Update README.md

README.md

@@ -1,9 +1,12 @@
 ---
 language:
 - en
+- zh
 pretty_name: GeoComp
 tags:
 - GeoLocation
+size_categories:
+- 10M<n<100M
 
 ---
 
@@ -23,53 +26,93 @@ To prevent cheating, external search engines are banned, and each round is time-
 To ensure predictions are human-generated rather than machine-generated, users must register with a phone number, enabling tracking of individual activities. 
 Using this platform, we collected **GeoComp**, a comprehensive dataset covering 1,000 days of user competition.
 
-## File introduction
+## File Introduction
 
-- [tuxun_combined_*](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/tuxun_combined_aa)
+The GeoComp dataset is now primarily provided in Parquet format within the `/data` directory for efficient access and processing. You can find the following files in this repository:
 
-  The splited files of tuxun_combined.csv, you can use "cat" to get the csv file.
+* [**`/data/tuxun_combined.parquet`**]([ShirohAO/tuxun at main](https://huggingface.co/datasets/ShirohAO/tuxun/tree/main/data)): This is the main dataset file containing combined competition history in Parquet format.
+* [**`tuxun_sample.csv`**]([tuxun_sample.csv · ShirohAO/tuxun at main](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/tuxun_sample.csv)): An example CSV file to preview the structure of the data.
+* [**`selected_panoids`**]([selected_panoids · ShirohAO/tuxun at main](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/selected_panoids)): The 500 panoids we used in our work. You can add a `.csv` or `.json` suffix to this file.
+* [**`download_panoramas.py`**]([download_panoramas.py · ShirohAO/tuxun at main](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/download_panoramas.py)): A script to download street view images using the provided panoids.
 
-- [tuxun_sample.csv](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/tuxun_sample.csv)
+## Requirement
 
-  An example to preview the structure of tuxun_combined.csv.
+The **GeoComp** is only for research.
 
-- [selected_panoids](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/selected_panoids)
+## Start
 
-  The 500 panoids we used in our work. You can add csv or json suffix to the file.
+### Data format of tuxun_combined.csv
 
-- [download_panoramas.py](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/download_panoramas.py)
+The `tuxun_combined.parquet` file contains data in a similar structure to the original `tuxun_combined.csv`.
 
-  The script to download street view images from the panoid. 
+**Example Schema:**
 
-## Requirement
+| id   | data                | gmt_create      | timestamp |
+| ---- | ------------------- | --------------- | --------- |
+| Game | Json style metadata | 1734188074762.0 |           |
 
-The **GeoComp** is only for reasearch.
+**Explanation:**
 
-## Start
+* We hide data items that may reveal personal privacy like changing the value of key "userId" to "User", "hostUserId" to "HostUser", "playerIds" to "Players", "id" to "Game".
+* The data under the "data" column is in JSON style. This column contains detailed geolocation information like "lat", "lng", "nation", and "panoId".
 
-### Get tuxun_combined.csv
+### Extracting Specific Fields from the 'data' Column
 
-Merge the splited files to tuxun_combined.csv
+The 'data' column contains rich game-specific information in a JSON string format. To access individual fields like `guessPlace`, `targetPlace`, `score`, or `panoId`, you'll need to parse this JSON string.
 
-```shell
-cat tuxun_comblined_* > tuxun_comblined.csv
+Here’s a Python example using `pandas` and `json` to extract these fields from the `tuxun_combined.parquet` file:
 
-ls -lh tuxun_comblined.csv
-```
+```python
+import pandas as pd
+import json
 
-### Data format of tuxun_combined.csv
+# Assuming your Parquet file is at 'data/tuxun_combined.parquet'
+# Adjust the file_path if necessary
+file_path = 'data/tuxun_combined.parquet'
 
-#### Example
+# Read the Parquet file into a DataFrame
+df = pd.read_parquet(file_path)
 
-| id   | data                | gmt_create      | timestamp |
-| ---- | ------------------- | --------------- | --------- |
-| Game | Json style metadata | 1734188074762.0 |           |
+# Define a function to parse the 'data' column and extract desired information
+def extract_game_details(data_json_str):
+    try:
+        # Parse the JSON string into a Python dictionary
+        game_data = json.loads(data_json_str)
 
-#### Explanation
+        # Initialize variables to None in case a field is missing
+        guess_place = None
+        target_place = None
+        score = None
+        pano_id = None
 
-- We hide data items that may reveal personal privacy like changing the value of key "userId" to "User", "hostUserId" to "HostUser", "playerIds" to "Players", "id" to "Game"
+        # Extract guessPlace, targetPlace, and score from 'player' -> 'lastRoundResult'
+        if 'player' in game_data and 'lastRoundResult' in game_data['player']:
+            last_round_result = game_data['player']['lastRoundResult']
+            guess_place = last_round_result.get('guessPlace')
+            target_place = last_round_result.get('targetPlace')
+            score = last_round_result.get('score')
 
-- The data under the "data" column is in json style. This column contains the detailed geolocation information like "lat", "lng", "nation" and "panoId".
+        # Extract panoId from the first element of the 'rounds' list
+        if 'rounds' in game_data and len(game_data['rounds']) > 0:
+            first_round = game_data['rounds'][0]
+            pano_id = first_round.get('panoId')
+
+        return guess_place, target_place, score, pano_id
+    except json.JSONDecodeError:
+        print(f"Error decoding JSON for row: {data_json_str[:100]}...") # Print first 100 chars for context
+        return None, None, None, None
+    except KeyError as e:
+        print(f"Missing key: {e} in row: {data_json_str[:100]}...") # Print first 100 chars for context
+        return None, None, None, None
+
+# Apply the function to the 'data' column and create new columns in the DataFrame
+df[['guessPlace', 'targetPlace', 'score', 'panoId']] = df['data'].apply(
+    lambda x: pd.Series(extract_game_details(x))
+)
+
+# Display the first few rows with the newly extracted columns
+print(df[['id', 'guessPlace', 'targetPlace', 'score', 'panoId']].head())
+```
 
 ## Additional Information