added all_markets()

Author: tuttlepower

predictit_markets/__pycache__/__init__.cpython-312.pyc

@@ -3,6 +3,7 @@
 import pandas as pd
 import requests
 
+
 def market_data(market, time=90, max_contracts=6):
     """
     Fetch market data from PredictIt's API and return as a DataFrame.
@@ -15,11 +16,11 @@ def market_data(market, time=90, max_contracts=6):
     Returns:
     - pd.DataFrame: DataFrame containing the market data or None if an error occurred.
     """
-    url = f'https://www.predictit.org/api/Public/GetMarketChartData/{market}'
+    url = f"https://www.predictit.org/api/Public/GetMarketChartData/{market}"
     params = {
-        'timespan': time,
-        'maxContracts': max_contracts,
-        'isTimespanInHours': 'false'
+        "timespan": time,
+        "maxContracts": max_contracts,
+        "isTimespanInHours": "false",
     }
     try:
         response = requests.get(url, params=params)
@@ -40,9 +41,54 @@ def market_data(market, time=90, max_contracts=6):
 
     return None
 
+
 def market_name(market):
-    # this may not be the best way to do it, but it worked pretty well for me 
-    df = pd.read_json('https://www.predictit.org/api/marketdata/markets/'+str(market))
-    # sets the market to a string and returns it. 
-    text = str(df['name'][0])
+    # this may not be the best way to do it, but it worked pretty well for me.
+
+    df = pd.read_json("https://www.predictit.org/api/marketdata/markets/" + str(market))
+    # sets the market to a string and returns it.
+
+    text = str(df["name"][0])
     return text
+
+
+def all_markets():
+    """
+    Fetch all available markets from the PredictIt API and return the data as a DataFrame.
+
+    This function sends a GET request to the PredictIt API endpoint to retrieve all market data.
+    The returned data includes market IDs and names, which are then structured into a pandas
+    DataFrame with two columns: 'Market ID' and 'Market Name'.
+
+    Returns:
+    --------
+    pd.DataFrame
+        A DataFrame containing market IDs and their corresponding names.
+
+    Raises:
+    -------
+    Exception
+        If the API request fails, an exception is raised with the HTTP status code.
+
+    Examples:
+    ---------
+    df = all_markets()
+    print(df.head())
+       Market ID                         Market Name
+    0       123                   Which party wins?
+    1       456  Who will be the next president?
+    """
+    url = "https://www.predictit.org/api/marketdata/all"
+    headers = {"Accept": "application/json"}
+    response = requests.get(url, headers=headers)
+
+    if response.status_code == 200:
+        data = response.json()
+        markets = data["markets"]
+        market_names = [(market["id"], market["name"]) for market in markets]
+        df = pd.DataFrame(market_names, columns=["Market ID", "Market Name"])
+        return df
+    else:
+        raise Exception(
+            f"Failed to retrieve data. HTTP Status Code: {response.status_code}"
+        )

predictit_markets/__pycache__/predictit_markets.cpython-312.pyc

@@ -3,7 +3,8 @@
 import unittest
 from unittest.mock import patch, Mock
 import requests
-from predictit_markets.predictit_markets import market_data
+import pandas as pd
+from predictit_markets.predictit_markets import all_markets, market_data
 
 class TestPredictItMarkets(unittest.TestCase):
 
@@ -63,6 +64,27 @@ def test_market_data_json_error(self, mock_get):
 
         # Assert the DataFrame is None due to JSON error
         self.assertIsNone(df)
+    
+    @patch('predictit_markets.requests.get')
+    def test_all_markets_success(self, mock_get):
+        mock_get.return_value.status_code = 200
+        mock_get.return_value.json.return_value = {
+        'markets': [
+            {'id': 123, 'name': 'Market 1'},
+            {'id': 456, 'name': 'Market 2'},
+            ]
+        }
+
+        df = all_markets()
+
+   
+        expected_df = pd.DataFrame([
+        {'Market ID': 123, 'Market Name': 'Market 1'},
+        {'Market ID': 456, 'Market Name': 'Market 2'},
+    ])
+
+        pd.testing.assert_frame_equal(df, expected_df)
+
 
 if __name__ == '__main__':
     unittest.main()