Kedro

Major features and improvements
* Added `KedroContext` base class which holds the configuration and Kedro's main functionality (catalog, pipeline, config, runner).
* Added a new CLI command `kedro jupyter convert` to facilitate converting Jupyter Notebook cells into Kedro nodes.
* Added support for `pip-compile` and new Kedro command `kedro build-reqs` that generates `requirements.txt` based on `requirements.in`.
* Running `kedro install` will install packages to conda environment if `src/environment.yml` exists in your project.
* Added a new `--node` flag to `kedro run`, allowing users to run only the nodes with the specified names.
* Added new `--from-nodes` and `--to-nodes` run arguments, allowing users to run a range of nodes from the pipeline.
* Added prefix `params:` to the parameters specified in `parameters.yml` which allows users to differentiate between their different parameter node inputs and outputs.
* Jupyter Lab/Notebook now starts with only one kernel by default.
* Added the following datasets:
- `CSVHTTPDataSet` to load CSV using HTTP(s) links.
- `JSONBlobDataSet` to load json (-delimited) files from Azure Blob Storage.
- `ParquetS3DataSet` in `contrib` for usage with pandas. (by [mmchougule](https://github.com/mmchougule))
- `CachedDataSet` in `contrib` which will cache data in memory to avoid io/network operations. It will clear the cache once a dataset is no longer needed by a pipeline. (by [tsanikgr](https://github.com/tsanikgr))
- `YAMLLocalDataSet` in `contrib` to load and save local YAML files. (by [Minyus](https://github.com/Minyus))

Bug fixes and other changes
* Documentation improvements including instructions on how to initialise a Spark session using YAML configuration.
* `anyconfig` default log level changed from `INFO` to `WARNING`.
* Added information on installed plugins to `kedro info`.
* Added style sheets for project documentation, so the output of `kedro build-docs` will resemble the style of `kedro docs`.

Breaking changes to the API
* Simplified the Kedro template in `run.py` with the introduction of `KedroContext` class.
* Merged `FilepathVersionMixIn` and `S3VersionMixIn` under one abstract class `AbstractVersionedDataSet` which extends`AbstractDataSet`.
* `name` changed to be a keyword-only argument for `Pipeline`.
* `CSVLocalDataSet` no longer supports URLs. `CSVHTTPDataSet` supports URLs.

Migration guide from Kedro 0.14.* to Kedro 0.15.0
Migration for Kedro project template
This guide assumes that:
* The framework specific code has not been altered significantly
* Your project specific code is stored in the dedicated python package under `src/`.

The breaking changes were introduced in the following project template files:
- `<project-name>/.ipython/profile_default/startup/00-kedro-init.py`
- `<project-name>/kedro_cli.py`
- `<project-name>/src/tests/test_run.py`
- `<project-name>/src/<python_package>/run.py`
- `<project-name>/.kedro.yml` (new file)

The easiest way to migrate your project from Kedro 0.14.* to Kedro 0.15.0 is to create a new project (by using `kedro new`) and move code and files bit by bit as suggested in the detailed guide below:

1. Create a new project with the same name by running `kedro new`

2. Copy the following folders to the new project:
- `results/`
- `references/`
- `notebooks/`
- `logs/`
- `data/`
- `conf/`

3. If you customised your `src/<package>/run.py`, make sure you apply the same customisations to `src/<package>/run.py`
- If you customised `get_config()`, you can override `config_loader` property in `ProjectContext` derived class
- If you customised `create_catalog()`, you can override `catalog()` property in `ProjectContext` derived class
- If you customised `run()`, you can override `run()` method in `ProjectContext` derived class
- If you customised default `env`, you can override it in `ProjectContext` derived class or pass it at construction. By default, `env` is `local`.
- If you customised default `root_conf`, you can override `CONF_ROOT` attribute in `ProjectContext` derived class. By default, `KedroContext` base class has `CONF_ROOT` attribute set to `conf`.

4. The following syntax changes are introduced in ipython or Jupyter notebook/labs:
- `proj_dir` -> `context.project_path`
- `proj_name` -> `context.project_name`
- `conf` -> `context.config_loader`.
- `io` -> `context.catalog` (e.g., `io.load()` -> `context.catalog.load()`)

5. If you customised your `kedro_cli.py`, you need to apply the same customisations to your `kedro_cli.py` in the new project.

6. Copy the contents of the old project's `src/requirements.txt` into the new project's `src/requirements.in` and, from the project root directory, run the `kedro build-reqs` command in your terminal window.

Migration for versioning custom dataset classes

If you defined any custom dataset classes which support versioning in your project, you need to apply the following changes:

1. Make sure your dataset inherits from `AbstractVersionedDataSet` only.
2. Call `super().__init__()` with the appropriate arguments in the dataset's `__init__`. If storing on local filesystem, providing the filepath and the version is enough. Otherwise, you should also pass in an `exists_function` and a `glob_function` that emulate `exists` and `glob` in a different filesystem (see `CSVS3DataSet` as an example).
3. Remove setting of the `_filepath` and `_version` attributes in the dataset's `__init__`, as this is taken care of in the base abstract class.
4. Any calls to `_get_load_path` and `_get_save_path` methods should take no arguments.
5. Ensure you convert the output of `_get_load_path` and `_get_save_path` appropriately, as these now return [`PurePath`s](https://docs.python.org/3/library/pathlib.html#pure-paths) instead of strings.
6. Make sure `_check_paths_consistency` is called with [`PurePath`s](https://docs.python.org/3/library/pathlib.html#pure-paths) as input arguments, instead of strings.

These steps should have brought your project to Kedro 0.15.0. There might be some more minor tweaks needed as every project is unique, but now you have a pretty solid base to work with. If you run into any problems, please consult the [Kedro documentation](https://docs.kedro.org).

Thanks for supporting contributions
[Dmitry Vukolov](https://github.com/dvukolov), [Jo Stichbury](https://github.com/stichbury), [Angus Williams](https://github.com/awqb), [Deepyaman Datta](https://github.com/deepyaman), [Mayur Chougule](https://github.com/mmchougule), [Marat Kopytjuk](https://github.com/kopytjuk), [Evan Miller](https://github.com/evanmiller29), [Yusuke Minami](https://github.com/Minyus)

Major features and improvements
* Tab completion for catalog datasets in `ipython` or `jupyter` sessions. (Thank you [datajoely](https://github.com/datajoely) and [WaylonWalker](https://github.com/WaylonWalker))
* Added support for transcoding, an ability to decouple loading/saving mechanisms of a dataset from its storage location, denoted by adding '' to the dataset name.
* Datasets have a new `release` function that instructs them to free any cached data. The runners will call this when the dataset is no longer needed downstream.

Bug fixes and other changes
* Add support for pipeline nodes made up from partial functions.
* Expand user home directory `~` for TextLocalDataSet (see issue 19).
* Add a `short_name` property to `Node`s for a display-friendly (but not necessarily unique) name.
* Add Kedro project loader for IPython: `extras/kedro_project_loader.py`.
* Fix source file encoding issues with Python 3.5 on Windows.
* Fix local project source not having priority over the same source installed as a package, leading to local updates not being recognised.

Breaking changes to the API
* Remove the max_loads argument from the `MemoryDataSet` constructor and from the `AbstractRunner.create_default_data_set` method.

Thanks for supporting contributions
[Joel Schwarzmann](https://github.com/datajoely), [Alex Kalmikov](https://github.com/kalexqb)

Major features and improvements
* Added Data Set transformer support in the form of AbstractTransformer and DataCatalog.add_transformer.

Breaking changes to the API
* Merged the `ExistsMixin` into `AbstractDataSet`.
* `Pipeline.node_dependencies` returns a dictionary keyed by node, with sets of parent nodes as values; `Pipeline` and `ParallelRunner` were refactored to make use of this for topological sort for node dependency resolution and running pipelines respectively.
* `Pipeline.grouped_nodes` returns a list of sets, rather than a list of lists.

Thanks for supporting contributions

[Darren Gallagher](https://github.com/dazzag24), [Zain Patel](https://github.com/mzjp2)

Major features and improvements
* New I/O module `HDFS3DataSet`.

Bug fixes and other changes
* Improved API docs.
* Template `run.py` will throw a warning instead of error if `credentials.yml`
is not present.

Breaking changes to the API
None

The initial release of Kedro.


Thanks for supporting contributions

Jo Stichbury, Aris Valtazanos, Fabian Peters, Guilherme Braccialli, Joel Schwarzmann, Miguel Beltre, Mohammed ElNabawy, Deepyaman Datta, Shubham Agrawal, Oleg Andreyev, Mayur Chougule, William Ashford, Ed Cannon, Nikhilesh Nukala, Sean Bailey, Vikram Tegginamath, Thomas Huijskens, Musa Bilal

We are also grateful to everyone who advised and supported us, filed issues or helped resolve them, asked and answered questions and were part of inspiring discussions.


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+ random_state: 8
+ features:
+ - engines
+ - passenger_capacity
+ - crew


* Optional: You can now remove all `params:` prefix when supplying values to `parameters` argument in a `pipeline()` call.
* If you pull modular pipelines with `kedro pipeline pull my_pipeline --alias other_pipeline`, now use `kedro micropkg pull my_pipeline --alias pipelines.other_pipeline` instead.
* If you package modular pipelines with `kedro pipeline package my_pipeline`, now use `kedro micropkg package pipelines.my_pipeline` instead.
* Similarly, if you package any modular pipelines using `pyproject.toml`, you should modify the keys to include the full module path, and wrapped in double-quotes, e.g:

diff
[tool.kedro.micropkg.package]
-data_engineering = {destination = "path/to/here"}
-data_science = {alias = "ds", env = "local"}
+"pipelines.data_engineering" = {destination = "path/to/here"}
+"pipelines.data_science" = {alias = "ds", env = "local"}

[tool.kedro.micropkg.pull]
-"s3://my_bucket/my_pipeline" = {alias = "aliased_pipeline"}
+"s3://my_bucket/my_pipeline" = {alias = "pipelines.aliased_pipeline"}


DataSets
* If you use `pandas.ExcelDataSet`, make sure you have `openpyxl` installed in your environment. This is automatically installed if you specify `kedro[pandas.ExcelDataSet]==0.18.0` in your `requirements.txt`. You can uninstall `xlrd` if you were only using it for this dataset.
* If you use`pandas.ParquetDataSet`, pass pandas saving arguments directly to `save_args` instead of nested in `from_pandas` (e.g. `save_args = {"preserve_index": False}` instead of `save_args = {"from_pandas": {"preserve_index": False}}`).
* If you use `spark.SparkHiveDataSet` with `write_mode` option set to `insert`, change this to `append` in line with the Spark styleguide. If you use `spark.SparkHiveDataSet` with `write_mode` option set to `upsert`, make sure that your `SparkContext` has a valid `checkpointDir` set either by `SparkContext.setCheckpointDir` method or directly in the `conf` folder.
* If you use `pandas~=1.2.0` and pass `storage_options` through `load_args` or `savs_args`, specify them under `fs_args` or via `credentials` instead.
* If you import from `kedro.io.lambda_data_set`, `kedro.io.memory_data_set`, or `kedro.io.partitioned_data_set`, change the import to `kedro.io.lambda_dataset`, `kedro.io.memory_dataset`, or `kedro.io.partitioned_dataset`, respectively (or import the dataset directly from `kedro.io`).
* If you have any `pandas.AppendableExcelDataSet` entries in your catalog, replace them with `pandas.ExcelDataSet`.
* If you have any `networkx.NetworkXDataSet` entries in your catalog, replace them with `networkx.JSONDataSet`.

Other
* Edit any scripts containing `kedro pipeline package --version` to use `kedro micropkg package` instead. If you wish to set a specific pipeline package version, set the `__version__` variable in the pipeline package's `__init__.py` file.
* To run a pipeline in parallel, use `kedro run --runner=ParallelRunner` rather than `--parallel` or `-p`.
* If you call `ConfigLoader` or `TemplatedConfigLoader` directly, update the keyword arguments `conf_root` to `conf_source` and `extra_params` to `runtime_params`.
* If you use `KedroContext` to access `ConfigLoader`, use `settings.CONFIG_LOADER_CLASS` to access the currently used `ConfigLoader` instead.
* The signature of `KedroContext` has changed and now needs `config_loader` and `hook_manager` as additional arguments of type `ConfigLoader` and `PluginManager` respectively.