Numpy and Pandas are two popular libraries in Python that are widely used for data manipulation and analysis. Both libraries have their unique features and are suited for different types of tasks. In this article, we will compare Numpy and Pandas and provide a table highlighting their differences and code examples to help you understand how to use each library.

Numpy, short for ‘Numerical Python,’ is a library that supports large, multi-dimensional arrays and matrices of numerical data and a large collection of mathematical functions to operate on these arrays. Numpy is particularly useful for operations on numerical data, such as mathematical computations and linear algebra.

Pandas, on the other hand, is a library that provides data structures and data analysis tools for handling and manipulating numerical tables and time series data. It is built on top of Numpy and is designed for working with structured data, such as tables and time series data.

Pandas provide powerful data manipulation capabilities, such as merging, grouping, and reshaping data.

Here is a table that summarizes the key differences between Numpy and Pandas:

NumPy Example

import numpy as np

# Creating a 2-dimensional array
arr = np.array([[1, 2, 3], [4, 5, 6]])

#Performing mathematical operations on the array
print(arr + 2)
print(np.sin(arr))  

The following will be the output:

[[3 4 5] [6 7 8]][[0.84147100 0.90929743 0.14112001] [-0.7568025  -0.95892427 -0.2794155 ]]

Pandas Example

import pandas as pd

# Creating a dataframe
data = {'name': ['John', 'Jane', 'Bob'], 'age': [30, 25, 35]}
df = pd.DataFrame(data)

#Performing operations on the dataframe
print(df.groupby(by='name').mean()) 

The following will be the output:

name     age
Bob 35
Jane 25
John 30

In conclusion, NumPy and Pandas are both powerful libraries for data manipulation and analysis in Python. NumPy is best suited for numerical operations on large arrays and matrices, while Pandas is designed for working with structured data, such as tables and time series data. Both libraries can be used together to perform complex data analysis tasks.