<jupyter_start><jupyter_text>`props_to_DataFrame`Extracts the scalar values from a ``regionprops_3D`` query and uses them to populate a ``pandas`` ``DataFrame``.<jupyter_code>import porespy as ps
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as spim
np.random.seed(7)
im = ~ps.generators.overlapping_spheres([100, 100], r=10, porosity=0.6)
plt.imshow(im, origin='lower', interpolation='none');
regions = spim.label(im)[0]
props = ps.metrics.regionprops_3D(regions)
plt.imshow(regions, origin='lower', interpolation='none');
df = ps.metrics.props_to_DataFrame(props)
df<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1362179487179487, 'fraction_numerical': 0.022435897435897436, 'mean_word_length': 8.057971014492754, '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': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '593917', 'license': 'MIT', 'n_tokens_mistral': 239, 'n_tokens_neox': 222, 'n_words': 50, 'path': 'examples/metrics/reference/props_to_DataFrame.ipynb', 'repo_name': 'xu-kai-xu/porespy', 'size': '624'}