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Data Wrangling with Python Test
Test duration:
15
min
No. of questions:
1
Level of experience:
Entry/Mid/Expert

Data Wrangling with Python Test

iMocha's data wrangling with Python test is the ideal pre-hire test for recruiters and hiring managers to assess candidates objectively. This test is useful for hiring Data Scientists, Data Science Engineer, Data Science Developer, Data Science Associate, Data Analyst, and Data Visualization Specialist. Our test helps recruiters to  increases interview-to-selection ratio by 62% and reduce time-to-hire by 45%.

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Data Wrangling with Python Test

Data Wrangling (Data Munging) with Python is a type of data science technique that is used effectively while coding data science problems.

It mainly involves carrying out information processing in numerous ways such as merging, grouping, concatenating, etc. for analyzing and obtaining knowledge from data. Python has an inbuilt option to perform Data Wrangling and Correction and further obtain the knowledge sets to attain the required analytical goal. Data wrangling with Python test helps tech recruiters & hiring managers to assess candidate’s skills of using Python programming for data wrangling.

Coding test for data wrangling with Python is designed by experienced Subject Matter Experts (SME) to evaluate and hire data scientists as per the industry standards.

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How it works

Test Summary

Data wrangling with Python test helps to screen the candidates who possess traits as follows:

  • Excellent Python programming skills for data wrangling and correction
  • Understanding of data, filtering unwanted data, using pivot, and melting the shifted data sets
  • Ability to use Python libraries like NumPy, Pandas, ScKit-learn for data wrangling
  • Excellent knowledge of data science concepts, steps, methods, and functions in Python

Data Wrangling with Python test for data wrangling with Python contains a coding simulator which will automatically evaluate and provide a score for the candidate’s written codes by compiling multiple test cases that generate discrete output. You will also get a detailed report for each test case execution along with execution-time and execution memory usage for the program written by the candidate. The Code-Replay feature records the coding screen of the candidate so that the reviewer can understand the coding and thinking patterns of the candidate.

The Coding test for data wrangling with Python may contain coding questions and innovative AI-LogicBox (Pseudo coding platform) questions to assess a candidate's coding skills in a fun and quick way.

Useful for hiring
  • Data Scientists
  • Data Science Engineer
  • Data Science Developer
  • Data Science Associate
  • Data Analyst
  • Data Visualization Specialist
Test Duration
15
min
No. of Questions
1
Level of Expertise
Entry/Mid/Expert
Topics Covered
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Python

Data Wrangling with Python test assesses candidate's proficiency in Python

Data Wrangling

Data Wrangling with Python test gauges candidates skills of cleaning and unifying messy and complex data sets for easy access and data analysis
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Data Science

Coding test for data wrangling with Python evaluates candidate's ability to deal with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions
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Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.
Question type
Coding
Topics covered
Data Wrangling (Mean Imputation)
Difficulty
Easy

Question:

Impute the missing Values

Mean Imputation is a method in which the missing value on a certain variable is replaced by the mean of the available cases.
You are given a list of numbers, there are certain values missing from the list. All you need to do is impute the missing values using the Mean Imputation method.

Input Format
Input 1: An integer “n”, giving the size of the list.
Input 2: n space-separated values of the list in which missing values are represented by “Nan”.

Output Format
Output the list in the space-separated format after mean Imputation. Note: round of the output values to 2 digits after the decimal

Sample Input10
Nan 13 18 14 15 19 20 16 15 Nan
Sample Output

16.25 13 18 14 15 19 20 16 15 16.25


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