Introducing you to the fundamentals of the quintessential Python data analysis library, pandas, and its core data structures – the Series and DataFrame objects.
pandas is one of today’s most celebrated data analysis libraries. A favorite of many, its versatile functionalities can be leveraged for manipulation of many types of data - numeric, text, Boolean, and more. That’s one of the features that make pandas the go-to choice for analysts, especially during the data cleaning and preprocessing stages. pandas is built on NumPy and takes advantage of its computational power and abilities. But what sets pandas apart is its ability to operate with data in an easy-to-use way, allowing you to focus almost entirely on your analytic task. In this course, you will learn how to work with this powerful Python library and its core data structures – the pandas Series and DataFrames.
This course introduces you to one of the most widely used data analysis libraries – pandas. With pandas you’ll be able to solve your analytic tasks in an easy and professional way!
In this section, you will develop a basic understanding of the pandas library and practice with fundamental programming tools such as methods, parameters, arguments, attributes, and index values. You will also learn how to work with the pandas Series and DataFrame objects. In the end, we will present the pandas documentation and will show how you can navigate through it.
Introduction to the pandas Library Free Installing and Running pandas Free Introduction to pandas Series Free Working with Attributes in Python Free Using an Index in pandas Free Label-based vs Position-based Indexing Free More on Working with Indices in Python Free Using Methods in Python - Part I Free Using Methods in Python - Part II Free Parameters vs Arguments Free The pandas Documentation Free Introduction to pandas DataFrames Free Creating DataFrames from Scratch - Part I Creating DataFrames from Scratch - Part II Additional Notes on Using DataFramesOnly about 20% of the work of a data analytics or science team goes to statistical analysis, making visualization or predictive models. The bulk of the time is consumed by collecting, cleaning, and preprocessing data. That is why in this section, we’ve provided a single lecture that aims at clarifying the meaning of and difference between the data cleaning and data preprocessing stages.
Data Cleaning and Data PreprocessingHere, we will introduce you to working with one of the two core data structures of pandas – the pandas Series object. You will also discover several common methods and learn how to apply them to a pandas Series.
.unique(), .nunique() Converting Series into Arrays .sort_values() Attribute and Method Chaining .sort_index()This section focuses on the other fundamental object in pandas - the DataFrame. The DataFrame is universally known as the most important structure in this library. Here, we will revise its characteristics as well as comment on several popular related methods. In addition, we will show you how to deal with various techniques for data selection in a DataFrame.
A Revision to pandas DataFrames Common Attributes for Working with DataFrames Data Selection in pandas DataFrames Data Selection - Indexing Data with .iloc[] Data Selection - Indexing Data with .loc[] A Few Comments on Using .loc[] and .iloc[]with Martin Ganchev
