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@@ -20,12 +20,13 @@ PyStreamAPI offers both sequential and parallel streams and utilizes lazy execut
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Now you might be wondering why another library when there are already a few implementations? Well, here are a few advantages of this particular implementation:
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* It provides both sequential and parallel versions.
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* It provides both sequential and parallel streams.
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* Lazy execution is supported, enhancing performance.
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* It boasts high speed and efficiency.
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* The implementation achieves 100% test coverage.
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* It follows Pythonic principles, resulting in clean and readable code.
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* It adds some cool innovative features such as conditions or error handling and an even more declarative look.
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* It provides loaders for various data sources such as CSV
Creates a new Stream from multiple Streams. Order doesn't change.
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## Use loaders: Load data from CSV files in just one line
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PyStreamAPI offers a convenient way to load data from CSV files. Like that you can start processing your CSV right away without having to worry about reading and parsing the file.
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You can import the loader with:
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```python
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from pystreamapi.loaders import csv
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```
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Now you can use the loader directly when creating your Stream:
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```python
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Stream.of(csv("data.csv", delimiter=";")) \
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.map(lambdax: x["name"]) \
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.for_each(print)
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```
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## API Reference
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For a more detailed documentation view the docs on GitBook: [PyStreamAPI Docs](https://pystreamapi.pickwicksoft.org/)
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