems and get help from the community.
- Versatility: NumPy is not limited to numerical computing. It's used in various domains, including data science, machine learning, scientific research, and engineering. You can use NumPy for tasks beyond numerical computing, such as working with images, text data, and more.
- Flexibility and Customization: NumPy allows for fine-grained control and customization. You can create custom data types, work with complex data structures, and implement specialized algorithms. You can also interface with other libraries and tools, including C/C++ code, through NumPy.
- Performance: NumPy is highly optimized for performance, especially for large datasets. It provides efficient array operations and can leverage multi-core processors.
- Machine Learning Ecosystem: NumPy is the foundation for many machine learning libraries in Python, including scikit-learn and TensorFlow. If you plan to work on machine learning projects, NumPy is a natural choice.
- Interactivity: Python, which uses NumPy, allows for interactive data exploration and analysis. Jupyter notebooks and interactive development environments are commonly used with NumPy for this purpose.
# Python Program # generate six random integers between 10 and 30 # import lib. import numpy as np size_num = int(input("Enter size of random integers number between 10 to 30: ")) num = np.random.randint(low= 10, high= 30, size= size_num) # size is output shape # low is start value # high is end value # number of values show based on size, let's see print(f" {size_num} Random integers between 10 and 30: ") print(num) # Thanks for Reading.
Output:
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