<jupyter_start><jupyter_text>Preprocessing Example with `ImpedanceDataFrame`<jupyter_code>from impedance.preprocessing import ImpedenceDataFrame
idf = ImpedenceDataFrame.readFile(filename='../../../data/exampleData.csv',
                                  format='csv')
# Iterating over with for loop
for entry in idf:
    f, z = entry
print(f, z)
# slicing based on boolean arrays
idf_cropped = idf[idf > 0]
idf_above_x = idf[idf.impendance < 0]
# printing capabilities
print(idf_cropped)
idf_cropped.writeCsv('../_static/processed.csv')
loaded = ImpedenceDataFrame.readFile('../_static/processed.csv', format='csv')
print(loaded)<jupyter_output>0.0031623 | (0.0494998977640506-0.020438698544418925j) 
 0.0039811 | (0.04776559257398882-0.0182856893045487j) 
 0.0050119 | (0.04613581757142157-0.016343158966700824j) 
 0.0063096 | (0.044596836301773274-0.014589168660649915j) 
 0.0079433 | (0.043142303861239205-0.01300096361736358j) 
 0.01 | (0.04181678802099761-0.011573009182824043j) 
 0.012589 | (0.040606740929859095-0.010282133623145187j) 
 0.015849 | (0.03951932383144551-0.009113366697002839j) 
 0.019953 | (0.03856629404354767-0.00804494958277692j) 
 0.025119 | (0.03773445890891119-0.007075702921918925j) 
 0.031623 | (0.037013908851197805-0.006209940124316647j) 
 0.039811 | (0.03639992299924442-0.005450664199993216j) 
 0.050119 | (0.035883179203561086-0.004804611324614652j) 
 0.063096 | (0.03544780816944048-0.0042630212172992624j) 
 0.079433 | (0.03506693121253139-0.003816723014957778j) 
 0.1 | (0.034721707243418394-0.003465230467686932j) 
 0.12589 | (0.03440366308110683-0.0031936182833490197j) 
 0.15849 | (0.03410783765793668-0[...]
{'fraction_non_alphanumeric': 0.12666666666666668, 'fraction_numerical': 0.4684848484848485, 'mean_word_length': 4.938848920863309, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '266001', 'license': 'MIT', 'n_tokens_mistral': 1191, 'n_tokens_neox': 734, 'n_words': 78, 'path': 'docs/source/examples/preprocessing_example.ipynb', 'repo_name': 'Bundaberg-Joey/impedance.py', 'size': '1650'}