--- title: pandas python Reviews meta_title: 'pandas python Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 100 reviews by the users' company size, role or industry to find out how pandas python works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 100 scale: '5' date_modified: '2026-08-07' parent_category: name: Development url: https://www.g2.com/categories/development --- # pandas python Reviews **Vendor:** pandas python **Category:** [Component Libraries Software](https://www.g2.com/categories/component-libraries) **Average Rating:** 4.6/5.0 **Total Reviews:** 100 ## About pandas python Pandas is a powerful and flexible open-source Python library designed for data analysis and manipulation. It provides fast, efficient, and intuitive data structures, such as DataFrame and Series, which simplify handling structured (tabular, multidimensional, potentially heterogeneous) and time series data. Pandas aims to be the fundamental high-level building block for practical, real-world data analysis in Python, offering a wide range of functionalities to streamline data processing tasks. Key Features and Functionality: - Handling Missing Data: Pandas offers easy handling of missing data, represented as `NaN`, `NA`, or `NaT`, in both floating point and non-floating point data. - Size Mutability: Columns can be inserted and deleted from DataFrame and higher-dimensional objects, allowing for dynamic data manipulation. - Data Alignment: Automatic and explicit data alignment ensures that objects can be aligned to a set of labels, facilitating accurate computations. - Group By Operations: Powerful and flexible group by functionality enables split-apply-combine operations on datasets for both aggregating and transforming data. - Data Conversion: Simplifies converting differently-indexed data in other Python and NumPy data structures into DataFrame objects. - Indexing and Subsetting: Provides intelligent label-based slicing, fancy indexing, and subsetting of large datasets. - Merging and Joining: Facilitates intuitive merging and joining of datasets. - Reshaping and Pivoting: Offers flexible reshaping and pivoting of datasets. - Hierarchical Labeling: Supports hierarchical labeling of axes, allowing multiple labels per tick. - Robust I/O Tools: Includes robust tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format. - Time Series Functionality: Provides time series-specific functionality, including date range generation, frequency conversion, moving window statistics, and date shifting and lagging. Primary Value and User Solutions: Pandas addresses the challenges of data analysis by offering a comprehensive suite of tools that simplify the process of data manipulation, cleaning, and analysis. Its intuitive data structures and functions allow users to perform complex operations with minimal code, enhancing productivity and enabling efficient handling of large datasets. By providing seamless integration with other Python libraries and tools, Pandas serves as a cornerstone for data science workflows, empowering users to extract insights and make data-driven decisions effectively. ## pandas python Pros & Cons **What users like:** - Users value the **ease of data management** in pandas, simplifying analysis and visualization tasks daily. (2 reviews) - Users find **pandas easy to use** , benefiting from intuitive syntax and seamless integration for data analysis tasks. (2 reviews) - Users appreciate the **easy integrations** of pandas, making it essential for seamless data analysis and visualization tasks. (2 reviews) - Users appreciate the **coding efficiency** of pandas, finding its syntax easy and integration with structured data seamless. (1 reviews) - Users appreciate the **high design quality** of pandas Python, enabling effective usability and visualization of data sets. (1 reviews) - Users appreciate the **intuitive and powerful data manipulation** of pandas, enabling efficient operations in just a few lines. (1 reviews) - Features (1 reviews) - Installation Ease (1 reviews) - Integrations (1 reviews) - Time-saving (1 reviews) **What users dislike:** - Users experience **performance issues** with pandas, noting slow operation and high memory consumption on large datasets. (2 reviews) - Users find the **complex installation** of pandas Python to be time-consuming and challenging to implement effectively. (1 reviews) - Users often find the **difficulty** in handling large datasets and the steep learning curve frustrating. (1 reviews) - Users often face **integration issues** with pandas, especially when attempting to connect it efficiently to various data sources. (1 reviews) ## pandas python Reviews ### 1. Easy, Coding-Friendly Data Analysis & Visualization for Everyday Projects **Rating:** 5.0/5.0 stars **Reviewed by:** Areeb A. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** February 22, 2026 **What do you like best about pandas python?** It has helped me a lot with data analysis and visualization. The syntax is easy to use and very coding-friendly, and it’s also straightforward to implement. I use it in almost every project, nearly every day. It’s especially easy to integrate when working with structured data. **What do you dislike about pandas python?** It’s a heavy library to implement, and it takes time. **What problems is pandas python solving and how is that benefiting you?** Pandas has helped a lot with understanding my data, as well as visualizing and preprocessing it before I use it in an ML model. ### 2. Intuitive and Powerful Data Manipulation for Every Analyst **Rating:** 5.0/5.0 stars **Reviewed by:** Sergio P. | Analytical Consultant, Enterprise (> 1000 emp.) **Reviewed Date:** December 09, 2025 **What do you like best about pandas python?** What I like best about pandas is how intuitive and powerful it makes data manipulation. Its DataFrame structure feels natural to work with, almost like handling an Excel sheet but with the full flexibility of Python. Operations that would take dozens of lines in raw Python—such as cleaning datasets, merging tables, filtering, grouping, or calculating statistics—can be done in just one or two lines with pandas. I also appreciate how well pandas integrates with the entire Python data ecosystem, especially NumPy, Matplotlib, and scikit-learn. This seamless workflow makes pandas an essential tool for any data science or analytical project. **What do you dislike about pandas python?** One of my main frustrations with pandas is that it tends to become slow and consume a lot of memory when handling very large datasets, as it loads all the data into RAM. Certain operations, such as complex groupby tasks or applying custom Python functions, can be significantly slower than what you might experience with optimized databases or distributed systems. The learning curve can also be quite steep for newcomers, given the wide range of methods, various indexing options, and the distinctions between Series and DataFrames. On top of that, debugging chained operations is sometimes tricky, and getting pandas to work efficiently with data sources like SQL databases or cloud storage often requires additional configuration. **What problems is pandas python solving and how is that benefiting you?** Pandas addresses the challenge of working efficiently with structured data. It enables me to clean, transform, filter, merge, and analyze datasets much more quickly and reliably than if I were using raw Python or spreadsheets. Many tasks that would typically require a database or several different tools can be accomplished entirely within pandas, streamlining the workflow for both data analysis and machine learning projects. In my academic work, research, and personal projects, pandas has made it much easier to process data, explore patterns, and prepare datasets for modeling with minimal effort. Its flexibility and comprehensive features let me concentrate on drawing insights rather than getting bogged down in low-level data manipulation. ## pandas python Discussions - [What is your experience with pandas for data analysis, and what features do you find most useful?](https://www.g2.com/discussions/what-is-your-experience-with-pandas-for-data-analysis-and-what-features-do-you-find-most-useful) - 1 comment, 1 upvote - [What is pandas python used for?](https://www.g2.com/discussions/what-is-pandas-python-used-for) - 1 comment - [View pandas python pricing details and edition comparison](https://www.g2.com/products/pandas-python/reviews?filters%5Bsentiment_snippet%5D=2425396&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-08+07%3A44%3A37+-0500&secure%5Bsession_id%5D=6351d2da-f3ef-4de7-9083-50fe835ca77b&secure%5Btoken%5D=74c614d91d90843b32a11e935020b407d66bb34d26a6cbff5c872142c310fd2d&format=llm_user) ## pandas python Integrations - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [Python](https://www.g2.com/products/python/reviews) - [PyTorch](https://www.g2.com/products/pytorch/reviews) - [Visual Studio](https://www.g2.com/products/visual-studio/reviews) ## pandas python Features **Functionality** - Language Contingency - Component Library - Unlocked Components **Management** - Framework Integration - Repository Management - Support ## Top pandas python Alternatives - [python xlrd](https://www.g2.com/products/python-xlrd/reviews) - 4.1/5.0 (20 reviews) - [Flutter](https://www.g2.com/products/flutter/reviews) - 4.6/5.0 (67 reviews) - [DevExpress](https://www.g2.com/products/devexpress/reviews) - 4.8/5.0 (112 reviews)