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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'code'}) and 2 missing columns ({'true_code', 'train_code'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Vedant-acharya/AQ_finetuning_data/AQ_test_dataset.csv (at revision 5501ab5bc2223cd3829ed62ba18d584d75f6c46b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
id: int64
category: string
og_question: string
code: string
question: string
correct_ans: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1064
to
{'Unnamed: 0': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'category': Value(dtype='string', id=None), 'og_question': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'true_code': Value(dtype='string', id=None), 'correct_ans': Value(dtype='string', id=None), 'train_code': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'code'}) and 2 missing columns ({'true_code', 'train_code'}).
This happened while the csv dataset builder was generating data using
hf://datasets/Vedant-acharya/AQ_finetuning_data/AQ_test_dataset.csv (at revision 5501ab5bc2223cd3829ed62ba18d584d75f6c46b)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Unnamed: 0
int64 | id
int64 | category
string | og_question
string | question
string | true_code
string | correct_ans
string | train_code
string |
|---|---|---|---|---|---|---|---|
0
| 1
|
area_based
|
Which state (excluding UTs) has the 3rd highest PM 10 concentration per square kilometer based on the median PM 10 values?
|
Which state (excluding Union Territories) shows the 3rd maximum PM10 concentration per square kilometer, using median PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Sikkim
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
1
| 4
|
area_based
|
Which state (excluding UTs) has the highest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?
|
Which state (excluding Union Territories) has the highest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Manipur
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
2
| 6
|
area_based
|
Which state (excluding UTs) has the 3rd highest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?
|
Which state (excluding Union Territories) exhibits the 3rd maximum PM2.5 concentration per square kilometer, based on average PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Sikkim
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].mean().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
3
| 10
|
area_based
|
Which state (excluding UTs) has the 3rd lowest PM 10 concentration per square kilometer based on the variance of PM 10 values?
|
Which state (excluding Union Territories) exhibits the 3rd lowest PM10 concentration per square kilometer, based on the variance of PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
print(max_area_state)
true_code()
|
Tamil Nadu
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
return max_area_state
</code>
|
4
| 16
|
area_based
|
Which state (excluding UTs) has the 2nd highest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?
|
Which state (excluding Union Territories) has the 2nd maximum PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Nagaland
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
5
| 23
|
area_based
|
Which state (excluding UTs) has the 2nd lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?
|
Which state (excluding Union Territories) presents the 2nd minimum PM10 concentration per square kilometer, according to 25th percentile PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
print(max_area_state)
true_code()
|
Madhya Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
return max_area_state
</code>
|
6
| 26
|
area_based
|
Which state (excluding UTs) has the 3rd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?
|
Which state (excluding Union Territories) exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
print(max_area_state)
true_code()
|
Maharashtra
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
return max_area_state
</code>
|
7
| 31
|
area_based
|
Which state (excluding UTs) has the 2nd highest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?
|
Which state (excluding Union Territories) presents the 2nd maximum PM2.5 concentration per square kilometer, according to median PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Nagaland
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
8
| 34
|
area_based
|
Which state (excluding UTs) has the 3rd lowest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?
|
Which state (excluding Union Territories) exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
print(max_area_state)
true_code()
|
Maharashtra
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].var().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state']
return max_area_state
</code>
|
9
| 40
|
area_based
|
Which union territory has the lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?
|
Which union territory shows the minimum PM2.5 concentration per square kilometer, using 75th percentile PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
print(max_area_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
return max_area_state
</code>
|
10
| 43
|
area_based
|
Which union territory has the highest PM 2.5 concentration per square kilometer based on the total PM 2.5 values?
|
Which union territory has the highest PM2.5 concentration per square kilometer, based on total PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].sum().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].sum().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
11
| 46
|
area_based
|
Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?
|
Which union territory presents the 2nd minimum PM2.5 concentration per square kilometer, according to 75th percentile PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
print(max_area_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
return max_area_state
</code>
|
12
| 47
|
area_based
|
Which union territory has the 2nd highest PM 10 concentration per square kilometer based on the total PM 10 values?
|
Which union territory has the 2nd highest PM10 concentration per square kilometer, based on total PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].sum().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
13
| 56
|
area_based
|
Which union territory has the lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?
|
Which union territory shows the minimum PM10 concentration per square kilometer, using 25th percentile PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
print(max_area_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state']
return max_area_state
</code>
|
14
| 57
|
area_based
|
Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?
|
Which union territory exhibits the 2nd lowest PM2.5 concentration per square kilometer, based on median PM2.5 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
print(max_area_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM2.5'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
return max_area_state
</code>
|
15
| 58
|
area_based
|
Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?
|
Which union territory presents the 2nd minimum PM10 concentration per square kilometer, according to 25th percentile PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
print(max_area_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].quantile(0.25).reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
return max_area_state
</code>
|
16
| 64
|
area_based
|
Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the median PM 10 values?
|
Which union territory shows the 2nd minimum PM10 concentration per square kilometer, using median PM10 values?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm25 = main_data.groupby('state')['PM10'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
print(max_area_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm25 = data.groupby('state')['PM10'].median().reset_index()
states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm25.merge(states_area, on='state', how='inner')
merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)']
max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state']
return max_area_state
</code>
|
17
| 73
|
area_based
|
Which state has the lowest number of monitoring stations relative to its area?
|
Which state possesses the smallest number of monitoring stations relative to its area?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
station_counts = main_data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
print(required_state)
true_code()
|
Arunachal Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
station_counts = data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
return required_state
</code>
|
18
| 74
|
area_based
|
Which state has the 4th highest number of monitoring stations relative to its area?
|
Which state has the 4th highest count of monitoring stations in proportion to its area?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
station_counts = main_data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
print(required_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
station_counts = data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
return required_state
</code>
|
19
| 77
|
area_based
|
Which union territory has the lowest number of monitoring stations relative to its area?
|
Which union territory possesses the smallest number of monitoring stations relative to its area?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
station_counts = main_data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
print(required_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
station_counts = data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2').iloc[0]['state']
return required_state
</code>
|
20
| 78
|
area_based
|
Which union territory has the 4th highest number of monitoring stations relative to its area?
|
Which union territory has the 4th highest count of monitoring stations in proportion to its area?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
station_counts = main_data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
print(required_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
station_counts = data.groupby('state')['station'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = station_counts.merge(filtered_states_data, on='state', how='inner')
merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)']
required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[3]['state']
return required_state
</code>
|
21
| 79
|
area_based
|
Report the total land area of the state (excluding UTs) with the highest combined PM2.5 and PM10 concentrations.
|
Provide the total land area of the state (excluding Union Territories) having the maximum combined PM2.5 and PM10 concentrations.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean()
state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
print(required_area)
true_code()
|
94163
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_averages = data.groupby('state')[['PM2.5', 'PM10']].mean()
state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
return required_area
</code>
|
22
| 83
|
area_based
|
Report the total land area of the union territory with the highest combined PM2.5 and PM10 concentrations.
|
State the total land area of the union territory with the highest combined PM2.5 and PM10 concentrations.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean()
state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
print(required_area)
true_code()
|
1484
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_averages = data.groupby('state')[['PM2.5', 'PM10']].mean()
state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10']
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_averages.merge(filtered_states_data, on='state', how='inner')
required_area = merged_df.sort_values('combined', ascending=False).iloc[0]['area (km2)']
return required_area
</code>
|
23
| 91
|
area_based
|
Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on median PM 10 levels?
|
Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to median PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Punjab
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
24
| 94
|
area_based
|
Which state(excuding UTs) has the 3rd highest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?
|
Which state (excluding Union Territories) possesses the 3rd largest land area among the top 5 most polluted states, based on the 25th percentile of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Jharkhand
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
25
| 95
|
area_based
|
Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on variance of PM 10 levels?
|
Which state (excluding Union Territories) has the smallest land area among the top 3 most polluted states, according to the variance of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Assam
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
26
| 102
|
area_based
|
Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on variance of PM 2.5 levels?
|
Which state (excluding Union Territories) possesses the 2nd largest land area among the top 5 most polluted states, based on the variance of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Bihar
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
27
| 109
|
area_based
|
Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on total PM 10 levels?
|
Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to total PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Punjab
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
28
| 110
|
area_based
|
Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?
|
Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 10 most polluted states, based on the 75th percentile of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
29
| 115
|
area_based
|
Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?
|
Which state (excluding Union Territories) has the smallest land area among the top 3 most polluted states, according to the 25th percentile of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
30
| 120
|
area_based
|
Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?
|
Which state (excluding Union Territories) possesses the largest land area among the top 10 most polluted states, based on the 75th percentile of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Rajasthan
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
31
| 125
|
area_based
|
Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on variance of PM 10 levels?
|
Which state (excluding Union Territories) has the 3rd minimum land area among the top 10 most polluted states, according to the variance of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
print(max_area_state)
true_code()
|
Assam
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
return max_area_state
</code>
|
32
| 127
|
area_based
|
Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?
|
Which state (excluding Union Territories) has the largest land area among the top 5 most polluted states, according to the 25th percentile of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Rajasthan
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
33
| 133
|
area_based
|
Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on median PM 2.5 levels?
|
Which state (excluding Union Territories) has the smallest land area among the top 5 most polluted states, according to median PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(5)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
34
| 138
|
area_based
|
Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?
|
Which state (excluding Union Territories) possesses the 2nd largest land area among the top 3 most polluted states, based on the 25th percentile of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Himachal Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
35
| 146
|
area_based
|
Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on 25th percentile of PM 10 levels?
|
Which state (excluding Union Territories) possesses the 3rd largest land area among the top 10 most polluted states, based on the 25th percentile of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Uttar Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
36
| 153
|
area_based
|
Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on average PM 2.5 levels?
|
Which state (excluding Union Territories) has the 3rd highest land area among the top 10 most polluted states, according to average PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Gujarat
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
37
| 154
|
area_based
|
Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on median PM 2.5 levels?
|
Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 10 most polluted states, based on median PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
print(max_area_state)
true_code()
|
Punjab
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state']
return max_area_state
</code>
|
38
| 156
|
area_based
|
Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on median PM 10 levels?
|
Which state (excluding Union Territories) possesses the largest land area among the top 10 most polluted states, based on median PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Rajasthan
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
39
| 177
|
area_based
|
Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on total PM 10 levels?
|
Which state (excluding Union Territories) has the 3rd highest land area among the top 3 most polluted states, according to total PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
print(max_area_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(3)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state']
return max_area_state
</code>
|
40
| 181
|
area_based
|
Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on standard deviation of PM 10 levels?
|
Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to the standard deviation of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Tripura
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(10)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
41
| 194
|
area_based
|
Which union territory has the highest land area among the top 4 most polluted union territories, based on average PM 2.5 levels?
|
Which union territory possesses the highest land area among the top 4 most polluted union territories, based on average PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
42
| 195
|
area_based
|
Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on 75th percentile of PM 10 levels?
|
Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to the 75th percentile of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
43
| 196
|
area_based
|
Which union territory has the lowest land area among the top 4 most polluted union territories, based on 75th percentile of PM 2.5 levels?
|
Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on the 75th percentile of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
44
| 209
|
area_based
|
Which union territory has the 2nd highest land area among the top 2 most polluted union territories, based on standard deviation of PM 10 levels?
|
Which union territory has the 2nd highest land area among the top 2 most polluted union territories, according to the standard deviation of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
45
| 219
|
area_based
|
Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on standard deviation of PM 10 levels?
|
Which union territory has the 2nd minimum land area among the top 4 most polluted union territories, according to the standard deviation of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].std().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
46
| 220
|
area_based
|
Which union territory has the lowest land area among the top 4 most polluted union territories, based on average PM 10 levels?
|
Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on average PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].mean().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
47
| 221
|
area_based
|
Which union territory has the highest land area among the top 2 most polluted union territories, based on median PM 2.5 levels?
|
Which union territory has the largest land area among the top 2 most polluted union territories, according to median PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
48
| 226
|
area_based
|
Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on variance of PM 2.5 levels?
|
Which union territory possesses the 2nd smallest land area among the top 2 most polluted union territories, based on the variance of PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state']
return max_area_state
</code>
|
49
| 230
|
area_based
|
Which union territory has the 2nd highest land area among the top 4 most polluted union territories, based on variance of PM 10 levels?
|
Which union territory possesses the 2nd largest land area among the top 4 most polluted union territories, based on the variance of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].var().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state']
return max_area_state
</code>
|
50
| 232
|
area_based
|
Which union territory has the highest land area among the top 2 most polluted union territories, based on 25th percentile of PM 10 levels?
|
Which union territory possesses the largest land area among the top 2 most polluted union territories, based on the 25th percentile of PM10 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
print(max_area_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM10', ascending=False)
top_polluted_states = merged_df.head(2)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state']
return max_area_state
</code>
|
51
| 234
|
area_based
|
Which union territory has the lowest land area among the top 4 most polluted union territories, based on median PM 2.5 levels?
|
Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on median PM2.5 levels?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
print(max_area_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm_avg = data.groupby('state')['PM2.5'].median().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner')
merged_df = merged_df.sort_values('PM2.5', ascending=False)
top_polluted_states = merged_df.head(4)['state'].tolist()
top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)]
max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state']
return max_area_state
</code>
|
52
| 238
|
area_based
|
Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on 25th percentile of PM 2.5 level?
|
Which state having a land area less than 50,000 km² registers the minimum PM2.5 level, based on its 25th percentile PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
print(required_state)
true_code()
|
Mizoram
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
return required_state
</code>
|
53
| 240
|
area_based
|
Which state with a land area lesser than 50,000 km² has the 5th highest PM 10 level, based on variance of PM 10 level?
|
Which state having a land area less than 50,000 km² registers the 5th maximum PM10 level, based on its variance of PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].var().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
print(required_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].var().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
return required_state
</code>
|
54
| 250
|
area_based
|
Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on total PM 2.5 level?
|
Which state having a land area exceeding 50,000 km² registers the 2nd minimum PM2.5 level, based on its total PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
print(required_state)
true_code()
|
Himachal Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].sum().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
return required_state
</code>
|
55
| 251
|
area_based
|
Which state with a land area greater than 50,000 km² has the 2nd highest PM 2.5 level, based on standard deviation of PM 2.5 level?
|
Which state with a land area greater than 50,000 km² shows the 2nd highest PM2.5 level, according to its standard deviation of PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
print(required_state)
true_code()
|
Bihar
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
return required_state
</code>
|
56
| 256
|
area_based
|
Which state with a land area greater than 50,000 km² has the 5th lowest PM 10 level, based on total PM 10 level?
|
Which state having a land area exceeding 50,000 km² registers the 5th minimum PM10 level, based on its total PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].sum().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10').iloc[4]['state']
print(required_state)
true_code()
|
Chhattisgarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].sum().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10').iloc[4]['state']
return required_state
</code>
|
57
| 279
|
area_based
|
Which state with a land area lesser than 50,000 km² has the lowest PM 10 level, based on median PM 10 level?
|
Which state with a land area below 50,000 km² shows the minimum PM10 level, according to its median PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].median().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
print(required_state)
true_code()
|
Sikkim
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].median().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
return required_state
</code>
|
58
| 287
|
area_based
|
Which state with a land area lesser than 50,000 km² has the 2nd highest PM 10 level, based on standard deviation of PM 10 level?
|
Which state with a land area below 50,000 km² shows the 2nd highest PM10 level, according to its standard deviation of PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state']
print(required_state)
true_code()
|
Haryana
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state']
return required_state
</code>
|
59
| 289
|
area_based
|
Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on average PM 2.5 level?
|
Which state with a land area below 50,000 km² shows the minimum PM2.5 level, according to its average PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
print(required_state)
true_code()
|
Mizoram
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[0]['state']
return required_state
</code>
|
60
| 293
|
area_based
|
Which state with a land area lesser than 50,000 km² has the 3rd highest PM 2.5 level, based on average PM 2.5 level?
|
Which state with a land area below 50,000 km² shows the 3rd highest PM2.5 level, according to its average PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state']
print(required_state)
true_code()
|
Tripura
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state']
return required_state
</code>
|
61
| 298
|
area_based
|
Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on 75th percentile of PM 10 level?
|
Which state having a land area less than 50,000 km² registers the 3rd minimum PM10 level, based on its 75th percentile PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10').iloc[2]['state']
print(required_state)
true_code()
|
Mizoram
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].quantile(0.75).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 50000]
required_state = filtered_data.sort_values('PM10').iloc[2]['state']
return required_state
</code>
|
62
| 306
|
area_based
|
Which state with a land area greater than 50,000 km² has the 5th highest PM 10 level, based on standard deviation of PM 10 level?
|
Which state having a land area exceeding 50,000 km² registers the 5th maximum PM10 level, based on its standard deviation of PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
print(required_state)
true_code()
|
West Bengal
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state']
return required_state
</code>
|
63
| 311
|
area_based
|
Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on standard deviation of PM 2.5 level?
|
Which state with a land area greater than 50,000 km² shows the 2nd lowest PM2.5 level, according to its standard deviation of PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
print(required_state)
true_code()
|
Chhattisgarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].std().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
return required_state
</code>
|
64
| 312
|
area_based
|
Which state with a land area greater than 50,000 km² has the lowest PM 10 level, based on 25th percentile of PM 10 level?
|
Which state having a land area exceeding 50,000 km² registers the minimum PM10 level, based on its 25th percentile PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
print(required_state)
true_code()
|
Arunachal Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
return required_state
</code>
|
65
| 315
|
area_based
|
Which state with a land area greater than 50,000 km² has the 2nd highest PM 2.5 level, based on average PM 2.5 level?
|
Which state with a land area greater than 50,000 km² shows the 2nd highest PM2.5 level, according to its average PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
print(required_state)
true_code()
|
Uttar Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].mean().reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
return required_state
</code>
|
66
| 327
|
area_based
|
Which state with a land area greater than 50,000 km² has the highest PM 10 level, based on 25th percentile of PM 10 level?
|
Which state with a land area greater than 50,000 km² shows the highest PM10 level, according to its 25th percentile PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
print(required_state)
true_code()
|
Himachal Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
merged_data = pd.merge(state_pm, states_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 50000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
return required_state
</code>
|
67
| 328
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on 25th percentile of PM 10 level?
|
Which union territory with a land area exceeding 1,000 km² shows the 2nd lowest PM10 level, based on its 25th percentile PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
print(required_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].quantile(0.25).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
return required_state
</code>
|
68
| 336
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on standard deviation of PM 10 level?
|
Which union territory with a land area greater than 1,000 km² shows the lowest PM10 level, based on its standard deviation of PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].std().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
print(required_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].std().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
return required_state
</code>
|
69
| 346
|
area_based
|
Which union territory with a land area lesser than 1,000 km² has the 2nd highest PM 2.5 level, based on 25th percentile of PM 2.5 level?
|
Which union territory with a land area below 1,000 km² shows the 2nd highest PM2.5 level, based on its 25th percentile PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 1000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
print(required_state)
true_code()
|
Puducherry
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].quantile(0.25).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 1000]
required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state']
return required_state
</code>
|
70
| 348
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on median PM 10 level?
|
Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM10 level, based on its median PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].median().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
print(required_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].median().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
return required_state
</code>
|
71
| 352
|
area_based
|
Which union territory with a land area lesser than 1,000 km² has the 2nd lowest PM 2.5 level, based on 75th percentile of PM 2.5 level?
|
Which union territory with a land area below 1,000 km² shows the 2nd lowest PM2.5 level, based on its 75th percentile PM2.5 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 1000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
print(required_state)
true_code()
|
Chandigarh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM2.5'].quantile(0.75).reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] < 1000]
required_state = filtered_data.sort_values('PM2.5').iloc[1]['state']
return required_state
</code>
|
72
| 354
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the highest PM 10 level, based on median PM 10 level?
|
Which union territory with a land area greater than 1,000 km² shows the maximum PM10 level, based on its median PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].median().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
print(required_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].median().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state']
return required_state
</code>
|
73
| 361
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on total PM 10 level?
|
Which union territory having a land area exceeding 1,000 km² registers the minimum PM10 level, according to its total PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].sum().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
print(required_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].sum().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[0]['state']
return required_state
</code>
|
74
| 370
|
area_based
|
Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on variance of PM 10 level?
|
Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM10 level, based on its variance of PM10 level?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_pm = main_data.groupby('state')['PM10'].var().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
print(required_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_pm = data.groupby('state')['PM10'].var().reset_index()
filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged_data = pd.merge(state_pm, filtered_data, on='state')
filtered_data = merged_data[merged_data['area (km2)'] > 1000]
required_state = filtered_data.sort_values('PM10').iloc[1]['state']
return required_state
</code>
|
75
| 374
|
funding_based
|
In which financial year was the variance of NCAP funding release the 2nd highest across cities?
|
During which financial year was the variance in NCAP funding release the 2nd highest among cities?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.var()
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1]
print(required_year)
true_code()
|
2021-22
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.var()
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1]
return required_year
</code>
|
76
| 383
|
funding_based
|
In which financial year was the 75th percentile of NCAP funding release the lowest across cities?
|
In which financial year did the 75th percentile of NCAP funding release reach its minimum across cities?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.75)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[0]['Year'].split()[-1]
print(required_year)
true_code()
|
2021-22
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.75)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[0]['Year'].split()[-1]
return required_year
</code>
|
77
| 385
|
funding_based
|
In which financial year was the 25th percentile of NCAP funding release the highest across cities?
|
In which financial year did the 25th percentile of NCAP funding release reach its peak across cities?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.25)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1]
print(required_year)
true_code()
|
2020-21
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.25)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1]
return required_year
</code>
|
78
| 389
|
funding_based
|
In which financial year was the 25th percentile of NCAP funding release the 3rd lowest across cities?
|
In which financial year did the 25th percentile of NCAP funding release rank 3rd lowest across cities?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.25)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[2]['Year'].split()[-1]
print(required_year)
true_code()
|
2020-21
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
df = ncap_funding_data[
['Amount released during FY 2019-20',
'Amount released during FY 2020-21',
'Amount released during FY 2021-22']
]
avg_series = df.quantile(0.25)
avg_series = avg_series.sort_values().reset_index()
avg_series.columns = ['Year', 'Amount']
required_year = avg_series.iloc[2]['Year'].split()[-1]
return required_year
</code>
|
79
| 393
|
funding_based
|
Report the state(excluding union territories) that received the highest NCAP funding relative to its land area on a per-square.
|
Report the state (excluding union territories) that received the maximum NCAP funding relative to its land area on a per-square basis.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[0]['state']
print(required_state)
true_code()
|
Punjab
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[0]['state']
return required_state
</code>
|
80
| 396
|
funding_based
|
Report the state(excluding union territories) that received the 4th highest NCAP funding relative to its land area on a per-square.
|
Provide the state (excluding union territories) that obtained the 4th maximum NCAP funding in proportion to its land area per square unit.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[3]['state']
print(required_state)
true_code()
|
Uttarakhand
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[3]['state']
return required_state
</code>
|
81
| 398
|
funding_based
|
Report the union territory that received the 2nd highest NCAP funding relative to its land area on a per-square.
|
Provide the union territory that obtained the 2nd highest NCAP funding in proportion to its land area per square unit.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[1]['state']
print(required_state)
true_code()
|
Delhi
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']]
merged = pd.merge(funding_per_state, filtered_states_data, on='state')
merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)']
required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[1]['state']
return required_state
</code>
|
82
| 399
|
funding_based
|
Which state has the lowest difference between allocated NCAP funding and actual utilisation as on June 2022?
|
Report the state with the lowest difference between allocated NCAP funding and actual utilization as of June 2022.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference').iloc[0]['state']
print(req_loc)
true_code()
|
Maharashtra
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference').iloc[0]['state']
return req_loc
</code>
|
83
| 404
|
funding_based
|
Which city has the 2nd lowest difference between allocated NCAP funding and actual utilisation as on June 2022?
|
Report the city with the second smallest difference between allocated NCAP funding and its actual utilization as of June 2022.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference').iloc[1]['city']
print(req_loc)
true_code()
|
Nashik
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference').iloc[1]['city']
return req_loc
</code>
|
84
| 407
|
funding_based
|
Which state has the 5th highest difference between allocated NCAP funding and actual utilisation as on June 2022?
|
Which state presents the 5th highest difference between allocated NCAP funds and their actual utilization as of June 2022?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference', ascending=False).iloc[4]['state']
print(req_loc)
true_code()
|
Jharkhand
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022']
df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index()
req_loc = df.sort_values('Difference', ascending=False).iloc[4]['state']
return req_loc
</code>
|
85
| 413
|
funding_based
|
Which state saw the 4th highest increment in funding between FY 2020-21 and FY 2021-22?
|
Report the state that observed the 4th highest increment in funding between FY 2020-21 and FY 2021-22.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2020-21'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['state']
print(result)
true_code()
|
Madhya Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2020-21'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['state']
return result
</code>
|
86
| 416
|
funding_based
|
Which city saw the 4th highest increment in funding between FY 2019-20 and FY 2020-21?
|
Identify the city that saw the 4th largest rise in funding between FY 2019-20 and FY 2020-21.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2020-21'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['city']
print(result)
true_code()
|
Patiala
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2020-21'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['city']
return result
</code>
|
87
| 420
|
funding_based
|
Which state saw the 3rd lowest decrement in funding between FY 2019-20 and FY 2021-22?
|
Identify the state that experienced the 3rd smallest decrease in funding from FY 2019-20 to FY 2021-22.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=False)
result = sorted_change.iloc[2]['state']
print(result)
true_code()
|
Andhra Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=False)
result = sorted_change.iloc[2]['state']
return result
</code>
|
88
| 429
|
funding_based
|
Which state saw the 5th highest decrement in funding between FY 2019-20 and FY 2020-21?
|
Report the state with the 5th most significant drop in funding between FY 2019-20 and FY 2020-21.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2020-21'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=False)
result = sorted_change.iloc[len(sorted_change)-5]['state']
print(result)
true_code()
|
Gujarat
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2020-21'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=False)
result = sorted_change.iloc[len(sorted_change)-5]['state']
return result
</code>
|
89
| 430
|
funding_based
|
Which state saw the 4th highest increment in funding between FY 2019-20 and FY 2021-22?
|
Determine which state observed the 4th highest increment in funding between FY 2019-20 and FY 2021-22.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['state']
print(result)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2019-20'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-4]['state']
return result
</code>
|
90
| 431
|
funding_based
|
Which state saw the 5th highest increment in funding between FY 2020-21 and FY 2021-22?
|
Which state saw the 5th largest increase in funding from FY 2020-21 to FY 2021-22?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
col_start = 'Amount released during FY 2020-21'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-5]['state']
print(result)
true_code()
|
West Bengal
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
col_start = 'Amount released during FY 2020-21'
col_end = 'Amount released during FY 2021-22'
ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start]
funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index()
sorted_change = funding_change.sort_values('change', ascending=True)
result = sorted_change.iloc[len(sorted_change)-5]['state']
return result
</code>
|
91
| 444
|
funding_based
|
Which city utilised the 4th highest percentage of its allocated NCAP funding as of June 2022?
|
Identify the city with the 4th highest percentage use of its allocated NCAP funds as of June 2022.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[3]['city']
print(ans)
true_code()
|
Indore
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[3]['city']
return ans
</code>
|
92
| 446
|
funding_based
|
Which state utilised the 3rd lowest percentage of its allocated NCAP funding as of June 2022?
|
Which state had the 3rd lowest percentage utilization of its NCAP funds as of June 2022?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent').iloc[2]['state']
print(ans)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent').iloc[2]['state']
return ans
</code>
|
93
| 447
|
funding_based
|
Which city utilised the 2nd lowest percentage of its allocated NCAP funding as of June 2022?
|
Report the city that showed the second smallest percentage utilization of its allocated NCAP funding by June 2022.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent').iloc[1]['city']
print(ans)
true_code()
|
Tuticorin
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent').iloc[1]['city']
return ans
</code>
|
94
| 449
|
funding_based
|
Which city utilised the highest percentage of its allocated NCAP funding as of June 2022?
|
Determine the city that utilized the maximum percentage of its allocated NCAP funding by June 2022.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[0]['city']
print(ans)
true_code()
|
Visakhapatnam
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[0]['city']
return ans
</code>
|
95
| 450
|
funding_based
|
Which state utilised the 5th highest percentage of its allocated NCAP funding as of June 2022?
|
Which state exhibited the 5th highest percentage utilization of its allocated NCAP funds as of June 2022?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[4]['state']
print(ans)
true_code()
|
Madhya Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index()
ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] /
ncap_funding_data['Total fund released']) * 100
ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[4]['state']
return ans
</code>
|
96
| 451
|
funding_based
|
Identify the state that has the 5th lowest number of cities receiving NCAP funding.
|
Report the state having the 5th lowest count of cities receiving NCAP funding.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city').iloc[4]['state']
print(max_cities_state)
true_code()
|
Tamil Nadu
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city').iloc[4]['state']
return max_cities_state
</code>
|
97
| 452
|
funding_based
|
Identify the state that has the highest number of cities receiving NCAP funding.
|
Which state possesses the highest number of cities with NCAP funding?
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[0]['state']
print(max_cities_state)
true_code()
|
Maharashtra
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[0]['state']
return max_cities_state
</code>
|
98
| 453
|
funding_based
|
Identify the state that has the 3rd highest number of cities receiving NCAP funding.
|
Identify the state that has the 3rd highest number of cities benefiting from NCAP funding.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[2]['state']
print(max_cities_state)
true_code()
|
Andhra Pradesh
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[2]['state']
return max_cities_state
</code>
|
99
| 456
|
funding_based
|
Identify the union territory that has the highest number of cities receiving NCAP funding.
|
Identify the union territory with the highest count of cities receiving NCAP funding.
|
def true_code():
import pandas as pd
main_data = pd.read_pickle("preprocessed/main_data.pkl")
states_data = pd.read_pickle("preprocessed/states_data.pkl")
ncap_funding_data = pd.read_pickle("preprocessed/ncap_funding_data.pkl")
city_count = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
merged_df = pd.merge(filtered_states_data, city_count, on='state', how='inner')
max_cities_state = merged_df.sort_values('city', ascending=False).iloc[0]['state']
print(max_cities_state)
true_code()
|
Jammu and Kashmir
|
<code>
import pandas as pd
def get_response(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
city_count = ncap_funding_data.groupby('state')['city'].nunique().reset_index()
filtered_states_data = states_data[states_data['isUnionTerritory'] == True]
merged_df = pd.merge(filtered_states_data, city_count, on='state', how='inner')
max_cities_state = merged_df.sort_values('city', ascending=False).iloc[0]['state']
return max_cities_state
</code>
|
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