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main.py
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458 lines (400 loc) · 18.8 KB
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.font_manager as fm
font_name = fm.FontProperties(fname='C:/Windows/Fonts/malgun.ttf').get_name()
matplotlib.rc('font', family=font_name)
matplotlib.rcParams['axes.unicode_minus'] = False
# 18년도 평균 평당 가격
def avg_18():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
year = 2018
d_ = avg_data[(avg_data["접수연도"] == year)]
plt.figure(figsize=(10, 5))
plt.title("2018년도 평균평당가격", fontsize=15)
plt.plot(d_["자치구명"], d_["평균평당가격(만원)"], "-", color='red', label=str(year))
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 19년도 평균 평당 가격
def avg_19():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
year = 2019
d_ = avg_data[(avg_data["접수연도"] == year)]
plt.figure(figsize=(10, 5))
plt.title("2019년도 평균평당가격", fontsize=15)
plt.plot(d_["자치구명"], d_["평균평당가격(만원)"], "-", color='red', label=str(year))
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 20년도 평균 평당 가격
def avg_20():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
year = 2020
d_ = avg_data[(avg_data["접수연도"] == year)]
plt.figure(figsize=(10, 5))
plt.title("2020년도 평균평당가격", fontsize=15)
plt.plot(d_["자치구명"], d_["평균평당가격(만원)"], "-", color='red', label=str(year))
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 평균 평당 가격 추이
def avg_total():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 평균 평당 가격 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = avg_data[(avg_data["접수연도"] == spot_)]
plt.plot(d_["자치구명"], d_["평균평당가격(만원)"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 살인 범죄 추이
def kill_total():
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 살인 범죄 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = crime_data[(crime_data["기간"] == spot_)]
plt.plot(d_["자치구"], d_["살인"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 강도 범죄 추이
def robbery_total():
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 강도 범죄 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = crime_data[(crime_data["기간"] == spot_)]
plt.plot(d_["자치구"], d_["강도"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 강간강제추행 범죄 추이
def molestation_total():
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 강간강제추행 범죄 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = crime_data[(crime_data["기간"] == spot_)]
plt.plot(d_["자치구"], d_["강간강제추행"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 절도 범죄 추이
def theft_total():
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 절도 범죄 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = crime_data[(crime_data["기간"] == spot_)]
plt.plot(d_["자치구"], d_["절도"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# 18년도, 19년도, 20년도 폭력 범죄 추이
def violence_total():
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 5))
plt.title("18년도, 19년도, 20년도 폭력 범죄 추이", fontsize=15)
for spot_ in [2018, 2019, 2020]:
d_ = crime_data[(crime_data["기간"] == spot_)]
plt.plot(d_["자치구"], d_["폭력"], "-", label=str(spot_), alpha=.6)
plt.grid()
plt.legend(fontsize=13)
plt.xticks(rotation=90)
plt.show()
# total 강남구 집값과 살인, 강도, 강간강제추행, 절도, 폭력의 상관관계
def crime_Gangnam():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 30))
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강남구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강남구 집값")
d_ = crime_data[(crime_data["자치구"] == "강남구")]
b, = ax2.plot(d_["기간"], d_["살인"], "-", color='blue', label="살인율")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.title("강남구 집값과 5대 범죄와 상관관계", fontsize=15)
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강남구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강남구 집값")
d_ = crime_data[(crime_data["자치구"] == "강남구")]
b, = ax2.plot(d_["기간"], d_["강도"], "-", color='blue', label="강도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 3)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강남구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강남구 집값")
d_ = crime_data[(crime_data["자치구"] == "강남구")]
b, = ax2.plot(d_["기간"], d_["강간강제추행"], "-", color='blue', label="강간강제추행")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강남구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강남구 집값")
d_ = crime_data[(crime_data["자치구"] == "강남구")]
b, = ax2.plot(d_["기간"], d_["절도"], "-", color='blue', label="절도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강남구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강남구 집값")
d_ = crime_data[(crime_data["자치구"] == "강남구")]
b, = ax2.plot(d_["기간"], d_["폭력"], "-", color='blue', label="폭력")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
# total 강북구 집값과 살인, 강도, 강간강제추행, 절도, 폭력의 상관관계
def crime_Gangbuk():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 30))
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강북구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강북구 집값")
d_ = crime_data[(crime_data["자치구"] == "강북구")]
b, = ax2.plot(d_["기간"], d_["살인"], "-", color='blue', label="살인")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.title("강북구 집값과 5대 범죄와 상관관계", fontsize=15)
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강북구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강북구 집값")
d_ = crime_data[(crime_data["자치구"] == "강북구")]
b, = ax2.plot(d_["기간"], d_["강도"], "-", color='blue', label="강도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 3)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강북구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강북구 집값")
d_ = crime_data[(crime_data["자치구"] == "강북구")]
b, = ax2.plot(d_["기간"], d_["강간강제추행"], "-", color='blue', label="강간강제추행")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강북구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강북구 집값")
d_ = crime_data[(crime_data["자치구"] == "강북구")]
b, = ax2.plot(d_["기간"], d_["절도"], "-", color='blue', label="절도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "강북구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="강북구 집값")
d_ = crime_data[(crime_data["자치구"] == "강북구")]
b, = ax2.plot(d_["기간"], d_["폭력"], "-", color='blue', label="폭력")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
# total 구로구 집값과 살인, 강도, 강간강제추행, 절도, 폭력의 상관관계
def crime_Guro():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 30))
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "구로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="구로구 집값")
d_ = crime_data[(crime_data["자치구"] == "구로구")]
b, = ax2.plot(d_["기간"], d_["살인"], "-", color='blue', label="살인")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.title("구로구 집값과 5대 범죄와 상관관계", fontsize=15)
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "구로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="구로구 집값")
d_ = crime_data[(crime_data["자치구"] == "구로구")]
b, = ax2.plot(d_["기간"], d_["강도"], "-", color='blue', label="강도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 3)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "구로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="구로구 집값")
d_ = crime_data[(crime_data["자치구"] == "구로구")]
b, = ax2.plot(d_["기간"], d_["강간강제추행"], "-", color='blue', label="강간강제추행")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "구로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="구로구 집값")
d_ = crime_data[(crime_data["자치구"] == "구로구")]
b, = ax2.plot(d_["기간"], d_["절도"], "-", color='blue', label="절도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "구로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="구로구 집값")
d_ = crime_data[(crime_data["자치구"] == "구로구")]
b, = ax2.plot(d_["기간"], d_["폭력"], "-", color='blue', label="폭력")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
# total 마포구 집값과 살인, 강도, 강간강제추행, 절도, 폭력의 상관관계
def crime_Mapo():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 30))
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "마포구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="마포구 집값")
d_ = crime_data[(crime_data["자치구"] == "마포구")]
b, = ax2.plot(d_["기간"], d_["살인"], "-", color='blue', label="살인")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.title("마포구 집값과 5대 범죄와 상관관계", fontsize=15)
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "마포구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="마포구 집값")
d_ = crime_data[(crime_data["자치구"] == "마포구")]
b, = ax2.plot(d_["기간"], d_["강도"], "-", color='blue', label="강도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 3)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "마포구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="마포구 집값")
d_ = crime_data[(crime_data["자치구"] == "마포구")]
b, = ax2.plot(d_["기간"], d_["강간강제추행"], "-", color='blue', label="강간강제추행")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "마포구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="마포구 집값")
d_ = crime_data[(crime_data["자치구"] == "마포구")]
b, = ax2.plot(d_["기간"], d_["절도"], "-", color='blue', label="절도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "마포구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="마포구 집값")
d_ = crime_data[(crime_data["자치구"] == "마포구")]
b, = ax2.plot(d_["기간"], d_["폭력"], "-", color='blue', label="폭력")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
# total 종로구 집값과 살인, 강도, 강간강제추행, 절도, 폭력의 상관관계
def crime_Jongro():
avg_data = pd.read_csv('./average_data.csv', encoding='cp949', low_memory=False)
crime_data = pd.read_csv('./crime.csv', encoding='cp949', low_memory=False)
plt.figure(figsize=(10, 30))
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "종로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="종로구 집값")
d_ = crime_data[(crime_data["자치구"] == "종로구")]
b, = ax2.plot(d_["기간"], d_["살인"], "-", color='blue', label="살인")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.title("종로구 집값과 5대 범죄와 상관관계", fontsize=15)
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "종로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="종로구 집값")
d_ = crime_data[(crime_data["자치구"] == "종로구")]
b, = ax2.plot(d_["기간"], d_["강도"], "-", color='blue', label="강도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(3, 1, 3)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "종로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="종로구 집값")
d_ = crime_data[(crime_data["자치구"] == "종로구")]
b, = ax2.plot(d_["기간"], d_["강간강제추행"], "-", color='blue', label="강간강제추행")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 1)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "종로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="종로구 집값")
d_ = crime_data[(crime_data["자치구"] == "종로구")]
b, = ax2.plot(d_["기간"], d_["절도"], "-", color='blue', label="절도")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.xticks(rotation=90)
ax1 = plt.subplot(2, 1, 2)
ax2 = ax1.twinx()
d_ = avg_data[(avg_data["자치구명"] == "종로구")]
a, = ax1.plot(d_["접수연도"], d_["평균평당가격(만원)"], "-", color='red', label="종로구 집값")
d_ = crime_data[(crime_data["자치구"] == "종로구")]
b, = ax2.plot(d_["기간"], d_["폭력"], "-", color='blue', label="폭력")
p = [a, b]
ax1.legend(p, [p_.get_label() for p_ in p])
plt.show()
def main():
# avg_18()
# avg_19()
# avg_20()
# avg_total()
# kill_total()
# robbery_total()
# molestation_total()
# theft_total()
# violence_total()
# crime_Gangnam()
# crime_Gangbuk()
# crime_Guro()
# crime_Mapo()
crime_Jongro()
if __name__ == '__main__':
main()