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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

Work with JSON orientations

JSON isn't a tabular format, so pandas makes assumptions about its orientation when loading data. Most JSON data you encounter will be in orientations that pandas can automatically transform into a dataframe.

Sometimes, like in this modified version of the Department of Homeless Services Daily Report, data is oriented differently. To reduce the file size, it has been split formatted. You'll see what happens when you try to load it normally versus with the orient keyword argument. The try/except block will alert you if there are errors loading the data.

pandas has been loaded as pd.

Instructions 1/2

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  • 1

    Try loading dhs_report_reformatted.json without any keyword arguments.

  • 2

    Load dhs_report_reformatted.json to a dataframe with orient specified.