Set up the data


Don’t forget to check the tutorial FAQ if you run into problems, or ask the community for help if you need it!

In this section, we discuss the data setup phase, which is the second part of the standard development workflow. The steps are as follows:

  • Add datasets to your data folder, according to data engineering convention

  • Register the datasets with the Kedro Data Catalog in conf/base/catalog.yml, which is the registry of all data sources available for use by the project. This ensures that your code is reproducible when it references datasets in different locations and/or environments.

You can find further information about the Data Catalog in specific documentation covering advanced usage.


If you are using the tutorial created by the spaceflights starter, you can omit the copy/paste steps below, but it is worth reviewing the files described.

Download datasets

The spaceflights tutorial makes use of three fictional datasets of companies shuttling customers to the Moon and back. The data comes in two different formats: .csv and .xlsx:

  • companies.csv contains data about space travel companies, such as their location, fleet count and rating

  • reviews.csv is a set of reviews from customers for categories, such as comfort and price

  • shuttles.xlsx is a set of attributes for spacecraft across the fleet, such as their engine type and passenger capacity

Download and save the files to the data/01_raw folder of your project:

Register the datasets

You now need to register the datasets so they can be loaded by Kedro. All Kedro projects have a conf/base/catalog.yml file, and you register each dataset by adding a named entry into the .yml file that includes the following:

  • File location (path)

  • Parameters for the given dataset

  • Type of data

  • Versioning

Register csv data

First, for the spaceflights data, register the two csv datasets by adding this snippet to the end of the conf/base/catalog.yml file and saving it:

  type: pandas.CSVDataSet
  filepath: data/01_raw/companies.csv

  type: pandas.CSVDataSet
  filepath: data/01_raw/reviews.csv

Register xlsx data

Now register the xlsx dataset by adding this snippet to the end of the conf/base/catalog.yml file, and saving it:

  type: pandas.ExcelDataSet
  filepath: data/01_raw/shuttles.xlsx
    engine: openpyxl # Use modern Excel engine (the default since Kedro 0.18.0)

This registration has an additional line: load_args, which is passed to the excel file read method (pd.read_excel) as a keyword argument. Although not specified here, the equivalent output is save_args and the value would be passed to pd.DataFrame.to_excel method.

Test that Kedro can load the csv data

Open a kedro ipython session in your terminal from the project root directory:

kedro ipython

Then type the following into the IPython prompt:

companies = catalog.load("companies")
  • The first command creates a variable (companies), which is of type pandas.DataFrame and loads the dataset (also named companies as per top-level key in catalog.yml) from the underlying filepath data/01_raw/companies.csv.

  • The head method from pandas displays the first five rows of the DataFrame.

INFO     Loading data from 'companies' (CSVDataSet)
      id company_rating       company_location  total_fleet_count iata_approved
0  35029           100%                   Niue                4.0             f
1  30292            67%               Anguilla                6.0             f
2  19032            67%     Russian Federation                4.0             f
3   8238            91%               Barbados               15.0             t
4  30342            NaN  Sao Tome and Principe                2.0             t

Test that Kedro can load the xlsx data

To test that everything works as expected, load the dataset within IPython and display its first five rows:

shuttles = catalog.load("shuttles")

You should see output such as the following:

INFO     Loading data from 'shuttles' (ExcelDataSet)
      id       shuttle_location shuttle_type engine_type  ... d_check_complete  moon_clearance_complete     price company_id
0  63561                   Niue      Type V5     Quantum  ...                f                        f  $1,325.0      35029
1  36260               Anguilla      Type V5     Quantum  ...                t                        f  $1,780.0      30292
2  57015     Russian Federation      Type V5     Quantum  ...                f                        f  $1,715.0      19032
3  14035               Barbados      Type V5      Plasma  ...                f                        f  $4,770.0       8238
4  10036  Sao Tome and Principe      Type V2      Plasma  ...                f                        f  $2,820.0      30342

When you have finished, close ipython session with exit().

Further information

Custom data

Kedro supports numerous datasets out of the box, but you can also add support for any proprietary data format or filesystem in your pipeline.

You can find further information about how to add support for custom datasets in specific documentation covering advanced usage.

Supported data locations

Kedro uses fsspec to read data from a variety of data stores including local file systems, network file systems, HDFS, and all of the widely-used cloud object stores.