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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.

What are the Different Job Titles That Encounter Pandas and Numpy Interview Questions?

Here are some common job titles that often encounter pandas in python interview questions.

1. Data Analyst

Data analysts often use Pandas to clean, preprocess, and analyze data for insights. They may be asked about their proficiency in using Pandas for data wrangling, summarization, and visualization.

2. Data Scientist

Data scientists use Pandas extensively for preprocessing and exploratory data analysis (EDA). During interviews, they may face questions related to Pandas for data manipulation and feature engineering.

3. Machine Learning Engineer

When building machine learning models, machine learning engineers leverage Pandas for data preparation and feature extraction. They may be asked Pandas-related questions in the context of model development.

4. Quantitative Analyst (Quant)

Quants use Pandas for financial data analysis, modeling, and strategy development. They may be questioned on their Pandas skills as part of the interview process.

5. Business Analyst

Business analysts use Pandas to extract meaningful insights from data to support decision-making. They may encounter Pandas interview questions related to data cleaning and visualization.

6. Data Engineer

Data engineers often work on data pipelines and ETL processes where Pandas can be used for data transformation tasks. They may be quizzed on their knowledge of Pandas in data engineering scenarios.

7. Research Analyst

Research analysts across various domains, such as market research or social sciences, might use Pandas for data analysis. They may be assessed on their ability to manipulate data using Pandas.

8. Financial Analyst

Financial analysts use Pandas for financial data analysis and modeling. Interview questions might focus on using Pandas to calculate financial metrics and perform time series analysis.

9. Operations Analyst

Operations analysts may use Pandas to analyze operational data and optimize processes. Questions might revolve around using Pandas for efficiency improvements.

10. Data Consultant

Data consultants work with diverse clients and datasets. They may be asked Pandas questions to gauge their adaptability and problem-solving skills in various data contexts.

What is the Importance of Pandas in Data Science?

Pandas is a crucial library in data science, offering a powerful and flexible toolkit for data manipulation and analysis. So, let’s explore Panda in detail: –

1. Data Handling

Pandas provides essential data structures, primarily the Data Frame and Series, which are highly efficient for handling and managing structured data. These structures make it easy to import, clean, and transform data, often the initial step in any data science project.

2. Data Cleaning

Data in the real world is messy and inconsistent. Pandas simplifies the process of cleaning and preprocessing data by offering functions for handling missing values, outliers, duplicates, and other data quality issues. This ensures that the data used for analysis is accurate and reliable.

3. Data Exploration

Pandas facilitate exploratory data analysis (EDA) by offering a wide range of tools for summarizing and visualizing data. Data scientists can quickly generate descriptive statistics, histograms, scatter plots, and more to gain insights into the dataset’s characteristics.

4. Data Transformation

Data often needs to be transformed to make it suitable for modeling or analysis. Pandas support various operations, such as merging, reshaping, and pivoting data, essential for feature engineering and preparing data for machine learning algorithms.

5. Time Series Analysis

Pandas are particularly useful for working with time series data, a common data type in various domains, including finance, economics, and IoT. It offers specialized functions for resampling, shifting time series, and handling date/time information.

6. Data Integration

It’s common to work with data from multiple sources in data science projects. Pandas enable data integration by allowing easy merging and joining of datasets, even with different structures or formats.

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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DataFrame Vs. Series: Their distinguishing features

In Pandas, DataFrame and Series are two fundamental data structures that play an important role in data analysis and manipulation. Here’s a concise overview of the key differences between DataFrame and series:

Feature DataFrame Series
Structure Two-dimensional tabular structure One-dimensional labeled array
Data Type Heterogeneous – Columns can have different data types Homogeneous – All elements must be of the same data type
Size Mutability Size Mutable – Can add or drop columns and rows after creation Size Immutable – Once created, size cannot be changed
Creation Created using dictionaries of Pandas Series, dictionaries of lists or ndarrays, lists of dictionaries, or another DataFrame Created using dictionaries, ndarrays, or scalar values, it serves as the basic building block for a DataFrame.
Dimensionality Two-dimensional One-dimensional
Data Type Flexibility Allows columns with different data types Requires homogeneity
Size Flexibility Can be changed after creation Cannot be changed after creation
Use Case Suitable for tabular data with multiple variables, resembling a database table Suitable for representing a single variable or a row/column in a DataFrame
Creation Flexibility Versatile creation from various data structures, including series Building block for a DataFrame, created using dictionaries, ndarrays, or scalar values

Understanding the distinction between DataFrame and Series is essential for efficiently working with Pandas, especially in scenarios involving data cleaning, analysis, and transformation. 

However, while DataFrame provides a comprehensive structure for handling diverse datasets, series offers a more focused, one-dimensional approach for individual variables or observations. 

Thus, we can say that both play integral roles in the toolkit of data scientists and analysts using Pandas for Python-based data manipulation.

Handling missing data in Panda

It is a crucial aspect of data analysis, as datasets often contain incomplete or undefined values. In Pandas, a famous Python library for data manipulation and analysis, various methods and tools are available to manage missing data effectively. Here is a detailed guide on how you can handle missing data in pandas:

1. Identifying Missing Data

Before addressing missing data, it’s crucial to identify its presence in the dataset. Missing values are conventionally represented as NaN (Not a Number) in pandas. By using functions like isnull() and sum(), you can systematically locate and quantify these missing values within your dataset.

2. Dropping Missing Values

A simplistic yet effective strategy involves the removal of rows or columns containing missing values. The dropna() method enables this, but caution is necessary as it might impact the dataset’s size and integrity.

3. Filling Missing Values

Instead of discarding data, another approach is to fill in missing values. The fillna() method facilitates this process, allowing you to replace missing values with a constant or values derived from the existing dataset, such as the mean.

4. Interpolation

Interpolation proves useful for datasets with a time series or sequential structure. The interpolate() method estimates missing values based on existing data points, providing a coherent approach to filling gaps in the dataset.

5. Replacing Generic Values

The replace() method offers flexibility in replacing specific values, including missing ones, with designated alternatives. This allows for a controlled substitution of missing data tailored to the requirements of the analysis.

6. Limiting Interpolation:

Fine-tuning the interpolation process is possible by setting constraints on consecutive NaN values. The limit and limit_direction parameters in the interpolate() method empower you to control the extent of filling, limiting the number of consecutive NaN values introduced since the last valid observation. These are some of the topics, which one might get pandas interview questions for experienced.

7. Using Nullable Integer Data Type:

For integer columns, pandas provide a special type called “Int64″ (dtype=”Int64”), allowing the representation of missing values in these columns. This nullable integer data type is particularly useful when dealing with datasets containing integer values with potential missing entries.

8. Experimental NA Scalar:

Pandas introduces an experimental scalar, pd.NA is designed to signify missing values consistently across various data types. While still in the experimental stage, pd.NA offers a unified representation for scalar missing values, aiding in standardized handling.

9. Propagation in Arithmetic and Comparison Operations:

In arithmetic operations involving pd.NA, the missing values propagate similarly to NumPy’s NaN. Logical operations adhere to three-valued logic (Kleene logic), where the outcome depends on the logical context and the values involved. Understanding the nuanced behavior of pd.NA in different operations is crucial for accurate analysis.

10. Conversion:

After identifying and handling missing data, converting data to newer dtypes is facilitated by the convert_dtypes() method. This is particularly valuable when transitioning from traditional types with NaN representations to more advanced integers, strings, and boolean types. This step ensures data consistency and enhances compatibility with the latest features offered by pandas.

Handling missing data is a detailed task that depends on the nature of your data and the goals of your analysis. Moreover, the choice of method should be driven by a clear understanding of the data and the potential impact of handling missing values on your results.

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 numpy and pandas interview 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 bit python 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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4. What is “mean data” in the Panda series? 

The mean, in the context of a Pandas series, serves as a crucial statistical metric that provides insights into the central tendency of the data. It is a measure of average that aims to represent a typical or central value within the series. The computation of the mean involves a two-step process that ensures a representative value for the entire dataset.

Firstly, all the numerical values in the Pandas series are summed up. This summation aggregates the individual data points, preparing for the next step. Subsequently, the total sum is divided by the count of values in the series. This division accounts for the varying dataset sizes and ensures that the mean is normalized with respect to the total number of observations

To perform this computation in Pandas, the mean() method is employed. This method abstracts away the intricate arithmetic operations, providing a convenient and efficient means of get the average. By executing mean() on a Pandas series, you gain valuable information about the central tendency of the data, aiding in the interpretation and analysis of the dataset.

5. How can data be obtained in a Pandas DataFrame using the Pandas DataFrame get() method?

Acquiring data in a Pandas DataFrame is a fundamental step in working with tabular data in Python. The Pandas library provides various methods for this purpose, and one such method is the `get()` method.

Moreover, the `get()` method in Pandas DataFrame is designed to retrieve specified column(s) from the DataFrame. Its functionality accommodates single and multiple-column retrievals, offering flexibility in data extraction.

When you utilize the `get()` method to fetch a single column, the return type is a Pandas Series object. A Series is a one-dimensional labeled array, effectively representing a single column of data. This is particularly useful when you need to analyze or manipulate data within a specific column especially when you solve pandas mcq questions.

Should you require multiple columns, you can specify them inside an array. This approach results in the creation of a new DataFrame object containing the selected columns. A DataFrame is a two-dimensional, tabular data structure with labeled axes (rows and columns), making it suitable for various analytical and data manipulation tasks.

The `get()` method in Pandas DataFrame is a versatile tool for extracting specific columns, allowing for seamless navigation and manipulation of tabular data based on your analytical requirements.

6. What are lists in Python?

In Python, a list is a versatile and fundamental data structure used for storing and organizing multiple items within a single variable. Lists are part of the four built-in data types in Python, which also include Tuple, Set, and Dictionary. Unlike other data types, lists allow for the sequential arrangement of elements and are mutable, meaning their contents can be modified after creation.

Lists in Python or python pandas interview questions are defined by enclosing a comma-separated sequence of elements within square brackets. These elements are of any data type like numbers, strings, or other lists. The ability to store heterogeneous data types within a single list makes it a flexible and powerful tool for managing collections of related information.

Furthermore, lists provide various methods and operations for manipulating and accessing their elements. Elements within a list are indexed, starting from zero for the first element, allowing for easy retrieval and modification. Additionally, lists support functions like appending, extending, and removing elements, making them dynamic and adaptable to changing data requirements.

Thus, we can say that a list in Python is a mutable data structure that allows storing multiple items in a single variable. Its flexibility, coupled with a range of built-in methods, makes lists a fundamental tool for handling collections of data in Python programming, to solve pandas practice questions.

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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