%global _empty_manifest_terminate_build 0
Name: python-googlewrapper
Version: 0.2.11
Release: 1
Summary: Simple API wrapper for Google Products
License: MIT
URL: https://github.com/jaceiverson/googlewrapper
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a5/a1/e8674ccac89dbc930b7ef837277325a080e3e79c12c3418d75a76eea1ee9/googlewrapper-0.2.11.tar.gz
BuildArch: noarch
Requires: python3-google-api-python-client
Requires: python3-oauth2client
Requires: python3-pandas
Requires: python3-pygsheets
Requires: python3-google-cloud-bigquery
Requires: python3-pandas-gbq
Requires: python3-pdcompare
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-mypy
Requires: python3-flake8
Requires: python3-tox
%description
# googlewrapper
[](https://pypi.org/project/googlewrapper/)
[](https://github.com/psf/black)
General Connector Classes for Google Products
__Current Wrappers Available__
- Google Analytics
- Google Search Console
- Google Calendar
- Google Big Query
- Google PageSpeed API
- Google Sheets
- Google Docs
_Wrappers In the Pipeline_
- Gmail
- Google Maps
# STEPS
1) Acquire Google Credentials from API Console
2) Install this package
3) Create Connection in Python
4) Use product wrapper to make API calls (see links to individual docs above)
## Acquire Google Credentials from API Console
First we will need to get our own Google Project set up so we can get our credentials. If you don't have experience, you can do so here Google API Console
After you have your project set up, oAuth configured, and the optional service account (only for Google Big Query connections), you are good to install this package.
Make sure to download your oAuth credentials and save them to your working directory as 'client_secret.json'.
## Installation
```
pip install googlewrapper
```
OR
```
python -m pip install googlewrapper
```
### Virtual Environment
For each project it is reccomended to create a virtualenv. Here is a simple guide on virtual environments.
## Combining Products Examples
### Example 1
> Take a list of URLs from Sheets, grab Search Console Data, and import it into Big Query.
```py
from googlewrapper import GoogleSearchConsole, GoogleSheets, GoogleBigQuery
import datetime as dt
# init our objects
sheets = GoogleSheets(YOUR_URL_HERE)
gsc = GoogleSearchConsole()
gbq = GoogleBigQuery()
# get our urls we want to pull
# remember that sheet1 is default
sites = sheets.get_column(1)
'''
this one is a bit more technical
we can pull our column Branded Words right
from sheets then assign it to a dictionary to use
in our GSC object.
Make sure that your url column is the index for
your df. This will happen by default if the urls
are in the first column in google sheets
'''
branded_list = sheets.df()['Branded Words'].to_dict()
# assign those sheets to GSC
gsc.set_sites(sites)
# assign other GSC variables
gsc.set_date(dt.date(2021,1,1))
gsc.set_dims(['page','date','query'])
# get our data
gsc_data = gsc.get_data()
# print the total clicks/impressions and avg position
# for all the sites we just pulled data for
# send them to Big Query
for site in gsc_data:
print(f"{site}'s Data\n"\
f"Clicks: {gsc_data[site]['Clicks'].sum()}\n"\
f"Impressions: {gsc_data[site]['Impressions'].sum()}\n"\
f"Avg Position: {gsc_data[site]['Position'].mean()}\n\n")
# now we will send our data into our GBQ tables for storage
# we will assign the dataset name to be our url
# we will assign table to be gsc
gbq.set_dataset(site)
gbq.set_table('gsc')
# send the data to GBQ
gbq.send(gsc_data[site])
```
## Pull Requests/Suggestions
I'd love to hear your feedback and suggestions. If you see something and you want to give it a shot and fix it, feel free to clone and make a pull request. OR you can submit and issue/feature request on GitHub.
## Thanks for using my code
If you found this library useful, I'd appreciate a coffee. Thanks.
%package -n python3-googlewrapper
Summary: Simple API wrapper for Google Products
Provides: python-googlewrapper
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-googlewrapper
# googlewrapper
[](https://pypi.org/project/googlewrapper/)
[](https://github.com/psf/black)
General Connector Classes for Google Products
__Current Wrappers Available__
- Google Analytics
- Google Search Console
- Google Calendar
- Google Big Query
- Google PageSpeed API
- Google Sheets
- Google Docs
_Wrappers In the Pipeline_
- Gmail
- Google Maps
# STEPS
1) Acquire Google Credentials from API Console
2) Install this package
3) Create Connection in Python
4) Use product wrapper to make API calls (see links to individual docs above)
## Acquire Google Credentials from API Console
First we will need to get our own Google Project set up so we can get our credentials. If you don't have experience, you can do so here Google API Console
After you have your project set up, oAuth configured, and the optional service account (only for Google Big Query connections), you are good to install this package.
Make sure to download your oAuth credentials and save them to your working directory as 'client_secret.json'.
## Installation
```
pip install googlewrapper
```
OR
```
python -m pip install googlewrapper
```
### Virtual Environment
For each project it is reccomended to create a virtualenv. Here is a simple guide on virtual environments.
## Combining Products Examples
### Example 1
> Take a list of URLs from Sheets, grab Search Console Data, and import it into Big Query.
```py
from googlewrapper import GoogleSearchConsole, GoogleSheets, GoogleBigQuery
import datetime as dt
# init our objects
sheets = GoogleSheets(YOUR_URL_HERE)
gsc = GoogleSearchConsole()
gbq = GoogleBigQuery()
# get our urls we want to pull
# remember that sheet1 is default
sites = sheets.get_column(1)
'''
this one is a bit more technical
we can pull our column Branded Words right
from sheets then assign it to a dictionary to use
in our GSC object.
Make sure that your url column is the index for
your df. This will happen by default if the urls
are in the first column in google sheets
'''
branded_list = sheets.df()['Branded Words'].to_dict()
# assign those sheets to GSC
gsc.set_sites(sites)
# assign other GSC variables
gsc.set_date(dt.date(2021,1,1))
gsc.set_dims(['page','date','query'])
# get our data
gsc_data = gsc.get_data()
# print the total clicks/impressions and avg position
# for all the sites we just pulled data for
# send them to Big Query
for site in gsc_data:
print(f"{site}'s Data\n"\
f"Clicks: {gsc_data[site]['Clicks'].sum()}\n"\
f"Impressions: {gsc_data[site]['Impressions'].sum()}\n"\
f"Avg Position: {gsc_data[site]['Position'].mean()}\n\n")
# now we will send our data into our GBQ tables for storage
# we will assign the dataset name to be our url
# we will assign table to be gsc
gbq.set_dataset(site)
gbq.set_table('gsc')
# send the data to GBQ
gbq.send(gsc_data[site])
```
## Pull Requests/Suggestions
I'd love to hear your feedback and suggestions. If you see something and you want to give it a shot and fix it, feel free to clone and make a pull request. OR you can submit and issue/feature request on GitHub.
## Thanks for using my code
If you found this library useful, I'd appreciate a coffee. Thanks.
%package help
Summary: Development documents and examples for googlewrapper
Provides: python3-googlewrapper-doc
%description help
# googlewrapper
[](https://pypi.org/project/googlewrapper/)
[](https://github.com/psf/black)
General Connector Classes for Google Products
__Current Wrappers Available__
- Google Analytics
- Google Search Console
- Google Calendar
- Google Big Query
- Google PageSpeed API
- Google Sheets
- Google Docs
_Wrappers In the Pipeline_
- Gmail
- Google Maps
# STEPS
1) Acquire Google Credentials from API Console
2) Install this package
3) Create Connection in Python
4) Use product wrapper to make API calls (see links to individual docs above)
## Acquire Google Credentials from API Console
First we will need to get our own Google Project set up so we can get our credentials. If you don't have experience, you can do so here Google API Console
After you have your project set up, oAuth configured, and the optional service account (only for Google Big Query connections), you are good to install this package.
Make sure to download your oAuth credentials and save them to your working directory as 'client_secret.json'.
## Installation
```
pip install googlewrapper
```
OR
```
python -m pip install googlewrapper
```
### Virtual Environment
For each project it is reccomended to create a virtualenv. Here is a simple guide on virtual environments.
## Combining Products Examples
### Example 1
> Take a list of URLs from Sheets, grab Search Console Data, and import it into Big Query.
```py
from googlewrapper import GoogleSearchConsole, GoogleSheets, GoogleBigQuery
import datetime as dt
# init our objects
sheets = GoogleSheets(YOUR_URL_HERE)
gsc = GoogleSearchConsole()
gbq = GoogleBigQuery()
# get our urls we want to pull
# remember that sheet1 is default
sites = sheets.get_column(1)
'''
this one is a bit more technical
we can pull our column Branded Words right
from sheets then assign it to a dictionary to use
in our GSC object.
Make sure that your url column is the index for
your df. This will happen by default if the urls
are in the first column in google sheets
'''
branded_list = sheets.df()['Branded Words'].to_dict()
# assign those sheets to GSC
gsc.set_sites(sites)
# assign other GSC variables
gsc.set_date(dt.date(2021,1,1))
gsc.set_dims(['page','date','query'])
# get our data
gsc_data = gsc.get_data()
# print the total clicks/impressions and avg position
# for all the sites we just pulled data for
# send them to Big Query
for site in gsc_data:
print(f"{site}'s Data\n"\
f"Clicks: {gsc_data[site]['Clicks'].sum()}\n"\
f"Impressions: {gsc_data[site]['Impressions'].sum()}\n"\
f"Avg Position: {gsc_data[site]['Position'].mean()}\n\n")
# now we will send our data into our GBQ tables for storage
# we will assign the dataset name to be our url
# we will assign table to be gsc
gbq.set_dataset(site)
gbq.set_table('gsc')
# send the data to GBQ
gbq.send(gsc_data[site])
```
## Pull Requests/Suggestions
I'd love to hear your feedback and suggestions. If you see something and you want to give it a shot and fix it, feel free to clone and make a pull request. OR you can submit and issue/feature request on GitHub.
## Thanks for using my code
If you found this library useful, I'd appreciate a coffee. Thanks.
%prep
%autosetup -n googlewrapper-0.2.11
%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-googlewrapper -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 31 2023 Python_Bot - 0.2.11-1
- Package Spec generated