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  • 21 Aug, 2019

  • By, Wikipedia

File:Healthcare Expenditures In Japan By Age.svg

graph data

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as ticker

df1 = pd.read_csv("data.tsv", index_col=0 , sep = "\t").T.div(1000)
df2 = pd.read_csv("consult.tsv", index_col=0 , sep = "\t")
df2 = df2.reindex(columns=['Total'])

fig, ax = plt.subplots(figsize=(10, 5))
ax2 = ax.twinx()

ax.plot(df2, label="Doctor consult", color="#5E95CD")

for i in range( len(df1) ):
	ax2.bar(df1.columns, df1.iloc[i] ,  bottom=df1.iloc[:i].sum(), label=df1.index[i])

lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, facecolor="#eeeeee" ,ncol=2, fontsize=11,loc='upper left')

ax.set_ylim([0,20])
ax2.set_ylim([0,1600])
ax2.axvspan(14.5, 20.5, color="#dddddd",zorder=-1)		#BG paint

ax.patch.set_alpha(0)
ax.set_zorder(2)
ax2.set_zorder(1)

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Noto Sans Display']
plt.subplots_adjust(left=0.07, bottom=0.13, right=0.90, top=0.92)

plt.title("Healthcare expenditure of Japan by Age (MHLW,2020)", fontsize=16)
plt.tick_params(labelsize=10, pad=4)
ax.set_xlabel("Age", size=10)
ax.set_ylabel("Doctor consult per person (count)", size=10)
ax2.set_ylabel("Healthcare expenditure per person (1000 JPY)", size=10)

plt.setp(ax.get_xticklabels(), fontsize=8, rotation=45)
plt.setp(ax.get_yticklabels(), fontsize=9 )

ax2.minorticks_on()
ax.set_axisbelow(True)
ax2.set_axisbelow(True)
ax.grid(False, which='both', axis="both")
ax2.grid(True, which='major',color='#cccccc',linestyle='-', axis="y")
ax2.grid(True, which='minor',color='#eeeeee',linestyle='--', axis="y")

plt.savefig("image.svg")
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27 April 2015

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current17:03, 25 June 2023Thumbnail for version as of 17:03, 25 June 2023900 × 450 (129 KB)Yuasanbg style
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