Streamlined Data Ingestion with pandas

Learn to acquire data from common file formats and systems such as CSV files, spreadsheets, JSON, SQL databases, and APIs.
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4 Hours16 Videos53 Exercises25,546 Learners
4500 XP

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Course Description

Before you can analyze data, you first have to acquire it. This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

  1. 1

    Importing Data from Flat Files

    Practice using pandas to get just the data you want from flat files, learn how to wrangle data types and handle errors, and look into some U.S. tax data along the way.
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  2. 2

    Importing Data From Excel Files

    Automate data imports from that staple of office life, Excel files. Import part or all of a workbook and ensure boolean and datetime data are properly loaded, all while learning about how other people are learning to code.
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  3. 3

    Importing Data from Databases

    Combine pandas with the powers of SQL to find out just how many problems New Yorkers have with their housing. This chapter features introductory SQL topics like WHERE clauses, aggregate functions, and basic joins.
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  4. 4

    Importing JSON Data and Working with APIs

    Learn how to work with JSON data and web APIs by exploring a public dataset and getting cafe recommendations from Yelp. End by learning some techniques to combine datasets once they have been loaded into data frames.
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In the following tracks
Data Analyst Data EngineerImporting & Cleaning Data
Adrián SotoHillary Green-Lerman
Amany Mahfouz Headshot

Amany Mahfouz

Data scientist via spatial analytics and geography.
A geographer by training, Amany drifted into data science via spatial analytics. These days, they spend a lot of time thinking about how best to structure data and streamline acquisition processes for reporting and analytics, mostly for government agencies and nonprofits. They enjoy demystifying data science and coding concepts.
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