Search test library by skills or roles
⌘ K

About the test:

The Python Pandas Online Test evaluates a candidate's ability to work with data using the Pandas library in Python. It assesses knowledge of reading and writing data, data manipulation, analysis, cleaning, data visualization, time series data handling, grouping and aggregating, merging and joining dataframes, missing data handling, applying statistical functions, and reshaping data.

Covered skills:

  • Reading and Writing Data
  • Data Analysis
  • Data Visualization
  • Grouping and Aggregating Data
  • Handling Missing Data
  • Reshaping Data
  • Data Manipulation
  • Data Cleaning and Preprocessing
  • Working with Time Series Data
  • Merging and Joining DataFrames
  • Applying Statistical Functions

9 reasons why
9 reasons why

Adaface Python Pandas Test is the most accurate way to shortlist Python Developers



Reason #1

Tests for on-the-job skills

The Python Pandas Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Reading and Writing Data efficiently using Python Pandas
  • Performing Data Manipulation operations using Python Pandas
  • Analyzing Data using Python Pandas library
  • Cleaning and Preprocessing Data using Python Pandas
  • Visualizing Data using Python Pandas
  • Working with Time Series Data in Python Pandas
  • Grouping and Aggregating Data using Python Pandas
  • Merging and Joining DataFrames in Python Pandas
  • Handling Missing Data using Python Pandas
  • Applying Statistical Functions on Data using Python Pandas
  • Reshaping Data using Python Pandas
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 10,000+ questions. The actual questions on this Python Pandas Test will be non-googleable.

🧐 Question

Medium

ZeroDivisionError and IndexError
Exceptions
Solve
What will the following Python code output?
 image

Medium

Session
File Handling
Dictionary
Solve
 image
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file:
Tony,10
Stark,12
Black,25
Your program should ignore a non-conforming line like this one.
Stark,3
Widow,6
Widow,14
The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 }
What should replace the CODE TO FILL line to complete the function?
 image

Medium

Max Code
Arrays
Solve
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
 image

Medium

Recursive Function
Recursion
Dictionary
Lists
Solve
Consider the following Python code:
 image
In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None.

nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target.

What will the output be after executing the above code?

Medium

Stacking problem
Stack
Linkedlist
Solve
What does the below function ‘fun’ does?
 image
A: Sum of digits of the number passed to fun.
B: Number of digits of the number passed to fun.
C: 0 if the number passed to fun is divisible by 10. 1 otherwise.
D: Sum of all digits number passed to fun except for the last digit.

Medium

Data Aggregation and Transformation
Data Aggregation
Data Transformation
Solve
You are working with a dataset, `df`, that contains columns 'A', 'B', and 'C'. You need to perform the following tasks:

1. Group the DataFrame `df` by column 'A'.
2. Compute the sum of column 'B' for each group.
3. Append this sum as a new column 'D' to the original DataFrame `df`.

You wrote the following code to perform these tasks:
 image
However, you notice that the new column 'D' contains many missing values. What is the cause of this issue?
A: The groupby method did not work as expected.
B: The sum method did not work as expected.
C: The new column 'D' should be appended to grouped instead of df.
D: The grouped object should be mapped to df['A'] before assigning to a new column in df
E: The groupby method should be called on df['A'] instead of df.

Easy

Handling Missing Data
Data Cleaning
Missing Data
Solve
You are working with a dataset, `df`, that contains several columns with missing values. You want to replace all missing values in the dataset with the mean of the non-missing values of their respective columns.

You wrote the following code to perform this task:
 image
However, you notice that some missing values are still not replaced. What is the cause of this issue?
A: The fillna method does not work with the mean method.
B: The mean method does not work with missing values.
C: The fillna method should be called on df.mean() instead of df.
D: The fillna method does not work inplace by default. You should use df.fillna(df.mean(), inplace=True).
E: The mean method should be called on df.fillna() instead of df.
🧐 Question🔧 Skill

Medium

ZeroDivisionError and IndexError
Exceptions

2 mins

Python
Solve

Medium

Session
File Handling
Dictionary

2 mins

Python
Solve

Medium

Max Code
Arrays

2 mins

Python
Solve

Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
Solve

Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
Solve

Medium

Data Aggregation and Transformation
Data Aggregation
Data Transformation

2 mins

Pandas
Solve

Easy

Handling Missing Data
Data Cleaning
Missing Data

2 mins

Pandas
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
ZeroDivisionError and IndexError
Exceptions
Python
Medium2 mins
Solve
Session
File Handling
Dictionary
Python
Medium2 mins
Solve
Max Code
Arrays
Python
Medium2 mins
Solve
Recursive Function
Recursion
Dictionary
Lists
Python
Medium3 mins
Solve
Stacking problem
Stack
Linkedlist
Python
Medium4 mins
Solve
Data Aggregation and Transformation
Data Aggregation
Data Transformation
Pandas
Medium2 mins
Solve
Handling Missing Data
Data Cleaning
Missing Data
Pandas
Easy2 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Python Pandas Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Python Pandas Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Python Pandas Assessment Test

Why you should use Pre-employment Python Pandas Online Test?

The Python Pandas Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Reading and Writing Data using Python
  • Data Manipulation using Pandas
  • Data Analysis using Python
  • Data Cleaning and Preprocessing
  • Data Visualization with Pandas
  • Working with Time Series Data using Pandas
  • Grouping and Aggregating Data with Pandas
  • Merging and Joining DataFrames with Pandas
  • Handling Missing Data with Pandas
  • Applying Statistical Functions using Pandas

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Python Pandas Online Test?

  • Reading and Writing Data

    This skill involves the ability to read and write data using the Python Pandas library. It includes tasks such as loading data from various file formats (e.g., CSV, Excel), extracting specific columns or rows, and saving the manipulated data back into files. This skill is important to measure because reading and writing data is a fundamental aspect of data analysis and manipulation workflows, and being proficient in this skill is essential for working with real-world datasets.

  • Data Manipulation

    Data manipulation refers to the process of transforming and modifying data to make it suitable for analysis. It includes tasks such as filtering rows based on certain conditions, changing data types, creating new columns, manipulating strings, and performing mathematical operations on data. This skill should be measured in this test because it is a crucial aspect of data analysis, allowing users to transform raw data into a structured and usable format for further analysis.

  • Data Analysis

    Data analysis involves exploring and making sense of data, identifying patterns, correlations, and trends, and extracting meaningful insights. It includes tasks such as computing summary statistics, calculating frequencies, performing aggregations, and applying statistical functions. Measuring this skill in the test is important as it assesses the candidate's ability to apply various data analysis techniques using the Python Pandas library, thereby determining their proficiency in analyzing and interpreting data.

  • Data Cleaning and Preprocessing

    Data cleaning and preprocessing involves identifying and handling missing or incorrect data, removing duplicates, dealing with outliers, normalizing data, and performing other data cleansing operations. This skill is essential to ensure data integrity and accuracy before conducting any further analysis. Measuring this skill in the test helps evaluate the candidate's ability to clean and preprocess data effectively, which is a critical step in the data analysis process.

  • Data Visualization

    Data visualization refers to representing data in a visual format, such as charts, graphs, and maps, to facilitate understanding and communication of information. It includes tasks such as creating plots, customizing visualizations, adding labels, colors, and legends, and visualizing trends and relationships in data. Measuring this skill in the test provides insight into the candidate's ability to visually represent data using the Python Pandas library, which is important for effective data storytelling and presentation.

  • Working with Time Series Data

    Working with time series data involves handling and analyzing data that is ordered and indexed by time or date. It includes tasks such as time-based indexing, resampling data at different frequencies, calculating rolling statistics, and working with time-related operations. Measuring this skill in the test assesses the candidate's capability to work with time series data using the Python Pandas library, which is crucial in domains such as finance, stock market analysis, and forecasting.

  • Grouping and Aggregating Data

    Grouping and aggregating data involves grouping data by one or more categorical variables and then applying aggregate functions to calculate summary statistics within each group. It includes tasks such as grouping data by specific columns, performing aggregate calculations such as mean, sum, count, and applying custom aggregation functions. Measuring this skill in the test evaluates the candidate's proficiency in grouping and summarizing data efficiently using the Python Pandas library, which is essential for data analysis and generating insights.

  • Merging and Joining DataFrames

    Merging and joining DataFrames involves combining multiple DataFrames based on common columns or indexes, thereby creating a new DataFrame that contains all the information from the merged datasets. It includes tasks such as inner and outer joins, merging on multiple keys, concatenating DataFrames vertically or horizontally, and handling overlapping column names. Measuring this skill in the test assesses the candidate's ability to merge and join DataFrames accurately and efficiently using the Python Pandas library, which is a vital skill for integrating and harmonizing data from different sources.

  • Handling Missing Data

    Handling missing data involves identifying, analyzing, and filling in missing values or deleting rows/columns with missing data. It includes tasks such as detecting missing values, imputing missing values using strategies like mean, median, or interpolation, and removing rows or columns with excessive missing data. Measuring this skill in the test helps evaluate the candidate's ability to handle missing data appropriately using the Python Pandas library, which is crucial to ensure data quality and integrity during the analysis process.

  • Applying Statistical Functions

    Applying statistical functions involves performing statistical calculations and analyses on data, such as computing correlation coefficients, conducting hypothesis tests, measuring central tendency and variability, and implementing statistical models. It includes tasks such as calculating mean, median, mode, variance, standard deviation, and applying inferential statistics methods. Measuring this skill in the test assesses the candidate's proficiency in utilizing statistical functions from the Python Pandas library to derive meaningful insights and conclusions from the data being analyzed.

  • Reshaping Data

    Reshaping data involves transforming the structure of data to suit specific analysis requirements or desired formats. It includes tasks such as pivoting data, melting data, stacking and unstacking data, and transforming wide-format data to long-format or vice versa. Measuring this skill in the test evaluates the candidate's ability to reshape, restructure and organize data efficiently using the Python Pandas library, which is essential for data analysis, modeling, and reporting purposes.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Python Pandas Test to be based on.

    Reading CSV files
    Writing CSV files
    Reading Excel files
    Writing Excel files
    Filtering data
    Sorting data
    Joining data
    Grouping data
    Aggregating data
    Handling duplicates
    Handling missing values
    Data visualization
    Line plots
    Histograms
    Scatter plots
    Box plots
    Time series analysis
    Resampling time series
    Handling time zones
    Reshaping data
    Pivoting data
    Melting data
    Statistical analysis
    Descriptive statistics
    Correlation analysis
    Hypothesis testing
    Linear regression
    Data cleaning techniques
    Data imputation
    Outlier detection
    Data transformation
    Data normalization

What roles can I use the Python Pandas Online Test for?

  • Python Developer
  • Python Data Engineer
  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • Database Administrator

How is the Python Pandas Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Reshaping Data using Pandas
  • Integrating Python with other technologies
  • Optimizing data processing pipelines in Python
  • Debugging and troubleshooting data-related issues
  • Efficiently processing large datasets
  • Applying machine learning algorithms to data analysis
  • Implementing data access and security measures
  • Building interactive data dashboards
  • Automating data analysis workflows
  • Collaborating with cross-functional teams for data-driven decision making
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Python Pandas Hiring Test FAQs

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

customers across world
Join 1200+ companies in 75+ countries.
Try the most candidate friendly skills assessment tool today.
g2 badges
Ready to use the Adaface Python Pandas Test?
Ready to use the Adaface Python Pandas Test?
logo
40 min tests.
No trick questions.
Accurate shortlisting.
Terms Privacy Trust Guide
ada
Ada
● Online
Previous
Score: NA
Next
✖️