init

.github/workflows/translate.yml

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+# .github/workflows/translate.yml
+name: Translate Readme
+
+on:
+    push:
+        branches: ['test']
+
+jobs:
+    translate:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout
+              uses: actions/checkout@v3
+              with:
+                fetch-depth: 3
+
+            - name: Auto Translate
+              uses: Lin-jun-xiang/action-translate-readme@v2 # Based on the tag
+              with:
+                token: ${{ secrets.Action_Bot }} # Based on step2 name
+                g4f_provider: g4f.Provider.DeepAi # You can change this provider

.gitignore

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+config.py
+test.py
+private/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+Pipfile.lock
+Pipfile
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2023 JunXiang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+<p align="center">
+    <img src='./static/images/pyGA4_logo.PNG' width='60%' />
+</p>
+
+[English version](README.md) | [Chinese version README.md](README.zh-TW.md)
+
+## Introduction
+
+* `pyGA4` is a Python toolkit designed for **extracting, processing, and analyzing** data from **Google Analytics 4 (GA4)**.
+* Whether you're a digital marketing professional, a data analyst, or anyone interested in gaining insights from GA4 data, this package simplifies the process of working with your GA4 data.
+
+## Features
+
+- **Data Extraction**: Easily connect to your GA4 property, retrieve data, and save it for analysis.
+- **Data Preprocessing**: Prepare and clean your GA4 data for analysis with built-in data preprocessing functions.
+- **Custom Queries**: Execute custom queries to filter and aggregate data based on your specific needs.
+- **Data Analysis**: Perform various types of analysis, including user behavior analysis, conversion tracking, and more.
+- **Data Visualization**: Create informative visualizations and reports to communicate your findings effectively.
+- **Simple Integration**: Seamlessly integrate `pyGA4` into your data pipeline or analytics workflow.

README.zh-TW.md

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+--------------------------------
+<p align="center">
+    <img src='./static/images/pyGA4_logo.PNG' width='60%' />
+</p>
+
+[English version](README.md) | [Chinese version README.md](README.zh-TW.md)
+
+## 簡介
+
+* `pyGA4` 是一個 Python 工具箱，設計用於從 **Google Analytics 4 (GA4)** 提取、處理和分析資料。
+* 無論您是數位行銷專業人士、數據分析師或有興趣從 GA4 資料中獲得洞見的任何人，這個套件簡化了處理 GA4 資料的流程。
+
+## 功能
+
+- **資料提取**: 輕鬆連接到您的 GA4 網站，擷取資料並儲存以進行分析。
+- **資料預處理**: 使用內建的資料預處理函數準備和清理 GA4 資料，以便進行分析。
+- **自訂查詢**: 根據您的特定需求執行自定查詢以篩選和彙總資料。
+- **資料分析**: 執行各種類型的分析，包括使用者行為分析、轉換追蹤等。
+- **資料視覺化**: 創建資訊豐富的視覺化和報告，以有效地傳達您的發現。
+- **簡單整合**: 無縫地將 `pyGA4` 集成到您的資料管道或分析工作流程中。

ga4/analytic/__init__.py

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+from ga4.analytic.data_transform import Transformer

ga4/analytic/analytic.py

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+from collections import Counter
+from typing import Any
+
+from ga4.model.bigquery import Ga4Table
+
+
+class User:
+    """The features of user"""
+    @staticmethod
+    def most_common_countries(table: Ga4Table, n: int = 3) -> list:
+        pass
+
+
+class Technology:
+    """The features of technology"""
+    @staticmethod
+    def most_common_os(table: Ga4Table, n: int = 3) -> list:
+        pass
+
+    @staticmethod
+    def most_common_browser(table: Ga4Table, n: int = 3) -> list:
+        pass
+
+
+class Page:
+    """The features of page"""
+
+    @staticmethod
+    def most_common_pages(table: Ga4Table, n: int = 3) -> list:
+        """Return most common pages for all users"""
+        all_pages_loc = table.page_location_list
+        counts = Counter(all_pages_loc)
+        top_n = counts.most_common(n)
+
+        return top_n
+
+    @staticmethod
+    def track_user_loc():
+        """Return most common pages for an user"""
+        pass
+
+    @staticmethod
+    def conversion_rate(engagement_events: Any, target_events: Any) -> float:
+        """Calculate the conversion rate
+
+        If your "target" is to measure how many people `place an order`(purchased), 
+        so your `ad clicks` would be the "engagement" because the orders resulting 
+        from the ad are your "conversions."
+
+        If your "target" is to measure how many people `clicking the ad`,
+        then your ad clicks would become the "conversions,"
+        and `ad impressions` would become the "engagement."
+
+        Parameters
+        ----------
+        action_events:
+            The engagement events that users perform.
+
+        target_events:
+            The target events that represent successful conversions.
+
+        Returns
+        -------
+        float
+            The calculated conversion rate as a percentage.
+
+        Examples
+        --------
+        If `action_events` contains 1000 page views and `target_events` contains 200 purchases,
+        the conversion rate would be calculated as:
+
+        >>> conversion_rate([...,1000], [...,200])
+        20.0
+
+        If you have multiple engagement events and target events, you can calculate the combined
+        conversion rate as well:
+
+        >>> conversion_rate([1000, 500], [200, 100])
+        18.18
+
+        Note that the input lists should contain counts or frequencies of events.
+
+        """
+        return len(target_events) / len(engagement_events) * 100.0