Python Pandas Online Test

Q: What are the main Data Analysis tests?

Our main Data Analysis tests include: Python Pandas Test Data Analysis Test Data Science Assessment Test Data Wrangling 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 Manipulation
Data Analysis
Data Cleaning and Preprocessing
Data Visualization
Working with Time Series Data
Grouping and Aggregating Data
Merging and Joining DataFrames
Handling Missing Data
Applying Statistical Functions
Reshaping Data

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About the Python Pandas Test

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

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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 15,000+ questions. The actual questions on this Python Pandas Test will be non-googleable.

🧐 Question
Medium ZeroDivisionError and IndexError Exceptions Exception Handling Error Handling	Solve
What will the following Python code output?
Medium Session File Handling Dictionary Csv Parsing Exception Handling In File Input/output	Solve
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?
Medium Max Code Arrays Code Analysis Algorithm Understanding	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:
Medium Recursive Function Recursion Dictionary Lists	Solve
Consider the following Python code: 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? 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 Data Structures	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: 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 Data Transformation Data Analysis	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: 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 Exception Handling Error Handling	2 mins Python	Solve
What will the following Python code output?
	Medium Session File Handling Dictionary Csv Parsing Exception Handling In File Input/output	2 mins Python	Solve
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?
	Medium Max Code Arrays Code Analysis Algorithm Understanding	2 mins Python	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:
	Medium Recursive Function Recursion Dictionary Lists	3 mins Python	Solve
Consider the following Python code: 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	4 mins Python	Solve
What does the below function ‘fun’ does? 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 Data Structures	2 mins Pandas	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: 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 Data Transformation Data Analysis	2 mins Pandas	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: 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	💪 Difficulty	⌛ Time
	ZeroDivisionError and IndexError Exceptions Exception Handling Error Handling	Python	Medium	2 mins	Solve
What will the following Python code output?
	Session File Handling Dictionary Csv Parsing Exception Handling In File Input/output	Python	Medium	2 mins	Solve
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?
	Max Code Arrays Code Analysis Algorithm Understanding	Python	Medium	2 mins	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:
	Recursive Function Recursion Dictionary Lists	Python	Medium	3 mins	Solve
Consider the following Python code: 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?
	Stacking problem Stack Linkedlist	Python	Medium	4 mins	Solve
What does the below function ‘fun’ does? 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.
	Data Aggregation and Transformation Data Aggregation Data Transformation Data Structures	Pandas	Medium	2 mins	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: 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.
	Handling Missing Data Data Cleaning Missing Data Data Transformation Data Analysis	Pandas	Easy	2 mins	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: 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.

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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

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Have questions about the Python Pandas Hiring Test?

What is the Python Pandas Test?

The Python Pandas Test is designed to assess a candidate's proficiency in using Python and the Pandas library for data manipulation and analysis. This test is used by recruiters to evaluate candidates for roles that require strong data handling skills.

Can I combine the Python Pandas Test with Data Analysis questions?

Yes, recruiters can request a custom test that includes both Python Pandas and Data Analysis questions. For more details on how we assess Data Analysis skills, check out our Data Analysis Test.

What topics are evaluated in the Python Pandas Test?

This test covers:

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

How to use the Python Pandas Test in my hiring process?

Use the test as a pre-screening tool. Add a link to the job post or invite candidates via email. Find skilled candidates faster.

Can I test Python and Pandas together?

Yes, you can test Python and Pandas together. It helps in evaluating a candidate's proficiency in both Python programming and data handling with Pandas. Check out our Python & Pandas Test.

What are the main Data Analysis tests?

Our main Data Analysis tests include:

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.

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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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