rafapolo
2446a0f78d
- Add 37 census documentation files for IBGE census datasets (1970-2010) - Add dataviz wordcloud scripts and images - Add relatorio_final.md with research findings on households and living conditions New data from DuckDB queries: - 90.7M households, 203M population - 53.2% Black population - 27.9% female-headed households - 46.6% urban sewage without collection/treatment - 15,816 favela sectors (2010) - 68% Black population in Fortaleza
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
#!/usr/bin/env python3
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import json
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import re
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from collections import Counter
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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STOPWORDS = {'de', 'do', 'da', 'a', 'ou', 'em', 'e', 'o', 'que', 'das', 'dos', 'nos', 'nas', 'um', 'uma', 'para', 'com', 'não', 'uma', 'à', 'ao', 'os', 'as', 'se', 'na', 'no', 'de', 'do', 'da', 'é', 'ser', 'seu', 'sua', 'isso', 'the', 'of', 'and', 'in', 'to', 'is', 'for', 'on', 'with', 'at', 'by', 'from'}
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with open('context/basedosdados-schema.json') as f:
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schema = json.load(f)
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words = []
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for dataset, tables in schema.items():
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for table, cols in tables.items():
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for col in cols:
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name = col.get('name', '').lower()
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desc = col.get('description', '').lower()
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if name and len(name) >= 3:
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words.append(name)
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if desc:
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for w in desc.split():
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w = re.sub(r'[^a-záàâãéèêíìîóòôõúùûç]', '', w)
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if len(w) >= 3 and w not in STOPWORDS:
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words.append(w)
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word_freq = Counter(words)
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wc = WordCloud(
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width=1600,
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height=800,
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background_color='white',
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max_words=200,
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colormap='viridis',
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min_font_size=8
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).generate_from_frequencies(word_freq)
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plt.figure(figsize=(20, 10))
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plt.imshow(wc, interpolation='bilinear')
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plt.axis('off')
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plt.tight_layout(pad=0)
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plt.savefig('docs/wordcloud_attributes.png', dpi=150, bbox_inches='tight')
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print("Saved docs/wordcloud_attributes.png")
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print(f"Total unique words: {len(word_freq)}")
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print("Top 30:", word_freq.most_common(30))
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