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17 Must Read Pandas Interview Questions & Answers [For Freshers & Experienced]

Pandas is a BSD-licensed and open-source Python library offering high-performance, easy-to-use data structures, and data analysis tools. Python with Pandas is used in a wide array of disciplines, including economics, finance, statistics, analytics, and more. In this article, we have listed some essential pandas interview questions and NumPy interview questions that a python learner must know. If you want to learn more about python, check out our data science programs.

Pandas Interview Questions & Answers

Question 1 – Define Python Pandas.

Pandas refer to a software library explicitly written for Python, which is used to analyze and manipulate data. Pandas is an open-source, cross-platform library created by Wes McKinney. It was released in 2008 and provided data structures and operations to manipulate numerical and time-series data. Pandas can be installed using pip or Anaconda distribution. Pandas make it very easy to perform machine learning operations on tabular data.

Question 2 – What Are The Different Types Of Data Structures In Pandas?

Panda library supports two major types of data structures, DataFrames and Series. Both these data structures are built on the top of NumPy. Series is a one dimensional and simplest data structure, while DataFrame is two dimensional. Another axis label known as the “Panel” is a 3-dimensional data structure and includes items such as major_axis and minor_axis.

Source

Question 3 – Explain Series In Pandas.

Series is a one-dimensional array that can hold data values of any type (string, float, integer, python objects, etc.). It is the simplest type of data structure in Pandas; here, the data’s axis labels are called the index.

Question 4 – Define Dataframe In Pandas.

A DataFrame is a 2-dimensional array in which data is aligned in a tabular form with rows and columns. With this structure, you can perform an arithmetic operation on rows and columns.

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Question 5 – How Can You Create An Empty Dataframe In Pandas?

To create an empty DataFrame in Pandas, type

import pandas as pd

ab = pd.DataFrame()

 

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Question 6 – What Are The Most Important Features Of The Pandas Library?

Important features of the panda’s library are:

  • Data Alignment
  • Merge and join
  • Memory Efficient
  • Time series
  • Reshaping

Read: Dataframe in Apache PySpark: Comprehensive Tutorial

Question 7 – How Will You Explain Reindexing In Pandas?

To reindex means to modify the data to match a particular set of labels along a particular axis.

Various operations can be achieved using indexing, such as-

  • Insert missing value (NA) markers in label locations where no data for the label existed.
  • Reorder the existing set of data to match a new set of labels.

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Question 8 – What are the different ways of creating DataFrame in pandas? Explain with examples.

DataFrame can be created using Lists or Dict of nd arrays.

Example 1 – Creating a DataFrame using List

import pandas as pd    

# a list of strings    

Strlist = [‘Pandas’, ‘NumPy’]    

# Calling DataFrame constructor on the list    

list = pd.DataFrame(Strlist)    

print(list)   

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Example 2 – Creating a DataFrame using dict of arrays

import pandas as pd    

list = {‘ID’: [1001, 1002, 1003],’Department’:[‘Science’, ‘Commerce’, ‘Arts’,]}    

list = pd.DataFrame(list)    

print (list)   

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Question 9 – Explain Categorical Data In Pandas?

Categorical data refers to real-time data that can be repetitive; for instance, data values under categories such as country, gender, codes will always be repetitive. Categorical values in pandas can also take only a limited and fixed number of possible values. 

Numerical operations cannot be performed on such data. All values of categorical data in pandas are either in categories or np.nan.

This data type can be useful in the following cases:

If a string variable contains only a few different values, converting it into a categorical variable can save some memory.

It is useful as a signal to other Python libraries because this column must be treated as a categorical variable.

A lexical order can be converted to a categorical order to be sorted correctly, like a logical order.

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Question 10 – Create A Series Using Dict In Pandas.

import pandas as pd    

import numpy as np    

ser = {‘a’ : 1, ‘b’ : 2, ‘c’ : 3}    

ans = pd.Series(ser)    

print (ans)   

Question 11 – How To Create A Copy Of The Series In Pandas?

To create a copy of the series in pandas, the following syntax is used:

pandas.Series.copy

Series.copy(deep=True)

* if the value of deep is set to false, it will neither copy data nor the indices.

Question 12 – How Will You Add An Index, Row, Or Column To A Dataframe In Pandas?

To add rows to a DataFrame, we can use .loc (), .iloc () and .ix(). The .loc () is label based, .iloc() is integer based and .ix() is booth label and integer based. To add columns to the DataFrame, we can again use .loc () or .iloc ().

Question 13 – What Method Will You Use To Rename The Index Or Columns Of Pandas Dataframe?

.rename method can be used to rename columns or index values of DataFrame

Question 14 – How Can You Iterate Over Dataframe In Pandas?

To iterate over DataFrame in pandas for loop can be used in combination with an iterrows () call.

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Question 15 – What Is Pandas Numpy Array?

Numerical Python (NumPy) is defined as an inbuilt package in python to perform numerical computations and processing of multidimensional and single-dimensional array elements. 

NumPy array calculates faster as compared to other Python arrays.

Question 16 – How Can A Dataframe Be Converted To An Excel File?

To convert a single object to an excel file, we can simply specify the target file’s name. However, to convert multiple sheets, we need to create an ExcelWriter object along with the target filename and specify the sheet we wish to export.

Question 17 – What Is Groupby Function In Pandas?

In Pandas, groupby () function allows the programmers to rearrange data by using them on real-world sets. The primary task of the function is to split the data into various groups.

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Frequently Asked Python Pandas Interview Questions For Experienced Candidates

Till now, we have looked at some of the basic pandas questions that you can expect in an interview. If you are looking for some more advanced pandas interview questions for the experienced, then refer to the list below. Seek reference from these questions and curate your own pandas interview questions and answers pdf.

1. What do we mean by data aggregation?

One of the most popular pandas questions that are frequently asked in interviews is this one. The main goal of data aggregation is to add some aggregation in one or more columns. It does so by using the following

Sum- It is specifically used when you want to return the sum of values for the requested axis.

Min-This is used to return the minimum values for the requested axis.

Max- Contrary to min, Max is used to return a maximum value for the requested axis. 

2. What do we mean by Pandas index? 

Yet another frequently asked pandas interview question is what do we mean by pandas index. Well, you can answer the same in the following manner. 

Pandas index basically refers to the technique of selecting particular rows and columns of data from a data frame. Also known as subset selection, you can either select all the rows and some of the columns, or some rows and all of the columns. It also allows you to select only some of the rows and columns. There are mainly four types of multi-axes indexing, supported by Pandas. They are 

  • Dataframe.[ ]
  • Dataframe.loc[ ]
  • Dataframe.iloc[ ]
  • Dataframe.ix[ ]

3. What do we mean by Multiple Indexing?

Multiple indexing is often referred to as essential indexing since it allows you to deal with data analysis and analysis, especially when you are working with high-dimensional data. Furthermore, with the help of this, you can also store and manipulate data with an arbitrary number of dimensions. 

These are some of the most common python pandas interview questions that you can expect in an interview. Therefore, it is important that you clear all your doubts regarding the same for a successful interview experience. Incorporate these questions in your pandas interview questions and answers pdf to get started on your interview preparation!

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Conclusion

We hope the above-mentioned Pandas interview questions and NumPy interview questions will help you prepare for your upcoming interview sessions. If you are looking for courses that can help you get a hold of Python language, upGrad can be the best platform. 

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Pandas library is used for which purpose?

The main reason behind the usage of Pandas is for data analysis. Pandas allows the users to import data from various formats like Microsoft Excel, SQL, JSON, and also comma-separated values. Pandas is considered to be very useful for data analysis because it allows the users to perform different data manipulation operations like selecting, reshaping, merging, and data cleaning too. Other than that, Pandas also provide various data wrangling features.

In simple terms, we can say that Pandas make it easy to perform various time-consuming and repetitive tasks that involve data. The tasks made easy with Pandas are:

1. Merging and joining Statistical
2.analysis Data
3. normalization Data
4. filling Data
5. cleansing Data
6. inspection Loading and saving data
7. Data visualization

These are just a few of the data manipulation tasks made easy with Pandas. Data Scientists vote Pandas to be the best tool available for data analysis and manipulation.

What are some of the essential features provided by Python Pandas?

For harnessing the true power of the Pandas library in Python, you should explore some of the essential features being offered to the users. When it comes to data analysis, Pandas is considered to be the most powerful tool with plenty of features to make things easier for users.

Some of the essential features that you should know about before starting your usage with Pandas library are:

1. Data handling
2. Data alignment and indexing
3. Data cleaning
4. Handling missing data
5. Various input and output tools for reading and writing data
6. Supports multiple file formats
7. Merge and join different datasets
8. Performance optimization
9. Data visualization
10. Grouping the data as per requirement
11. Performing different mathematical operations on the available data
12. Masking out irrelevant data to only use the required data
13. Taking out unique data from various repetitions in the dataset

What is the reason behind importing Pandas library in Python?

Pandas is an open-source Python library that is the most widely used one for performing various data analysis, data science, and machine learning tasks. Pandas is the most popular package for data wrangling, and it works pretty well with various other data science modules in the Python ecosystem. Pandas library is the first preference for anything when it comes to data for every data science and data analysis professional.

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