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  3. Streamlined Data Ingestion with pandas
  • 1

    Importing Data from Flat Files

    Free

    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.

  • 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.

  • 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.

  • 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.

Initializing

Exercise

Flatten nested JSONs

A feature of JSON data is that it can be nested: an attribute's value can consist of attribute-value pairs. This nested data is more useful unpacked, or flattened, into its own dataframe columns. The pandas.io.json submodule has a function, json_normalize(), that does exactly this.

The Yelp API response data is nested. Your job is to flatten out the next level of data in the coordinates and location columns.

pandas (as pd) and requests have been imported. The results of the API call are stored as response.

Instructions

100 XP
  • Load the json_normalize() function from pandas' io.json submodule.
  • Isolate the JSON data from response and assign it to data.
  • Use json_normalize() to flatten and load the businesses data to a dataframe, cafes. Set the sep argument to use underscores (_), rather than periods.
  • Show the data head.