Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Last Updated: Mar 27, 2024
Difficulty: Easy

Difference Between NumPy and Pandas

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Hello Ninjas, do you know what NumPy and Pandas are? Have you ever performed any of these two libraries in your Python applications? If not, and you want to know about these libraries, then don't worry, ninjas. Coding Ninjas got your back. We will clear all your doubts.

difference between NumPy and Pandas

In this article, we will discuss the difference between NumPy and Pandas. We will discuss what they are. Libraries in Python are the most important asset. Because using the libraries, we can write simpler code and can get fast execution of it.  Before moving on to the main topic, let us understand what NumPy and Pandas are and their features.

What is Numpy in Python?

In Python, we have lists that work as arrays for us. But when we perform operations on the lists with large-size data, it becomes slow. There NumPy comes into the picture. NumPy is an open-source library in Python. NumPy stands for Numerical Python. It is used for various purposes like data analysis, numerical computation, and scientific computation. 

NumPy is very much faster than a list. NumPy provides an array object which is known as ndarray. This array object provides us with efficient computation and manipulation of multidimensional arrays. On these arrays, we can apply various functions that are provided by NumPy. These functions are:

  • Linear algebra,
     
  • Fourier transform,
     
  • Statistics functions, etc.
     

Let us look at some features of NumPy.

Features 

There are different features of using NumPy:

features of NumPy
  • NumPy supports vectorized operations: This is the most important feature of NumPy. It supports vectorized operations. Vectorized operations means that we can perform operations on the entire array at once. This thing makes them more efficient and faster than the traditional loops in Python.
     
  • NumPy can broadcast the arrays: NumPy provides the ability to broadcast the arrays. We can broadcast any of the arrays with any shape and size. It also allows us to perform operations on them. If we do the same thing with others, it requires more complex loops and indexing.
     
  • NumPy provides a random number generation ability: NumPy can help us to generate random numbers. Because NumPy comes up with several functions that can help us to generate random numbers. It can generate normally distributed random numbers, uniformly distributed random numbers, and so on.
     
  • NumPy can integrate with other libraries: NumPy allows it to integrate with other libraries. It is designed to integrate with other scientific libraries in Python. These libraries are SciPy, matplotlib, etc.
     

Let us understand NumPy with the help of an example.

Example of NumPy

Here is an example to understand the NumPy library in Python:

# Importing NumPy library
import numpy as np


# Creating a two-dimensional array 
# Shape of the array is (2, 3) and filling it with random values
myArr = np.random.rand(2, 3)
print("Array is: ",myArr)


# Finding the shape of the myArr
print("Shape of the Array is:",myArr.shape)


# Getting the number of dimensions of the myArr
print("Number of dimensions:",myArr.ndim)


# Finding the sum of all elements of the myArr
print("Sum of all elements is:",myArr.sum())


# Finding the total number of elements available in the myArr
print("Size of the Array is:",myArr.size)


# Finding the minimum and maximum values of the myArr
print("Minimum value is:",myArr.min(),"and Maximum value is:",myArr.max())

 

Output

Array is:  [[0.74335629 0.63534874 0.70232367]
 [0.62727798 0.40585586 0.4298033 ]]
Shape of the Array is: (2, 3)
Number of dimensions: 2
Sum of all elements is: 3.543965831067365
Size of the Array is: 6
Minimum value is: 0.40585585920675815 and Maximum value is: 0.7433562913101969


Also see, Swapcase in Python and Convert String to List Python.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

What is Pandas in Python?

Pandas is also an open-source library in Python. It is also used for data manipulation and analysis. We can use Pandas because it gives us the easiest way to store and manipulate structured and tabular data. Pandas also provides a data structure which is known as DataFrame. 

DataFrame in Python is a 2D(two-dimensional) labeled data structure. It has columns with different data types, such as numeric, string, boolean, and row indices. It is similar to a spreadsheet or a SQL table. Pandas allows for the efficient handling of large datasets.

Pandas provides us with a variety of functions for data cleaning, data transformation, and analysis. We can also perform 

  • Data filtering
     
  • Sorting
     
  • Grouping
     
  • Aggregating
     
  • Merging 
     
  • Reshaping 

Features

There are different features of using pandas:

features of Pandas
  • Pandas provides data cleaning and preprocessing: Pandas can provide us to help to clean data. Pandas has a variety of functions that can also help us to preprocess the data. It helps us to handle missing or null data.
     
  • Pandas can do data visualization: If we want to create some charts based on some data, then Pandas can help us to create charts. Pandas can visualize data by creating graphs, plots, histograms, etc. 
     
  • Pandas can read data from anywhere: Pandas can help us to read data from various sources. These sources can be an Excel file, CSV, SQL database, etc.
     
  • Pandas provides good performance: The design of Pandas library is like that it can handle large datasets easily. That’s why it provides good performance while doing operations on these datasets.
     

Let us understand Pandas with the help of an example.

Example of Pandas

Here is an example to understand the Pandas library in Python:

# Importing the Pandas library
import pandas as pd


# Creating a dictionary
ninjasData = {'name': ['Ninja1', 'Ninja2', 'Ninja3', 'Ninja4', 'Ninja5'],'age': [20, 30, 18, 42, 22],'city': ['Mathura', 'Vrindavan', 'Lucknow', 'Delhi','Patna']}


# Create a DataFrame from the dictionary
dataFrame = pd.DataFrame(ninjasData)


# Print the DataFrame
print(dataFrame)
print()


# Get the mean age of the people in the DataFrame
mean_age = dataFrame['age'].mean()
print("Mean age is:", mean_age)


# Select a subset of the DataFrame using boolean indexing
subset = dataFrame[dataFrame['age'] > 25]
print("Subset of DataFrame is:")
print(subset)

 

Output 

name  age       city
0  Ninja1   20    Mathura
1  Ninja2   30  Vrindavan
2  Ninja3   18    Lucknow
3  Ninja4   42      Delhi
4  Ninja5   22      Patna

Mean age is: 26.4
Subset of DataFrame is:
name  age       city
1  Ninja2   30  Vrindavan
3  Ninja4   42      Delhi

 

Must Read Python List Operations

Difference Between Numpy and Pandas in Python

There are several differences between NumPy and pandas, as mentioned below:

NumPy Pandas
It supports multi-dimensional arrays and matrices. It supports data frames and series.
It is used for numerical computing. It is used for data manipulation and analysis.
NumPy arrays are indexed by integers only. Pandas data frames and series can be indexed by both integers and labels.
NumPy arrays do not have built-in support for handling missing data. Pandas provides several methods for dealing with missing data.
It is faster than Pandas when performing numerical operations on arrays. It is faster when working with large data sets that require data manipulation and analysis.
NumPy arrays are generally more memory-efficient than Pandas data frames. Pandas data frames store data in a two-dimensional table with column and row labels.
It supports a wide range of data types than Pandas data frames. It is limited to basic data types such as integers, floats, and strings.

Must Read, Python for Data Science

Frequently Asked Questions 

What do you mean by NumPy in Python?

NumPy is a library in Python. It is used for scientific computing. It gives a multidimensional array object. It also provides masked arrays and matrices. We can perform several operations on it, as like arrays.

How can we install NumPy?

We can install the NumPy library by entering the pip command in the Python console. Pip is a Python package manager. It is used to install libraries in Python.

What do you understand by Pandas in Python?

Pandas is also a library in Python. It is primarily used for data manipulation and data analysis. By using the Pandas library, you can store large datasets. You can perform various operations on these datasets. You can also perform operations like read and write from CSV or Excel files.

How can we install Pandas?

We can install the Pandas library using the pip command on the Python console. You just need to write “pip install pandas” on it. 

Conclusion

This article delves into the difference between NumPy and pandas. We have discussed the NumPy and pandas with an example and what they are used for. You can check out our other blogs to enhance your knowledge:

We hope this blog helped you to understand the difference between NumPy and pandas. You can refer to our guided paths on the Coding Ninjas Studio platform. You can check our course to learn more about DSADBMSCompetitive ProgrammingPythonJavaJavaScript, etc. 

To practice and improve yourself in the interview, you can also check out Top 100 SQL problemsInterview experienceCoding interview questions, and the Ultimate guide path for interviews

Happy Learning!!

Topics covered
1.
Introduction
2.
What is Numpy in Python?
2.1.
Features 
2.2.
Example of NumPy
3.
What is Pandas in Python?
3.1.
Features
3.2.
Example of Pandas
4.
Difference Between Numpy and Pandas in Python
5.
Frequently Asked Questions 
5.1.
What do you mean by NumPy in Python?
5.2.
How can we install NumPy?
5.3.
What do you understand by Pandas in Python?
5.4.
How can we install Pandas?
6.
Conclusion