--- title: pandas python Reviews meta_title: 'pandas python Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 98 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: 98 scale: '5' date_modified: '2026-06-24' 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:** 98 ## 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 praise the **intuitive data management** of pandas, finding it essential for efficient analysis and visualization. (2 reviews) - Users find the **ease of use** of pandas invaluable, enabling intuitive data manipulation and seamless integration with Python. (2 reviews) - Users appreciate the **easy integrations** of pandas, enhancing their productivity within the Python data ecosystem. (2 reviews) - Users appreciate the **coding efficiency** of pandas, finding its syntax easy and integration with structured data straightforward. (1 reviews) - Users commend the **design quality** of pandas for its excellent usability and graphical representation 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 face notable **performance issues** with pandas, experiencing slowness and high memory consumption with large datasets. (2 reviews) - Users find the **complex installation** of pandas challenging and time-consuming, impacting their overall experience. (1 reviews) - Users often face **difficulty** with pandas due to its steep learning curve and performance issues with large datasets. (1 reviews) - Users face **integration issues** with pandas, particularly when connecting to data sources, which complicates data handling and performance. (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. ### 3. Pandas Makes Structured Data Work Fast, Practical, and Readable **Rating:** 4.0/5.0 stars **Reviewed by:** Zharina F. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 12, 2026 **What do you like best about pandas python?** We need pandas because it makes working with structured data in Python practical, fast, and readable. Without pandas, most real‑world data tasks would be slow, error‑prone, and much more code‑heavy. **What do you dislike about pandas python?** Need some time and practice to intergrate **What problems is pandas python solving and how is that benefiting you?** ETL development Reading and cleaning data ### 4. Data Analysis Powerhouse for Python **Rating:** 5.0/5.0 stars **Reviewed by:** Luca P. | Chief Operations Officer DEQUA Studio | Formerly CTO in MarTech, Marketing and Advertising, Small-Business (50 or fewer emp.) **Reviewed Date:** July 04, 2025 **What do you like best about pandas python?** Pandas is a mature, open-source Python library for data manipulation and analysis. Its core components, `DataFrame` and `Series`, provide robust abstractions for handling structured, labeled data. Here’s what stands out from a developer’s perspective: ✅ Expressive Data Structures • `DataFrame`: Two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns). • `Series`: One-dimensional labeled array, capable of holding any data type. ✅ Comprehensive I/O Support • Native functions for reading/writing CSV, Excel, SQL, JSON, Parquet, HDF5, and more. Methods like `read_csv()`, `to_excel()`, and `read_sql()` streamline integration with external data sources. ✅ Efficient Data Manipulation • Powerful indexing, slicing, and subsetting using intuitive label-based or integer-based selectors. • Vectorized operations built on top of NumPy enable fast, memory-efficient computations on large datasets. • Built-in support for handling missing data (`NaN`, `NA`, `NaT`) without breaking workflows. ✅ Advanced Grouping and Aggregation • Flexible `groupby` operations for split-apply-combine workflows, supporting complex aggregations and transformations. ✅ Time Series and Categorical Data • Specialized types and methods for time series (e.g., `Timestamp`, `Period`, resampling) and categorical data, improving both performance and memory usage. ✅ Interoperability • Seamless integration with the broader Python data stack: NumPy for numerical operations, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning pipelines. ✅ Reshape, Merge, and Pivot • Functions like `pivot_table`, `melt`, `merge`, and `concat` enable flexible data reshaping and joining. ✅ Extensive Documentation and Community • Large, active community and extensive documentation, with a wealth of tutorials and examples for most use cases. **What do you dislike about pandas python?** Pandas is optimized for in-memory operations and single-threaded execution. Handling very large datasets (that don’t fit in RAM) or leveraging multi-core CPUs requires external tools or libraries (e.g., Dask, cuDF). **What problems is pandas python solving and how is that benefiting you?** Pandas has become the de facto standard for structured data manipulation in Python. In practice, it has enabled: • Rapid prototyping and exploration of tabular datasets, replacing manual data wrangling with concise, readable code. • Efficient data cleaning, transformation, and feature engineering for analytics and machine learning workflows. • Reliable integration with a variety of data sources and formats, reducing friction when moving between different stages of a data pipeline. • Streamlined collaboration between developers, analysts, and data scientists, thanks to a consistent and expressive API. For any Python developer working with structured or semi-structured data, pandas is an essential part of the toolkit—well-suited for everything from quick data inspection to building robust ETL pipelines. ### 5. Python for data analysis using Pandas **Rating:** 4.5/5.0 stars **Reviewed by:** Chiradeep B. | Senior Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** September 16, 2025 **What do you like best about pandas python?** Created visualization and reports using extensive python libraries, Pandas, Numpy, Matplotlib. **What do you dislike about pandas python?** Nothing as such, everything at par my expectation. **What problems is pandas python solving and how is that benefiting you?** Used for data analysis for multiple data set layer ### 6. Reviewing Panda python as user and integration **Rating:** 5.0/5.0 stars **Reviewed by:** Shaik Aleem Ur R. | Silicon Engineer 2, Enterprise (> 1000 emp.) **Reviewed Date:** October 31, 2024 **What do you like best about pandas python?** Usability and Graphical representation of various data sets **What do you dislike about pandas python?** Nothing much to dislike about, It's still developing hoping to mature enough to be the best **What problems is pandas python solving and how is that benefiting you?** Post processing logs and visualizing the plots using matplotlib or pandas ### 7. Excellent Python Library for Data Manipulation **Rating:** 4.0/5.0 stars **Reviewed by:** ROSHAN S. | Small-Business (50 or fewer emp.) **Reviewed Date:** February 11, 2024 **What do you like best about pandas python?** It is easy to understand. It is perfect for small-sized data manipulation. **What do you dislike about pandas python?** It tends to be slower as the size of the data increases. **What problems is pandas python solving and how is that benefiting you?** I am using pandas to manipulate tabular data. It makes it easier to view the tabular data, and manipulate it how you see fit. I am performing data transformation using pandas in some of my ETL projects. ### 8. Good data processing library **Rating:** 4.5/5.0 stars **Reviewed by:** Kush R. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** March 16, 2024 **What do you like best about pandas python?** It has multiple functions for dataset processing **What do you dislike about pandas python?** Syntax keeps changing with updates, so that causes some confusion sometimes **What problems is pandas python solving and how is that benefiting you?** I use it in my daily data science analysis and projects ### 9. Pandas python: data processing **Rating:** 4.5/5.0 stars **Reviewed by:** Nikhil A. | Software product analyst , Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** September 22, 2023 **What do you like best about pandas python?** Pandas python is very powerful library in python,Pandas has incredible features like data analysis for file's like CSV file , Excel file, json file, dollar file, .text file etc it will convert all file types into dataframe and you can do easily operation on this dataframe. **What do you dislike about pandas python?** I'm using pandas since 1 year and no dislike about pandas because it is very powerful library.but i want to say pandas only visualise the data into dataframe if we want to visualise the data then we need to use another library for this,but rather than pandas is very great Library **What problems is pandas python solving and how is that benefiting you?** In my company I will use python Pandas for processing the raw files like CSV, dollar, Excel,.text,json etc and from this file I will cleaning the data remove unnecessary data and create another file from raw file and this is very easy and save my time because of using pandas python. ### 10. Python Pandas **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Enterprise (> 1000 emp.) **Reviewed Date:** February 12, 2024 **What do you like best about pandas python?** - Ease of use - Ease of Implementation - Ease of Integration - Versatility - Updated library **What do you dislike about pandas python?** There is no dislikes that I can think of. **What problems is pandas python solving and how is that benefiting you?** - Data Manipulation - Data Creation - ETL ## 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/pandas-python-review-1774673?section=pricing&secure%5Bexpires_at%5D=2026-06-27+06%3A06%3A36+-0500&secure%5Bsession_id%5D=24d50b69-c6ad-4c1c-a915-bb3b2798dd8f&secure%5Btoken%5D=e8748cca8b91d615f75a821d40d133fa1be22d233b9e10b41660edabaf316360&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)