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Data Wrangling with Python Test
Clock
Test duration:
15
min
Notes
No. of questions:
1
Tie
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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About

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

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

<ul>

<li>Excellent Python programming skills for data wrangling and correction</li>

<li>Understanding of data, filtering unwanted data, using pivot, and melting the shifted data sets</li>

<li>Ability to use Python libraries like NumPy, Pandas, ScKit-learn for data wrangling</li>

<li>Excellent knowledge of data science concepts, steps, methods, and functions in Python</li>

</ul>

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&rsquo;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.<br />

&nbsp;<br />

The Coding test for data wrangling with Python may contain coding questions and innovative AI-LogicBox (Pseudo coding platform) questions to assess a candidate&#39;s coding skills in a fun and quick way.

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

Data Wrangling with Python test assesses candidate&

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&
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Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.

Question types :

Multiple Option

Topic:

Difficulty:

Hard


Q 1. You have been asked to train « Naive Bayes model for spam detection dataset

Using Python command, what wil you add in the given code at XXXX to deciore, it and train your modal for the given dataset?

import numpy os np

X= nporroyll 3. 2b[1 21 [2.21.[3,21)
Y= nporray(fi.a,a.1))

XXXX

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

Question types :

Multiple Option

Topic:

Machine teaming

Difficulty:

Hard


Q 1. Q2 Lets assume that you have 6 Pandas DataFrome(af) in the below tole.

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074

When the following commend is executed, what hoppens to the DatoFrome?

df= defiinat()
Y= nporray(fi.a,a.1))

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
it gives an error becouse of the third row which has on empty string.
None of these

Question types :

Coding

Topic:

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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