Python has become an integral tool used by data analysts nowadays. Every tool has some packages and libraries’ add-ins, which helps in different analysis. One such vital library in Python is the Pandas. It provides relational data in a labeled and easy-to-comprehend format. It helps in manipulating and analyzing data.
Origin of Pandas
The name ‘Pandas’ is derived from the word Panel Data – an econometric term from Multidimensional data. Earlier, Python was basically used for data wrangling and preparation. Later in 2008, developer Wes McKinney started to develop pandas for flexibility and data analysis. It usually is installed with pip within the Python environment. In Anaconda (which is an open source distribution of Python as well as R), however, pandas and other libraries come inbuilt. Anaconda is kind of a large package which supports multiple sub packages that help in real-time analysis. It is important to know that python programming can be done via pandas both with and without installing Anaconda.
Components and Uses of Pandas
The pandas is a vital component of data analysis in Python. It supports and is able to work on some prominent data structure like Series (1-dimensional), DataFrame (2-dimensional) and Panel (3D tables).
It is appropriate for varied types of data. Be it ordered or unordered; labeled or not labeled; tabular or unstructured; statistical or observational. It has all the ingredients required for accurate analysis. They help in indexing, missing value (NaN) treatment, pivot or reshaping data, slicing or subsetting, appending, extending deletion or insertion. Not only these, they provide immense help in time series analysis. Data cleaning as we know is a humongous process. If we try to use Excel, then it might take around hours for clean. As every process needs to be done manually. Whereas, in Python, it is only a matter of running the saved codes once again. Working with pandas is easy for a programmer for analysis purpose or have a notebook-like environment for data exploration. The good thing about it is that it provides shortcuts for common functions that are performed on data in a frequent basis.
Among the available tools, Python is so far the the most lucid for all programmers as well as analysts to explain their output to their respective clients. Pandas aided by Python is also supported in C++ and Java to a certain extent. In Java, it comes with a JPandas format. Thus, compatibility over other areas adds to its popularity.
