What Is the Pandas in Machine learning?
Machine learning is a complex discipline. The implementation of machine learning models is now far much easier than it used to be, this is as a result of Machine learning frameworks such as pandas. Wait!! isn't panda an animal? As I recall panda is an animal, this was my reaction in a Data science class by the end of the class I had completely grasped the concept of pandas.
Pandas is an open-source library, free to use (under theBSD license) and it was originally written by Wes McKinney back in 2009. Today we look at Pandas Library an entirely different kind of panda that is not only powerful but also the most used Library when it comes to data munging/wrangling.
This article is purely for others like me who might be confused of the connection between the animal and the Data. Note: there is no connection between pandas the animal and the library.
What is Pandas.
Pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool. It is the most common tool used by Data analyst Data scientists working with data and use the python platform.
According to Wikipedia it is derived from the term ““panel data”, an econometrics term for data sets that include observations over multiple time periods for the same individuals. [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.’’
Before you work with pandas you have to install it in your system. Depending on the type of system the installation differs.The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. It is the recommended installation method for most users. The anaconda distribution is the most used platform that is used when it comes to working with data it comes intergrated with a number of tools that are used in working with data.
Why pandas?
Have you ever tried working with data without the pandas’ library? If not, this will be a hard task you will have to perform when it comes to working with data unless you are using a language like R where the case is different. If you tried working without pandas then you understand the need for the library.
The reason why pandas are the most used library is that when working with tabular data, exploration, cleaning, and processing of your data is the very first and most important steps. These steps ensure that you get to understand the structure of the data. In this case, identifying the missing values, the size of the data frame the type of data. With pandas, you get a general view of the kind of data that you are working with.
Pandas are suited for many different kinds of data:
-Arbitrary matrix data with row and column labels.
-Ordered and unordered time-series data.
- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet, working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you-Any other form of observational/statistical data sets.
The fact that pandas support the integration with many file formats or data sources out of the box (CSV, Excel, SQL, JSON, parquet,. . . ) this is a bonus to pandas being the most popular library used in python. Pandas are commonly used for data analysis. The library allows various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.
Pandas provide a platform to visualize the data this allows one to draw conclusions based on the relationships in the plots. Plots are a useful tool when it comes to understanding the relationship in the data. You are sure to use plots to get a conclusion based on the data. You also get the chance to choose the plot type (scatter, bar, boxplot,… ) corresponding to your data.
Summary
Pandas is a package that provides a fast, flexible, and expressive library designed to make working with “relational” or “labeled” data both easy and intuitive. Its goal is to be a fundamental high-level building block for practicing, real-world data analysis in Python.
With Pandas you are offered the power to work with a variety of data including, Arbitrary matrix data with row and column labels, Ordered and unordered time-series data, Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet and any other form of observational/statistical data sets.
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