pandas python Reviews & Product Details

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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

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. Review collected by and hosted on G2.com.

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

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Pandas makes it easy to manipulate data in data frames. Review collected by and hosted on G2.com.

Sometimes the options and features available can be limited. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The best thing I like about Pandas is how fast and easily it handles huge sets of data and organizes it according to our needs. Also, coding in Pandas is very fast; I can do a lot of work in very little time. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Ready optimized implementations for basic statistical calculations like central tendency measures and quantiles. Review collected by and hosted on G2.com.

Though the library is excellent, it is still not optimized enough to be used on big data. Review collected by and hosted on G2.com.