pandas python Reviews & Product Details

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.

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Alvaro R.

Profesor titular

Enterprise (> 1000 emp.)

9/30/2018

"The most used library for managing table like data in python"

4.5/5

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. Review collected by and hosted on G2.com.

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

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Verified User in Computer Software

Small-Business (50 or fewer emp.)

11/4/2021

"Excellent and essential framework for data analyst and scientist"

5/5

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. Review collected by and hosted on G2.com.

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

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

GouriS S.

Data Scientist

Mid-Market (51-1000 emp.)

9/11/2021

Business partner of the seller or seller's competitor, not included in G2 scores.

"Pandas for data pre-processing"

5/5

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

deniz y.

Business Intelligence Manager

Enterprise (> 1000 emp.)

9/21/2021

"The best data manipulation tool"

4.5/5

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Pablo S.

Data Science Fellow

Small-Business (50 or fewer emp.)

7/28/2019

"Manipulating data with pandas"

4/5

What do you like best about pandas python?

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

What do you dislike about pandas python?

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

Diwakar B.

Software Engineer

Mid-Market (51-1000 emp.)

9/1/2021

"Best ever library for reading the CSV and sheets."

4.5/5

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Neeraj J.

DevOps Engineer

Small-Business (50 or fewer emp.)

7/29/2021

"Best Python library for data analysis"

5/5

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Vaibhav C.

Data Scientist

Enterprise (> 1000 emp.)

10/20/2021

"One stop shop for data analysis"

5/5

What do you like best about pandas python?

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

What do you dislike about pandas python?

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.

Joao V.

trainee RPA

Small-Business (50 or fewer emp.)

12/29/2021

"pandas review"

5/5

What do you like best about pandas python?

the way that pandas work with datas, and organize them Review collected by and hosted on G2.com.

What do you dislike about pandas python?

I dislike the way that you need put on python script Review collected by and hosted on G2.com.

Verified User in Commercial Real Estate

Enterprise (> 1000 emp.)

12/14/2021

"Standard scientific computing package"

5/5

What do you like best about pandas python?

It makes it easy to work with large datasets. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.