- Ease of use

- Ease of Implementation

- Ease of Integration

- Versatility

- Updated library Review collected by and hosted on G2.com.

There is no dislikes that I can think of. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

Pandas has to work on their support center because some of problems are not solved in any other tools, like pandas os error Review collected by and hosted on G2.com.

-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 Review collected by and hosted on G2.com.

-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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.