Pandas for Data Analytics
Understand Series, DataFrames, indexes, rows, columns, and how data structure connects to real-world analytics questions.
Complete the activity and related question to unlock the next concept.
Concept 1 of 8
Core Structures
Pandas mainly works with two important structures: Series and DataFrame. A Series represents a single labeled column of data, while a DataFrame represents a complete table containing rows and multiple columns.
Compare the Two Structures
Analyst Workflow
A Pandas analyst should first understand the business question and the structure of the dataset before selecting data or performing calculations.
Understand what the business needs to know.
Understand rows, columns, values and indexes.
Choose fields relevant to the question.
Use Pandas operations to investigate the data.
Pandas for Data Analytics
Pandas Series and DataFrames are fundamental data structures used in Python data analytics. A Series stores one-dimensional labeled data, while a DataFrame organizes data into rows and columns.
Understanding the difference between a Series and DataFrame is important before learning filtering, sorting, grouping, missing-data handling, and other Pandas techniques.
In practical data analysis, columns usually represent variables and rows commonly represent records or observations. This structure allows an analyst to connect a business question with the relevant fields in a dataset.
In the next lesson, you will learn how to create DataFrames using Python and Pandas.