In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. Its name is a play on the phrase "Python data analysis" itself. Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.
What Is Pandas In Python?
Pandas is an open-source library that is made mainly for working with relational or labeled data both easily and intuitively. It provides various data structures and operations for manipulating numerical data and time series. This library is built on the top of the NumPy library. Pandas is fast and it has high-performance & productivity for users.The Pandas library is core to any Data Science work in Python. This introduction will walk you through the basics of data manipulating, and features many of Pandas important features.
Pandas is mainly used for data analysis. Pandas allows importing data from various file formats such as comma-separated values, JSON, SQL, Microsoft Excel. Pandas allows various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.
PANDA makes PYTHON better
Pandas geniunely makes Python a more viable language for Data Science just by being built in it. This isn’t to say that Python doesn’t have a multitude of wonderful packages that emulate this exact effect, because Python has an uncountable number of packages for machine-learning and data processing. Pandas makes things that are relatively difficult, or more of a pain in other languages, incredibly easy in Python.
Pandas is an essential package for Data Science in Python because it’s versatile and really good at handling data. One component I really like about Pandas is its wonderful IPython and Numpy integration. This is to say, Pandas is made to be directly intertwined with Numpy just as peanut butter is to be with jelly. It’s no wonder that both of those combinations are sold together in one full, package.
The key points of Pandas Library are :-
- DataFrame object for data manipulation with integrated indexing.
- Tools for reading and writing data between in-memory data structures and different file formats.
- Data alignment and integrated handling of missing data.
- Reshaping and pivoting of data sets.
- Label-based slicing, fancy indexing, and subsetting of large data sets.
- Data structure column insertion and deletion.
- Group by engine allowing split-apply-combine operations on data sets.
- Data set merging and joining.
- Hierarchical axis indexing to work with high-dimensional data in a lower-dimensional data structure.
- Time series-functionality: Date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging.
- Provides data filtration.
Indeed, the Pandas library of Python has a lot more functions that makes it such a flexible and powerful data analytics tool in Python. In this article, I just organised the basic ones that I believe are the most useful. If one can nail all of them, definitely can start to use Pandas to perform some simple data analytics. Of course, there is still a lot to learn to become a master.
I love Pandas.
Pandas is such a great package, and makes Data Science a complete and total breeze for the most part. I certainly hope that DataFrames.jl can emulate what Pandas has created for the Python Data Science community. What is truly great about Pandas is how the entire tech stack around it flows seamlessly with it.
The Python universe is certainly a pretty one.