Q. What is the difference between view and materialized view?
A. View:– Tail raid data representation is provided by a view to access data from its table. – It has logical structure that does not occupy space. – Changes get affected in corresponding tables. Materialized view:– Pre-calculated data persists in materialized view. – It has physical data space occupation. – Changes will not get affected in corresponding tables.
Q. How make you 3D plots/visualizations using NumPy/SciPy?
A. Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in this 2D example, packages exist that integrate with NumPy. Matplotlib provides primary 3D plotting in the mplot3d subpackage, whereas Mayavi produces a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.
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Q. What are the types of biases that can occur during sampling ?
A. Example Under coverage occurs when some members of the population live badly represented inside the sample. The survey relied on a service unit, drawn of telephone directories and car registration lists.
Selection bias
Under coverage bias
Survivorship bias.
Q. Which Python library is used for data visualization Data Science?
A. Plotly. The fifth tool is Plotly, also called as Plot.ly because of its main platform online. It is an interactive online visualization tool that is being used for data analytics, scientific graphs, and other visualization. This contains some great API including one for Python.
Q. Why is an import statement required in Python?
A. To be able to use any functionality, the respective code logic needs to be accessible for the Python interpreter. With import statement, we can use specific scripts. Many of scripts are available, Hence we import statement to use only the scripts that we want to use on the situation and requirement
import pandas as pd
import numpy as np
Q. How to get indices of N maximum values in a NumPy array in data science?
A. We can get the indices of N maximum values in a NumPy array using the below Example code:
import numpy as np
arr = np.array([1, 3, 2, 4, 5])
print(arr.argsort()[-3:][::-1])
Output: [ 4 3 1 ].
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