NOTE: Koalas supports Apache Spark 3.1 and below as it will be officially included to PySpark in the upcoming Apache Spark 3.2. This repository is now in maintenance mode. For Apache Spark 3.2 and above, please use PySpark directly.
pandas API on Apache Spark
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The Koalas project makes data scientists more productive when interacting with big data, by implementing the pandas DataFrame API on top of Apache Spark.
pandas is the de facto standard (single-node) DataFrame implementation in Python, while Spark is the de facto standard for big data processing. With this package, you can:
- Be immediately productive with Spark, with no learning curve, if you are already familiar with pandas.
- Have a single codebase that works both with pandas (tests, smaller datasets) and with Spark (distributed datasets).
We would love to have you try it and give us feedback, through our mailing lists or GitHub issues.
Try the Koalas 10 minutes tutorial on a live Jupyter notebook here. The initial launch can take up to several minutes.
Getting Started
Koalas can be installed in many ways such as Conda and pip.
# Conda
conda install koalas -c conda-forge
# pip
pip install koalas
See Installation for more details.
For Databricks Runtime, Koalas is pre-installed in Databricks Runtime 7.1 and above. Try Databricks Community Edition for free. You can also follow these steps to manually install a library on Databricks.
Lastly, if your PyArrow version is 0.15+ and your PySpark version is lower than 3.0, it is best for you to set ARROW_PRE_0_15_IPC_FORMAT environment variable to 1 manually.
Koalas will try its best to set it for you but it is impossible to set it if there is a Spark context already launched.
Now you can turn a pandas DataFrame into a Koalas DataFrame that is API-compliant with the former:
import databricks.koalas as ks
import pandas as pd
pdf = pd.DataFrame({'x':range(3), 'y':['a','b','b'], 'z':['a','b','b']})
# Create a Koalas DataFrame from pandas DataFrame
df = ks.from_pandas(pdf)
# Rename the columns
df.columns = ['x', 'y', 'z1']
# Do some operations in place:
df['x2'] = df.x * df.x
For more details, see Getting Started and Dependencies in the official documentation.
Contributing Guide
See Contributing Guide and Design Principles in the official documentation.
FAQ
See FAQ in the official documentation.
Best Practices
See Best Practices in the official documentation.
Koalas Talks and Blogs
See Koalas Talks and Blogs in the official documentation.
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Release files for koalas 1.8.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| koalas-1.8.2.tar.gz | 357.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| koalas-1.8.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 748.5 kB
Release files / koalas-1.8.2.tar.gz
| Download URL | koalas-1.8.2.tar.gz |
|---|---|
| Size | 357.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / koalas-1.8.2-py3-none-any.whl
| Download URL | koalas-1.8.2-py3-none-any.whl |
|---|---|
| Size | 390.8 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/0.0.0 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7
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