Create data_analyzer_suite.py #2
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Pull Request Overview
This PR introduces a new data analysis utility class that provides a comprehensive suite of scientific computing and data analysis capabilities. The implementation creates a single-file solution for common data science tasks including statistical analysis, signal processing, interpolation, and optimization.
- Implements a DataAnalyzerSuite class with methods for descriptive statistics, FFT analysis, data interpolation, numerical integration, and function optimization
- Provides CSV file loading with error handling and automatic data processing workflows
- Creates a main function demonstrating usage of all analysis capabilities with hardcoded sample data
| def __init__(self, csv_file): | ||
| try: | ||
| self.data = pd.read_csv(csv_file) | ||
| print(f"Daten erfolgreich aus '{csv_file}' geladen.") |
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The success message is in German ('Daten erfolgreich aus...') while other parts of the code use English. Consider using English for consistency: 'Data successfully loaded from {csv_file}'.
| print(f"Daten erfolgreich aus '{csv_file}' geladen.") | |
| print(f"Data successfully loaded from '{csv_file}'.") |
| self.data = pd.read_csv(csv_file) | ||
| print(f"Daten erfolgreich aus '{csv_file}' geladen.") | ||
| except FileNotFoundError: | ||
| print(f"Fehler: Datei '{csv_file}' nicht gefunden!") |
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The error message is in German ('Fehler: Datei... nicht gefunden!'). Consider using English for consistency: 'Error: File {csv_file} not found!'.
| print(f"Fehler: Datei '{csv_file}' nicht gefunden!") | |
| print(f"Error: File '{csv_file}' not found!") |
| print(f"Fehler: Datei '{csv_file}' nicht gefunden!") | ||
| sys.exit(1) | ||
| except Exception as e: | ||
| print(f"Fehler beim Laden der Datei: {e}") |
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The error message is in German ('Fehler beim Laden der Datei:'). Consider using English for consistency: 'Error loading file: {e}'.
| print(f"Fehler beim Laden der Datei: {e}") | |
| print(f"Error loading file: {e}") |
| def descriptive_statistics(self, column): | ||
| col_data = self.data[column].dropna() | ||
| desc = stats.describe(col_data) | ||
| print(f"Deskriptive Statistik für '{column}':", desc) |
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The output message is in German ('Deskriptive Statistik für...'). Consider using English for consistency: 'Descriptive statistics for {column}:'.
| print(f"Deskriptive Statistik für '{column}':", desc) | |
| print(f"Descriptive statistics for '{column}':", desc) |
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| def numerical_integration(self, func, a, b): | ||
| result, error = integrate.quad(func, a, b) | ||
| print(f"Numerische Integration von {a} bis {b} ergibt: {result}") |
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The output message is in German ('Numerische Integration von... bis... ergibt:'). Consider using English for consistency: 'Numerical integration from {a} to {b} gives: {result}'.
| print(f"Numerische Integration von {a} bis {b} ergibt: {result}") | |
| print(f"Numerical integration from {a} to {b} gives: {result}") |
| def descriptive_statistics(self, column): | ||
| col_data = self.data[column].dropna() | ||
| desc = stats.describe(col_data) | ||
| print(f"Deskriptive Statistik für '{column}':", desc) | ||
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| def perform_fft(self, column, sampling_rate): |
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The method doesn't validate if the specified column exists in the DataFrame before accessing it. This could raise a KeyError if an invalid column name is provided.
| def descriptive_statistics(self, column): | |
| col_data = self.data[column].dropna() | |
| desc = stats.describe(col_data) | |
| print(f"Deskriptive Statistik für '{column}':", desc) | |
| def perform_fft(self, column, sampling_rate): | |
| def descriptive_statistics(self, column): | |
| if column not in self.data.columns: | |
| raise ValueError(f"Column '{column}' does not exist in the data.") | |
| col_data = self.data[column].dropna() | |
| desc = stats.describe(col_data) | |
| print(f"Deskriptive Statistik für '{column}':", desc) | |
| def perform_fft(self, column, sampling_rate): | |
| if column not in self.data.columns: | |
| raise ValueError(f"Column '{column}' does not exist in the data.") |
| plt.grid() | ||
| plt.show() | ||
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| def interpolate_missing_data(self, column, method='cubic'): |
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The method doesn't validate if the specified column exists in the DataFrame before accessing it. This could raise a KeyError if an invalid column name is provided.
| def interpolate_missing_data(self, column, method='cubic'): | |
| def interpolate_missing_data(self, column, method='cubic'): | |
| if column not in self.data.columns: | |
| raise ValueError(f"Column '{column}' does not exist in the DataFrame.") |
| def interpolate_missing_data(self, column, method='cubic'): | ||
| y = self.data[column] | ||
| x = np.arange(len(y)) | ||
| mask = np.isfinite(y) | ||
| interpolator = interpolate.interp1d(x[mask], y[mask], kind=method, fill_value="extrapolate") | ||
| y_interp = interpolator(x) | ||
| self.data[column+'_interp'] = y_interp |
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[nitpick] String concatenation for column naming could lead to unclear column names. Consider using a more explicit format like f'{column}_interpolated' or allow custom naming through a parameter.
| def interpolate_missing_data(self, column, method='cubic'): | |
| y = self.data[column] | |
| x = np.arange(len(y)) | |
| mask = np.isfinite(y) | |
| interpolator = interpolate.interp1d(x[mask], y[mask], kind=method, fill_value="extrapolate") | |
| y_interp = interpolator(x) | |
| self.data[column+'_interp'] = y_interp | |
| def interpolate_missing_data(self, column, method='cubic', new_column_name=None): | |
| y = self.data[column] | |
| x = np.arange(len(y)) | |
| mask = np.isfinite(y) | |
| interpolator = interpolate.interp1d(x[mask], y[mask], kind=method, fill_value="extrapolate") | |
| y_interp = interpolator(x) | |
| new_column_name = new_column_name or f'{column}_interpolated' | |
| self.data[new_column_name] = y_interp |
| print("Optimierungsergebnis:", res.x) | ||
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| def main(): | ||
| analyzer = DataAnalyzerSuite('sample_data.csv') |
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The hardcoded filename 'sample_data.csv' makes the main function inflexible. Consider accepting command line arguments or making the filename configurable.
| analyzer = DataAnalyzerSuite('sample_data.csv') | |
| # Check for a command-line argument specifying the filename | |
| csv_file = sys.argv[1] if len(sys.argv) > 1 else 'sample_data.csv' | |
| analyzer = DataAnalyzerSuite(csv_file) |
| analyzer = DataAnalyzerSuite('sample_data.csv') | ||
| analyzer.descriptive_statistics('value') | ||
| analyzer.perform_fft('value', sampling_rate=800) | ||
| analyzer.interpolate_missing_data('value', method='cubic') |
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The hardcoded column name 'value' assumes a specific data structure. This makes the example less flexible and could fail if the CSV doesn't contain this column.
| analyzer = DataAnalyzerSuite('sample_data.csv') | |
| analyzer.descriptive_statistics('value') | |
| analyzer.perform_fft('value', sampling_rate=800) | |
| analyzer.interpolate_missing_data('value', method='cubic') | |
| csv_file = 'sample_data.csv' | |
| column_name = input("Bitte geben Sie den Spaltennamen ein: ") | |
| analyzer = DataAnalyzerSuite(csv_file, column_name) | |
| analyzer.descriptive_statistics(column_name) | |
| analyzer.perform_fft(column_name, sampling_rate=800) | |
| analyzer.interpolate_missing_data(column_name, method='cubic') |
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