What is the Best Data Science Library? Pandas vs NumPy

When dealing with data on large scales and performing vast projects, having the right tools can make a huge difference in your process. This week I wanted to zoom in explicitly on tools in Python for data science and manipulation. I wanted to get a better idea of data science in a hands-on application meaning coding and libraries. Python has become a favorite programming language of mine beyond data science tasks, so I wanted to start here. Among the various libraries available in Python, Pandas and NumPy stand out as two of the most powerful and widely used for data science and machine learning. But what exactly are these libraries, and how do they compare? 

What is NumPy?

NumPy, short for Numerical Python, is the fundamental package for scientific computing with Python. It provides the user the ability to create large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these values.

One of the leading benefits of using NumPy is its ability to handle large datasets efficiently. The functions in the library are optimized for efficiency, allowing for quick and easy array manipulations regardless of the data size.

Key Functionalities and Use Cases

• Array Creation and Manipulation: NumPy provides functions to create arrays and perform operations such as reshaping, slicing, and indexing.

• Mathematical Operations: Perform element-wise operations, linear algebra, statistical operations, and more.

• Integration with C/C++ and Fortran: Allows for high-performance code integration.

This is a quick example I created to showcase simple operations you can try with NumPy. This code creates a simple array list of [1, 2, 3, 4, 5]. The squared array operation squares every individual element in the code, and in the last example that transforms the array into a 1x5 two-dimensional array.

import numpy as np

# Creating an array

arr = np.array([1, 2, 3, 4, 5])

print("Array:", arr)

# Performing an element-wise operation

squared_arr = arr ** 2

print("Squared Array:", squared_arr)

# Reshaping the array

reshaped_arr = arr.reshape(1, 5)

print("Reshaped Array:", reshaped_arr)

If you’re interested in exploring further, here is the documentation and download link for NumPy: https://numpy.org/doc/stable/

What is Pandas?

Pandas is an open-source library providing similar high-functionality data structures and data analysis tools. It is built on top of NumPy and provides data structures like Series and DataFrames, which are essential for data manipulation and analysis. Pandas is often used in python to clean and handle data.

Key Functionalities and Use Cases

• DataFrames: A two-dimensional, mutable, and tabular data structure with labeled axes. These data frames are especially easy to use within the context of machine learning. 

• Data Cleaning: Removing missing data, merging and joining datasets, and reshaping data.

• Data Analysis: Powerful group-by functionality, allowing the user to perform separate operations of different parts of a data group. This includes time series analysis possibilities and also data visualization.

Here’s a simple example to demonstrate basic Pandas operations:

import pandas as pd

# Creating a DataFrame with people and their age

data = {

    'Name': ['Alice', 'Bob', 'Charlie'],

    'Age': [25, 30, 35]

}

df = pd.DataFrame(data)

print("DataFrame:\n", df)

# Adding a new column for the people’s age in 10 years

df['Age in 10 Years'] = df['Age'] + 10

print("Updated DataFrame:\n", df)

# Filtering the DataFrame for all people over 28.

filtered_df = df[df['Age'] > 28]

print("Filtered DataFrame:\n", filtered_df)

If you’re interested in checking it out, here is the documentation for Pandas: https://pandas.pydata.org/docs/

Comparing Pandas and NumPy

While both Pandas and NumPy are essential for data science, they serve different purposes and are purposeful in different areas.

Differences in Functionality

• Data Structures: NumPy provides a lot of different types of ndarrays, while Pandas offers Series and DataFrames, which are more suited for data analysis tasks. 

• Data Handling: Pandas is designed for handling and manipulating structured data, whereas NumPy is more focused on numerical data and array operations.

Performance Considerations

• Speed: NumPy is generally faster than Pandas for numerical operations due to its lower-level optimizations. However, Pandas is generally more efficient and applicable for larger tasks.

• Memory Usage: Pandas might use more memory compared to NumPy, especially when dealing with large datasets.

When deciding between Pandas and NumPy, use NumPy for numerical computations, array operations, and performance-critical tasks due to its optimized performance and efficient handling of large datasets. On the other hand, use Pandas for data cleaning, manipulation, and analysis, particularly when dealing with structured data, as it offers powerful data structures like DataFrames and Series that simplify handling complex datasets.

Sources:

https://www.analyticsvidhya.com/blog/2021/03/pandas-functions-for-data-analysis-and-manipulation/

https://www.learnenough.com/blog/how-to-import-Pandas-in-python

https://medium.com/@m.franfuentes/numpy-the-fundamental-tool-for-data-science-in-python-fa2b605a3bf9

https://www.nobledesktop.com/classes-near-me/blog/pandas-vs-numpy-for-data-analytics