Python Pandas is a renowned software library written explicitly for Python. You will most likely be questioned on it when you attend a Python interview, so it’s safe to review a few Python Pandas questions before your interview. Our article has 25 common recommendations to help you ace your upcoming interview. Let’s look at them.
1. What Is Python Pandas?
Pandas, or Python Pandas, is a common data analysis and manipulation software library. This open-source library was explicitly created for Python by Wes McKinney in 2008. It has useful data structures and operations for effectively manipulating time series and numerical data and is mostly used for machine learning operations on tabular data. Lastly, Python Pandas can be installed using Anaconda or pip distribution.
2. What Do You Understand By Categorical Data In Pandas?
Python Panda has categorical data, which refers to real-time repetitive data, e.g., values under different categories such as gender and country. These categorical values only allow a fixed or limited number of potential values and can’t support any numerical operations. All the values in this type of data either exist in np. nan or categories. Categorical data informs other Python libraries that a certain column should be treated as a categorical variable or to convert a string variable with limited values into a categorical value to save memory. It also allows the conversion of lexical orders into categorical orders for correct sorting.
3. What Are The Main Features Of Python Pandas?
Python Pandas has several features allowing it to support various data formats such as SQL Database, JSON, HTML, and CSS. They include:
- A range of powerful data manipulation and cleaning tools
- Several flexible pivoting and reshaping options
- Time-series data handling features ( built-in)
- Powerful alignment and indexing features
- Simple yet intuitive data visualization tools
- Extremely fast and efficient data processing tools
4. Walk Us Through How You Normally Sort A Dataframe
The simplest way to sort a DataFrame in Python Pandas is the sort. values () method. It sorts the data frame by its columns or rows and uses three main attributes, i.e., by, ascending, and axis. Axis specifies whether to sort for columns or rows while (by) contributes to the sorting order by specifying the columns or rows responsible for the operation. Lastly, ascending sorts the DataFrame into ascending order by default. To sort the data in descending order, you only need to specify ascending= false.
5. How Do You Normally Merge Two Data Frames?
Combining two data frames with SQL-style join methods is possible using the merge() method. You must also pass the different types of existing joins to an optional argument. They include:
- Outer join- This join is passed for records with matching values in the right or left data frames. Values are only shown if the rows match; otherwise, NaN is obtained.
- Inner join- The inner join works where there are similar values in both data frames
- Right join- This join shows all the records from the right data frame and only matched records from the left.
- Left join- Left join shows all records from the left data frame and only matching ones from the right.
6. Walk Us Through How You Would Delete A Column Or Row From A Pandas Dataframe
I would use the drop () method with the help of the axis, in place, and ascending attributes to delete a row or column from the data frame. The axis attribute is generally used to determine whether to sort for columns or rows, while the in-place attribute can delete a column without reassigning the DataFrame when set to delete or True. On the other hand, the ascending DataFrame sorts the DataFrame in ascending order by default.
7. Mention The Main Uses Of Python Pandas
Python Pandas has several features that allow data cleaning, transformation, and analysis. Main uses include:
- Data cleaning- Python Pandas allows you to clean data using different criteria to filter rows and columns and remove missing values.
- Data visualization- You can visualize data using tools such as histograms, plot bars, lines, and Matplotlib.
- Data storage- You can store the cleaned data in files and databases or transform it into a CSV file.
- Answering questions about the data- You can calculate statistics and answer questions such as the median, max, min, and average of every column.
8. What’s The Relationship Between Data Pandas And Jupyter Notebooks?
Jupyter Notebook offers Panda users a conducive environment to use Pandas for data exploration and modelling. It allows code execution in specific cells, saving users from running an entire file. Jupyter Notebooks come in handy when manipulating large datasets that require heavy transformations. Lastly, users can easily visualize Python Pandas plots and data frames using Notebooks, explaining why it is a popular platform among Panda users.
9. Can Someone Use Python Pandas Without Any Python Coding Experience?
It would be best if you were adept with all the basics of the Python programming language before learning or thinking of using Python Pandas. People without experience coding with Python should therefore consider learning it and obtaining a level of experience before picking up Python Pandas. A few things to note when learning Python that plays a huge role in Pandas include dictionaries, tuples, lists, iterations, and functions. It’s also important to familiarize oneself with Numpy when learning Python since Pandas is built on top of it, meaning that most Numpy structures are replicated in Pandas.
10. Why Should Someone Learn Python Pandas?
Python Pandas is a powerful, flexible, and easy-to-use library that coders and software developers should learn. Some of the capabilities that should entice people to learn it include:
- Quick data visualization through simple interface plotting
- Dataframes manipulation through delete, add, and insert functions
- Familiar data aggregation methods and tools through the Pandas pivot_table function
- Easier data grouping through the group_by method
- Easier reshaping of datasets
- Versatile merging and joining of datasets
- Effective time-series functionality thanks to capabilities such as moving windows, lagging, and frequency conversion
- Easier addition of data depth thanks to hierarchical axes
- Easier management of missing data
- Ability to read, access, and view data in several tabular formats
11. What Do You Understand By Concatenation In Python Pandas?
Concatenation is a common method of combining data in Python Pandas. It combines datasets by placing the rows of one of them underneath that of another. It is also commonly referred to as the process of appending data. It gives you several ways to combine and manipulate data, e.g., you can give the datasets a similar column or include any mismatched columns. Concatenation, therefore, results in a longer, not wider, dataset. It is mostly focused on merging data based on columns, not records.
12. In Your Opinion, What Are Some Of The Advantages Of Python Pandas?
After using Python Pandas for several years, here are the advantages I can think of:
- Better data representation- Python Pandas has streamlined data representation methods that offer better data analysis and manipulation. Its simple data representation means have significantly helped me in my data science projects
- A powerful set of features- Pandas has an array of features and commands that allow better data manipulation. Such features make it easy to filter data depending on the needed condition and segment/segregate data according to preference.
- Handling large datasets- Python Pandas can effectively handle large datasets, saving time. It’s easier to import large data amounts at a relatively faster rate.
- Less writing- Python Pandas saves coders and programmers from writing multiple lines. It shortens the data handling procedure, giving coders more time to focus on different data analysis algorithms.
- Flexibility and customization ability- Panda’s powerful features allow developers to edit, pivot and customize data depending on their desired results.
- It is explicitly made for Python- Python Pandas is designed for Python, a programming language with several important features, allowing it to offer maximum productivity.
13. What Don’t You Like About Python Pandas?
Even though I have found Python Pandas useful since its unveiling, here are a few of its shortcomings I have to point out:
- Compatibility issues- Python Pandas has poor compatibility with 3D matrices. One normally has to turn to NumPy or other libraries if they have to work on something other than 2D matrices.
- Poor documentation- Pandas’ documentation is not up to standards, making it difficult to understand the library’s harder functions. It can therefore be difficult for beginners to understand how it works.
- Different Syntax From Python- Pandas was explicitly designed for Python, but its code syntax differs from the programming language. Switching back and forth can therefore be hectic.
14. What Are The Main Differences Between Pandas And Numpy?
NumPy is an extension module of Python written in C Language. It mainly differs from Pandas in the following ways:
- Type of data- Whereas the Pandas module works better with tabular data, NumPy works with numerical data.
- Data analysis tools- Pandas’ main data analysis tools are data frame and series, while the NumPy module has arrays.
- 50K rows< performance – When it comes to 50K rows or less, NumPy performs better than Panda.
- Memory Consumption- Pandas consume more memory than NumPy.
- 500K rows> performance- Pandas performs better than NumPy when it comes to 500K rows or more.
15. What Are The Advantages Of Pandas Over Numpy?
Pandas towers over NumPy regarding performance, industrial coverage, and tools. It offers more powerful tools and performs better for 500K rows or more than NumPy. As for industrial coverage, Pandas is mentioned in more than 70 company stacks and 46 developer stacks compared to NumPy’s 62 company stacks and 32 developer stacks.
16. Do You Know The Right Audience For Python Pandas?
Python Pandas serves five main audiences. The first and most obvious are people interested in learning Python, given that the library was explicitly designed for the programming language. Others include:
- People interested in becoming Python application developers.
- People aspiring to become Python analysts, architects, and testers
- People intend to acquire more skills and develop professionally.
- People interested in learning and obtaining more experience in analytics
Python Pandas is a good option for anyone wanting to advance in Python-related studies and professional skills.
17. Mention The Data Structures In Panda
There are two main data structures in Panda, namely series and DataFrames, which are both built on NumPy. The main difference between the two is that series is a one-dimensional data structure while a data frame is two-dimensional. Series can hold any data type, including strings, floating point numbers, and integers, while a DataFrame contains data, columns, and rows. The data frame is usually created from the dictionary, lists, or a list of dictionaries, while the series data structure must be called. The axis labels in series are generally referred to as the index. Lastly, Python Panda has a 3D data structure called a panel, which stores data heterogeneously.
18. Mention Some Of The Basic Time Series Functions Supported By Pandas
The time series-related functions supported by Python Pandas include:
- Resampling, which is basically converting a time series to a different frequency
- Converting time series information from different formats and sources
- Calculating data and time with relative or absolute time increments
- Using timezone information to manipulate or convert date and time
- Generating time span or fixed-frequency data sequences
19. Define Matplotlib
Matplotlib is one of the most common data visualization libraries used by coders, developers, and data scientists to plot data. John D. Hunter developed it in 2003 as an open-source library to create animated, static, and interactive data visualizations. Matplotlib users get access to several toolkits with extended functionalities such as Excel, Basemap, GTK tools, and Cartopy. A common feature in Matplotlib is Pylab, a module that acts as the open-source platform’s interface, allowing for graphical plotting.
20. Define Pandas Numpy
Like Python Pandas, Pandas NumPy is another great open-source library designed for Python. Thanks to its powerful mathematical functions and sophisticated N-dimensional array objects, it works with several datasets and allows scientific computing with Python. NumPy has several functionalities, such as random number capabilities, Fourier transforms, and linear algebra. Additionally, Pandas NumPy can integrate with Fortran code and C/C++ thanks to its powerful integration tools.
21. Mention The Use Of Reindexing And Groupby In Python Pandas
Reindexing is a common method in Python Pandas that changes the DataFrame’s column and row labels, allowing the data to match certain sets of labels along a target axis. It can also insert missing value markers in label locations with missing data. On the other hand, GroupBy is a common tool on Python Pandas used to split data into groups based on given criteria. It also allows labels to be mapped to the group names and has several variations that can fasten and ease the data-splitting process.
22. Define Vectorization In Python Pandas And List Some Of Its Alternatives
Vectorization allows operations to be run on the entire array, thus reducing the number of iterations performed by different functions. A few vectorized functions offered by Python Pandas include string functions and aggregations optimized to work on series and data frames. The vectorized pandas functions allow quick execution of operations. Python Pandas alternatives include SciPy, NumPy, R Language, Anaconda, Dask, Panda, and Pentaho Data to answer the second part of the question.
23. How Many Statistical Functions Do Python Pandas Have? Mention Them And Their Uses
Python Pandas has seven main statistical functions. The sum () returns the sum of all values, while the min () returns the minimum value. The mean () function returns the average of different values (mean), while std () returns the standard deviation of numerical columns. Max () returns the maximum value, while prod () returns the products of the values. Lastly, abs () brings back the absolute value.
24. Why Would You Encourage Someone To Use Python Pandas?
Python Pandas come with several advantages that people should explore. They include:
- It has time series functionality that can be used to manipulate dates, times, and time frames.
- It has powerful functionalities for pivoting and reshaping datasets
- It allows dataset merging and joining
- It has a fast and efficient means of handling data thanks to its DataFrame feature
- It allows fast merging and joining of data
- It supports operations such as fancy indexing, label-based slicing, and large dataset sunsetting
- It allows the loading of data into in-memory data objects. This happens from several file formats
In short, Python Pandas has several functionalities that I’d love other people to explore.
25. How Would You Convert A Data Frame To An Excel File? Also, Mention How To Add Row, Index, Or Column To A Pandas Dataframe
I would specify the target’s file name to convert a single object to an Excel file. I would create an ExcelWriter object for several sheets and then specify the target filename and the sheet to export to. On the second part of the question, I would add rows by using .loc (), which is label-based, i.loc () for integer-based functions, and .ix () for both label and integer-based functions. I would use .loc () and .iloc () to add columns to the data frame.
These 25 recommendations are some of the most asked questions in Python Pandas interviews. Ensure you have the answers at your fingertips before entering the interview room. We wish you all the best and remember to work on your first impression and verbal and non-verbal language.