Created
October 30, 2020 00:15
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sf_crime_5.py
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train['IsOnBlock'] = train['Address'].str.contains('block', case=False) | |
train['IsAtIntersection'] = train['Address'].str.contains('/', case=False) | |
def clean_road(text): | |
return re.sub(r"[0-9]+ [bB]lock of ", "", text) | |
def make_counts(values): | |
counts = Counter() | |
for value in values: | |
cur_counts = list(map(clean_road, value.split(" / "))) | |
counts.update(cur_counts) | |
return counts | |
# compute road counts, in preparation of the log road probability feature | |
counts = make_counts(train["Address"]) | |
common_roads = pd.Series(dict(counts.most_common(20))) | |
# have a look at the most common roads in the data | |
plt.figure(figsize=(10, 10)) | |
with sns.axes_style("whitegrid"): | |
ax = sns.barplot( | |
(common_roads / common_roads.sum()) * 100, | |
common_roads.index, | |
orient='h', | |
palette="Blues_r") | |
plt.title('Most Common Roads', fontdict={'fontsize': 16}) | |
plt.xlabel('P(x)') | |
plt.show() |
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