What are the differences between NumPy and Pandas with examples?

Can you explain the differences between NumPy and Pandas with examples?

Sure! NumPy is primarily used for numerical computations and provides support for arrays, while Pandas is used for data manipulation and analysis with DataFrames. For example, NumPy can perform element-wise operations on arrays, while Pandas can handle missing data and perform group operations.

Can you give me a specific example of how to use both?

Absolutely! For NumPy, you can create an array and perform operations like this:

import numpy as np
array = np.array([1, 2, 3])
result = array * 2  # Output: [2, 4, 6]

For Pandas, you can create a DataFrame and manipulate it:

import pandas as pd
data = {'A': [1, 2, None], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
df.fillna(0)  # Output: DataFrame with NaN replaced by 0

What are the advantages of using Pandas over NumPy?

Pandas offers more flexibility for data manipulation, such as handling missing values, merging datasets, and performing group operations. It's designed for working with structured data, making it easier to analyze and visualize.

When should I choose NumPy over Pandas?

You should choose NumPy when you need high-performance numerical computations, especially with large arrays, and when you don't require the additional features of Pandas for data manipulation.