Python is well-known within the world of data science, in part because of its many packages and libraries. An extremely popular software libraries for Python users, Pandas is used to analyze and manipulate datasets. Through multiple functions and unique capabilities, there are many ways that you can use the Pandas library for working on both small and large scale data analysis projects. This article outlines some of the key functions and features that makes Pandas the go-to library for data scientists.

What is Pandas?

Pandas, which is short for Python Data Analysis Library, is used for data analysis and machine learning based projects that require advanced quantitative methods. The Pandas library allows users to read and write data in different formats, sort and organize data sets, in addition to other methods of data manipulation and expression. As a newer open-source library, the Pandas library was created to be free and easily accessible to users while maintaining high-powered technological capabilities. Pandas can be used with any language, industry, or file format suited to data analysis.

Using Pandas for Data Science

There are dozens of ways that the Pandas library is used within data science. This list includes some of the primary functions and features of this versatile library, with a focus on data manipulation, organization, and management.

Importing Data Files

Being able to import data from different file types is useful for data scientists who work with different programs, applications, and projects. While some programming libraries can only be used with one or two file formats, the Pandas library can be used with multiple formats from different data sources, such as .csv files, Microsoft Excel, JSON, etc. The Pandas library can even import data from other databases!

All you have to do in order to import these different files is to first import the Pandas library into the environment that you are working in, whether that be Jupyter Notebook or some other Python based platform, then you can easily reference the Pandas data frame when writing code. After importing these data files into Pandas, you can then convert these files into data frames or to create objects.

Working with Data Frames

When working in the Pandas library, one of the primary uses of the library is creating and editing data frames. Data frames are simply the rows and columns structure of most programming platforms, and they are similar in appearance to a spreadsheet or table. Using the dataframe constructor, you are also able to create labels, or metadata, for these rows and columns.

While there are several ways that data scientists can use Pandas data frames, primarily this feature is used to compare two dimensions or aspects of a dataset. For example, if you have one type of data in a row and another type of data in a column, you can use the data frame structure to compare or understand the relationship between the two dimensions. Data frames also allows you to visually represent data in a way that is orderly and easy to understand.

Missing Data and Values

For data scientists, it is important to perform an exploratory analysis of a dataset in order to learn more about what is going on within the dataset. When exploring the dataset it is also important to check for what is not going on in the dataset i.e. whether or not there is missing data or entries in the dataset. Within the Pandas library there are multiple functions which assist data scientists in discovering missing values in the dataset across data types.

Then, once you discover the missing values, the Pandas library also includes functions which allow you to replace the missing data through inserting or filling in those missing values. These functions are helpful when there is an oversight in the data collection process and data needs to be replaced or included within a dataset after the initial collection process. By filling in or inserting values, using Pandas saves you the time and hassle of inputting data values entry by entry.

Indexing, Indicators, and Data Manipulation

Since Pandas is known for its data manipulation capabilities, one of the ways that data scientists use this library is to label and restructure different parts of a dataset. One of the most important functions within the Pandas library is the indexing of data. Indexing is a form of data organization that lets the data scientist select and assign a numerical value to an object within the dataset.

These functions allow data to be indexed based on data type and then sliced to further examine the data in specific rows and columns. This is useful when working within a large database, because you can use indexing to quickly recall the data that you need without having to search row by row. In addition to indexing, the Pandas library also allows you to create different types of metadata within your dataset which is an important part of sorting and grouping data.

Grouping, Sorting, and Visualizing Data

Besides the many functions and features which focus on manipulating data, the Pandas library can also be used to display a dataset in different ways. Through using the groupby function, data scientists can place columns together or apart in whatever way makes the most sense to them.

Sorting values also allows you to change the arrangement or order of a list. Similar to other Python libraries, Pandas also includes a plot function which can be used to create data visualizations through graphs. These different grouping, sorting, and graphing functions each make it easier to present and visualize data in ways that simplify the process of making inferences and communicating what is going on within the dataset.

Interested in learning more about Pandas?

The Noble Desktop Data Science Certificate includes instruction on the Pandas library as well as other Python programming libraries. Additionally, Noble Desktop offers multiple data science classes where you can learn more about how to use Python libraries in the process of data analysis, organization, and sorting. Through taking an in-person Python class in your area or one of the many live online Python classes you can continue your instruction in this popular programming language and the Pandas library!