Difference between Pandas VS NumPy

Last Updated : 22 Jul, 2024

Python is one of the most popular languages for Machine Learning, Data Analysis, and Deep learning tasks. It is powerful because of its libraries that provide the user full command over the data.

Today, we will look into the most popular libraries i.e. NumPy and Pandas in Python, and then we will compare them.

Pandas

Pandas is an open-source, BSD-licensed library written in Python Language. Pandas provide high-performance, fast, easy-to-use data structures, and data analysis tools for manipulating numeric data and time series.

Pandas is built on the NumPy library and written in languages like Python, Cython, and C. In Pandas, we can import data from various file formats like JSON, SQL, Microsoft Excel, etc.

Example: Pandas Library

Python

# Importing pandas library
import pandas as pd

# Creating and initializing a nested list
age = [['Aman', 95.5, "Male"], ['Sunny', 65.7, "Female"],
       ['Monty', 85.1, "Male"], ['toni', 75.4, "Male"]]

# Creating a pandas dataframe
df = pd.DataFrame(age, columns=['Name', 'Marks', 'Gender'])

# Printing dataframe
df

Output:

    Name    Marks    Gender
0    Aman    95.5    Male
1    Sunny    65.7    Female
2    Monty    85.1    Male
3    toni    75.4    Male

Numpy

Numpy is the fundamental library of Python, used to perform scientific computing. It provides high-performance multidimensional arrays and tools to deal with them.

A Numpy array is a grid of values (of the same type) that are indexed by a tuple of positive integers, Numpy arrays are fast, easy to understand, and give users the right to perform calculations across arrays.

Example: Numpy Library

Python

# Importing Numpy package
import numpy as np

# Creating a 3-D numpy array using np.array()
org_array = np.array([[23, 46, 85],
                      [43, 56, 99],
                      [11, 34, 55]])

# Printing the Numpy array
print(org_array)

Output:

[[23 46 85]
 [43 56 99]
 [11 34 55]]

Difference between Pandas and Numpy

Let's look at the side-by-side comparison of Pandas and Numpy in this table:

Pandas vs NumPy
Pandas	NumPy
When we have to work on Tabular data, we prefer the pandas module.	When we have to work on Numerical data, we prefer the NumPy module.
The powerful tools of pandas are DataFrame and Series.	Whereas the powerful tool of NumPy is Arrays.
Pandas consume more memory.	Numpy is memory efficient.
Pandas have a better performance when the number of rows is 500K or more.	Numpy has a better performance when number of rows is 50K or less.
Indexing of the Pandas series is very slow as compared to Numpy arrays.	Indexing of Numpy arrays is very fast.
Pandas have a 2D table object called DataFrame.	Numpy is capable of providing multi-dimensional arrays.
It was developed by Wes McKinney and was released in 2008.	It was developed by Travis Oliphant and was released in 2005.
It is used in a lot of organizations like Kaidee, Trivago, Abeja Inc., and a lot more.	It is being used in organizations like Walmart Tokopedia, Instacart, and many more.
It has a higher industry application.	It has a lower industry application.

Read More: Python Libraries

Conclusion

We have done a side-by-side comparison of Pandas and NumPy, explaining all the major differences between them. We have also briefly discussed Pandas and NumPy libraries with examples to give you a better understanding.

Both NumPy and Pandas are very important libraries in Python Programming, both serving their purpose. Pandas is useful for organizing data into rows and columns making it easy to clean, analyze, and manipulate data whereas NumPy is useful for efficient math on raw numbers.