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|
%global _empty_manifest_terminate_build 0
Name: python-databricks-utils
Version: 0.0.7
Release: 1
Summary: Ease-of-use utility tools for databricks notebooks.
License: Apache License 2.0
URL: https://github.com/e2fyi/databricks-utils
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/89/05/4e40e0546bd2415b3fb38eab0d7fd48bead8877cf6121b5e64dc5401c69b/databricks-utils-0.0.7.tar.gz
BuildArch: noarch
%description
# databricks-utils
[](https://shields.io/)
[](https://shields.io/)
[](https://travis-ci.org/e2fyi/databricks-utils)
`databricks-utils` is a python package that provide several utility classes/func
that improve ease-of-use in databricks notebook.
### Installation
```bash
pip install databricks-utils
```
### Features
- `S3Bucket` class to easily interact with a [S3 bucket](https://aws.amazon.com/s3/) via [`dbfs`](https://docs.databricks.com/user-guide/dbfs-databricks-file-system.html) and databricks spark.
- `vega_embed` to render charts from [Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/) specifications.
### Documentation
API documentation can be found at [https://e2fyi.github.io/databricks-utils/](https://e2fyi.github.io/databricks-utils/).
### Quick start
**S3Bucket**
```python
import json
from databricks_utils.aws import S3Bucket
# need to attach notebook's dbutils
# before S3Bucket can be used
S3Bucket.attach_dbutils(dbutils)
# create an instance of the s3 bucket
bucket = (S3Bucket("somebucketname", "SOMEACCESSKEY", "SOMESECRETKEY")
.allow_spark(sc) # local spark context
.mount("somebucketname")) # mount location name (resolves as `/mnt/somebucketname`)
# show list of files/folders in the bucket "resource" folder
bucket.ls("resource/")
# read in a json file from the bucket
data = json.load(open(bucket.local("resource/somefile.json", "r")))
# read from parquet via spark
dataframe = spark.read.parquet(bucket.s3("resource/somedf.parquet"))
# umount
bucket.umount()
```
**Vega**
[Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/)
are high-level grammars of interactive graphics. They provide concise JSON
syntax for rapidly generating visualizations to support analysis.
```python
from databricks_utils.vega import vega_embed
# vega-lite spec for a bar chart
spec = {
"data": {
"values": [
{"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
{"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
{"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
]
},
"mark": "bar",
"encoding": {
"x": {"field": "a", "type": "ordinal"},
"y": {"field": "b", "type": "quantitative"}
}
}
# plot out the vega chart in databricks notebook
displayHTML(vega_embed(spec=spec))
```
### Developer
```bash
# add a version to git tag and publish to pypi
. add_tag.sh <VERSION>
```
%package -n python3-databricks-utils
Summary: Ease-of-use utility tools for databricks notebooks.
Provides: python-databricks-utils
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-databricks-utils
# databricks-utils
[](https://shields.io/)
[](https://shields.io/)
[](https://travis-ci.org/e2fyi/databricks-utils)
`databricks-utils` is a python package that provide several utility classes/func
that improve ease-of-use in databricks notebook.
### Installation
```bash
pip install databricks-utils
```
### Features
- `S3Bucket` class to easily interact with a [S3 bucket](https://aws.amazon.com/s3/) via [`dbfs`](https://docs.databricks.com/user-guide/dbfs-databricks-file-system.html) and databricks spark.
- `vega_embed` to render charts from [Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/) specifications.
### Documentation
API documentation can be found at [https://e2fyi.github.io/databricks-utils/](https://e2fyi.github.io/databricks-utils/).
### Quick start
**S3Bucket**
```python
import json
from databricks_utils.aws import S3Bucket
# need to attach notebook's dbutils
# before S3Bucket can be used
S3Bucket.attach_dbutils(dbutils)
# create an instance of the s3 bucket
bucket = (S3Bucket("somebucketname", "SOMEACCESSKEY", "SOMESECRETKEY")
.allow_spark(sc) # local spark context
.mount("somebucketname")) # mount location name (resolves as `/mnt/somebucketname`)
# show list of files/folders in the bucket "resource" folder
bucket.ls("resource/")
# read in a json file from the bucket
data = json.load(open(bucket.local("resource/somefile.json", "r")))
# read from parquet via spark
dataframe = spark.read.parquet(bucket.s3("resource/somedf.parquet"))
# umount
bucket.umount()
```
**Vega**
[Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/)
are high-level grammars of interactive graphics. They provide concise JSON
syntax for rapidly generating visualizations to support analysis.
```python
from databricks_utils.vega import vega_embed
# vega-lite spec for a bar chart
spec = {
"data": {
"values": [
{"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
{"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
{"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
]
},
"mark": "bar",
"encoding": {
"x": {"field": "a", "type": "ordinal"},
"y": {"field": "b", "type": "quantitative"}
}
}
# plot out the vega chart in databricks notebook
displayHTML(vega_embed(spec=spec))
```
### Developer
```bash
# add a version to git tag and publish to pypi
. add_tag.sh <VERSION>
```
%package help
Summary: Development documents and examples for databricks-utils
Provides: python3-databricks-utils-doc
%description help
# databricks-utils
[](https://shields.io/)
[](https://shields.io/)
[](https://travis-ci.org/e2fyi/databricks-utils)
`databricks-utils` is a python package that provide several utility classes/func
that improve ease-of-use in databricks notebook.
### Installation
```bash
pip install databricks-utils
```
### Features
- `S3Bucket` class to easily interact with a [S3 bucket](https://aws.amazon.com/s3/) via [`dbfs`](https://docs.databricks.com/user-guide/dbfs-databricks-file-system.html) and databricks spark.
- `vega_embed` to render charts from [Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/) specifications.
### Documentation
API documentation can be found at [https://e2fyi.github.io/databricks-utils/](https://e2fyi.github.io/databricks-utils/).
### Quick start
**S3Bucket**
```python
import json
from databricks_utils.aws import S3Bucket
# need to attach notebook's dbutils
# before S3Bucket can be used
S3Bucket.attach_dbutils(dbutils)
# create an instance of the s3 bucket
bucket = (S3Bucket("somebucketname", "SOMEACCESSKEY", "SOMESECRETKEY")
.allow_spark(sc) # local spark context
.mount("somebucketname")) # mount location name (resolves as `/mnt/somebucketname`)
# show list of files/folders in the bucket "resource" folder
bucket.ls("resource/")
# read in a json file from the bucket
data = json.load(open(bucket.local("resource/somefile.json", "r")))
# read from parquet via spark
dataframe = spark.read.parquet(bucket.s3("resource/somedf.parquet"))
# umount
bucket.umount()
```
**Vega**
[Vega](https://vega.github.io/vega/) and [Vega-Lite](https://vega.github.io/vega-lite/)
are high-level grammars of interactive graphics. They provide concise JSON
syntax for rapidly generating visualizations to support analysis.
```python
from databricks_utils.vega import vega_embed
# vega-lite spec for a bar chart
spec = {
"data": {
"values": [
{"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
{"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
{"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
]
},
"mark": "bar",
"encoding": {
"x": {"field": "a", "type": "ordinal"},
"y": {"field": "b", "type": "quantitative"}
}
}
# plot out the vega chart in databricks notebook
displayHTML(vega_embed(spec=spec))
```
### Developer
```bash
# add a version to git tag and publish to pypi
. add_tag.sh <VERSION>
```
%prep
%autosetup -n databricks-utils-0.0.7
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-databricks-utils -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.7-1
- Package Spec generated
|
