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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 (SMEs) to evaluate and hire data scientists as per the industry standards.
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
Coding 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.
This test may contain coding questions and innovative LogicBox (an AI-based pseudo coding platform) questions to assess a candidate's coding skills in a fun & quick way.
This Data Wrangling with Python Test is useful for hiring
Data Science Engineer
Data Science Developer
Data Science Associate
Data Visualization Specialist
Q 1.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 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 the list in the space-separated format after mean Imputation. Note: round of the output values to 2 digits after the decimal