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from itertools import count, takewhile | |
def frange(start, stop, step): | |
return takewhile(lambda x: x < stop, count(start, step)) | |
def show_avg_hist(df, column, buckets=40): | |
counts = df[column].value_counts() | |
counts = counts[counts > 20] # Drop rare values | |
min_val = counts.index.min() | |
max_val = counts.index.max() | |
step = (max_val - min_val) / buckets | |
buckets = zip(frange(min_val, max_val + 0.1, step), frange(min_val + step + 0.001, max_val + 0.1 + step, step)) | |
print(column, "from-to\tmean\tdays", sep="\n") | |
s = 0 | |
for (_from, _to) in buckets: | |
chosen = df[column].between(_from, _to) | |
days = sum(chosen) | |
s = s + days | |
print( | |
"{_from:.1f}-{_to:.1f}\t{mean:.1f}\t{days:d}".format(_from=_from, _to=_to, mean=df[chosen]['crimes'].mean(), | |
days=days).replace(".", ",")) | |
def add_day_month_year(df): | |
dates = df["Date"].map(lambda x: x[:10]) | |
df["day_of_month"] = dates.map(lambda x: x.split("/")[1]) | |
df["month"] = dates.map(lambda x: x.split("/")[0]) | |
df["year"] = dates.map(lambda x: x.split("/")[2]) | |
df["date"] = dates | |
return df | |
from pandas import read_csv | |
crimes_df = read_csv("CrimeData.csv") | |
crimes_df = add_day_month_year(crimes_df) | |
crimes_df = crimes_df[crimes_df.year <= "2019"] | |
weather_df = read_csv("Weather.csv") | |
weather_df["date"] = weather_df["DATE"].map(lambda x: x[5:7] + "/" + x[8:11] + "/" + x[0:4]) | |
crimes_count_df = crimes_df \ | |
.groupby(["date"]) \ | |
.size() \ | |
.to_frame("crimes") \ | |
.reset_index() | |
df = crimes_count_df.merge(weather_df, on="date", how='left') | |
show_avg_hist(df, column="TMAX", buckets=20) |
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