--- title: pandas python Reviews meta_title: 'pandas python Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 99 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: 99 scale: '5' date_modified: '2026-07-17' 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:** 99 ## 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 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?page=4&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-07-21+07%3A43%3A24+-0500&secure%5Bsession_id%5D=1e934582-6482-4fff-81e3-1b8202a30a67&secure%5Btoken%5D=692bfa82a69a7231cd48f05a8e5b065f3e0f6e6b8a667ad5e84800977afe7aa0&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 (19 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)