yjyoon / Choropleth_Chart_Function star
윤영준 윤영준 2023-06-14
renamed and cleared unused codes
@bd85aef23c039888f44077a8b557be9f3caa7925
 
bd85aef
example.py (added)
+++ example.py
@@ -0,0 +1,23 @@
+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/동별종합.csv")
+
+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)(파일 끝에 줄바꿈 문자 없음)
877460f
 
test.py (deleted)
--- test.py
@@ -1,96 +0,0 @@
-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/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)
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