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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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.) Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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). Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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). Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

as It is easy to use, almost all functions are helpful. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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? Review collected by and hosted on G2.com.