Chapter 3 – 100 Most Asked Pandas Interview Questions
Topic – 100 Most Asked Pandas Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
Most Asked Python Interview Questions for Analytics
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Coming back to the topic, below is the list of 250 SQL questions to Ace any Analytics Interview
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Interview
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with solution
450. What is Pandas ?
451. What is python pandas used for ?
452. Write Steps to install Pandas on Windows.
453. What are the key features of pandas library ?
454. What is pandas dataframe ?
455. How to Import Pandas Library and also check the version of Library.
456. Mention the different types of Data Structures in Pandas?
457. How to read the different – different format files using pandas??
458. Define Series in Pandas?
459. How to create a Series from a numpy array ?
460.. Create a series using disctionary
461. What is the describe() method in pandas ?
462. What is a Data Frame?
463. Define the different ways a DataFrame can be created in pandas?
464. Create a dataframe form a List
465. Create a DataFrame from dict of ndarrays:
466. Describe how you will get the names of columns of a DataFrame in Pandas?
467. How to Covert Json(Javascript object notation) data into Dataframe ?
468 .Read a file using read_csv()
469. head() and tail()
470.xyz.info()
471. You can also take a look at the shape and size of the dataset. We have 100 rows and 6 columns in the original dataset
472. Choose n number of random sample from the dataset
473. Get all the standard mathematical analysis of each column of data set
474. Find number of distinct values – This is an important function as it will directly tell you how many categorical variables are there in your dataset
475. How to find if there is any variable/column with missing values in it?
476. isnull()
477. Find the number of null values in each column, this set of function tells you if you can ignore a column or not
478. Get the name of all the columns
479. Get the nsmallest or nlargest values from a column
480. Now comes loc and iloc – There are a few interviewers who tries to check your basics with loc and iloc
481. Slice the date – It means cutting the dataset vertically or horizontally
482. Group by in Pandas – Very useful pandas function
483. Sort the complete data frame according to one column
484. Query in data frame
485. Get unique values from a column
486. If you want. to know how many space columns are taking into your computer then use memory_usage
487. How do you select a column in a DataFrame?
488. How do you select multiple columns in a DataFrame?
489. How do you filter rows based on a condition in a DataFrame?
490. How do you merge two DataFrames in Pandas?
491. How do you handle missing values in a DataFrame?
492. How do you group data in a DataFrame?
493. How do you sort a DataFrame by one or more columns?
494. How do you apply a function to a DataFrame?
495. How do you rename columns in a DataFrame?
496. How do you create a new column in a DataFrame?
497. How do you remove a column from a DataFrame?
498. How do you export data to a CSV file using Pandas?
499. Suppose you have a DataFrame named sales_data that contains columns Year, Month, and Sales. Write a code snippet to calculate the total sales for each year and output the results in a new DataFrame with columns Year and Total Sales.
500. Suppose you have a DataFrame named customer_data that contains columns CustomerID, Name, Age, and City. Write a code snippet to filter the data to only include customers who are 18 years old or older and who live in either “New York” or “San Francisco”.
501. How do you select a subset of rows and columns from a DataFrame based on certain conditions?
502. How do you group a DataFrame by a certain column and perform an aggregation function on each group?
503. How do you merge two DataFrames based on a common column?
504. How do you create a new column in a DataFrame that is the result of a calculation involving other columns?
505. How do you rename a column in a DataFrame?
506. How do you sort a DataFrame by one or more columns?
507. How do you check if a DataFrame has any missing values?
508. How do you fill in missing values in a DataFrame with a certain value or method?
509. How do you write a pandas DataFrame to a CSV file?
510. What are some ways to handle missing data in pandas?
511. How can you handle duplicates in a pandas DataFrame?
512. How can you merge two pandas DataFrames together?
513. What are some ways to improve the performance of pandas operations?
514. How can you use pandas to group data by a certain column and perform aggregate functions on the groups?
515. How can you pivot a pandas DataFrame?
516. How can you use pandas to create a time series plot?
517. How can you use pandas to handle categorical data?
518. What are some common methods for data manipulation in pandas?
519. How can you use pandas to perform statistical analysis on a dataset?
520. How can you use pandas to handle time series data?
521. What are some techniques for reshaping data in pandas?
513. What are some ways to improve the performance of pandas operations?
514. How can you use pandas to group data by a certain column and perform aggregate functions on the groups?
515. How can you pivot a pandas DataFrame?
516. How can you use pandas to create a time series plot?
517. How can you use pandas to handle categorical data?
518. What are some common methods for data manipulation in pandas?
519. How can you use pandas to perform statistical analysis on a dataset?
520. How can you use pandas to handle time series data?
521. What are some techniques for reshaping data in pandas?
522. How can you use pandas to handle text data?
523. What are some ways to handle outliers and anomalies in a pandas DataFrame?
524. How can you use pandas to handle large datasets that don’t fit in memory?
525. How can you use pandas to handle imbalanced datasets?
526. How can you use pandas to handle multi-index DataFrames?
527. What are some techniques for feature engineering in pandas?
528. How can you use pandas to handle data from multiple sources or files?
529. What are some advanced visualization techniques in pandas?
530. How can you create a scatter plot in pandas?
531. How can you create a bar chart in pandas?
532. How can you create a line plot in pandas?
533. How can you create a histogram in pandas?
534. How can you create a box plot in pandas?
535. How can you create a heatmap in pandas?
536. How can you customize the appearance of a plot in pandas?
537. How can you create multiple plots on a single figure in pandas?
538. How can you convert a series of strings to a series of numbers in pandas, where some of the strings may contain non-numeric characters or missing values?
539. How can you identify and handle multicollinearity in a pandas DataFrame?
540. How can you select rows from a pandas DataFrame that satisfy a condition based on the values in multiple columns?
541. How can you compute the rolling standard deviation of a time series in pandas, where the rolling window size varies over time?
542. How can you handle imbalanced data in a pandas DataFrame for a classification problem?
543. How can you handle data with a mix of categorical and continuous variables in a pandas DataFrame for a regression problem?
544. How can you perform anomaly detection in a pandas DataFrame using statistical methods?
545. How can you perform sentiment analysis on text data in a pandas DataFrame?
546. How can you use pandas to perform feature selection and feature extraction for a machine learning model?
547. How can you use pandas to perform hyperparameter tuning for a machine learning model?
548. How can you optimize the memory usage of a pandas DataFrame?
549. How can you handle missing values in time series data in a way that preserves the time series structure?
550. How can you handle imbalanced data in a multi-class classification problem in a way that maintains class balance during training?
551. How can you use pandas to perform time series forecasting using machine learning models?
552. How can you use pandas to handle very large datasets that don’t fit into memory on a single machine?
553. How can you use pandas to perform natural language processing on text data in a DataFrame?
554. How can you use pandas to perform unsupervised learning on a DataFrame with mixed data types?
555. How can you use pandas to perform deep learning on a DataFrame with image data?
556. How can you use pandas to perform distributed computing across a cluster of machines?
557. How can you use pandas to handle streaming data in real-time?
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