# Python SciPy MCQs

**SciPy** is an open-source library written in Python that is utilized to resolve mathematical, scientific, engineering, and technological issues. A large variety of high-level Python commands are made available to users, enabling them to alter data as well as view it.

NumPy is an extension of Python that serves as the foundation for SciPy. It's possible to say "Sigh Pi" when you're referring to SciPy. NumPy is an extension of Python that serves as the foundation for SciPy, which is a library of mathematical methods and convenience functions. It provides the user with high-level commands and classes for manipulating and displaying data, which adds a substantial amount of capability to the Python session that is being used for interactive programming. An interactive Python session may be transformed into a data-processing and system-prototyping environment with the help of SciPy. This environment can compete with other software packages such as MATLAB, IDL, Octave, R-Lab, and SciLab.

**Python SciPy MCQs**: This section contains multiple-choice questions and answers on Python SciPy. These MCQs are written for beginners as well as advanced, practice these MCQs to enhance and test the knowledge of Python SciPy.

## List of Python SciPy MCQs

**1. SciPy stands for ____.**

- Science in Python
- Scientific Python
- Seconds in Python
- None of the mentioned above

**Answer:** B) Scientific Python

**Explanation:**

The abbreviation for Scientific Python is "SciPy." SciPy is an open-source library written in Python that is utilized for the purpose of resolving mathematical, scientific, engineering, and technological issues. A large variety of high-level Python commands are made available to users, enabling them to alter data as well as view it. NumPy is an extension of Python that serves as the foundation for SciPy.

**2. Amongst which of the following is correct with reference to install SciPy package in Python for windows?**

- pip install --user numpy scipy
- pip install --user scipy
- install --user numpy scipy
- All of the mentioned above

**Answer:** A) pip install --user numpy scipy

**Explanation:**

to install SciPy package we use pip install --user numpy scipy.

**3. SciPy is a collection of mathematical algorithms and convenience functions built on ____.**

- Pandas
- Matplotlib
- NumPy
- All of the mentioned above

**Answer:** C) NumPy

**Explanation:**

NumPy is an extension of Python that serves as the foundation for SciPy, which is a library of mathematical methods and convenience functions. It provides the user with high-level commands and classes for manipulating and displaying data, which adds a substantial amount of capability to the Python session that is being used for interactive programming.

**4. Numpy and SciPy both are used for ____ analysis.**

- Mathematical and numerical
- Visualization
- Graphical analysis
- None of the mentioned above

**Answer:** A) Mathematical and numerical

**Explanation:**

Both NumPy and SciPy are useful for mathematical and numerical analysis in various contexts. Because it stores array data, NumPy is more suited for fundamental operations such as sorting and indexing than SciPy, which stores just numerical data. On the other hand, SciPy stores all of the numerical data.

**5. scipy.cluster is uses for ____ which are used to vector quantization/ Kmeans.**

- Statistical algorithms
- Scientific algorithms
- Cluster algorithms
- None of the mentioned above

**Answer:** C) Cluster algorithms

**Explanation:**

The scipy.cluster package is utilised for cluster algorithms, such as those utilised for vector quantization and Kmeans.

**6. Identify the purpose of including SciPy.linalg.**

- To solve linear algebra problems
- It has very fast linear algebra capabilities
- linear algebra routines expect an object that can be converted into a two-dimensional array
- All of the mentioned above

**Answer:** D) All of the mentioned above

**Explanation:**

SciPy.linalg is the library that is used to solve issues involving linear algebra. It is capable of performing linear algebra at a very high speed. The procedures of linear algebra anticipate the presence of an object that is capable of being transformed into a two-dimensional array.

**7. scipy.linalg.solve is used to solve the linear equations.**

- True
- False

**Answer:** A) True

**Explanation:**

scipy.linalg.solve is used to solve the linear equations.

**8. What will be the output of following code?**

from scipy import linalg import numpy as np A = np.array([[2,3],[5,6]]) x = linalg.det(A) print (x)

- -2.000000000000001
- -3.000000000000001
- -4.000000000000001
- None of the mentioned above

**Answer:** B) -3.000000000000001

**Explanation:**

when we run the code, we will get -3.000000000000001 as an output.

**9. What will be the eigen values after run the code?**

from scipy import linalg import numpy as np A = np.array([[2,3],[5,6]]) x, y = linalg.eig(A) print (x) print (y)

[-0.35889894+0.j 8.35889894+0.j] [[-0.78609474 -0.4266791] [0.61810602 -0.90440309]]

[-0.37889894+0.j 8.35889214+0.j] [[-0.88609474 -0.4266791] [0.881810602 -0.90440309]]

[-0.55889894+0.j 6.35889894+0.j] [[-0.68609474 -0.4266791] [0.51810602 -0.10440309]]

[-0.25889894+0.j 1.35889894+0.j] [[-0.765609474 -0.4266791] [0.51810602 -0.50440309]]

**Answer:** A)

[-0.35889894+0.j 8.35889894+0.j] [[-0.78609474 -0.4266791] [0.61810602 -0.90440309]]

**Explanation:**

Eigen values of the inputted matrix will be:

[-0.35889894+0.j 8.35889894+0.j] [[-0.78609474 -0.4266791] [0.61810602 -0.90440309]]

**10. The scipy.optimize package provides different commonly used optimization ____.**

- Data Flow Diagram
- Flowchart
- Algorithms
- All of the mentioned above

**Answer:** C) Algorithms

**Explanation:**

The scipy.optimize package contains a variety of various optimization methods that are often utilized. SciPy optimise offers functions for reducing (or maximizing), depending on the context, the impact of restrictions on objective function evaluations. It features linear programming, restricted and nonlinear least-squares, root finding, and curve fitting, as well as solvers for nonlinear problems (with support for both local and global optimization techniques).

**11. The interp1d class in scipy.interpolate is a convenient method to create a function based on ____.**

- Variable and constants
- Fixed data points
- Arbitrary arguments
- None of the mentioned above

**Answer:** B) Fixed data points

**Explanation:**

The interp1d class in scipy.interpolate is a convenient method to create a function based on fixed data points.

**12. scipy.fftpack is used for ____.**

- Matrix calculation
- Fourier transform
- Basic arithmetic computation
- None of the mentioned above

**Answer:** B) Fourier transforms

**Explanation:**

The Fourier transform may be accomplished with scipy.fftpack. The Fourier analysis is a technique that may be used to define a function as a sum of periodic components, and it can also be used to recover the signal from those components. The term "discrete Fourier transform" refers to a situation in which the function and its corresponding Fourier transform have both been replaced with discretized versions (DFT). The Discrete Fourier Transform has emerged as a fundamental component of numerical computing in large part as a result of the development of an extremely efficient algorithm for its computation known as the Fast Fourier Transform (FFT).

**13. The ____ sub-package provides several integration techniques including an ordinary differential equation integrator.**

- scipy.image
- scipy.integrate
- scipy.statistics
- None of the mentioned above

**Answer:** B) scipy.integrate

**Explanation:**

The scipy.integrate sub-package provides several integration techniques including an ordinary differential equation integrator.

**14. fft(a[, n, axis, norm]) can be used to ____.**

- Computes 3-dimensional Fourier transforms
- Computes 2-dimensional Fourier transforms
- Computes 1-dimensional Fourier transforms
- None of the mentioned above

**Answer:** C) computes 1-dimensional Fourier transforms

**Explanation:**

fft(a[, n, axis, norm]) can be used to computes 1-dimensional Fourier transforms.

**15. To compute 1-dimensional inverse discrete Fourier transforms, we use ____.**

- ifft(a[, n, axis, norm])
- fft(a[, n, axis, norm])
- ufft(a[, n, axis, norm])
- None of the mentioned above

**Answer:** A) ifft(a[, n, axis, norm])

**Explanation:**

The Fourier transform (FT) is a type of mathematical transform that decomposes functions that are space- or time-dependent into functions that are either space- or time-frequency-dependent. Decomposing the waveform of a musical chord into terms of the strength of its constituent pitches is an illustration of one possible application of this technique. The frequency domain representation as well as the mathematical operation that relates the frequency domain representation to a function of space or time is both included in the definition of the word "Fourier transform," which applies to both of these concepts.

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