Created
September 20, 2024 01:25
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| import pandas as pd | |
| csv_file = '00_zenkoku_all_20240830.csv' | |
| df = pd.read_csv(csv_file, header=None, encoding='utf-8', low_memory=False) | |
| df.columns = [ | |
| 'sequence_number', 'corporate_number', 'process', 'correct', 'update_date', | |
| 'change_date', 'name', 'name_image_id', 'kind', 'prefecture_name', 'city_name', | |
| 'street_number', 'prefecture_code', 'city_code', 'postcode', 'address_outside', | |
| 'address_outside_image_id', 'close_date', 'close_cause', 'successor_corporate_number', | |
| 'change_cause', 'extra_column', 'assignment_date', 'latest', 'name_en', 'prefecture_name_en', 'city_name_en', | |
| 'address_outside_en', 'furigana', 'hihyoji' | |
| ] | |
| new_establishments = df[df['process'] == 1] | |
| new_establishments['assignment_date'] = pd.to_datetime(new_establishments['assignment_date'], errors='coerce') | |
| def calculate_fiscal_year(date): | |
| if pd.isnull(date): | |
| return None | |
| if date.month >= 4: | |
| return date.year | |
| else: | |
| return date.year - 1 | |
| new_establishments['fiscal_year'] = new_establishments['assignment_date'].apply(calculate_fiscal_year) | |
| new_establishments = new_establishments.dropna(subset=['fiscal_year']) | |
| result = new_establishments.groupby(['city_name', 'fiscal_year']).size().reset_index(name='count') | |
| print(result) | |
| result.to_csv('new_establishments_by_city_fiscal_year.csv', index=False, encoding='utf-8') |
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