Pandas VS Seaborn

Compare Pandas VS Seaborn and see what are their differences

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Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Seaborn

Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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2023-05-12

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2023-10-20

Pandas

Website: pandas.pydata.org
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Seaborn

Website: seaborn.pydata.org
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Pandas features and specs

Data Wrangling
Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
Flexible Data Structures
Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
Integration with Other Libraries
Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
Performance with Data Size
For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
Rich Feature Set
Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
Community and Documentation
Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

Memory Consumption
Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
Single-threaded
Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
Steep Learning Curve
For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
Less Suitable for Real-time Analytics
Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
Error Handling
Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Seaborn features and specs

High-Level Interface
Seaborn provides a high-level interface for drawing attractive statistical graphics, simplifying the process of creating complex plots with just a few lines of code.
Integration with Pandas
Seaborn automatically works well with Pandas data structures, making it easy to visualize data directly from DataFrames without additional data manipulation.
Built-in Themes
Seaborn offers built-in themes and color palettes that allow users to quickly improve the aesthetics of their plots, making them more appealing and informative.
Statistical Plotting
Seaborn includes a wide array of statistical plots like heatmaps, violin plots, and box plots, which help in understanding data distribution and relationships.
Customization
It provides extensive options for customizing plots, giving users the flexibility to tailor their visualizations to specific needs and preferences.

Possible disadvantages of Seaborn

Dependence on Matplotlib
Seaborn is built on top of Matplotlib, and users may need to understand Matplotlib to handle more intricate customizations that Seaborn does not directly support.
Learning Curve
While Seaborn simplifies plotting, there is still a learning curve involved, especially for users unfamiliar with statistical data visualization.
Limited Interactivity
Seaborn primarily generates static plots, which may not provide the level of interactivity required for dynamic data exploration compared to other tools such as Plotly or Bokeh.
Performance
For very large datasets, Seaborn may become slow, and performance can be an issue compared to more optimized visualization libraries.
3D Plotting Support
Seaborn does not natively support 3D plotting, limiting its use for visualizations that require three-dimensional data representation.

Analysis of Pandas

Overall verdict

Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Pandas videos

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Ozzy Man Reviews: Pandas

Seaborn videos

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Seaborn Review

Category Popularity

0-100% (relative to Pandas and Seaborn)

Pandas

Seaborn

Data Science And Machine Learning

90 90%

Data Science And Machine Learning

10% 10

Data Science Tools

100 100%

Data Science Tools

0% 0

Development

0 0%

Development

100% 100

Python Tools

100 100%

Python Tools

0% 0

User comments

Share your experience with using Pandas and Seaborn. For example, how are they different and which one is better?

Reviews

These are some of the external sources and on-site user reviews we've used to compare Pandas and Seaborn

Pandas Reviews

25 Python Frameworks to Master

Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.

Source: kinsta.com

Python & ETL 2020: A List and Comparison of the Top Python ETL Tools

When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.

Source: www.xplenty.com

Seaborn Reviews

5 Best Python Libraries For Data Visualization in 2023

Seaborn is working hard to make visualization a central part of understanding and exploring data. Its dataset-oriented plotting functions run on data frames carrying whole datasets. Seaborn internally performs the necessary semantic mapping and statistical aggregation to provide informative plots. Lastly, Seaborn is fully integrated with the PyData stack including support...

Source: www.technotification.com

Top 8 Python Libraries for Data Visualization

Seaborn is a Python data visualization library that is based on Matplotlib and closely integrated with the NumPy and pandas data structures. Seaborn has various dataset-oriented plotting functions that operate on data frames and arrays that have whole datasets within them. Then it internally performs the necessary statistical aggregation and mapping functions to create...

Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Pandas should be more popular than Seaborn. It has been mentiond 220 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Pandas mentions (220)

Top 5 GitHub Repositories for Data Science in 2026
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…. - Source: dev.to / 14 days ago
Top Programming Languages for AI Development in 2025
Libraries for data science and deep learning that are always changing. - Source: dev.to / 5 months ago
How to import sample data into a Python notebook on watsonx.ai and other questions…
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 6 months ago
How I Hacked Uber’s Hidden API to Download 4379 Rides
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 6 months ago
Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 8 months ago

Seaborn mentions (37)

How I Hacked Uber’s Hidden API to Download 4379 Rides
Below are the key insights. If you want to see the Python code I used to do this analysis and generate the charts using Seaborn, you can find my full analysis Jupyter notebook on my Github repo here: Tip Analysis.ipynb. - Source: dev.to / 6 months ago
Scientific Visualization: Python and Matplotlib, by Nicolas Rougier
Additionally, Seaborn (https://seaborn.pydata.org/) is a great mention for people that want to use Matplotlib with better default aesthetics, amongst other conveniences: "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.". - Source: Hacker News / about 1 year ago
Data Visualisation Basics
Seaborn: built on top of matplotlib, adds a number of functions to make common statistical visualizations easier to generate. - Source: dev.to / about 1 year ago
Useful Python Libraries for AI/ML
Pandas - The standard data analysis and manipulation tool Numpy - scientific computing library Seaborn - statistical data visualization Sklearn - basic machine learning and predictive analysis CausalML - a suite of uplift modeling and causal inference methods PyTorch - professional deep learning framework PivotTablejs - Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook LazyPredict - build... - Source: dev.to / about 1 year ago
Essential Deep Learning Checklist: Best Practices Unveiled
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / over 1 year ago