prompt,Desired Answer,Category ,llama3_answer_1721726247,llama3_score_1721726247,mixtral_answer_1721726407,mixtral_score_1721726407,gemma_answer_1721726499,gemma_score_1721726499,Codestral Mamba_answer_1721759526,Codestral Mamba_score_1721759526,Codestral_answer_1721759762,Codestral_score_1721759762
Which month has the highest average PM2.5 in 2023 for Mumbai?,January,,The month with the highest average PM2.5 in 2023 for Mumbai is January with an average of 93.31 µg/m³.,TRUE,The month with the highest average PM2.5 in 2023 for Mumbai is January with an average of 93.31277266048505 µg/m³.,TRUE,The highest average PM2.5 in 2023 for Mumbai was 93.31 µg/m³. It occurred in January.,TRUE,"content='To find the month with the highest average PM2.5 in 2023 for Mumbai, you can follow these steps:\n\n1. Filter the data for the year 2023 and the city of Mumbai.\n2. Group the data by month and calculate the average PM2.5 for each month.\n3. Find the month with the highest average PM2.5.\n\nHere\'s the code to accomplish this:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Filter data for the year 2023 and Mumbai\nmumbai_2023 = df[(df[""city""] == ""Mumbai"") & (df[""Timestamp""].dt.year == 2023)]\n\n# Group data by month and calculate average PM2.5\naverage_pm25 = mumbai_2023.groupby(mumbai_2023[""Timestamp""].dt.month)[""PM2.5""].mean()\n\n# Find the month with the highest average PM2.5\nhighest_month = average_pm25.idxmax()\n\n# Print the result\nprint(f""The month with the highest average PM2.5 in 2023 for Mumbai is {highest_month}."")\n```\n\nThis code will output the month with the highest average PM2.5 in 2023 for Mumbai.\n\nThe unit of the data is micrograms per cubic meter (µg/m³).' response_metadata={'token_usage': {'prompt_tokens': 313, 'total_tokens': 696, 'completion_tokens': 383}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-a1f7518c-da50-4559-8aa5-46df78dfb29c-0' usage_metadata={'input_tokens': 313, 'output_tokens': 383, 'total_tokens': 696}",TRUE,The month with the highest average PM2.5 in 2023 for Mumbai is January. The average PM2.5 is 93.31 µg/m³,TRUE
Which month generally has the highest pollution?,November,,The month with the highest pollution is 11 with an average PM2.5 of 106.61 µg/m³.,TRUE,"The month with the highest pollution (on average) is month 11, with an average PM2.5 of 106.61250133484131 µg/m³.",TRUE,"The month with the highest average PM2.5 is November, with an average of 106.61 µg/m³.",TRUE,"content='To find out which month generally has the highest pollution, we can group the data by month and calculate the average PM2.5 for each month. Then, we can find the month with the highest average PM2.5.\n\nHere\'s the code to accomplish this:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Group the data by month and calculate the average PM2.5 for each month\nmonthly_avg = df.groupby(df[""Timestamp""].dt.month)[""PM2.5""].mean()\n\n# Find the month with the highest average PM2.5\nhighest_month = monthly_avg.idxmax()\n\n# Print the result\nprint(""The month with the highest pollution is:"", highest_month)\n```\n\nThis code will output the month with the highest average PM2.5.\n\nThe unit of the data is µg/m³, as PM2.5 is measured in micrograms per cubic meter.' response_metadata={'token_usage': {'prompt_tokens': 300, 'total_tokens': 554, 'completion_tokens': 254}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-a1dd6414-e96c-4672-99dc-6cf79be7925b-0' usage_metadata={'input_tokens': 300, 'output_tokens': 254, 'total_tokens': 554}",FALSE,The month with the highest pollution is November with an average PM2.5 of 106.61250133484131 µg/m³,TRUE
Which city has the highest PM2.5 level in July 2022?,Saharsa,,The city with the highest PM2.5 level in July 2022 is Saharsa with an average PM2.5 level of 65.47860816896052 µg/m³.,TRUE,"Mumbai has the highest PM2.5 level in July 2022, with a value of 833.8 µg/m³.",FALSE,"Error: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",FALSE,"content='To find the city with the highest PM2.5 level in July 2022, you can follow these steps:\n\n1. Filter the data for the month of July 2022.\n2. Group the data by city and calculate the mean PM2.5 level for each city.\n3. Find the city with the highest mean PM2.5 level.\n\nHere\'s the code to accomplish this:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Filter data for July 2022\njuly_2022_data = df[(df[""Timestamp""].dt.month == 7) & (df[""Timestamp""].dt.year == 2022)]\n\n# Group data by city and calculate mean PM2.5 level\ncity_pm25_levels = july_2022_data.groupby(""city"")[""PM2.5""].mean()\n\n# Find city with the highest PM2.5 level\nhighest_pm25_city = city_pm25_levels.idxmax()\n\n# Print the result\nprint(""The city with the highest PM2.5 level in July 2022 is:"", highest_pm25_city)\n```\n\nThis code will output the city with the highest PM2.5 level in July 2022.\n\nThe unit of the PM2.5 data is micrograms per cubic meter (µg/m³).' response_metadata={'token_usage': {'prompt_tokens': 310, 'total_tokens': 678, 'completion_tokens': 368}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-196fb218-c16e-48ad-880a-563573e05cc2-0' usage_metadata={'input_tokens': 310, 'output_tokens': 368, 'total_tokens': 678}",FALSE,The city with the highest PM2.5 level in July 2022 is Saharsa with an average PM2.5 level of 65.47860816896052 µg/m³,TRUE
"Which (month, year) has the highest PM2.5?",2017 November,,"The month and year with the highest PM2.5 is (11.0, 2017.0) with an average PM2.5 of 149.09 µg/m³.",TRUE,The month and year with the highest PM2.5 value is 11/2017.,TRUE,"Error: invalid decimal literal (<string>, line 30)",FALSE,Nov-17,TRUE,"The month and year with the highest average PM2.5 is November, 2017 with a value of 149.09468767706852 µg/m³",TRUE
Which state has the highest average PM2.5?,Delhi,,The state with the highest average PM2.5 is Delhi with an average of 104.96 µg/m³.,TRUE,The state with the highest average PM2.5 is Delhi with an average of 104.96 µg/m³.,TRUE,"Error: invalid decimal literal (<string>, line 30)",FALSE,"content='To find the state with the highest average PM2.5, you can group the data by state and calculate the mean PM2.5 for each group. Then, you can find the state with the maximum mean PM2.5.\n\nHere\'s the code to accomplish this:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Group the data by state and calculate the mean PM2.5 for each group\nmean_pm25_by_state = df.groupby(\'state\')[\'PM2.5\'].mean()\n\n# Find the state with the maximum mean PM2.5\nstate_with_max_pm25 = mean_pm25_by_state.idxmax()\n\n# Print the result\nprint(""The state with the highest average PM2.5 is:"", state_with_max_pm25)\n```\n\nThis code will output the state with the highest average PM2.5.\n\nThe unit of the data is micrograms per cubic meter (µg/m³).' response_metadata={'token_usage': {'prompt_tokens': 303, 'total_tokens': 569, 'completion_tokens': 266}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-4ce9fd47-5024-4dcb-b933-d1ea9ec4cfd7-0' usage_metadata={'input_tokens': 303, 'output_tokens': 266, 'total_tokens': 569}",FALSE,The state with the highest average PM2.5 is Delhi with an average of 104.96301991330218 µg/m³,TRUE
What is the name of the month with the highest average PM2.5 overall?,November,,The month with the highest average PM2.5 is November with an average of 149.09 µg/m³.,TRUE,The average PM2.5 in the month of December is 106.61 µg/m³,FALSE,Error: 'str' object has no attribute 'strftime',FALSE,November,TRUE,The month with the highest average PM2.5 overall is November. The average PM2.5 is 106.61 µg/m³,TRUE
"Which city had the lowest average PM2.5 level on January 15th, 2023?",Silchar,,"The city with the lowest average PM2.5 level on January 15th, 2023 is Silchar with an average PM2.5 of 6.209687499999999 µg/m³.",TRUE,Error: attempt to get argmin of an empty sequence,FALSE,"Error: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",FALSE,"content='To find the city with the lowest average PM2.5 level on January 15th, 2023, you can use the following code:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Filter data for January 15th, 2023\nfiltered_data = df[(df[""Timestamp""].dt.month == 1) & (df[""Timestamp""].dt.day == 15) & (df[""Timestamp""].dt.year == 2023)]\n\n# Group data by city and calculate the average PM2.5 level\naverage_pm25 = filtered_data.groupby(""city"")[""PM2.5""].mean()\n\n# Find the city with the lowest average PM2.5 level\nlowest_city = average_pm25.idxmin()\n\n# Print the result\nprint(""The city with the lowest average PM2.5 level on January 15th, 2023 is:"", lowest_city)\n```\n\nThe unit of the PM2.5 data in this case is micrograms per cubic meter (µg/m³).' response_metadata={'token_usage': {'prompt_tokens': 316, 'total_tokens': 607, 'completion_tokens': 291}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-136e6aed-6f13-4d1d-b4c6-f5c73ddfb742-0' usage_metadata={'input_tokens': 316, 'output_tokens': 291, 'total_tokens': 607}",FALSE,"The city with the lowest average PM2.5 level on January 15th, 2023 is Silchar with an average PM2.5 level of 6.209687499999999 µg/m³",TRUE
What was the PM2.5 level in Kolkata on Diwali day in 2022?,18.31,,The average PM2.5 level in Kolkata on Diwali day in 2022 is 18.31 µg/m³,TRUE,83.29255319,FALSE,"content='```python\n# What was the PM2.5 level in Kolkata on Diwali day in 2022?\n\ndf_filtered = df[(df[\'city\'] == \'Kolkata\') & (df[\'Timestamp\'].dt.month == 11) & (df[\'Timestamp\'].dt.day == 22)]\n\npm2_5_diwali = df_filtered[\'PM2.5\'].values[0]\n\nprint(f""The PM2.5 level in Kolkata on Diwali day in 2022 was {pm2_5_diwali} µg/m³."")\n```' response_metadata={'token_usage': {'completion_time': 0.148857874, 'completion_tokens': 130, 'prompt_time': 0.200585383, 'prompt_tokens': 296, 'queue_time': None, 'total_time': 0.349443257, 'total_tokens': 426}, 'model_name': 'gemma-7b-it', 'system_fingerprint': 'fp_7d8efeb0b1', 'finish_reason': 'stop', 'logprobs': None} id='run-b65480e1-1191-434e-8da6-c2abe3016d05-0'",FALSE,41.98108108,FALSE,The PM2.5 level in Kolkata on Diwali day in 2022 was 83.29255319148938 µg/m³,FALSE
How many days did Chennai exceed the PM2.5 level of 150 in March 2023?,0,,0,TRUE,0,TRUE,1011,FALSE,0,TRUE,0,TRUE
What was the average PM2.5 level in Delhi for the year 2023?,102.18,,The average PM2.5 level in Delhi for the year 2023 is 102.18 µg/m³,TRUE,The average PM2.5 level in Delhi for the year 2023 was 102.18 µg/m³.,TRUE,102.1839229,TRUE,"content='To find the average PM2.5 level in Delhi for the year 2023, you can use the following code:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\n# Filter data for Delhi in 2023\ndelhi_2023 = df[(df[""city""] == ""Delhi"") & (df[""Timestamp""].dt.year == 2023)]\n\n# Calculate average PM2.5 level\naverage_pm25 = delhi_2023[""PM2.5""].mean()\n\n# Print the result\nprint(""The average PM2.5 level in Delhi for the year 2023 is"", average_pm25, ""µg/m³"")\n```\n\nThis code first filters the data to include only the rows for Delhi in the year 2023. Then, it calculates the average PM2.5 level by taking the mean of the ""PM2.5"" column. Finally, it prints the result.\n\nThe unit of the data is µg/m³ (micrograms per cubic meter).' response_metadata={'token_usage': {'prompt_tokens': 312, 'total_tokens': 598, 'completion_tokens': 286}, 'model': 'open-codestral-mamba', 'finish_reason': 'stop'} id='run-cc197e4c-cc1a-4353-8e59-6ee572266271-0' usage_metadata={'input_tokens': 312, 'output_tokens': 286, 'total_tokens': 598}",FALSE,The average PM2.5 level in Delhi for the year 2023 was 102.18392291539847 µg/m³,TRUE
Which city had the highest PM2.5 levels in January 2023?,Begusarai,,The city with the highest PM2.5 levels in January 2023 is Begusarai with an average PM2.5 of 318.5035825572216 µg/m³.,TRUE,"Error: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",FALSE,"Error: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",FALSE,Begusarai,TRUE,The city with the highest PM2.5 levels in January 2023 is Begusarai with an average PM2.5 level of 318.5035825572216 µg/m³,TRUE
What are the top three most polluted cities based on PM2.5 levels?,Byrnihat Begusarai Angul,,"The top three most polluted cities in India based on PM2.5 levels are: Byrnihat, Begusarai, and Angul with average PM2.5 levels of 151.39 µg/m³, 133.71 µg/m³, and 118.56 µg/m³ respectively.",TRUE,"Series([], Name: PM2.5, dtype: float64)",FALSE,"Empty DataFrame
Columns: [city, PM2.5]
Index: []",FALSE,"city
Byrnihat     151.394592
Begusarai    133.711359
Angul        118.555268
Name: PM2.5, dtype: float64",TRUE,"city
Byrnihat     151.394592
Begusarai    133.711359
Angul        118.555268
Name: PM2.5, dtype: float64",TRUE
How many days in 2023 did Mumbai exceed the WHO's PM2.5 guidelines?,365,,"In 2023, Mumbai exceeded the WHO's PM2.5 guidelines on 5954 days.",FALSE,0,FALSE,Error: Cannot perform 'rand_' with a dtyped [object] array and scalar of type [bool],FALSE,5954,FALSE,5954,FALSE
What was the maximum PM2.5 level recorded in Kolkata in 2023?,195.69,,The maximum PM2.5 level recorded in Kolkata in 2023 is 195.6911578947369 µg/m³,TRUE,The maximum PM2.5 level recorded in Kolkata in 2023 was 195.69 µg/m³.,TRUE,The maximum PM2.5 level recorded in Kolkata in 2023 was 195.69 µg/m³.,TRUE,195.6911579,TRUE,The maximum PM2.5 level recorded in Kolkata in 2023 is 195.6911578947369 µg/m³,TRUE
What is the difference between the pollution level of the month before lockdown start and the month after lockdown end in Hyderabad in 2020,-25.76,,The difference in pollution levels before and after lockdown in Hyderabad in 2020 is -25.76 µg/m³.,TRUE,-25.76412152,TRUE,Error: 'year',FALSE,-3.692312722,FALSE,The difference in the average PM2.5 pollution level between the month before lockdown start and the month after lockdown end in Hyderabad in 2020 is -7.76 µg/m³,FALSE
What is the percentage change in PM2.5 levels in Delhi from 2019 to 2023?,-5.89,,The percentage change in PM2.5 levels in Delhi from 2019 to 2023 is -5.89%.,TRUE,The percentage change in PM2.5 levels in Delhi from 2019 to 2023 is -5.89% (µg/m³).,TRUE,"content='```python\n# Filter the data to Delhi station\ndelhi = df[df[\'city\'] == \'Delhi\']\n\n# Filter the data to the year range\ndf_filtered = delhi[df[\'Timestamp\'].between(\'2019-01-01\', \'2023-12-31\')]\n\n# Calculate the average PM2.5 levels for each year\npm25_2019 = df_filtered[df_filtered[\'Timestamp\'].dt.year == 2019][\'PM2.5\'].mean()\npm25_2023 = df_filtered[df_filtered[\'Timestamp\'].dt.year == 2023][\'PM2.5\'].mean()\n\n# Calculate the percentage change\npercentage_change = (pm25_2023 - pm25_2019) / pm25_2019 * 100\n\n# Print the result\nprint(""The percentage change in PM2.5 levels in Delhi from 2019 to 2023 is approximately {:.2f}%"".format(percentage_change))\n\n#answer = percentage_change\n```' response_metadata={'token_usage': {'completion_time': 0.298705402, 'completion_tokens': 254, 'prompt_time': 0.208471803, 'prompt_tokens': 302, 'queue_time': None, 'total_time': 0.5071772050000001, 'total_tokens': 556}, 'model_name': 'gemma-7b-it', 'system_fingerprint': 'fp_e3b292b95f', 'finish_reason': 'stop', 'logprobs': None} id='run-073c43dc-8e74-4b7c-9265-b504d60f3fa7-0'",FALSE,7.285826926,FALSE,The percentage change in PM2.5 levels in Delhi from 2019 to 2023 is -5.89%,TRUE
Which state had the lowest average PM2.5 levels in 2023?,Mizoram,,The state with the lowest average PM2.5 levels in 2023 is Mizoram with an average of 10.010255033721341 µg/m³.,TRUE,The state with the lowest average PM2.5 levels in 2023 was Mizoram with an average of 10.01 µg/m³.,TRUE,"Error: invalid syntax (<string>, line 30)",FALSE,The state with the lowest average PM2.5 levels in 2023 was Mizoram with an average of 10.010255033721341 µg/m³.,TRUE,The state with the lowest average PM2.5 levels in 2023 is Mizoram with an average of 10.010255033721341 µg/m³,TRUE
"Which (month,year) show the most impact on reducing PM2.5 levels?",2020 August,,The month and year that show the most impact on reducing PM2.5 levels are 8.0 2020.0 with an average PM2.5 of 20.8460273003573 µg/m³.,TRUE,"43    The month and year that shows the most impact ...
Name: month_year, dtype: object",FALSE,"Error: invalid syntax (<string>, line 30)",FALSE,"(8, 2020)",TRUE,The month and year that show the most impact on reducing PM2.5 levels is 8-2020 with an average PM2.5 of 20.8460273003573 µg/m³,FALSE
Which city in India has the best air quality?,Silchar,,Error: attempt to get argmin of an empty sequence,FALSE,Error: 'country',FALSE,Error: 'float' object is not subscriptable,FALSE,Error: attempt to get argmin of an empty sequence,FALSE,The city in India with the best air quality is Silchar. The average PM2.5 is 9.564287909611249 µg/m³.,TRUE
Number of cities had PM2.5 levels above the WHO guideline in November 2023?,266,,There were 266 cities that had PM2.5 levels above the WHO guideline (15 µg/m³) in November 2023.,TRUE,266,TRUE,Error: 'Series' object has no attribute 'columns',FALSE,266,TRUE,274,FALSE
What was the PM2.5 level during the spring season in 2022?,61.59,,The average PM2.5 level during the spring season in 2022 is 61.59 µg/m³,TRUE,61.58655549,TRUE,61.58655549,TRUE,61.58655549,TRUE,The average PM2.5 level during the spring season in 2022 was 61.59 µg/m³,TRUE
By how much is the PM2.5 levels in Delhi on weekdays more than the weekends in 2023?,-2.83,,The PM2.5 levels in Delhi on weekdays are -2.83 µg/m³ more than the weekends in 2023.,TRUE,The PM2.5 levels in Delhi are -2.83 µg/m³ higher on weekdays than on weekends in 2023.,TRUE,"Error: invalid decimal literal (<string>, line 36)",FALSE,Error: name 'weekdays' is not defined,FALSE,The PM2.5 levels in Delhi on weekdays are -2.83 µg/m³ more than the weekends in 2023.,TRUE
What was the percentage change in PM2.5 levels in Varanasi before and after the implementation of the National Clean Air Programme (NCAP)?,-61.208,,The percentage change in PM2.5 levels in Varanasi before and after the implementation of the National Clean Air Programme (NCAP) is -61.21%.,TRUE,"content='To find the percentage change in PM2.5 levels in Varanasi before and after the implementation of the National Clean Air Programme (NCAP), we first need to filter the data for Varanasi station and then find the average PM2.5 levels before and after the implementation date.\n\nLet\'s assume the NCAP was implemented on 2019-01-01. We can filter the data using the following code:\n\n```python\nvaranasi_df = df[df[""station""] == ""Varanasi""]\nbefore_ncap_df = varanasi_df[varanasi_df[""Timestamp""] < ""2019-01-01""]\nafter_ncap_df = varanasi_df[varanasi_df[""Timestamp""] >= ""2019-01-01""]\n```\n\nNow, we can find the average PM2.5 levels before and after NCAP using the `mean()` function:\n\n```python\nbefore_ncap_avg = before_ncap_df[""PM2.5""].mean()\nafter_ncap_avg = after_ncap_df[""PM2.5""].mean()\n```\n\nFinally, we can calculate the percentage change using the following formula:\n\n```python\npercentage_change = ((after_ncap_avg - before_ncap_avg) / before_ncap_avg) * 100\n```\n\nSo, the final code would look like this:\n\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(""Data.csv"")\ndf[""Timestamp""] = pd.to_datetime(df[""Timestamp""])\n\nvaranasi_df = df[df[""station""] == ""Varanasi""]\nbefore_ncap_df = varanasi_df[varanasi_df[""Timestamp""] < ""2019-01-01""]\nafter_ncap_df = varanasi_df[varanasi_df[""Timestamp""] >= ""2019-01-01""]\n\nbefore_ncap_avg = before_ncap_df[""PM2.5""].mean()\nafter_ncap_avg = after_ncap_df[""PM2.5""].mean()\n\npercentage_change = ((after_ncap_avg - before_ncap_avg) / before_ncap_avg) * 100\n\nanswer = f""The percentage change in PM2.5 levels in Varanasi before and after the implementation of the National Clean Air Programme (NCAP) is {percentage_change:.2f}%.""\n```\n\nNote: The actual percentage change may vary depending on the actual implementation date of the NCAP.' response_metadata={'token_usage': {'completion_time': 1.008675864, 'completion_tokens': 634, 'prompt_time': 0.016755465, 'prompt_tokens': 330, 'queue_time': None, 'total_time': 1.025431329, 'total_tokens': 964}, 'model_name': 'mixtral-8x7b-32768', 'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop', 'logprobs': None} id='run-158bdf2f-55bd-4b7c-8a46-c03efde070f3-0'",FALSE,"Error: invalid decimal literal (<string>, line 34)",FALSE,"The percentage change in PM2.5 levels in Varanasi before the implementation of the National Clean Air Programme (NCAP) was 2.12%. After the implementation, the percentage change was 7.35%.",FALSE,The percentage change in PM2.5 levels in Varanasi before and after the implementation of the National Clean Air Programme (NCAP) is -61.96%.,TRUE
"Which season of Summer, Winter, Spring, Autumn experiences high pollution levels.",Winter,,The season with high pollution levels is Winter with an average PM2.5 of 95.60 µg/m³.,TRUE,The season with the highest average PM2.5 level is Winter with an average of 91.53 µg/m³.,TRUE,Error: 'Series' object is not callable,FALSE,Winter,TRUE,The season that experiences high pollution levels is Winter. The average PM2.5 is 95.60 µg/m³,TRUE
"In which city in Gujarat, GIDC has highest pollution?",Vapi,,Error: attempt to get argmax of an empty sequence,FALSE,Error: attempt to get argmax of an empty sequence,FALSE,"The city in Gujarat with the highest PM2.5 pollution is GIDC, with a reading of nan µg/m³. ",FALSE,Surat,FALSE,Error: attempt to get argmax of an empty sequence,FALSE
How many cities had PM2.5 levels below the national standard in 2022?,201,,,,,,,,,,,
Which city experienced the highest percentage increase in PM2.5 levels in summer 2023?,Fatehabad,,,,,,,,,,,
What were the PM2.5 levels in Kochi on Christmas Day in 2023?,54.77,,,,,,,,,,,
What was the average PM2.5 level in Patna for the winter season in 2020?,132.44,,,,,,,,,,,
Which city has the lowest PM2.5 in India's largest rice producing state.,Haldia,,,,,,,,,,,
Which city has the highest PM2.5 in India's largest milk producing state.,Ghaziabad,,,,,,,,,,,
What is the difference between average pollution level of most polluted and least polluted city of Rajasthan?,76.95,,,,,,,,,,,
Which among the top 3 cities selected under smart city mission have the least pollution?,Pune,,,,,,,,,,,
Which metro city has the highest pollution?,Delhi,,,,,,,,,,,
Which out of the 9 metro city has the lowest pollution?,Bengaluru,,,,,,,,,,,
Which city is the most affected in terms of pollution due Christmas?,Begusarai,,,,,,,,,,,
Which of the cities that have top 9 IITs has the highest PM2.5?,Delhi,,,,,,,,,,,
Which of the cities that have top 2 IIMs has the highest PM2.5?,Ahmedabad,,,,,,,,,,,
How many stations noted PM2.5 levels exceeding the WHO guideline for all the 365 days in year 2023?,10,,,,,,,,,,,
Which of the 7 north-eastern states experiences lowest pollution?,Mizoram,,,,,,,,,,,
Which city showed the maximum drop between the pollution level of the month before lockdown start and the month after lockdown end in 2020,Muzaffarpur,,,,,,,,,,,
"Which season of Summer, Winter, Spring, Autumn experiences lowest pollution levels?",Summer,,,,,,,,,,,
What is the average PM2.5 of Ahmedabad on weekends in 2022?,61.44,,,,,,,,,,,
What is the average PM2.5 of Indore on weekdays in 2021?,49.26,,,,,,,,,,,
How many stations noted PM2.5 levels exceeding the Indian guideline for all the 365 days in year 2023?,0,,,,,,,,,,,