# 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 appreciate the **intuitive data management** capabilities of pandas, enhancing their data analysis and visualization experiences. (2 reviews) - Users find the **ease of use** of pandas invaluable for efficient data analysis and visualization in their projects. (2 reviews) - Users commend the **easy integrations** of pandas with the Python ecosystem, enhancing their data analysis workflow significantly. (2 reviews) - Users find pandas Python enhances **coding efficiency** , making data analysis and visualization tasks easier and quicker to implement. (1 reviews) - Users appreciate the **design quality** of pandas, enhancing usability and effective 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 report **performance issues** with pandas, particularly when handling large datasets, leading to slow and memory-intensive operations. (2 reviews) - Users find the **complex installation** of pandas python time-consuming and challenging to implement effectively. (1 reviews) - Users find the **difficulty in handling large datasets** and steep learning curve with pandas quite challenging. (1 reviews) - Users face **integration issues** with pandas, especially when connecting to external data sources like SQL or cloud storage. (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 ### 11. Pandas Python **Rating:** 5.0/5.0 stars **Reviewed by:** BANDA M. | Enterprise (> 1000 emp.) **Reviewed Date:** June 13, 2023 **What do you like best about pandas python?** DataFrames in Pandas are useful to handle and analyse data very efficiently. Also pandas provides built-in methods to filter and sort data, handle missing data. Pandas allows/supports reading data from excel, CSV fil e etc which is another advantage. **What do you dislike about pandas python?** Pandas has few weak areas. When large datasets are provided as inputs, Pandas encounter performance issues as interacting over large DataFrames and performing operations on them is time consuming. **What problems is pandas python solving and how is that benefiting you?** Pandas offer powerful tools for analysing structured data. Pandas datasets allows integration of different structured/format datasets, which allows us to join, merge and concatenate datasets. Pandas can be integrated with Matplotlib and other data visualisation tools. ### 12. Cleaning made easy with Pandas **Rating:** 5.0/5.0 stars **Reviewed by:** Aakash T. | Senior Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** June 04, 2023 **What do you like best about pandas python?** Pandas in Python have the ability to handle and manipulate large datasets with ease. It provides a rich set of functions and methods that make data cleaning, transformation, and analysis efficient and intuitive. **What do you dislike about pandas python?** Pandas work slowly for very large datasets, pandas data frames are mutable which means that can be changed anytime, this can be advantageous but can be confusing or wont work well if not handled properly **What problems is pandas python solving and how is that benefiting you?** Pandas simplifies the process of cleaning, transforming, and analyzing tabular and time series data. It provides intuitive data structures, like DataFrames, and powerful functions for operations such as filtering, aggregation, and joining, making data manipulation tasks more accessible and efficient. My firm works with datsets with many null or empty columns and Pandas works best to clean it ### 13. Powerful library for data analysis **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** September 05, 2023 **What do you like best about pandas python?** Pandas is widely used for data manipulation and data analysis. We can read datasets files such as CSV, Excel and process those files. Panda has tabular data structures like dataframes, series. It has more functions for manipulation of data. Empty records are handled properly. **What do you dislike about pandas python?** Pandas consume more memory when working with larger datasets. That's why there are performance limitations. It is dependent on external libraries. Support and performance should be improved. **What problems is pandas python solving and how is that benefiting you?** Pandas is used in project for processing of Excel files for the validation. Finding out missing records has done easily. Used for getting duplicate records. Satisfied all business requirements. ### 14. Structure your data with pandas **Rating:** 5.0/5.0 stars **Reviewed by:** PREM R. | Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 06, 2023 **What do you like best about pandas python?** Pandas can structure our data with a variety of extensions like pandas support html, xlsx,CSV extension etc. with pandas, we can also manipulate our data and analyze them **What do you dislike about pandas python?** Pandas has to work on their support center because some of problems are not solved in any other tools, like pandas os error **What problems is pandas python solving and how is that benefiting you?** Provide structure view with analytics like we can view our data mean, mode and work with large-scale data ### 15. Pandas a product not at full potential **Rating:** 1.5/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** August 04, 2023 **What do you like best about pandas python?** -very flexyble -a lot of support (community, chat, tutorial, courses,...) -a great community at support to develop the library -huge amount of projects, companies and people using it **What do you dislike about pandas python?** -very complex syntax, unnecessary -very slow, great lack of performances -great issues when using dataframes that does not fit in memory -new versions does not guarantee that code developed with previous versions will work properly **What problems is pandas python solving and how is that benefiting you?** I use it for: -data management -analytics -ETL and ELT process ### 16. Pandas Python for data wrangling **Rating:** 4.5/5.0 stars **Reviewed by:** Ruchi S. | Enterprise (> 1000 emp.) **Reviewed Date:** June 14, 2023 **What do you like best about pandas python?** Pandas provide a user-friendly tool for filtering, reshaping, modifying, and transforming your data; you can add, delete, and create rows and columns, just like in Excel, and it supports different data types. It requires less coding. **What do you dislike about pandas python?** Pandas memiliki kurva pembelajaran yang sangat curam dan menjadi sangat kompleks. Saat Anda maju dan mendalami, hal-hal menjadi lebih sulit untuk memahami bagaimana perpustakaan ini bekerja, dan juga dokumentasi yang buruk. **What problems is pandas python solving and how is that benefiting you?** Data cleansing, wrangling, imputation, reshaping, and transformation. Pandas helped clean and transform the data with less code and performed all the tasks on extensive data faster. ### 17. Pandas the ally with data in python **Rating:** 5.0/5.0 stars **Reviewed by:** Erik C. | Innovation Engineer, Industrial Automation, Mid-Market (51-1000 emp.) **Reviewed Date:** November 22, 2022 **What do you like best about pandas python?** The best thing about pandas is the compatibility with data sets that you can manipulate as excel files, csv, json, you can also handle lists or sqlalchemy dataframes, it is very important this part of the data with pandas if you want to send to call them elsewhere for example a web page. **What do you dislike about pandas python?** ult schemas, it is difficult to understand them because if you convert for example a sqlalchemy dataframe that already has a defined schema pandas completely ignores it and puts everything in one, you must define it yourself and that is a tedious task but not impossible. **What problems is pandas python solving and how is that benefiting you?** Pandas python helps me to manipulate the data in an easier way and to join several dataframes in one, where I look for relationships between tables, where the user can request through a web form the data they request with a coherent relationship, and transport them back to the web for their convenience. ### 18. Pandas is a LifeSaver **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** June 06, 2023 **What do you like best about pandas python?** I find pandas best of best for data processing and analytics. With so many functions and methods, pandas allows to process and analyse data as per our needs. My favorite part is to use groupby with lambda function to get some detail analysis. **What do you dislike about pandas python?** Its hard to dislike pandas when you use it in every one of your project and data work. But still pandas do not support parallel processing as much as pyspark does. That is one down-side but still it is more than beneficial. **What problems is pandas python solving and how is that benefiting you?** Pandas is solving the problem of writing complex sql codes. It allows to process data eagerly in an interactive way. Pandas makes data analytics much simpler compared to one with SQL codes. ### 19. Pandas is an excellent library to work with data **Rating:** 4.5/5.0 stars **Reviewed by:** Nathan P. | Machine Learning Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 20, 2023 **What do you like best about pandas python?** Pandas is a great way to work with tabular data. I really appreciate the C++ implementations which allow for performant manipulation of data in python. There are also excellent ways to visualize the data. **What do you dislike about pandas python?** I find some of the indexing semantics very confusing. The ways of using .loc, [colname] are redundant and give warnings in some implementations. I wish this was more straightforward **What problems is pandas python solving and how is that benefiting you?** Pandas allows me to quickly load, manipulate, edit, aggrigate, save, and plot my data. It makes me more productive and efficient. I am able to more quickly explore new datasets. ### 20. Pandas awesome **Rating:** 4.5/5.0 stars **Reviewed by:** Dharvi J. | Tech intern, Small-Business (50 or fewer emp.) **Reviewed Date:** January 09, 2023 **What do you like best about pandas python?** Using Pandas we can easily manipulation data like sorting, structure form, merge data, etc Read any files like csv or other that time pandas better than use file features. **What do you dislike about pandas python?** You have much data that time, not property visible use some function and pandas not have too much visulization graph that use another library and not use for unstructured data. **What problems is pandas python solving and how is that benefiting you?** Where you deal with files and featch data and do some change so we use loop and all so it's time - consuming and space complexity come that time use pandas it's benifit ### 21. Great functionality **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Management Consulting | Enterprise (> 1000 emp.) **Reviewed Date:** April 21, 2023 **What do you like best about pandas python?** Very useful for any type of data science/machine learning pipeline. Vectorized functions make editing the tables very easy and it works well with all kinds of tabular data (csv, txt, etc.) **What do you dislike about pandas python?** It can have a very steep learning curve, meaning new users have trouble accessing the full array of features offered. It also can be difficult to understand exactly what's going on when grouping/filtering. **What problems is pandas python solving and how is that benefiting you?** Pandas helps me solve the problem of reading in tabular data from different file types and transforming them into a form that machine learning models can use. It benefits me by quickly allowing me to transform and load datasets for ML. ### 22. It is very great experience to do work in pandas **Rating:** 4.5/5.0 stars **Reviewed by:** Harsh T. | intern, Small-Business (50 or fewer emp.) **Reviewed Date:** January 09, 2023 **What do you like best about pandas python?** Pandas is the best Python framework I most probably use before the Machine learning process for data cleaning and data overview, where we do null value handling outlier treatment and for appropriately creating data. **What do you dislike about pandas python?** I do not dislike pandas because it will be easy for us when we do data preprocessing, and we use some pandas in-built functions, making it easy to do code without any manual logic. **What problems is pandas python solving and how is that benefiting you?** Python is a straightforward coding language where the programmer can write code with a small line of code. For business purposes, we can complete our project in a significantly less period. ### 23. Pandas in Python **Rating:** 5.0/5.0 stars **Reviewed by:** Rohan F. | Senior Business Intelligence Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** June 09, 2022 **What do you like best about pandas python?** It is very flexible in handling large data and provides great help to the Analyst/Data Scientist to perform basic day-to-day operations which are mostly used in the industry. **What do you dislike about pandas python?** The documentation is good for a basic understanding but if you need to go deeper the documentation is not that great or easy to find. Also for higher dimensions pandas will not be the right choice and the analyst/data scientist will have to use other libraries. **What problems is pandas python solving and how is that benefiting you?** Pandas handles large data very well and also is amazing at customization of the data by manipulating it accordingly. The same can be achieved in SQL where we can preprocess the data but it won't be an optimum solution given the fact pandas handles it very well and fast. ### 24. Excellent library for data analysis **Rating:** 5.0/5.0 stars **Reviewed by:** ISAIAS G. | Pentester, Mid-Market (51-1000 emp.) **Reviewed Date:** September 30, 2022 **What do you like best about pandas python?** What I like most about the pandas framework for Python is its ease of use and great documentation. Currently, being pandas an extension of numpy, it has one of the best documentations possible. **What do you dislike about pandas python?** Even with good documentation, the main problem with Python (and consequently pandas) is that they need to improve calculation efficiency. Sometimes it tends to be a bit slow. **What problems is pandas python solving and how is that benefiting you?** The greatest benefit that pandas offers is the large number of resources already developed. This way, the time needed to create solutions we need to develop is reduced. ### 25. Best python library to represent the data ! **Rating:** 4.5/5.0 stars **Reviewed by:** Yash R. | Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** February 08, 2022 **What do you like best about pandas python?** Pandas provide a lot of prebuild features to modify and play with data, one of the most used libraries in data science space, pandas is easy to install thanks to pip (package installer of python). **What do you dislike about pandas python?** Pandas is indeed a great library but there is a learning curve which is a really big problem for beginners, also documentation is not well written so its hard to refer and work with official documentation **What problems is pandas python solving and how is that benefiting you?** we are looking for a library built on python to use in one of the data science projects, pandas is a great library when it comes to installation, ease of use, prebuild features, and data manipulation! ### 26. Data processing made easy! **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) **Reviewed Date:** May 30, 2022 **What do you like best about pandas python?** The superiority comes in the way of using pandas - user-friendly. It provides an ample amount of flexibility to the user to use it the way he/she wants to. The support is strong and huge. **What do you dislike about pandas python?** When it comes to disliking, it is confusing at the beginning. A beginner requires guidance when he wants to start using pandas. There are ample amount of resources which itself makes it confusing. However, one can easily learn by investing time and getting hands dirty (coding). **Recommendations to others considering pandas python:** A hundred percent recommended. **What problems is pandas python solving and how is that benefiting you?** Once a programmer is accustomed, pandas can be used anywhere to handle/manipulate data. It makes it super easy with the use of data frames and series to use it for application development. ### 27. Pandas is a Python based module used for data import, processing, manipulation and analysis. **Rating:** 5.0/5.0 stars **Reviewed by:** Jai Chand P. | Postdoctoral Research Associate, Enterprise (> 1000 emp.) **Reviewed Date:** January 07, 2022 **What do you like best about pandas python?** Pandas framework gives a variety of options to import data with a very simple function. Pandas have various small functions with minimal modifications that can be used to manipulate the data. **What do you dislike about pandas python?** Pandas should have some good visualization tools included. Like in the seaborn package, pandas library van also be upgraded and may include the options for colorful plots and other diagrams **What problems is pandas python solving and how is that benefiting you?** Nowadays i am working on deep learning algorithms. I am trying to develop pipelines for cancer biomarker prediction using deep learning. So the high throughput data analysis required pandas. Pandas can handle large matrices and can manipulate them easily. ### 28. pandas python review **Rating:** 5.0/5.0 stars **Reviewed by:** AMIT J. | Data Scientist L3, Small-Business (50 or fewer emp.) **Reviewed Date:** December 10, 2021 **What do you like best about pandas python?** To read CSV or excel files, I generally use pandas library in python every time. Also, I sometimes prefer it for visualazation. Once I read csv file in python, So with the help of pandas dataframe, performing a statistical analysis is very easy, lots of built-in functions available to use. A single line of program can help you. **What do you dislike about pandas python?** as It is easy to use, almost all functions are helpful. **Recommendations to others considering pandas python:** I will recoomend to everyone, use pandas library. It is beneficial library. Sometimes, we can use it at scale also. **What problems is pandas python solving and how is that benefiting you?** Mainly I use it to read csv or excel files with a single line of command. So Once I read the dataframe , lots of built-in functions are available for different types of work like statistical analysis and visualization. ### 29. Data Engineer first tool **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Hospital & Health Care | Mid-Market (51-1000 emp.) **Reviewed Date:** November 29, 2021 **What do you like best about pandas python?** There is a method for everything and an even efficient way to do what you already do in python! this is not just adding functionality but improving the functionality that you already have **What do you dislike about pandas python?** Nothing! I really love Pandas, I use it every day since a year now, and everything is so easy since then, and my code has improved so much in efficency that how could I dislike pandas? **Recommendations to others considering pandas python:** see which methods are more efficient for what are you doing, python and pandas have a lot of wait to accomplish the same things but some are way more efficient than others **What problems is pandas python solving and how is that benefiting you?** migrating information from files and DBs to a data warehouse, as a centralization tool works great and if you know pandas you know how to use spark dataframes and dask ### 30. use of panda package for data science is the best experience for me **Rating:** 3.0/5.0 stars **Reviewed by:** Mahesh S. | Senior Embedded Firmware Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** July 10, 2021 **What do you like best about pandas python?** I like the numpy and ipython integration most, which is very useful for any application. I like the PANDA packages, which are helpful for multiple data processing and machine learning applications. Julia and scipy also I like it. Data frame is essential for data manipulation and easy to link with SQL. It gives the same output in fewer lines of code compared to C++ and C **What do you dislike about pandas python?** Students can not use it efficiently because the switch to panda from standard python is very tuff. Less effective documentation leads hard to understand library features compare to other packages. Not essential for IoT-based embedded applications. **Recommendations to others considering pandas python:** Users can get more output by writing less code. It has mind-blowing features of the set for the data science field as it handles a large amount of data, so it is beneficial for organizations. It can be helpful for customized applications. **What problems is pandas python solving and how is that benefiting you?** It solved my coding development timing as it provides powerful library packages. It resolved my data representation skills. It settles my team's productivity issue because it has developed for the python platform. ### 31. Best Python Library for Tabular Data **Rating:** 4.5/5.0 stars **Reviewed by:** Chandresh M. | System Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 25, 2021 **What do you like best about pandas python?** My most favorite thing about Pandas that how they can easily represent your data. By using only two lines of code, you can import your data. One more thing is it easily handles heavy data. It also provides a data visualization function that helps me to visualize my data. It offers a large number of functions to do data manipulations. For me, it is the best library for tabular data. **What do you dislike about pandas python?** One thing I wouldn't say I like is that some functions in pandas come with very complex syntax. I cannot remember it. So, sometimes I have to check the documentation of Pandas to use it. **Recommendations to others considering pandas python:** I recommend it to those people who want to learn data analytics using Python. **What problems is pandas python solving and how is that benefiting you?** I am using Pandas for my CSV data manipulation and extracting information from it. I am also using Pandas for visualization. ### 32. The most used library for managing table like data in python **Rating:** 4.5/5.0 stars **Reviewed by:** Alvaro R. | Profesor titular, Enterprise (> 1000 emp.) **Reviewed Date:** September 30, 2018 **What do you like best about pandas python?** Pandas is the most common library in python when you have to deal with table like data. This makes of pandas a library with a lot of help available around the web. I like the way of importing data to pandas from text format, spreadsheets, csv, tsv, etc. I also like the way to select rows and columns and to operate with them. Although it is a little bit confusing at the beginning, once you get used to the way to manage data with pandas DataFrames, it is quite easy to play with data. **What do you dislike about pandas python?** If you are not careful managing data with pandas, the internal structures of pandas can use a high amount of memory. This is because pandas uses, by default, the object type, which requires a lot of memory. To solve this issue, you have to convert numeric types to int types. Then, you can reduce space by more than 50%. **Recommendations to others considering pandas python:** I recommend to read tutorials about how to reduce the memory usage with pandas. I also recommend to see several tutorials about how to manage rows and columns. **What problems is pandas python solving and how is that benefiting you?** I use pandas for importing data from other sources such as spreadsheets or csv files. Then, I can operate with that data and find patterns among the data. Pandas allow me to perform several operations with very few instructions. Moreover, some scientific libraries require to use as input pandas DataFrames. So, I have to convert my data to pandas DataFrames in order to use such libraries. ### 33. Excellent and essential framework for data analyst and scientist **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** November 04, 2021 **What do you like best about pandas python?** At the heart of the Pandas library is the data frame, which makes using the Pandas framework interoperable from a skills-building standpoint. Not only will learning the methods in Pandas be valuable within Python, but you can quickly transfer your knowledge of the framework to R or even Spark (for big data applications). Further, the framework itself implemented in Python is beneficial for data analysis, providing numerous helper functions on the data frame object, that include aggregation methods, standard statistical calculation methods, and handy join/merge, and subsetting functionality that all data analysts will likely use. On top of that, it is built on top of Numpy for easy transference between those types for more heavy-duty/actual work or even pushing it up to a higher level of abstraction for more data-viz/communications/analysis work. **What do you dislike about pandas python?** There's not much to dislike, except perhaps memory and some run-time constraints. By adding a lot of 'extra' structure on top of the NumPy array, the data frame isn't the most efficient data type, but what you get is worth the extra resources needed to run it, though maybe not at extreme scale (several dozen gigs or more than a couple million rows depending on how many columns of data is included in your frame). **Recommendations to others considering pandas python:** If you like the data frame-like environment, you should also consider using R if you are interested in more statistical-heavy applications and don't have time to implement many of those bespoke algorithms by-hand, or Spark if you need to operate in big data scale use-cases **What problems is pandas python solving and how is that benefiting you?** Pandas allow us to build a programmatic 'spreadsheet' object within the Python data-stack environment. This data frame will enable us to operate efficiently with 'mixed-type tables every day for almost all domain applications in data analysis/data science. ### 34. Pandas for data pre-processing **Rating:** 5.0/5.0 stars **Reviewed by:** GouriS S. | Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2021 **What do you like best about pandas python?** The best thing about pandas library in python is, it provides vast functionality to manipulate the data in all the angle. It processes the CSV files with great speed. it provide the facility to process all kind of data, either from file, json file, from databases etc. **What do you dislike about pandas python?** What I not like about pandas is on the large dataset, it occupies a lot of memory, and because of that, it hangs the system due to full memory error. **What problems is pandas python solving and how is that benefiting you?** I am using python pandas to pre-process the data, deriving new features, handling null values, and calculating descriptive statistics using the pandas library. ### 35. The best data manipulation tool **Rating:** 4.5/5.0 stars **Reviewed by:** deniz y. | Business Intelligence Manager, Enterprise (> 1000 emp.) **Reviewed Date:** September 21, 2021 **What do you like best about pandas python?** An excellent python module that can be used for data analysis. It can be easily manipulated by converting the data into a table structure very easily. It is installed with matplotlib. It supports many different file types. Excel, CSV, Pickle.. It is very ideal for processing rows and columns, expanding data, data sorting, filtering, label-based classification, data cleaning. **What do you dislike about pandas python?** I can't find the letter "i" by filtering after reducing the "i" character with lower. So I fix my data first and then load it into the data frame. **What problems is pandas python solving and how is that benefiting you?** I always import pandas first. Because I edit the data with pandas before running my models. I quickly create tables and filters. It is essential for text analysis. ### 36. Manipulating data with pandas **Rating:** 4.0/5.0 stars **Reviewed by:** Pablo S. | Data Science Fellow, Small-Business (50 or fewer emp.) **Reviewed Date:** July 28, 2019 **What do you like best about pandas python?** Pandas makes it easy to manipulate data in data frames. **What do you dislike about pandas python?** Sometimes the options and features available can be limited. **Recommendations to others considering pandas python:** Works great with Jupiter notebook **What problems is pandas python solving and how is that benefiting you?** Viewing data trends and being able to make changes and manipulation to large datasets quickly. ### 37. Best ever library for reading the CSV and sheets. **Rating:** 4.5/5.0 stars **Reviewed by:** Diwakar B. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 01, 2021 **What do you like best about pandas python?** The features of this library to handle the data if fabulous and with easy function read and write the data and also searching is also good in the data frame converted by panda. **What do you dislike about pandas python?** The speed of all operations is a bit slower and with millions o data reading this took lots of time but with the Modin package, we can increase the operational speeds by 3 times. **What problems is pandas python solving and how is that benefiting you?** I don't found a better package for reading and writing the Excel and sheets file and performs all king of the files operations easily. ### 38. Best Python library for data analysis **Rating:** 5.0/5.0 stars **Reviewed by:** Neeraj J. | DevOps Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** July 29, 2021 **What do you like best about pandas python?** The best thing i like about Pandas is how fast and easily it handles huge set of Data in and organize it according to our need. also coding in panda is very fast, I can do a lot of work in very little time. **What do you dislike about pandas python?** there is not much to dislike about pandas except it has a very complex syntax. and it is a little difficult to learn for beginners. **Recommendations to others considering pandas python:** If you are learning Data science or dealing with huge sets of data please learn pandas, it will make your job a lot easy **What problems is pandas python solving and how is that benefiting you?** I am using pandas to analyze,clen and modify Data for my organization ### 39. One stop shop for data analysis **Rating:** 5.0/5.0 stars **Reviewed by:** Vaibhav C. | Data Scientist, Enterprise (> 1000 emp.) **Reviewed Date:** October 20, 2021 **What do you like best about pandas python?** Ready optimized implementations for basic statistical calculations like central tendancy measures and quantiles. **What do you dislike about pandas python?** Though the library is excellent, it is still not optimized enough to be used on big data. **What problems is pandas python solving and how is that benefiting you?** I am solving fundamental data analysis for reporting and getting data ready for visualization in a dashboard. Benefits include quick turnaround for many use cases like sales efficiency and process optimizations. ### 40. pandas review **Rating:** 5.0/5.0 stars **Reviewed by:** Joao V. | trainee RPA, Small-Business (50 or fewer emp.) **Reviewed Date:** December 29, 2021 **What do you like best about pandas python?** the way that pandas work with datas, and organize them **What do you dislike about pandas python?** I dislike the way that you need put on python script **What problems is pandas python solving and how is that benefiting you?** organize my tables and datas ### 41. Standard scientific computing package **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Commercial Real Estate | Enterprise (> 1000 emp.) **Reviewed Date:** December 14, 2021 **What do you like best about pandas python?** It makes it easy to work with large datasets. **What do you dislike about pandas python?** The pandas package is a different ecosystem from vanilla Python. It takes some investment to learn and make full use of it. **What problems is pandas python solving and how is that benefiting you?** We are working with large data sets and iterating quickly to see how results change when variables are updated. ### 42. Amazing way to structure data **Rating:** 5.0/5.0 stars **Reviewed by:** Eduardo Javier C. | C, Mid-Market (51-1000 emp.) **Reviewed Date:** September 14, 2021 **What do you like best about pandas python?** I found an easy way to manage my data, just writing few lines of code. **What do you dislike about pandas python?** I don't like the way how I can develop graphs, should be more attractive like seaborn. **What problems is pandas python solving and how is that benefiting you?** I could work with large databases without having problems with memory and resources in a computer with basic resources. ### 43. The best of pandas **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Program Development | Small-Business (50 or fewer emp.) **Reviewed Date:** October 19, 2021 **What do you like best about pandas python?** Efficiently handles large data and akes data flexible and customizable **What do you dislike about pandas python?** Poor 3D matrix compatibility and sometimes the use of iloc and loc **What problems is pandas python solving and how is that benefiting you?** A lot of them. data cleaning, filetrin data ### 44. Pandas **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Banking | Enterprise (> 1000 emp.) **Reviewed Date:** October 20, 2021 **What do you like best about pandas python?** For doing mathematical operations Analysis purpose **What do you dislike about pandas python?** Lack of more inbuilt functions. Better documentation in detai **Recommendations to others considering pandas python:** Easy to use, well updated library. **What problems is pandas python solving and how is that benefiting you?** Calculations Data analysis Data exporting and importing ### 45. Add graphic and statistical data for any Python program **Rating:** 4.5/5.0 stars **Reviewed by:** Charles P. | Small-Business (50 or fewer emp.) **Reviewed Date:** October 12, 2020 **What do you like best about pandas python?** Python panda provides instant visual data for your programming results and complex methods. Big data and customer database predictions help keep you one step ahead using customizable analytics and statistical modeling. **What do you dislike about pandas python?** Difficult to implement at first, although there are lots of resources online with past project examples and forum answers. Data can develop into complex feedback unless careful. The method integration could be better and some of the documentation is lacking. **Recommendations to others considering pandas python:** Data sets and complex multi-functions need work and clear visuals. The documentation could offer clearer examples with an improved help function. **What problems is pandas python solving and how is that benefiting you?** Modelling statistical data, panda gives you the edge of competitors by adding graphs and statistical details to formula driven methods. This is great for in-game development and any science or mathematical based function. Algorithm precision, website analytics or even data modelling can be visualised in diagram patterned detail with customisable options. ### 46. Its ease of integration with the rest of my applications created in python is very practical **Rating:** 4.0/5.0 stars **Reviewed by:** Bpagadala P. | Small-Business (50 or fewer emp.) **Reviewed Date:** November 29, 2020 **What do you like best about pandas python?** Its ease of installation and integration with the rest of my applications created in python is something quite useful and practical, the fact that it allows me to perform data analysis in a much simpler, more precise and secure way is something that ultimately gives it A great plus to my developments, the fact that it supports a multiple number of files and files is something of great utility when I have the data in different file formats, so since it does not have exclusive use of a single format the data entry It can be much more comprehensive, its ease of use and implementation is so simple that even people who are not experts in data analysis can perform this type of task without any problem, the precision of the results is something really surprising and in fact allow making valuable decisions within the company. **What do you dislike about pandas python?** Its learning curve can be a bit slow at first, but nevertheless after you get used to using the application you realize that everything is really simple, easy and fast, sometimes it would get stuck, although I'm not sure if it was because of the application itself or because of my processor. **What problems is pandas python solving and how is that benefiting you?** It has been a very helpful option when I want to implement statistical data analysis options to my python developments in a much simpler and more precise way, which allows me to extend the type in a much simpler and more practical way of applications that I usually develop, allowing me to expand my professional services to another level. ### 47. Definitely a learning curve, but worth it if time **Rating:** 4.0/5.0 stars **Reviewed by:** Chloe' H. | Graduate Research Assistant, Small-Business (50 or fewer emp.) **Reviewed Date:** October 13, 2020 **What do you like best about pandas python?** This programming language has a learning curve for sure, but I really like that once you learn it, it’s pretty easy to remember. Pandas has a pretty simple language to write and code, but just like any other programming, you have to be careful about your language to get it to work properly. **What do you dislike about pandas python?** I dislike that it is hard to save my programming to a flash drive. I know it save son the internet but I do like a backup. **What problems is pandas python solving and how is that benefiting you?** It is so easy to run simulations with pandas because you can easily just run multiple simulations at one time that really help solve those questions. ### 48. Powerful python package for data analysis **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2020 **What do you like best about pandas python?** Pandas is very useful and easy to use. It gives very high-performance. It is very easy to install and setup. We can read various kinds of files using it like ssv, xls, etc. It makes Data analysis very easy and we can play around with the data set to grasp maximum knowledge from it by its various useful functions and features. **What do you dislike about pandas python?** Pandas python is one of the best tools but sometimes it took very long time for large datasets **Recommendations to others considering pandas python:** Highly recommended to every person who has to work on the dataset and to analyze it. It is the best tool I know for data manipulations and analysis. **What problems is pandas python solving and how is that benefiting you?** I use pandas for analyzing various datasets and to do data analysis in the best possible ways at my organization to get the best-optimized results. I also use matplotlib along with pandas to visualize the data very easily. ### 49. Easy to use data import and analyzing tool **Rating:** 3.5/5.0 stars **Reviewed by:** Mehmed Buğrahan D. | Machine Learning Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 06, 2020 **What do you like best about pandas python?** Pandas is a great tool to visualize, import and analyze the data in many formats. Its support for different file formats and its self-explanatory commands makes it user-friendly even for an inexperienced user. Conversions from pandas dataframe to numpy arrays is also another great feature of Pandas since not all other python libraries have Pandas support. They have a really rich and useful documentation page. Its user base is huge compared to other similar libraries. Meaning you can find answer to your many questions. **What do you dislike about pandas python?** Pandas isn't great at memory handling. It imports all of the files even if you need just couple of rows from a file. It also doesn't support multiprocessing and its functions only runs on CPU, not GPU. Another thing that I don't like about Pandas is that its error messages. For instance, KeyError when it can't find the specified column in the dataframe. These kinds of errors must provide human-friendly errors instead of robotic messages like 'KeyError 'some_column'. **Recommendations to others considering pandas python:** I would highly recommend Pandas, especially for inexperienced users. It has a great documentation page and it is used by many people, meaning you can find answers to your many questions about the library. But keep in mind that its lack of support for multiprocessing makes it slower compared to some other libraries. **What problems is pandas python solving and how is that benefiting you?** I like to use Pandas in order to visualize and analyze the file before I do some memory heavy processes. This helps me to reduce the time by helping me to decide what I need to do beforehand. ### 50. Best Python Library for making Dataframes **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** October 20, 2020 **What do you like best about pandas python?** Python Pandas is used to generate structured data from unstructed form of data like json data **What do you dislike about pandas python?** The most disliked thing is that in pandas the data is structured in slow pattern **Recommendations to others considering pandas python:** Highly recommended others to use pandas **What problems is pandas python solving and how is that benefiting you?** The problem of making the plots using matplotlib or other python library is become easier ## 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 - [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?qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-05-29+15%3A25%3A18+-0500&secure%5Bsession_id%5D=69ceea7f-af61-4661-a68c-75884c32edef&secure%5Btoken%5D=7aa4409cc5839ce6bd28cf5a1109b048b22877e264add02cbab8d587f5c19a80&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 (66 reviews) - [DevExpress](https://www.g2.com/products/devexpress/reviews) - 4.8/5.0 (112 reviews)