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|
%global _empty_manifest_terminate_build 0
Name: python-analytix
Version: 4.2.0
Release: 1
Summary: A simple yet powerful wrapper for the YouTube Analytics API.
License: BSD 3-Clause 'New' or 'Revised' License
URL: https://github.com/parafoxia/analytix
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/af/7a/e6cafa1a0fcd18ac924c659dec60eb0d6125846bf3e5ba83f6df318ecbb7/analytix-4.2.0.tar.gz
BuildArch: noarch
Requires: python3-aiofiles
Requires: python3-aiohttp
Requires: python3-dateutil
Requires: python3-openpyxl
Requires: python3-types-aiofiles
Requires: python3-types-python-dateutil
Requires: python3-types-setuptools
%description
# analytix
[](https://pypi.python.org/pypi/analytix/)
[](https://pypi.python.org/pypi/analytix/)
[](https://pepy.tech/project/analytix)
[](https://github.com/parafoxia/analytix)
[](https://github.com/parafoxia/analytix/blob/main/LICENSE)
[](https://github.com/parafoxia/analytix/actions/workflows/ci.yml)
[](https://parafoxia.github.io/analytix)
[](https://codeclimate.com/github/parafoxia/analytix/maintainability)
[](https://codeclimate.com/github/parafoxia/analytix/test_coverage)
A simple yet powerful wrapper for the YouTube Analytics API.
CPython versions 3.7 through 3.11 and PyPy versions 3.7 through 3.9 are officially supported.
Python 3.12 will be supported once AIOHTTP supports it.
Windows, MacOS, and Linux are all supported.
## Features
- Pythonic syntax lets you feel right at home
- Dynamic error handling saves hours of troubleshooting and makes sure only valid requests count toward your API quota
- A clever interface allows you to make multiple requests across multiple sessions without reauthorising
- Extra support allows the native saving of CSV files and conversion to DataFrame objects
- Easy enough for beginners, but powerful enough for advanced users
## What does *analytix* do?
The YouTube Studio provides a fantastic interface where creators can view some incredibly detailed analytics for their channel.
However, there's no way to perform programmatical operations on the data to do some proper analysis on it.
This is where analytix comes in.
The process of analysing data on the YouTube Studio is comprised of two steps:
1. Retrieving the data to be analysed and visualised
2. Presenting that data to the user
analytix aims to handle step one as comprehensively as possible, allowing analysts to use tools such as *pandas* and *Matplotlib* to work on the data without having to faff around with Google's offerings.
## Installation
To install the latest stable version of analytix, use the following command:
```sh
pip install analytix
```
You can also install the latest development version using the following command:
```sh
pip install git+https://github.com/parafoxia/analytix
```
You may need to prefix these commands with a call to the Python interpreter depending on your OS and Python configuration.
## Additional support
You can also install analytix with additional libraries to provide extra functionality:
* `analytix[excel]` — support for exporting reports to Excel spreadsheets
* `analytix[types]` — type stubs for type-hinted projects
To install multiple at once, use commas:
```sh
pip install "analytix[excel,types]"
```
Support for DataFrame and Arrow table conversions is also present.
analytix supports these libraries natively:
* `pandas` — 0.23.2 or greater
* `pyarrow` — 2.0.0 or greater
* `polars` — 0.15.17 or greater
Note: You will need to install your library of choice manually to be able to use it.
## OAuth authentication
All requests to the YouTube Analytics API need to be authorised through OAuth 2.
In order to do this, you will need a Google Developers project with the YouTube Analytics API enabled.
You can find instructions on how to do that in the [API setup guide](https://parafoxia.github.io/analytix/starting/googleapp/), or on [this video](https://www.youtube.com/watch?v=1Xday10ZWeg).
When analytix boots up for the first time, it will display a link.
You'll need to follow that link and run through the OAuth workflow.
Once that's done, analytix saves the tokens to the disk (if you plan to run analytix on a server, make sure these are in a safe place).
This includes your refresh token, which analytix will automatically use to refresh your access token when needed.
This means you should only have to authorise analytix, at most, every week.
More details regarding how and when refresh tokens expire can be found on the [Google Identity documentation](https://developers.google.com/identity/protocols/oauth2#expiration).
## Logging
If you want to see what analytix is doing, you can enable the packaged logger:
```py
import analytix
analytix.enable_logging()
```
If anything is going wrong, or analytix appears to be taking a long time to fetch data, try enabling the logger in DEBUG mode.
## Usage
### Retrieving reports
Retrieving reports from the YouTube Analytics API is easy.
The below example loads credentials from a secrets file, and gets day-by-day data on views, likes, and comments from the US over the last 28 days:
```py
from analytix import Client # or AsyncClient
client = Client("./secrets.json")
report = client.retrieve_report(
dimensions=("day",),
filters={"country": "US"},
metrics=("views", "likes", "comments"),
)
report.to_csv("./analytics.csv")
```
If you want to analyse this data using additional tools such as *pandas*, you can directly export the report as a DataFrame or table.
```py
df = report.to_pandas()
table = report.to_arrow()
df = report.to_polars()
```
### Fetching group information
You can also fetch groups and group items:
```py
from analytix import Client
client = Client("./secrets.json")
groups = client.fetch_groups()
# If you want to get the items within a group:
items = client.fetch_group_items(groups[0].id)
```
To read up further, [have a look at the documentation](https://parafoxia.github.io/analytix), or [have a look at some examples](https://github.com/parafoxia/analytix/tree/main/examples).
## Contributing
Contributions are very much welcome! To get started:
* Familiarise yourself with the [code of conduct](https://github.com/parafoxia/analytix/blob/main/CODE_OF_CONDUCT.md)
* Have a look at the [contributing guide](https://github.com/parafoxia/analytix/blob/main/CONTRIBUTING.md)
## License
The analytix module for Python is licensed under the [BSD 3-Clause License](https://github.com/parafoxia/analytix/blob/main/LICENSE).
%package -n python3-analytix
Summary: A simple yet powerful wrapper for the YouTube Analytics API.
Provides: python-analytix
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-analytix
# analytix
[](https://pypi.python.org/pypi/analytix/)
[](https://pypi.python.org/pypi/analytix/)
[](https://pepy.tech/project/analytix)
[](https://github.com/parafoxia/analytix)
[](https://github.com/parafoxia/analytix/blob/main/LICENSE)
[](https://github.com/parafoxia/analytix/actions/workflows/ci.yml)
[](https://parafoxia.github.io/analytix)
[](https://codeclimate.com/github/parafoxia/analytix/maintainability)
[](https://codeclimate.com/github/parafoxia/analytix/test_coverage)
A simple yet powerful wrapper for the YouTube Analytics API.
CPython versions 3.7 through 3.11 and PyPy versions 3.7 through 3.9 are officially supported.
Python 3.12 will be supported once AIOHTTP supports it.
Windows, MacOS, and Linux are all supported.
## Features
- Pythonic syntax lets you feel right at home
- Dynamic error handling saves hours of troubleshooting and makes sure only valid requests count toward your API quota
- A clever interface allows you to make multiple requests across multiple sessions without reauthorising
- Extra support allows the native saving of CSV files and conversion to DataFrame objects
- Easy enough for beginners, but powerful enough for advanced users
## What does *analytix* do?
The YouTube Studio provides a fantastic interface where creators can view some incredibly detailed analytics for their channel.
However, there's no way to perform programmatical operations on the data to do some proper analysis on it.
This is where analytix comes in.
The process of analysing data on the YouTube Studio is comprised of two steps:
1. Retrieving the data to be analysed and visualised
2. Presenting that data to the user
analytix aims to handle step one as comprehensively as possible, allowing analysts to use tools such as *pandas* and *Matplotlib* to work on the data without having to faff around with Google's offerings.
## Installation
To install the latest stable version of analytix, use the following command:
```sh
pip install analytix
```
You can also install the latest development version using the following command:
```sh
pip install git+https://github.com/parafoxia/analytix
```
You may need to prefix these commands with a call to the Python interpreter depending on your OS and Python configuration.
## Additional support
You can also install analytix with additional libraries to provide extra functionality:
* `analytix[excel]` — support for exporting reports to Excel spreadsheets
* `analytix[types]` — type stubs for type-hinted projects
To install multiple at once, use commas:
```sh
pip install "analytix[excel,types]"
```
Support for DataFrame and Arrow table conversions is also present.
analytix supports these libraries natively:
* `pandas` — 0.23.2 or greater
* `pyarrow` — 2.0.0 or greater
* `polars` — 0.15.17 or greater
Note: You will need to install your library of choice manually to be able to use it.
## OAuth authentication
All requests to the YouTube Analytics API need to be authorised through OAuth 2.
In order to do this, you will need a Google Developers project with the YouTube Analytics API enabled.
You can find instructions on how to do that in the [API setup guide](https://parafoxia.github.io/analytix/starting/googleapp/), or on [this video](https://www.youtube.com/watch?v=1Xday10ZWeg).
When analytix boots up for the first time, it will display a link.
You'll need to follow that link and run through the OAuth workflow.
Once that's done, analytix saves the tokens to the disk (if you plan to run analytix on a server, make sure these are in a safe place).
This includes your refresh token, which analytix will automatically use to refresh your access token when needed.
This means you should only have to authorise analytix, at most, every week.
More details regarding how and when refresh tokens expire can be found on the [Google Identity documentation](https://developers.google.com/identity/protocols/oauth2#expiration).
## Logging
If you want to see what analytix is doing, you can enable the packaged logger:
```py
import analytix
analytix.enable_logging()
```
If anything is going wrong, or analytix appears to be taking a long time to fetch data, try enabling the logger in DEBUG mode.
## Usage
### Retrieving reports
Retrieving reports from the YouTube Analytics API is easy.
The below example loads credentials from a secrets file, and gets day-by-day data on views, likes, and comments from the US over the last 28 days:
```py
from analytix import Client # or AsyncClient
client = Client("./secrets.json")
report = client.retrieve_report(
dimensions=("day",),
filters={"country": "US"},
metrics=("views", "likes", "comments"),
)
report.to_csv("./analytics.csv")
```
If you want to analyse this data using additional tools such as *pandas*, you can directly export the report as a DataFrame or table.
```py
df = report.to_pandas()
table = report.to_arrow()
df = report.to_polars()
```
### Fetching group information
You can also fetch groups and group items:
```py
from analytix import Client
client = Client("./secrets.json")
groups = client.fetch_groups()
# If you want to get the items within a group:
items = client.fetch_group_items(groups[0].id)
```
To read up further, [have a look at the documentation](https://parafoxia.github.io/analytix), or [have a look at some examples](https://github.com/parafoxia/analytix/tree/main/examples).
## Contributing
Contributions are very much welcome! To get started:
* Familiarise yourself with the [code of conduct](https://github.com/parafoxia/analytix/blob/main/CODE_OF_CONDUCT.md)
* Have a look at the [contributing guide](https://github.com/parafoxia/analytix/blob/main/CONTRIBUTING.md)
## License
The analytix module for Python is licensed under the [BSD 3-Clause License](https://github.com/parafoxia/analytix/blob/main/LICENSE).
%package help
Summary: Development documents and examples for analytix
Provides: python3-analytix-doc
%description help
# analytix
[](https://pypi.python.org/pypi/analytix/)
[](https://pypi.python.org/pypi/analytix/)
[](https://pepy.tech/project/analytix)
[](https://github.com/parafoxia/analytix)
[](https://github.com/parafoxia/analytix/blob/main/LICENSE)
[](https://github.com/parafoxia/analytix/actions/workflows/ci.yml)
[](https://parafoxia.github.io/analytix)
[](https://codeclimate.com/github/parafoxia/analytix/maintainability)
[](https://codeclimate.com/github/parafoxia/analytix/test_coverage)
A simple yet powerful wrapper for the YouTube Analytics API.
CPython versions 3.7 through 3.11 and PyPy versions 3.7 through 3.9 are officially supported.
Python 3.12 will be supported once AIOHTTP supports it.
Windows, MacOS, and Linux are all supported.
## Features
- Pythonic syntax lets you feel right at home
- Dynamic error handling saves hours of troubleshooting and makes sure only valid requests count toward your API quota
- A clever interface allows you to make multiple requests across multiple sessions without reauthorising
- Extra support allows the native saving of CSV files and conversion to DataFrame objects
- Easy enough for beginners, but powerful enough for advanced users
## What does *analytix* do?
The YouTube Studio provides a fantastic interface where creators can view some incredibly detailed analytics for their channel.
However, there's no way to perform programmatical operations on the data to do some proper analysis on it.
This is where analytix comes in.
The process of analysing data on the YouTube Studio is comprised of two steps:
1. Retrieving the data to be analysed and visualised
2. Presenting that data to the user
analytix aims to handle step one as comprehensively as possible, allowing analysts to use tools such as *pandas* and *Matplotlib* to work on the data without having to faff around with Google's offerings.
## Installation
To install the latest stable version of analytix, use the following command:
```sh
pip install analytix
```
You can also install the latest development version using the following command:
```sh
pip install git+https://github.com/parafoxia/analytix
```
You may need to prefix these commands with a call to the Python interpreter depending on your OS and Python configuration.
## Additional support
You can also install analytix with additional libraries to provide extra functionality:
* `analytix[excel]` — support for exporting reports to Excel spreadsheets
* `analytix[types]` — type stubs for type-hinted projects
To install multiple at once, use commas:
```sh
pip install "analytix[excel,types]"
```
Support for DataFrame and Arrow table conversions is also present.
analytix supports these libraries natively:
* `pandas` — 0.23.2 or greater
* `pyarrow` — 2.0.0 or greater
* `polars` — 0.15.17 or greater
Note: You will need to install your library of choice manually to be able to use it.
## OAuth authentication
All requests to the YouTube Analytics API need to be authorised through OAuth 2.
In order to do this, you will need a Google Developers project with the YouTube Analytics API enabled.
You can find instructions on how to do that in the [API setup guide](https://parafoxia.github.io/analytix/starting/googleapp/), or on [this video](https://www.youtube.com/watch?v=1Xday10ZWeg).
When analytix boots up for the first time, it will display a link.
You'll need to follow that link and run through the OAuth workflow.
Once that's done, analytix saves the tokens to the disk (if you plan to run analytix on a server, make sure these are in a safe place).
This includes your refresh token, which analytix will automatically use to refresh your access token when needed.
This means you should only have to authorise analytix, at most, every week.
More details regarding how and when refresh tokens expire can be found on the [Google Identity documentation](https://developers.google.com/identity/protocols/oauth2#expiration).
## Logging
If you want to see what analytix is doing, you can enable the packaged logger:
```py
import analytix
analytix.enable_logging()
```
If anything is going wrong, or analytix appears to be taking a long time to fetch data, try enabling the logger in DEBUG mode.
## Usage
### Retrieving reports
Retrieving reports from the YouTube Analytics API is easy.
The below example loads credentials from a secrets file, and gets day-by-day data on views, likes, and comments from the US over the last 28 days:
```py
from analytix import Client # or AsyncClient
client = Client("./secrets.json")
report = client.retrieve_report(
dimensions=("day",),
filters={"country": "US"},
metrics=("views", "likes", "comments"),
)
report.to_csv("./analytics.csv")
```
If you want to analyse this data using additional tools such as *pandas*, you can directly export the report as a DataFrame or table.
```py
df = report.to_pandas()
table = report.to_arrow()
df = report.to_polars()
```
### Fetching group information
You can also fetch groups and group items:
```py
from analytix import Client
client = Client("./secrets.json")
groups = client.fetch_groups()
# If you want to get the items within a group:
items = client.fetch_group_items(groups[0].id)
```
To read up further, [have a look at the documentation](https://parafoxia.github.io/analytix), or [have a look at some examples](https://github.com/parafoxia/analytix/tree/main/examples).
## Contributing
Contributions are very much welcome! To get started:
* Familiarise yourself with the [code of conduct](https://github.com/parafoxia/analytix/blob/main/CODE_OF_CONDUCT.md)
* Have a look at the [contributing guide](https://github.com/parafoxia/analytix/blob/main/CONTRIBUTING.md)
## License
The analytix module for Python is licensed under the [BSD 3-Clause License](https://github.com/parafoxia/analytix/blob/main/LICENSE).
%prep
%autosetup -n analytix-4.2.0
%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-analytix -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 4.2.0-1
- Package Spec generated
|
