27 lines
1.1 KiB
Python
Executable File
27 lines
1.1 KiB
Python
Executable File
#!/usr/bin/env python3
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import pandas as pd
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import numpy as np
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import pathlib
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# Load the cleaned CSV file
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file_path = pathlib.Path('/home/user/bachelorarbeit_importer/data/', 'cleaned_st_neu_verlustausschluss.csv')
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df = pd.read_csv(file_path)
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# Select relevant columns for 2022 and drop rows with missing ek2022 or st2022
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df_2022 = df[['name', 'ek2022', 'st2022']].dropna()
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# Sort by Eigenkapital (ek2022) ascending
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df_2022.sort_values('ek2022', inplace=True)
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# Define 5 classes based on equity size (in EUR):
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bins = [0, 500000, 2000000, 10000000, 50000000, np.inf]
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labels = ['< 500K EK', '500K EK < x < 2M EK', '2M < 10M EK', '10M EK < 50M EK', '> 50M EK']
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df_2022['Klasse'] = pd.cut(df_2022['ek2022'], bins=bins, labels=labels)
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# Calculate percentage of companies with negative taxes per class
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for klasse, group in df_2022.groupby('Klasse', observed=False):
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total_companies = len(group)
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negative_tax_companies = len(group[group['st2022'] < 0])
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percentage = (negative_tax_companies / total_companies * 100) if total_companies > 0 else 0
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print(f"Prozent Unternehmen mit negativen Steuern in {klasse}: {percentage:.2f}%")
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