| import os |
| import pandas as pd |
|
|
| def get_data(): |
| df = pd.read_csv(os.path.join(os.path.dirname(__file__), "../", "air-pollution-final.csv")) |
|
|
| return df |
|
|
| def get_categorized_data(): |
| df = pd.read_csv(os.path.join(os.path.dirname(__file__), "../", "air-pollution-final-categorized.csv")) |
|
|
| return df |
|
|
| def get_years(): |
| options = list() |
|
|
| for year in range(1850, 2022): |
| item = dict() |
|
|
| item['label'] = str(year) |
| item['value'] = str(year) |
|
|
| options.append(item) |
|
|
| return options |
|
|
| def get_countries(): |
| df = get_data() |
| countries = df['Country'].unique() |
|
|
| options = list() |
|
|
| for country in countries: |
| item = dict() |
|
|
| item['label'] = country |
| item['value'] = country |
|
|
| options.append(item) |
|
|
| return options |
|
|
| def get_data_by_year(year): |
| df = get_data() |
| df_subset = df[['Location', 'City', 'Country', 'City_Latitude', 'City_Longitude', year]] |
|
|
| return df_subset |
|
|
| def get_categorized_data_by_year(year): |
| df = get_categorized_data() |
| df_subset = df[['City', 'Country', year]] |
|
|
| return df_subset |
|
|
| def get_quality_counts(quality_values): |
| quality_dict = { |
| "Good": 0, |
| "Moderate": 0, |
| "Unhealthy for Sensitive Groups": 0, |
| "Unhealthy": 0, |
| "Very Unhealthy": 0, |
| "Hazardous": 0 |
| } |
| |
| for quality in quality_values: |
| if quality <= 9.0: |
| quality_dict["Good"] += 1 |
| elif quality <= 35.4: |
| quality_dict["Moderate"] += 1 |
| elif quality <= 55.4: |
| quality_dict["Unhealthy for Sensitive Groups"] += 1 |
| elif quality <= 125.4: |
| quality_dict["Unhealthy"] += 1 |
| elif quality <= 225.4: |
| quality_dict["Very Unhealthy"] += 1 |
| else: |
| quality_dict["Hazardous"] += 1 |
|
|
| return quality_dict |
