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Great Expectations
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*Always know what to expect from your data.*
What is great_expectations?
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Great Expectations helps teams save time and promote analytic integrity by offering a unique approach to automated testing: pipeline tests. Pipeline tests are applied to data (instead of code) and at batch time (instead of compile or deploy time). Pipeline tests are like unit tests for datasets: they help you guard against upstream data changes and monitor data quality.
Software developers have long known that automated testing is essential for managing complex codebases. Great Expectations brings the same discipline, confidence, and acceleration to data science and engineering teams.
Why would I use Great Expectations?
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To get more done with data, faster. Teams use great_expectations to
* Save time during data cleaning and munging.
* Accelerate ETL and data normalization.
* Streamline analyst-to-engineer handoffs.
* Monitor data quality in production data pipelines and data products.
* Simplify debugging data pipelines if (when) they break.
* Codify assumptions used to build models when sharing with distributed teams or other analysts.
How do I get started?
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It's easy!
First use pip install:
$ pip install great_expectations
Then run this command in the root directory of the project you want to try Great Expectations on:
$ great_expectations init
You can also clone the repository, which includes examples of using great_expectations.
$ git clone https://github.com/great-expectations/great_expectations.git
$ pip install great_expectations/
What expectations are available?
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Expectations include:
- `expect_table_row_count_to_equal`
- `expect_column_values_to_be_unique`
- `expect_column_values_to_be_in_set`
- `expect_column_mean_to_be_between`
- ...and many more
Visit the [glossary of expectations](http://great-expectations.readthedocs.io/en/latest/glossary.html) for a complete list of expectations that are currently part of the great expectations vocabulary.
Can I contribute?
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Absolutely. Yes, please. Start [here](https://github.com/great-expectations/great_expectations/blob/develop/CONTRIBUTING.md), and don't be shy with questions!
How do I learn more?
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For full documentation, visit [Great Expectations on readthedocs.io](http://great-expectations.readthedocs.io/en/latest/).
[Down with Pipeline Debt!](https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a) explains the core philosophy behind Great Expectations. Please give it a read, and clap, follow, and share while you're at it.
For quick, hands-on introductions to Great Expectations' key features, check out our walkthrough videos:
* [Introduction to Great Expectations](https://www.youtube.com/watch?v=-_0tG7ACNU4)
* [Using Distributional Expectations](https://www.youtube.com/watch?v=l3DYPVZAUmw&t=20s)
What's the best way to get in touch with the Great Expectations team?
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If you have questions, comments, feature requests, etc., [opening an issue](https://github.com/great-expectations/great_expectations/issues/new) is definitely the best path forward.
We also have a slack channel, which you can join here: https://tinyurl.com/great-expectations-slack
Great Expectations doesn't do X. Is it right for my use case?
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It depends. If you have needs that the library doesn't meet yet, please [upvote an existing issue(s)](https://github.com/great-expectations/great_expectations/issues) or [open a new issue](https://github.com/great-expectations/great_expectations/issues/new) and we'll see what we can do. Great Expectations is under active development, so your use case might be supported soon.
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资源分类:Python库 所属语言:Python 资源全名:great_expectations-0.7.5.tar.gz 资源来源:官方 安装方法:https://lanzao.blog.csdn.net/article/details/101784059
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Python库 | great_expectations-0.7.5.tar.gz (245个子文件)
setup.cfg 83B
distributional_expectations_data_base.csv 74KB
distributional_expectations_data_test.csv 74KB
Titanic.csv 69KB
fixed_distributional_test_dataset.csv 64KB
toy_data_complete.csv 100B
toy_data_incomplete.csv 80B
same_column_names.csv 68B
strf_test.csv 62B
unicode.csv 23B
null_file.csv 0B
.gitkeep 0B
2019-08-02T175019.495089Z-BasicDatasetProfiler.html 70KB
test_render_descriptive_page_view.html 69KB
test_full_oobe_flow.html 69KB
test_render_profiled_fixture_evrs.html 69KB
test_render_descriptive_column_section_renderer_with_exception.html 67KB
BasicDatasetProfiler.html 22KB
2019-08-02T175019.659420Z-BasicDatasetProfiler.html 20KB
2019-08-02T175019.659420Z-BasicDatasetProfiler.html 20KB
titanic_dataset_profiler_expectations_with_distribution.html 20KB
test_render_profiled_fixture_expectations.html 20KB
BasicDatasetProfiler.html 12KB
BasicDatasetProfiler.html 12KB
index.html 9KB
MANIFEST.in 170B
integrate_validation_into_pipeline.ipynb 8KB
create_expectations.ipynb 6KB
component.j2 6KB
page.j2 5KB
index_page.j2 2KB
navbar.j2 712B
ge_info.j2 668B
breadcrumbs.j2 467B
section.j2 329B
test_render_bullet_list_content_block.json 431KB
fixed_distributional_test_dataset.json 139KB
expect_column_kl_divergence_to_be_less_than.json 123KB
test_expect_column_bootstrapped_ks_test_p_value_to_be_greater_than.json 103KB
distributional_expectations_data_test.json 83KB
distributional_expectations_data_base.json 83KB
expect_column_quantile_values_to_be_between.json 82KB
expect_column_parameterized_distribution_ks_test_p_value_to_be_greater_than.json 56KB
expectations_suite_2.json 51KB
evr_suite_3.json 48KB
BasicDatasetProfiler_evrs_with_exception.json 48KB
expected_evrs_BasicDatasetProfiler_on_titanic.json 29KB
BasicDatasetProfiler_evrs.json 29KB
test_render_descriptive_page_renderer.json 26KB
expectation_suite_3.json 14KB
test_dataset_implementations.json 12KB
test_partitions_definition_fixture.json 12KB
expect_column_values_to_be_of_type.json 12KB
expect_column_values_to_be_between.json 11KB
expect_column_chisquare_test_p_value_to_be_greater_than.json 10KB
BasicDatasetProfiler_expectations_with_distribution.json 9KB
BasicDatasetProfiler_expectations.json 9KB
test_partitions.json 9KB
expect_column_values_to_be_in_type_list.json 8KB
expect_column_values_to_be_between_test_set.json 7KB
expected_cli_results_default.json 6KB
expect_column_mean_to_be_between.json 6KB
expect_column_distinct_values_to_be_in_set.json 5KB
expected_results_20180303.json 5KB
expect_column_values_to_be_in_set.json 5KB
expect_column_min_to_be_between.json 4KB
expect_column_values_to_match_regex.json 4KB
expect_column_median_to_be_between.json 4KB
expect_column_max_to_be_between.json 4KB
expect_column_values_to_match_strftime_format.json 4KB
expect_column_value_lengths_to_be_between.json 4KB
test_render_descriptive_column_section_renderer__Age.json 4KB
expect_column_values_to_not_match_regex.json 4KB
expect_column_stdev_to_be_between.json 3KB
test_render_descriptive_column_section_renderer__PClass.json 3KB
expect_column_values_to_be_unique.json 3KB
expect_column_values_to_not_be_null.json 3KB
expect_column_pair_values_to_be_equal.json 3KB
expect_column_values_to_not_be_in_set.json 3KB
expect_column_sum_to_be_between.json 3KB
expect_multicolumn_values_to_be_unique.json 3KB
expect_column_most_common_value_to_be_in_set.json 3KB
expect_column_distinct_values_to_equal_set.json 3KB
expect_column_distinct_values_to_contain_set.json 3KB
expect_column_values_to_be_null.json 3KB
test_render_descriptive_column_section_renderer__Name.json 3KB
evr_suite_1.json 2KB
expect_column_values_to_be_decreasing.json 2KB
expect_column_values_to_be_increasing.json 2KB
expect_column_pair_values_A_to_be_greater_than_B.json 2KB
expect_table_row_count_to_be_between.json 2KB
expect_column_value_lengths_to_equal.json 2KB
test_render_prescriptive_column_section_rendererAge.json 2KB
expected_cli_results_custom.json 2KB
expect_column_values_to_match_regex_list.json 2KB
test_render_descriptive_column_section_renderer__Survived.json 2KB
test_render_descriptive_column_section_renderer__SexCode.json 2KB
test_render_descriptive_column_section_renderer__Sex.json 2KB
expect_column_pair_values_to_be_in_set.json 2KB
expect_table_row_count_to_equal.json 2KB
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