Add traffic_agencies.parquet and README.md

README.md

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+---
+icon: traffic-light-slow
+description: >-
+  This dataset provides insights into web traffic patterns for various U.S.
+  government agencies and domains.
+---
+
+# Government Traffic
+
+> **Data Notice**: This dataset provides academic research access with a 6-month data lag. 
+> For real-time data access, please visit [sov.ai](https://sov.ai) to subscribe. 
+> For market insights and additional subscription options, check out our newsletter at [blog.sov.ai](https://blog.sov.ai).
+
+```python
+from datasets import load_dataset
+df_agencies = load_dataset("sovai/government/traffic/agencies", split="train").to_pandas().set_index(["date"])
+```
+    
+
+`Tutorials` are the best documentation — [<mark style="color:blue;">`Government Traffic Analysis Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Government%20Internet.ipynb)
+
+## Description
+
+This dataset provides web traffic data for U.S. government agencies and domains, offering insights into public engagement with government websites.
+
+It enables analysis of traffic trends, inter-agency comparisons, and patterns of citizen interaction with government online resources.
+
+## Data Access
+
+```python
+import sovai as sov
+sov.token_auth(token="your_token_here")
+
+# Agency-level traffic data
+df_agencies = sov.data("government/traffic/agencies")
+
+# Domain-level traffic data
+df_domains = sov.data("government/traffic/domains")
+```
+
+<figure><img src="https://raw.githubusercontent.com/sovai-research/sovai-documentation/main/.gitbook/assets/government_traffic_1 (2).png" alt=""><figcaption></figcaption></figure>
+
+### Dataset Contents
+
+1. **Agency Traffic (df\_agencies)**
+   * Provides traffic data aggregated at the agency level.
+   * Allows for high-level analysis of government agency website usage.
+2. **Domain Traffic (df\_domains)**
+   * Offers more granular data on traffic to specific government domains.
+   * Enables analysis of individual website performance within agencies.
+
+### Analysis Capabilities
+
+* Time series analysis of traffic patterns
+* Correlation analysis between different domains or agencies
+* Calculation of statistical measures like coefficient of variation
+* Filtering for specific types of domains (e.g., embassies)
+
+### Example Analyses
+
+1.  Plotting agency-level traffic:
+
+    ```python
+    df_agencies.plot()
+    ```
+2.  Analyzing embassy website traffic:
+
+    ```python
+    df_embassy = df_domains.loc[:, df_domains.columns.str.contains('embassy', case=False)]
+    df_embassy.plot()
+    ```
+3.  Correlation analysis:
+
+    ```python
+    df_embassy.corr()
+    ```
+4.  Advanced statistics (e.g., coefficient of variation):
+
+    ```python
+    cv = df_embassy.std().div(df_embassy.mean()).sort_values()
+    ```
+
+This dataset is valuable for understanding government web presence, analyzing public engagement with government resources, and identifying trends in how citizens interact with government websites.

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