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import geopandas as gpd
import pandas as pd
import glob
import numpy as np
from choropleth import choropleth_chart
# 데이터는 행정구역 가나다순으로 넣으세요
shp = gpd.read_file('map/영천시 행정동.shp', encoding='utf-8')
shp = shp.sort_values('EMD_KOR_NM')
shp = shp.reset_index()
df = pd.read_csv("data/stat/동별종합.csv")
colorscale = [\
[0,'#E02C4E'],
[0.33, '#F0E32E'],
[0.57, '#6FEB75'],
[1, '#5FB199']
]
datas = glob.glob(f'data/stat/*현황.xlsx')
datas = sorted(datas)
total = [None] * 16
for i, data in enumerate(datas):
data = pd.read_excel(data)
total[i] = data.loc[:, '계'].sum()
print(total)
sum = np.sum(total)
sum = total/sum
print(sum)
man = [None] * 16
for i, data in enumerate(datas):
data = pd.read_excel(data)
man[i] = data.loc[:,'남'].sum()
woman = [None] * 16
for i, data in enumerate(datas):
data = pd.read_excel(data)
woman[i] = data.loc[:,'여'].sum()
# total = pd.DataFrame(total,index=shp['EMD_KOR_NM'].values)
# choropleth_chart(shp,total,' ','읍면동별 1인가구수', show_legend=False, unit="가구" ,colorscheme="OrRd", adaptive_annotation=True)
# choropleth_chart(shp,sum,' ','읍면동별 1인가구수백분율', show_legend=False, unit="%", colorscheme="OrRd", adaptive_annotation=True)
# choropleth_chart(shp,man,' ','읍면동별 1인가구수남자', show_legend=False, unit="가구", colorscheme="OrRd", adaptive_annotation=True)
# choropleth_chart(shp,woman,' ','읍면동별 1인가구수여자', show_legend=False, unit="가구", colorscheme="OrRd", adaptive_annotation=True)
# colorscale = [\
# [0,'#E07C0E'],
# [0.33, '#F0E32E'],
# [0.57, '#6FEB75'],
# [1, '#5FB1F5']
# ]
colorscale = [\
[1-0,'#E02C4E'],
[1-0.4, '#F0E32E'],
[1-0.7, '#6FEB75'],
[1-1, '#5FB1F5']
]
cdf = df.iloc[:,1]
cdf += df.iloc[:,3]
choropleth_chart(shp, cdf, '2022 영천시 1인가구중 중년이상비율', f"1인가구 중년이상",
colorscheme=colorscale[::-1], adaptive_legend_font_size=True)
# df = pd.read_csv("data/stat/경상북도 영천시_경로당_20221108.csv")
#
# cdf = df['행정동명'].value_counts()
# #
# colorscale = [\
# [0,'#E02C4E'],
# [0.33, '#F0E32E'],
# [0.57, '#6FEB75'],
# [1, '#5FB199']
# ]
# #
# choropleth_chart(shp, cdf, '', f"figure2022노인정test",
# colorscheme=colorscale, adaptive_annotation=True)
# df = pd.read_excel("data/영천시 2021년 bc카드 행정구역 데이터.xlsx")
# df = df.sort_values('가맹점_읍면동명')
# df = df.reset_index()
# col_index = df.columns
# print(col_index)
# for col in col_index:
# cdf = df[col]
# choropleth_chart(shp, cdf, f"2021년 bc카드 {col}", f"figure2021{col}", adaptive_annotation=True)
#
# df = pd.read_excel("data/2022년 bc카드 행정구역 별 데이터.xlsx")
# col_index = df.columns
# print(col_index)
# # cdf = df['행정동명'].value_counts()
# for col in col_index:
# cdf = df[col]
# choropleth_chart(shp, cdf, f"2022년 bc카드 {col}", f"figure2022{col}", adaptive_annotation=True)