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Introduction to Importing Data in Python

BasicSkill Level
4.8+
1,316 reviews
Updated 12/2025
Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.
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PythonData Preparation3 hr15 videos50 Exercises3,900 XP330K+Statement of Accomplishment

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Course Description

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos. The course glossary can be found on the right in the resources section. To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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What you'll learn

  • Connect to an SQLite database using SQLAlchemy, execute SELECT queries, and load query results into pandas DataFrames
  • Evaluate SQL JOIN operations that merge related tables and confirm that the combined data are correctly loaded into a DataFrame.
  • Recognize the file structures, delimiters, and headers that characterize flat files and hierarchical HDF5 formats
  • Use appropriate Python functions and libraries for importing plain text, CSV, Excel, SAS, Stata, MATLAB, HDF5, and pickled data files
  • Use NumPy to load homogeneous numeric data, use pandas to load tabular data, and convert between NumPy arrays and pandas DataFrames when needed.

Prerequisites

Intermediate Python
1

Introduction and flat files

In this chapter, you'll learn how to import data into Python from all types of flat files, which are a simple and prevalent form of data storage. You've previously learned how to use NumPy and pandas—you will learn how to use these packages to import flat files and customize your imports.
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2

Importing data from other file types

You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this chapter, you'll learn how to import data into Python from a wide array of important file types. These include pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.
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3

Working with relational databases in Python

In this chapter, you'll learn how to extract meaningful data from relational databases, an essential skill for any data scientist. You will learn about relational models, how to create SQL queries, how to filter and order your SQL records, and how to perform advanced queries by joining database tables.
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Introduction to Importing Data in Python
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FAQs

Is this course suitable for beginners?

Yes, this course is suitable for beginners. It covers the fundamentals of importing data into Python, and does not require any prior knowledge of programming.

What topics are covered in this course?

This course covers topics such as how to import data from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. It also covers how to use NumPy and pandas to customize data imports.

Will I receive a certificate at the end of the course?

Yes, you will receive a certificate upon completion of the course.

How long would it take to complete this course?

It would take approximately 3 hours to complete this course.

Who will benefit from this course?

This course would benefit anyone working in data-related jobs such as data scientists, data engineers, and business analysts. It will also benefit anyone interested in learning about importing data into Python for their own projects.

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