Update

README.md

@@ -4,16 +4,129 @@
 
 [English version](README.md) | [Chinese version README.md](README.zh-TW.md)
 
+[Documents](https://lin-jun-xiang.github.io/pyga4/) | [Pypi](https://pypi.org/project/pyga4/)
+
+## Overview
+
+- [Overview](#overview)
+- [Introduction](#introduction)
+- [How to Stream GA4 Data to Bigquery in Real-time?](#how-to-stream-ga4-data-to-bigquery-in-real-time)
+- [Features](#features)
+- [How to Use?](#how-to-use)
+    - [Download the Package](#download-the-package)
+    - [Connect to Your Bigquery](#connect-to-your-bigquery)
+    - [Connect to GA4 Tables](#connect-to-ga4-tables)
+    - [Evaluate Query Cost with dry run](#evaluate-query-cost-with-dry-run)
+    - [Analyze User Properties](#analyze-user-properties)
+    - [Analyze Device Properties](#analyze-device-properties)
+    - [Analyze Events](#analyze-events)
+
+---
+
 ## 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.
 
+## How to Stream GA4 Data to Bigquery in Real-time?
+
+First, we assume that everyone has already integrated GA4 data into their respective platform websites (there are many online tutorials).
+
+Next, we will use a free third-party service to stream data into Bigquery. For detailed instructions, please refer to the [official documentation](https://support.google.com/analytics/answer/9823238?hl=en#zippy=%2Cin-this-article).
+
+If successful, you will see tables in Bigquery similar to the following (`analytics_xxxx`), [ref](https://analyticscanvas.com/knowledge-base/ga4-bigquery-export-tutorial-002-querying-event-params/):
+
+![https://analyticscanvas.com/knowledge-base/ga4-bigquery-export-tutorial-002-querying-event-params/](static/images/2023-09-21-15-04-30.png)
+
 ## Features
 
+- **Query Cost Estimation**: Provides the Bigquery `dry run` feature to estimate query cost before execution.
 - **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.
+
+## How to Use?
+
+For more features, please refer to the [package documentation](https://lin-jun-xiang.github.io/pyga4/).
+
+#### Download the Package
+
+`pip install pyga4`
+
+#### Connect to Your Bigquery 
+```python
+from google.cloud import bigquery
+
+client = bigquery.Client()
+# Or you can use:
+# client = bigquery.Client.from_service_account_json(
+#    './private/service-project-data-dev-01d11c742ba1.json'
+# )
+```
+
+#### Connect to GA4 Tables
+```python
+from pyga4.model import Ga4Table
+
+# Use your project_id, dataset_name (analytics_xxxx)
+ga4_table = Ga4Table(client, PROJECT_ID, DATASET_NAME)
+
+# Show the tables list in dataset, e.g., analytics_date1, analytics_date2
+table_id_list = ga4_table all_tables_list
+print(table_id_list)
+
+# Select the table you want to analyze
+ga4_table.table_id = 'events_intraday_20200812'
+```
+
+#### Evaluate Query Cost with dry run
+```python
+    # Query with dry run:
+    ga4_table.query_config.dry_run = True
+    query = f"""
+    SELECT event_timestamp FROM `<project_id>.<dataset_name>.<data_table>`
+    """
+    results = ga4_table.query(query) # return None, but you can see the query usage!
+```
+
+#### Analyze User Properties
+
+**Query User ID and Country List**
+
+```python
+# User attribute
+user_id_list = ga4_table.user_id_list
+user_country_list = ga4_table.geo_country_list
+```
+
+**Query User ID and Country Distribution**
+
+```python
+from pyga4.analytic import UserAnalytic
+
+# UserAnalytic
+user_analytic = UserAnalytic(ga4_table)
+countries_dist = user_analytic.countries_distribution
+userid_dist = user_analytic.user_id_distribution
+```
+
+#### Analyze Device Properties
+
+```python
+# DeviceAnalytic
+device_analytic = DeviceAnalytic(ga4_table)
+mobile_brand_dist = device_analytic.mobile_brand_distribution
+```
+
+#### Analyze Events
+
+```python
+# EventAnalytic
+event_analytic = EventAnalytic(ga4_table)
+page_loc_dist = event_analytic.pages_distribution
+```
+
+<a href="#top">Back to top</a>

README.zh-TW.md

@@ -32,9 +32,9 @@
 
 接下來，我們會透過免費第三方服務，將資料串流到 Bigquery 中，詳細操作請參考[官方文檔](https://support.google.com/analytics/answer/9823238?hl=en#zippy=%2Cin-this-article)
 
-如果成功串接，你會在 Bigquery 中看到類似以下的資料表(`analytics_xxxx`):
+如果成功串接，你會在 Bigquery 中看到類似以下的資料表(`analytics_xxxx`)，[圖片來源](https://analyticscanvas.com/knowledge-base/ga4-bigquery-export-tutorial-002-querying-event-params/):
 
-![](static/imgs/2023-09-21-14-15-55.png)
+![https://analyticscanvas.com/knowledge-base/ga4-bigquery-export-tutorial-002-querying-event-params/](static/images/2023-09-21-15-04-30.png)
 
 
 ## 功能