Top Python Interview Questions for Data Science

Top Python Interview Questions for Data Science
Top Python Interview Questions for Data Science

Python’s increasing use in data science has positioned it as a rival to the R programming language. Many people are switching from R to Python since its numerous libraries have matured over time to meet all data science needs. This may appear to be the most sensible scenario. R, on the other hand, would continue to be the most popular choice among data scientists. People are leaning toward Python, but not so much that they are abandoning R entirely. In our Python vs R article, we discuss the advantages and disadvantages of both of these data science languages. As can be seen, many data scientists study both Python and R to overcome the limits of either language. In data science job interviews, being prepared in both languages will be beneficial.

Python is a “friendly” programming language that anybody can use and runs on any platform. So it’s no surprise that Python has several modules that effectively deal with data and is thus utilized in data science. Python has just recently become popular in the field of data science. On the other hand, Python programming is not going away now that it has firmly established itself as a key language for Data Science. Python is typically used for data analysis when you need to incorporate data analysis results into web apps or add mathematical/statistical algorithms for production.

Top Python Interview Questions for Data Science

1) Name a few libraries in Python used for Data Analysis and Scientific computations?

A few of the Python Libraries for Data Analysis and Scientific computations are:-

  1. Numpy and Scipy – Fundamental Scientific Computing
  2. Pandas – Data Manipulation and Analysis
  3. Matplotlib – Plotting and Visualization
  4. Scikit-learn – Machine Learning and Data Mining
  5. StatsModels – Statistical Modeling, Testing, and Analysis
  6. Seaborn – For Statistical Data Visualization
http://makemeanalyst.com/data-science-with-python/python-libraries-for-data-analysis/

2) Which library would you prefer for plotting in Python language: Seaborn or Matplotlib?

The python package used for plotting is Matplotlib, however, it requires a lot of fine-tuning to guarantee that the charts appear well. Seaborn assists data scientists in creating relevant charts that are both statistically and visually pleasing.

3) How can you check if a data set or time series is Random?

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.

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4) What are the advantages of NumPy over regular Python lists?

NumPy’s arrays are more compact than Python lists — a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB. Access to reading and writing items is also faster with NumPy.

5) What is the Difference between Data Profiling and Data Mining?

Data profiling is the process of evaluating data’s individual characteristics. It primarily focuses on delivering useful data characteristics such as data type, frequency, length, and null value occurrence.

Data mining refers to the process of analyzing data in order to identify previously unknown relationships. It primarily focuses on finding odd records, determining dependencies, and doing cluster analysis.

6) What is the Process of Data Analysis?

Data analysis is the process of gathering, cleaning, analyzing, manipulating, and modeling data in order to acquire business insights and create reports. The many phases involved in the process are depicted in the figure below.

  1. Collect Data – Data is collected from a variety of sources and kept in order to be cleansed and processed. All missing data and outliers are eliminated in this stage.
  2. Analyse Data – After the data has been collected, the following step is to analyse it. A model is run several times to see whether it can be improved. The model is next verified to see if it satisfies the business requirements.
  3. Create Reports – Finally, the model is put into action, and the resulting reports are sent to the stakeholders.

7) Can the lambda forms in Python contain statements?

No, because their syntax is limited to single expressions and they’re only used to create runtime function instances.

8) How will you transpose a NumPy array?

nparr.T

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9) What are boolean arrays? 

The components of a boolean array must be of the boolean data type. It’s important to note that Python keywords like and and or don’t operate with boolean arrays.

10) What is NaT in Python’s Pandas library?

NaT stands for Not a Time. It is the NA value for timestamp data.

Written by

XR Developer responsible for end-to-end development of XR solutions spanning multiple domains, by using various XR and WebXR libraries.

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