pandas python Reviews & Product Details - Page 2

Pandas give you a user-friendly tool for filtering, reshaping, modify transform your data; you can add/delete & create rows and columns, same as in Excel, and support different data types. It needs less coding Review collected by and hosted on G2.com.

Pandas have a very steep learning curve and get very complex. As you advance and go deeper, things get harder to understand how this library works, and also poor documentation. 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.

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 the ease of use and its great documentation. Currently, being pandas an extension of numpy, it has one of the best possible documentations. 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.