What Is Pandas Python Library?

Key takeaways

Pandas is an open-source Python library built for data analysis, manipulation, and visualization.

• Pandas provides options for sorting, cleaning, and restructuring large data sets, helping you remove irrelevant values and correct missing data.

• Beyond data analysis, pandas integrates with machine learning libraries, such as TensorFlow, and data science libraries, such as NumPy.

• You can use pandas across multiple stages of your data science projects, from cleaning and analyzing data to preparing it for machine learning models.

Learn how pandas can help simplify your data analysis and machine learning projects. Afterward, build hands-on data skills by enrolling in Google's Data Analysis with Python Specialization. You'll have a chance to apply data analysis techniques and use pandas to manipulate data. Upon completion, you'll have earned a career certificate. 

Python in AI and machine learning

Programming for artificial intelligence and developing machine learning applications requires a language that can meet specific needs. Python is well-equipped to handle the demands of this space. One factor that makes this general-purpose language stand out is its data analysis and classification capabilities, two essential aspects of AI and machine learning projects.

Additionally, Python provides many data visualization tools and integrates well with other programming languages. Another reason Python can excel in this area is due to the several libraries it offers, including pandas, which brings it to the top of the list for the best AI and machine learning programming languages. 

What is pandas Python library?

Pandas is an open-source programming library offering programmers working in Python a more efficient way to analyze data, create visualizations, and manipulate data sets. Although the primary use for pandas is data analysis, this library also supports machine learning, allowing you to prepare the data that you will ultimately use when training your machine learning model.

The pandas library has several features that can help simplify your job. When working with large data sets, you can use pandas to sort through all that information and find the data you’re looking for based on specific conditions. It also helps to improve the overall quality of your data, with the ability to remove irrelevant values, empty sections of your data set, and correct missing values. In some cases, you may need to manipulate your data, and pandas conveniently offers features that allow you to do things such as restructure and combine data sets. Additionally, you can create data visualizations with pandas visualization tools or integrate them with other Python libraries.

Pandas has applications beyond data analysis. The machine learning models built in other frequently used Python libraries, such as TensorFlow, can use the structured data sets put together in pandas. The pandas library is also popular in the data science community since it integrates well with data science Python libraries and provides you with more options regarding what you can accomplish with your data. 

Learn more: Machine Learning Models and How to Build Them

What is the difference between pandas and NumPy?

NumPy, which stands for Numerical Python, is a core library for scientific computing in Python. While pandas is better suited for data manipulation and analysis, NumPy is ideal for numerical computations and raw data management.

You can get hands-on with the libraries that data science professionals rely on with Google's Data Analysis with Python Specialization. In as little as one month, you'll build a strong foundation in analyzing and cleaning real-world data sets using NumPy and pandas.

How to install pandas in Python

You have multiple options for installing pandas. Pandas documentation recommends using the Miniforge distribution to install the Conda package manager. This lets you install pandas and several other libraries on different platforms, including Windows, macOS, and Linux.

If you’re already set up with Python, you can install pandas through the pip package manager from PyPI. To do this, simply enter the command “pip install pandas.” Pandas officially supports Python versions 3.11 or higher, so be sure to have one of these versions on your device [1].

Pros and cons of pandas

The pandas library offers several benefits; however, it also has some challenges and shortcomings. Here’s a quick overview of some of the main pros and cons of pandas:

Pros

• Thanks to the fact that pandas is open source, it’s easily accessible.

• It can support tasks in multiple areas, such as machine learning, data visualization, and analytics.

• Pandas can function at higher speeds than many other Python libraries.

• Since pandas is part of Python, a beginner-friendly programming language, it’s easy to use. 

• It has several beneficial features relating to managing data and data sets, such as automatic data alignment, managing missing data, flexible aggregating and transforming data, and tools that allow you to upload data from different sources.

Cons

• Other Python libraries may better suit your needs than pandas when working with exceptionally large data sets.

• Pandas is useful for machine learning with regard to preparing data for training a model. However, when building deep learning models, you must transition to other Python libraries.

• Before using pandas, you first need to establish the ability to program in Python, which may mean learning an entirely new language if you don’t have any previous experience. 

Other Python libraries for machine learning

Python offers numerous programming libraries alongside pandas, many of which apply to machine learning. 

TensorFlow

TensorFlow is a Python library for machine learning, helping you to process data for building and training machine learning models. You can accomplish this from almost anywhere, whether using a desktop, mobile device, or even the cloud. Some specific machine learning applications that TensorFlow supports include image processing and natural language processing.

Matplotlib

While Matplotlib isn’t ideal for building actual machine learning models, its strengths relating to machine learning come from its ability to create data visualizations to represent insights provided by the data. Another advantage of Matplotlib is that it integrates well with pandas.

PyTorch

PyTorch is a popular Python machine learning library that simplifies the process of implementing neural networks and creating deep learning models. Specific machine learning applications for PyTorch include natural language processing, image recognition, and computer vision. 

Explore free Python and data science resources

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