Update questions data

data/questions/3/answer.txt

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+Winter Average PM2.5: 67.4923443634478
+Monsoon Average PM2.5: 34.42519611317571
+Summer Average PM2.5: nan

data/questions/3/code.py

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+def true_code():
+    import pandas as pd
+    
+    df = pd.read_csv('data/raw_data/Data.csv', sep=",")
+    
+    df['Timestamp'] = pd.to_datetime(df['Timestamp'])
+    df['Year'] = df['Timestamp'].dt.year
+    df['Month'] = df['Timestamp'].dt.month
+    
+    data = df[df['Year'] == 2018]
+    data = data[data['station'] == 'Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB']
+    
+    winter_data = data[(data['Month'] == 12) | (data['Month'] <= 2)]
+    summer_data = data[(data['Month'] >= 3) & (data['Month'] <= 5)]
+    monsoon_data = data[(data['Month'] >= 6) & (data['Month'] <= 9)]
+    
+    summer_avg = summer_data['PM2.5'].mean()
+    winter_avg = winter_data['PM2.5'].mean()
+    monsoon_avg = monsoon_data['PM2.5'].mean()
+    
+    print("Winter Average PM2.5:", winter_avg)
+    print("Monsoon Average PM2.5:", monsoon_avg)
+    print("Summer Average PM2.5:", summer_avg)
+
+true_code()

data/questions/3/metadata.json

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+{
+    "question_id": 3,
+    "category": "temporal",
+    "answer_category": "multiple",
+    "plot": false,
+    "libraries": [
+        "pandas"
+    ]
+}

data/questions/3/question.txt

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+For the year 2018, calculate the average PM2.5 concentration for each season (Winter: December-February, Summer: March-May, and Monsoon: June-September) for station Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB. Identify the season with the highest pollution and suggest potential factors contributing to the increase.

output.jsonl

@@ -1,3 +1 @@
-{"folder": "0", "question": "Which state has the highest average PM2.5 concentration across all stations?", "answer": "Delhi", "code": "def true_code():\n    import pandas as pd\n    \n    df = pd.read_csv('data/raw_data/Data.csv', sep=\",\")\n    \n    data = df.groupby(['state','station'])['PM2.5'].mean()\n    ans = data.idxmax()[0]\n    print(ans)\n\ntrue_code()", "metadata": {"question_id": 0, "category": "spatial", "answer_category": "single", "plot": false, "libraries": ["pandas"]}}
-{"folder": "1", "question": "Report the station that recorded the highest value of PM 2.5 for the month Aug of 2020", "answer": "Lal Bahadur Shastri Nagar, Kalaburagi ", "code": "def true_code():\n    import pandas as pd\n    \n    df = pd.read_csv('data/raw_data/Data.csv', sep=\",\")\n    \n    df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n    \n    df['Year'] = df['Timestamp'].dt.year\n    df['Month'] = df['Timestamp'].dt.month\n    data = df[(df['Year'] == 2020) & (df['Month'] == 8)]\n    ans = data.groupby('station')['PM2.5'].max().idxmax()\n    print(ans)\n\ntrue_code()", "metadata": {"question_id": 2, "category": "temporal", "answer_category": "double", "plot": false, "libraries": ["pandas"]}}
-{"folder": "2", "question": "Which state had the most days with hazardous PM2.5 levels (above 300 µg/m³)?", "answer": "Andhra Pradesh", "code": "def true_code():\n    import pandas as pd\n    \n    df = pd.read_csv('data/raw_data/Data.csv', sep=\",\")\n    \n    data = df[df['PM2.5'] > 300]\n    ans = data.groupby(['state', 'station']).value_counts().idxmax()[0]\n    print(ans)\n\ntrue_code()", "metadata": {"question_id": 2, "category": "spatial", "answer_category": "single", "plot": false, "libraries": ["pandas"]}}
+{"folder": 3, "question": "For the year 2018, calculate the average PM2.5 concentration for each season (Winter: December-February, Summer: March-May, and Monsoon: June-September) for station Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB. Identify the season with the highest pollution and suggest potential factors contributing to the increase.", "answer": "Winter Average PM2.5: 67.4923443634478\nMonsoon Average PM2.5: 34.42519611317571\nSummer Average PM2.5: nan", "code": "def true_code():\n    import pandas as pd\n    \n    df = pd.read_csv('data/raw_data/Data.csv', sep=\",\")\n    \n    df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n    df['Year'] = df['Timestamp'].dt.year\n    df['Month'] = df['Timestamp'].dt.month\n    \n    data = df[df['Year'] == 2018]\n    data = data[data['station'] == 'Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB']\n    \n    winter_data = data[(data['Month'] == 12) | (data['Month'] <= 2)]\n    summer_data = data[(data['Month'] >= 3) & (data['Month'] <= 5)]\n    monsoon_data = data[(data['Month'] >= 6) & (data['Month'] <= 9)]\n    \n    summer_avg = summer_data['PM2.5'].mean()\n    winter_avg = winter_data['PM2.5'].mean()\n    monsoon_avg = monsoon_data['PM2.5'].mean()\n    \n    print(\"Winter Average PM2.5:\", winter_avg)\n    print(\"Monsoon Average PM2.5:\", monsoon_avg)\n    print(\"Summer Average PM2.5:\", summer_avg)\n\ntrue_code()", "metadata": {"question_id": 3, "category": "temporal", "answer_category": "multiple", "plot": false, "libraries": ["pandas"]}}