Python is a popular programming language for data scientists, with many learning Python in order to work collaboratively on projects and product development. This is because Python is an open-source programming language, giving data scientists and developers a significant amount of freedom when engaging with products and tools that are compatible with the language. Through the creation of shared resources, such as libraries and packages, data scientists are able to access unique features and techniques which make the process of working with specific types of data significantly easier.
When choosing a library to work with, it is important to identify the features and resources of that library. For example, many data scientists use libraries such as Matplotlib for data visualization, while the Python library Scikit-learn is primarily used for selecting machine learning models. The Pandas library is known for several key features which can be used for analyzing and manipulating data. The DataFrames function is a feature that allows data scientists to display tabular data using the Pandas library. Every data scientist that is working with charts and/or spreadsheet data should know how to create DataFrames with the Pandas library.
Introduction to the Pandas Library
Of Python’s many data science libraries, Pandas is considered to be the go-to library for mathematical formulas and statistical modeling. The Pandas library, based on the NumPy package, was created to assist data scientists and developers working with real-world data. Pandas is one of the easiest ways to import and manage several different types of files and data formats, such as .CSV, text, and Microsoft Excel sheets. Pandas is a data science library popular among data science students and professionals across fields and disciplines, with many users coming from academic research, as well as industries that rely heavily on numerical data, such as economics and statistics.
What are DataFrames?
DataFrames are one of many data structures within the Pandas library available to users. DataFrames can be used to format a dataset in a two-dimensional structure that is very similar to a traditional chart or spreadsheet format. Depending on the type of data that you are using, there are several types of input that can be used to create a DataFrame, with many data scientists using lists, series, or another object to make their comparisons between categories of a dataset. DataFrames are also regarded as one of the most popular objects within Pandas. This function is easily accessible once you have imported the Pandas library into your terminal or environment of choice.
How Data Scientists Create and Use DataFrames
A DataFrame is created by defining a dataset that will make up the contents of a chart. When working in a terminal or interface that uses Python, data scientists and developers can create a DataFrame by importing the Pandas library and simply calling on the DataFrame function. So, a data scientist creating a DataFrame enters the required data in a row and column format and then writes a prompt for the output, which becomes the data visualization for that chart.
DataFrames are useful when working with structured data that needs to be organized in a tabular format for the purpose of comparison. By creating a small chart displaying the data, data scientists can get a general overview of the important relationships with different variables in a dataset. Creating DataFrames can be useful when exploring a dataset or after reading an Excel file into the environment. By presenting data in the format of a chart, DataFrames offer a preliminary visualization of the available data and how it compares to other lists and datasets.
Interested in learning more about the Pandas Python Library?
The popularity of Python is due in part to its generous community of data scientists and developers who contribute to the maintenance of its libraries and packages. Of the many resources that are managed by the Python community, the Pandas library was created for data analysis due to its compatibility with libraries such as NumPy and Matplotlib. Noble Desktop’s Data Science classes include hands-on training with the most popular Python libraries, including Pandas. For example, the Data Science Certificate offers instruction on cleaning data with Pandas, as well as working with Scikit-learn to solve problems using real-world datasets.
Noble Desktop’s Python bootcamps combine teaching students machine learning models with an introduction to Python’s data science libraries. Specifically, the Python for Data Science Bootcamp focuses on data science libraries such as Pandas, NumPy, and Matplotlib for data analysis and visualization. In addition, the Python Data Science and Machine Learning Bootcamp includes all of the libraries discussed (Pandas, NumPy, Matplotlib, and Scikit-learn) for the purpose of learning automated machine learning. Any data science student or professional interested in learning more about Python’s data science libraries has several classes and certificate programs to choose from!