一、数据说明
数据有每个国家对应的近的对于儿童超重和营养不良占比和人数的三种类型（estimate字段）评估数据，一共四个sheet，就是儿童超重比例、儿童超重人数、儿童营养不良比例、儿童营养不良人数。
二、绘图思路和部分绘图效果
part1
要求：儿童超重情况可视化分析：对各国儿童超重的比例和人数进行划分，根据数值的大小区分超重的程度，绘制图表，统计并对比 2000 年与 年不同程度分布情况；
思路：先提取出表中每个国家超重比例和超重人数，然后再提取对应的2000年与数据，表中数据要从字符串型转为浮点型数据才能画图。查看每年数据的数值分布，确定轻度超重比例小于5.0，重度超重比例大于11.0，中度居中；轻度超重人数小于27万人，重度超重人数大于257万人，中度居中，画出四个饼图
part2
要求：绘制地理图，分析不同国家儿童营养不良的情况；
解决思路：先提取出每个国家重度营养不良的数据，对其所有年份数据求和，根据和还有国家名称绘制世界地图，世界地图生成html文件在同一目录下，打开文件即可在浏览器观察世界地图
part3
要求：根据不同程度，抽取排名前 3 的国家数据，整合数据分析儿童营养不良不同程度数据的差距情况；
解决思路：
根据第二部取出营养不良总和前三的国家，去原表中找到对应的数据，根据数据绘制三个国家轻度营养不良、中度营养不良、重度营养不良的折线图对比
part4：
要求：分析对比 2000 年与 年各国儿童超重的比例和人数变动情况
解决思路：根据第一部处理的数据，提取前30个国家的数据绘制2000年与的超重比例和人数柱状图
三、代码
存一下源码，方便以后用到回来看看
import pandas as pddata_proportation = pd.read_excel('Country_Estimates_.xlsx',sheet_name = 'Overweight Proportion (Model)')data_numbers = pd.read_excel('Country_Estimates_.xlsx',sheet_name = 'Overweight Numb Affected(Model)')data_proportation_over = data_proportation[data_proportation['Estimate'] == 'Upper Uncertainty Bound']data_numbers_over = data_numbers[data_numbers['Estimate'] =='Upper Uncertainty Bound']data_proportation_over['2000'] = pd.to_numeric(data_proportation_over['2000'],errors='coerce')data_proportation_over[' 1'] = pd.to_numeric(data_proportation_over[' 1'],errors='coerce')data_numbers_over['2000'] = pd.to_numeric(data_numbers_over['2000'],errors='coerce')data_numbers_over[' 1'] = pd.to_numeric(data_numbers_over[' 1'],errors='coerce')from matplotlib import pyplot as pltfrom jupyterthemes import jtplotjtplot.style(theme='chesterish') #选择一个绘图主题plt.rcParams['font.sans-serif'] = 'SimHei' # 设置中文显示plt.rcParams['axes.unicode_minus'] = Falseprint(data_proportation_over['2000'].describe())data_proportation_over_2000 = []data_proportation_over_2000.append(data_proportation_over['2000'][data_proportation_over['2000']<=5.0].count())data_proportation_over_2000.append(data_proportation_over['2000'][(data_proportation_over['2000']>=5.0) & (data_proportation_over['2000']<=11.0)].count())data_proportation_over_2000.append(data_proportation_over['2000'][data_proportation_over['2000']>=11.0].count())label = ['轻度超重', '中度超重', '重度超重'] # 刻度标签plt.figure(figsize=(6, 6)) # 将画布设定为正方形，则绘制的饼图是正圆explode = [0.01, 0.01, 0.01] # 设定各项离心n个半径plt.pie(data_proportation_over_2000,explode=explode, labels=label, autopct='%1.1f%%') # 绘制饼图plt.title('2000年世界超重儿童比例饼图') # 添加图表标题# plt.savefig('../tmp/各年龄段年末总人口饼图.png')plt.show()print(data_proportation_over[' 1'].describe())data_proportation_over_ = []data_proportation_over_.append(data_proportation_over[' 1'][data_proportation_over[' 1']<=5.0].count())data_proportation_over_.append(data_proportation_over[' 1'][(data_proportation_over[' 1']>=5.0) & (data_proportation_over[' 1']<=11.0)].count())data_proportation_over_.append(data_proportation_over[' 1'][data_proportation_over[' 1']>=11.0].count())label = ['轻度超重', '中度超重', '重度超重'] # 刻度标签plt.figure(figsize=(6, 6)) # 将画布设定为正方形，则绘制的饼图是正圆explode = [0.01, 0.01, 0.01] # 设定各项离心n个半径plt.pie(data_proportation_over_,explode=explode, labels=label, autopct='%1.1f%%') # 绘制饼图plt.title('2000年世界超重儿童比例饼图') # 添加图表标题# plt.savefig('../tmp/各年龄段年末总人口饼图.png')plt.show()print(data_numbers_over['2000'].describe())data_numbers_over_2000 = []data_numbers_over_2000.append(data_numbers_over['2000'][data_numbers_over['2000']<=27.0].count())data_numbers_over_2000.append(data_numbers_over['2000'][(data_numbers_over['2000']>=27.0) & (data_numbers_over['2000']<=257.0)].count())data_numbers_over_2000.append(data_numbers_over['2000'][data_numbers_over['2000']>=257].count())label = ['轻度超重', '中度超重', '重度超重'] # 刻度标签plt.figure(figsize=(6, 6)) # 将画布设定为正方形，则绘制的饼图是正圆explode = [0.01, 0.01, 0.01] # 设定各项离心n个半径plt.pie(data_numbers_over_2000,explode=explode, labels=label, autopct='%1.1f%%') # 绘制饼图plt.title('世界超重儿童比例饼图') # 添加图表标题# plt.savefig('../tmp/各年龄段年末总人口饼图.png')plt.show()print(data_numbers_over[' 1'].describe())data_numbers_over_ = []data_numbers_over_.append(data_numbers_over[' 1'][data_numbers_over[' 1']<=27.0].count())data_numbers_over_.append(data_numbers_over[' 1'][(data_numbers_over[' 1']>=27.0) & (data_numbers_over[' 1']<=257.0)].count())data_numbers_over_.append(data_numbers_over[' 1'][data_numbers_over[' 1']>=257].count())label = ['轻度超重', '中度超重', '重度超重'] # 刻度标签plt.figure(figsize=(6, 6)) # 将画布设定为正方形，则绘制的饼图是正圆explode = [0.01, 0.01, 0.01] # 设定各项离心n个半径plt.pie(data_numbers_over_,explode=explode, labels=label, autopct='%1.1f%%') # 绘制饼图plt.title('世界超重儿童比例饼图') # 添加图表标题# plt.savefig('../tmp/各年龄段年末总人口饼图.png')plt.show()data_proportation_Stunting = pd.read_excel('Country_Estimates_.xlsx',sheet_name = 'Stunting Proportion (Model)')#提取各国严重营养不良的数据data_proportation_Stunting_over_e = pd.DataFrame()data_proportation_Stunting_over = data_proportation_Stunting[data_proportation_Stunting['Estimate'] == 'Upper Uncertainty Bound']for i in ['2000','2001','2002','','','','','','','','','','','','','','','','','',' 1']:data_proportation_Stunting_over_e[i] = pd.to_numeric(data_proportation_Stunting_over[i],errors='coerce')data_proportation_Stunting_over_e['行的和'] = data_proportation_Stunting_over_e.sum(axis=1)print(data_proportation_Stunting_over_e['行的和'])data_proportation_Stunting_over_e['country'] = data_proportation_Stunting_over['Country and areas']print(data_proportation_Stunting_over_e.head())list_data = []for i in data_proportation_Stunting_over_e.itertuples():list_data.append([getattr(i, 'country'),getattr(i, '行的和')])list_datafrom pyecharts import options as optsfrom pyecharts.charts import Mapimport randodef create_world_map(data):''' 作用：生成世界地图'''( # 大小设置Map().add(series_name="严重营养不良", data_pair=data, maptype="world", )# 全局配置项.set_global_opts(# 设置标题title_opts=opts.TitleOpts(title="世界地图"),# 设置标准显示visualmap_opts=opts.VisualMapOpts(max_=1000, is_piecewise=False),)# 系列配置项.set_series_opts(# 标签名称显示，默认为Truelabel_opts=opts.LabelOpts(is_show=False, color="blue"))# 生成本地html文件.render("世界地图.html"))create_world_map(list_data)data_top_3 = data_proportation_Stunting_over_e.sort_values(by="行的和") data_top_3 = data_top_3.tail(3)print(data_top_3)list_top_3 = list(data_top_3['country'])print(list_top_3)data_top_3 =data_proportation_Stunting[(data_proportation_Stunting['Country and areas'] == 'Burundi')|(data_proportation_Stunting['Country and areas'] == 'Eritrea')|(data_proportation_Stunting['Country and areas'] == 'Timor-Leste')]data_top_3_point = data_top_3[data_top_3['Estimate'] =='Point Estimate']data_top_3_lower = data_top_3[data_top_3['Estimate'] =='Lower Uncertainty Bound']data_top_3_upper = data_top_3[data_top_3['Estimate'] =='Upper Uncertainty Bound']data_top_3_upperlist_columns =['2000','2001','2002','','','','','','','','','','','','','','','','','',' 1']plt.figure(figsize=(15, 10)) # 设置画布plt.plot(list_columns, data_top_3_point.iloc[0][list_columns], marker='o', c='red') # 绘制散点图plt.plot(list_columns, data_top_3_point.iloc[1][list_columns], marker='D', c='blue') # 绘制散点图plt.plot(list_columns, data_top_3_point.iloc[2][list_columns], marker='v', c='yellow') # 绘制散点图'''plt.xlabel('年份') # 添加横轴标签plt.ylabel('轻度营养不良') # 添加纵轴标签plt.title('2000-各前三国家轻度营养不良散点图') # 添加图表标题plt.legend(['Burundi', 'Eritrea', 'Timor-Leste']) #添加图例#plt.savefig('../tmp/2000-各年龄段年末总人口散点图.png')plt.show()plt.figure(figsize=(15, 10)) # 设置画布plt.plot(list_columns, data_top_3_lower.iloc[0][list_columns], marker='o', c='red') # 绘制散点图plt.plot(list_columns, data_top_3_lower.iloc[1][list_columns], marker='D', c='orange') # 绘制散点图plt.plot(list_columns, data_top_3_lower.iloc[2][list_columns], marker='v', c='yellow') # 绘制散点图'''plt.xlabel('年份') # 添加横轴标签plt.ylabel('轻度营养不良') # 添加纵轴标签plt.title('2000-各前三国家重度营养不良散点图') # 添加图表标题plt.legend(['Burundi', 'Eritrea', 'Timor-Leste']) #添加图例#plt.savefig('../tmp/2000-各年龄段年末总人口散点图.png')plt.show()plt.figure(figsize=(15, 10)) # 设置画布plt.plot(list_columns, data_top_3_upper.iloc[0][list_columns], marker='o', c='red',linestyle='--') # 绘制散点图plt.plot(list_columns, data_top_3_upper.iloc[1][list_columns], marker='D', c='green') # 绘制散点图plt.plot(list_columns, data_top_3_upper.iloc[2][list_columns], marker='v', c='yellow') # 绘制散点图'''plt.xlabel('年份') # 添加横轴标签plt.ylabel('轻度营养不良') # 添加纵轴标签plt.title('2000-各前三国家中度营养不良散点图') # 添加图表标题plt.legend(['Burundi', 'Eritrea', 'Timor-Leste']) #添加图例#plt.savefig('../tmp/2000-各年龄段年末总人口散点图.png')plt.show()data_proportation_over = data_proportation_over.head(20)data_numbers_over = data_numbers_over.head(20)import numpy as nplength = len(data_proportation_over['2000'])x = np.arange(length) plt.figure()total_width, n = 0.8, 2 # 柱状图总宽度，有几组数据width = total_width / n # 单个柱状图的宽度x1 = x - width / 2 # 第一组数据柱状图横坐标起始位置x2 = x1 + width # 第二组数据柱状图横坐标起始位置plt.figure(figsize=(30, 10)) plt.title("不同国家2000年与超重比例指标对比") # 柱状图标题plt.ylabel("超重比例") # 纵坐标labelplt.bar(x1, data_proportation_over['2000'], width=width, label="2000年")plt.bar(x2, data_proportation_over[' 1'], width=width, label="")plt.xticks(x, data_proportation_over['Country and areas']) # 用星期几替换横坐标x的值plt.legend() # 给出图例plt.show()length = len(data_numbers_over['2000'])x = np.arange(length) plt.figure()total_width, n = 0.8, 2 # 柱状图总宽度，有几组数据width = total_width / n # 单个柱状图的宽度x1 = x - width / 2 # 第一组数据柱状图横坐标起始位置x2 = x1 + width # 第二组数据柱状图横坐标起始位置plt.figure(figsize=(30, 10)) plt.title("不同国家2000年与超重人数对比") # 柱状图标题plt.ylabel("超重人数") # 纵坐标labelplt.bar(x1, data_numbers_over['2000'], width=width,color ='yellow' ,label="2000年")plt.bar(x2, data_numbers_over[' 1'], width=width,color='red' , label="")plt.xticks(x, data_numbers_over['Country and areas']) # 用星期几替换横坐标x的值plt.legend() # 给出图例plt.show()
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