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|
%global _empty_manifest_terminate_build 0
Name: python-ssb-ipython-kernels
Version: 0.3.3
Release: 1
Summary: Jupyter kernels for working with dapla services
License: MIT
URL: https://github.com/statisticsnorway/dapla-ipython-kernels
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c1/50/0dbd3944013356007b66c31ca1102cd26dbe060e7fd02b7699fa4bd1146d/ssb-ipython-kernels-0.3.3.tar.gz
BuildArch: noarch
Requires: python3-ipython
Requires: python3-pyspark
Requires: python3-jupyterhub
Requires: python3-oauthenticator
Requires: python3-requests
Requires: python3-requests-cache
Requires: python3-responses
Requires: python3-ipykernel
Requires: python3-notebook
Requires: python3-tornado
Requires: python3-gcsfs
Requires: python3-pyarrow
Requires: python3-pandas
Requires: python3-google-auth
Requires: python3-google-auth-oauthlib
Requires: python3-ipywidgets
Requires: python3-pyjwt
%description
# dapla-ipython-kernels
Python module for use within Jupyter notebooks. It contains kernel extensions for integrating with Apache Spark,
Google Cloud Storage and custom dapla services.
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
## Getting Started
Install the module from pip:
```bash
# pip
pip install dapla-ipython-kernels
```
Now the module is ready to use with a single import:
```python
import dapla as dp
```
This module is targeted to python kernels in Jupyter, but it may work in any IPython environment.
It also depends on a number of custom services, e.g. [the custom auth service](dapla/jupyterextensions/authextension.py)
To test, simply create any Pandas dataframe. This can be stored in Google Cloud Storage at a specific path:
```python
import pandas as pd
import dapla as dp
data = {
'apples': [3, 2, 0, 1],
'oranges': [0, 3, 7, 2]
}
# Create pandas DataFrame
purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])
# Write pandas DataFrame to parquet
dp.write_pandas(purchases, '/testfolder/python/purchases', valuation='INTERNAL', state= 'INPUT')
```
Conversely, parquet files can be read from a path directly into a pandas DataFrame.
```python
import dapla as dp
# Read path into pandas dataframe
purchases = dp.read_pandas('/testfolder/python/purchases')
```
## Other functions
Since the python module integrates with Google Cloud Storage and custom dapla services,
some other functions exist as well:
```python
import dapla as dp
# List path by prefix
dp.show('/testfolder/python')
```
| Path | Timestamp |
| ----------------------------- | ------------- |
| /testfolder/python/purchases | 1593120298095 |
| /testfolder/python/other | 1593157667793 |
```python
import dapla as dp
# Show file details
dp.details('/testfolder/python/purchases')
```
| Size | Name |
| ----- | -------------------------------------- |
| 2908 | 42331105444c9ca0ce049ef6de7160.parquet |
See also the [example notebook](examples/dapla_notebook.ipynb) written for Jupyter.
## Deploy to SSB jupyter
### Release version pypi
Make sure you have a clean master branch.<br>
run `make bump-version-patch` - this will update version and commit to git.<br>
run `git push --tags origin master` - important to have --tags to make it auto deploy to pypi
If everything was ok we should see a new release her: https://pypi.org/project/ssb-ipython-kernels/
### Update jupyter image on staging
* Bump ssb-ipython-kernels in dapla-gcp-jupyter [Dockerfile](https://github.com/statisticsnorway/dapla-gcp-jupyter/blob/master/jupyter/Dockerfile) <br>
* Example of previous [update]( https://github.com/statisticsnorway/dapla-gcp-jupyter/commit/8027dc1cbad15dadb1347fe452c78711463e9f3c) <br>
* Check new tag from build on [azure piplines](https://dev.azure.com/statisticsnorway/Dapla/_build/results?buildId=11202&view=logs&jobId=2143f898-48de-5476-aeb8-70e74f8d7c33&j=667c30d6-a912-540e-a406-35cd05a9f751&t=fb539ba6-e537-5346-19c8-c46f7dd4b185)
* update [platform dev jupyter-kubespawner-config](https://github.com/statisticsnorway/platform-dev/blob/master/flux/staging-bip-app/dapla-spark/jupyter/kubespawner-config.yaml) with tag
* [Example](https://github.com/statisticsnorway/platform-dev/commit/b063b830deb6bc0d6a485d7f08fda473cf340ff6)
For now, we have to delete the running jupyer hub instance to make it use this new config
%package -n python3-ssb-ipython-kernels
Summary: Jupyter kernels for working with dapla services
Provides: python-ssb-ipython-kernels
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ssb-ipython-kernels
# dapla-ipython-kernels
Python module for use within Jupyter notebooks. It contains kernel extensions for integrating with Apache Spark,
Google Cloud Storage and custom dapla services.
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
## Getting Started
Install the module from pip:
```bash
# pip
pip install dapla-ipython-kernels
```
Now the module is ready to use with a single import:
```python
import dapla as dp
```
This module is targeted to python kernels in Jupyter, but it may work in any IPython environment.
It also depends on a number of custom services, e.g. [the custom auth service](dapla/jupyterextensions/authextension.py)
To test, simply create any Pandas dataframe. This can be stored in Google Cloud Storage at a specific path:
```python
import pandas as pd
import dapla as dp
data = {
'apples': [3, 2, 0, 1],
'oranges': [0, 3, 7, 2]
}
# Create pandas DataFrame
purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])
# Write pandas DataFrame to parquet
dp.write_pandas(purchases, '/testfolder/python/purchases', valuation='INTERNAL', state= 'INPUT')
```
Conversely, parquet files can be read from a path directly into a pandas DataFrame.
```python
import dapla as dp
# Read path into pandas dataframe
purchases = dp.read_pandas('/testfolder/python/purchases')
```
## Other functions
Since the python module integrates with Google Cloud Storage and custom dapla services,
some other functions exist as well:
```python
import dapla as dp
# List path by prefix
dp.show('/testfolder/python')
```
| Path | Timestamp |
| ----------------------------- | ------------- |
| /testfolder/python/purchases | 1593120298095 |
| /testfolder/python/other | 1593157667793 |
```python
import dapla as dp
# Show file details
dp.details('/testfolder/python/purchases')
```
| Size | Name |
| ----- | -------------------------------------- |
| 2908 | 42331105444c9ca0ce049ef6de7160.parquet |
See also the [example notebook](examples/dapla_notebook.ipynb) written for Jupyter.
## Deploy to SSB jupyter
### Release version pypi
Make sure you have a clean master branch.<br>
run `make bump-version-patch` - this will update version and commit to git.<br>
run `git push --tags origin master` - important to have --tags to make it auto deploy to pypi
If everything was ok we should see a new release her: https://pypi.org/project/ssb-ipython-kernels/
### Update jupyter image on staging
* Bump ssb-ipython-kernels in dapla-gcp-jupyter [Dockerfile](https://github.com/statisticsnorway/dapla-gcp-jupyter/blob/master/jupyter/Dockerfile) <br>
* Example of previous [update]( https://github.com/statisticsnorway/dapla-gcp-jupyter/commit/8027dc1cbad15dadb1347fe452c78711463e9f3c) <br>
* Check new tag from build on [azure piplines](https://dev.azure.com/statisticsnorway/Dapla/_build/results?buildId=11202&view=logs&jobId=2143f898-48de-5476-aeb8-70e74f8d7c33&j=667c30d6-a912-540e-a406-35cd05a9f751&t=fb539ba6-e537-5346-19c8-c46f7dd4b185)
* update [platform dev jupyter-kubespawner-config](https://github.com/statisticsnorway/platform-dev/blob/master/flux/staging-bip-app/dapla-spark/jupyter/kubespawner-config.yaml) with tag
* [Example](https://github.com/statisticsnorway/platform-dev/commit/b063b830deb6bc0d6a485d7f08fda473cf340ff6)
For now, we have to delete the running jupyer hub instance to make it use this new config
%package help
Summary: Development documents and examples for ssb-ipython-kernels
Provides: python3-ssb-ipython-kernels-doc
%description help
# dapla-ipython-kernels
Python module for use within Jupyter notebooks. It contains kernel extensions for integrating with Apache Spark,
Google Cloud Storage and custom dapla services.
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
[](https://pypi.python.org/pypi/ssb-ipython-kernels/)
## Getting Started
Install the module from pip:
```bash
# pip
pip install dapla-ipython-kernels
```
Now the module is ready to use with a single import:
```python
import dapla as dp
```
This module is targeted to python kernels in Jupyter, but it may work in any IPython environment.
It also depends on a number of custom services, e.g. [the custom auth service](dapla/jupyterextensions/authextension.py)
To test, simply create any Pandas dataframe. This can be stored in Google Cloud Storage at a specific path:
```python
import pandas as pd
import dapla as dp
data = {
'apples': [3, 2, 0, 1],
'oranges': [0, 3, 7, 2]
}
# Create pandas DataFrame
purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])
# Write pandas DataFrame to parquet
dp.write_pandas(purchases, '/testfolder/python/purchases', valuation='INTERNAL', state= 'INPUT')
```
Conversely, parquet files can be read from a path directly into a pandas DataFrame.
```python
import dapla as dp
# Read path into pandas dataframe
purchases = dp.read_pandas('/testfolder/python/purchases')
```
## Other functions
Since the python module integrates with Google Cloud Storage and custom dapla services,
some other functions exist as well:
```python
import dapla as dp
# List path by prefix
dp.show('/testfolder/python')
```
| Path | Timestamp |
| ----------------------------- | ------------- |
| /testfolder/python/purchases | 1593120298095 |
| /testfolder/python/other | 1593157667793 |
```python
import dapla as dp
# Show file details
dp.details('/testfolder/python/purchases')
```
| Size | Name |
| ----- | -------------------------------------- |
| 2908 | 42331105444c9ca0ce049ef6de7160.parquet |
See also the [example notebook](examples/dapla_notebook.ipynb) written for Jupyter.
## Deploy to SSB jupyter
### Release version pypi
Make sure you have a clean master branch.<br>
run `make bump-version-patch` - this will update version and commit to git.<br>
run `git push --tags origin master` - important to have --tags to make it auto deploy to pypi
If everything was ok we should see a new release her: https://pypi.org/project/ssb-ipython-kernels/
### Update jupyter image on staging
* Bump ssb-ipython-kernels in dapla-gcp-jupyter [Dockerfile](https://github.com/statisticsnorway/dapla-gcp-jupyter/blob/master/jupyter/Dockerfile) <br>
* Example of previous [update]( https://github.com/statisticsnorway/dapla-gcp-jupyter/commit/8027dc1cbad15dadb1347fe452c78711463e9f3c) <br>
* Check new tag from build on [azure piplines](https://dev.azure.com/statisticsnorway/Dapla/_build/results?buildId=11202&view=logs&jobId=2143f898-48de-5476-aeb8-70e74f8d7c33&j=667c30d6-a912-540e-a406-35cd05a9f751&t=fb539ba6-e537-5346-19c8-c46f7dd4b185)
* update [platform dev jupyter-kubespawner-config](https://github.com/statisticsnorway/platform-dev/blob/master/flux/staging-bip-app/dapla-spark/jupyter/kubespawner-config.yaml) with tag
* [Example](https://github.com/statisticsnorway/platform-dev/commit/b063b830deb6bc0d6a485d7f08fda473cf340ff6)
For now, we have to delete the running jupyer hub instance to make it use this new config
%prep
%autosetup -n ssb-ipython-kernels-0.3.3
%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-ssb-ipython-kernels -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.3-1
- Package Spec generated
|
