利用税务数据分析美国人群收入情况

一、案例描述：

本案例数据集中记录了从过去大约100年内全球各国高收入信息，通过这些信息我们能分析群人的收入变化情况，以及专注在美国的各个收入阶层之间的差别关系。本案例集的下载地址为：

https://old.datahub.io/dataset/world-top-incomes-database

二、相关代码：

#coding=utf-8
import numpy as np
import matplotlib as plt
import matplotlib.pyplot as plt
import csv

data_file="income_dist.csv"

#过滤出特定的国家
def dataset(path,Country="United States"):
    with open(data_file,'r') as csvfile:
        reader=csv.DictReader(csvfile)
        for row in filter(lambda row:row["Country"]==Country,reader):
            yield row

#创建时间序列
def timeseries(data,column):
    for row in filter(lambda row:row[column],data):
        yield (int(row["Year"]),row[column])

#对matplotlib进行包装，传参进行
def linechart(series,**kwargs):
    fig=plt.figure()
    ax=plt.subplot(111)
    for line in series:
        line=list(line)
        xvals=[v[0] for v in line]
        yvals=[v[1] for v in line]
        ax.plot(xvals,yvals)

    if 'ylabel' in kwargs:
        ax.set_ylabel(kwargs['ylabel'])
    if 'title' in kwargs:
        plt.title(kwargs['title'])

    if'labels'in kwargs:
        ax.legend(kwargs.get('labels'))
    return fig

def percent_income_share(source):
    column={
        "Top 10% income share",
        "Top 5% income share",
        "Top 1% income share",
        "Top 0.5% income share",
        "Top 0.1% income share"
    }
    source=list(dataset(source))

    return linechart([timeseries(source,col) for col in column],
                     labels=column,
                     title="U.S Percentage Income Share",
                     ylabel="Percentage")

# percent_income_share(data_file)
# plt.show()

#计算各个阶层收入的中位数并且作图
def normalizes(data):
    data=list(data)
    norm=np.array(list(d[1] for d in data),dtype="f8")
    mean=norm.mean()
    norm/=mean
    return zip((d[0]for d in data),norm)


def norm_percent_income_share(source):
    column={
        "Top 10% income share",
        "Top 5% income share",
        "Top 1% income share",
        "Top 0.5% income share",
        "Top 0.1% income share"
    }
    source=list(dataset(source))

    return linechart([normalizes(timeseries(source,col)) for col in column],
                     labels=column,
                     title="U.S Percentage Income Share",
                     ylabel="Percentage")

# norm_percent_income_share(data_file)
# plt.show()

#不同收入组的各年平均收入，并且进行绘图
def average_income(source):
    column={
        "Top 10% average income",
        "Top 5% average income",
        "Top 1% average income",
        "Top 0.5% average income",
        "Top 0.1% average income"
    }
    source=list(dataset(source))
    return linechart([timeseries(source,col)for col in column],labels=column,title="U.S Average Income",
                     ylabel="2008 US Dollars")

# average_income(data_file)
# plt.show()

#堆积图来分析富裕人群
def stackedarea(series,**kwargs):
    fig=plt.figure()
    axe=fig.add_subplot(111)
    fnx=lambda s:np.array(list(v[1] for v in s),dtype="f8")
    yax=np.row_stack(fnx(s) for s in series)
    xax=np.arange(1917,2008)
    polys=axe.stackplot(xax,yax)
    axe.margins(0,0)

    if 'ylabels'in kwargs:
        axe.set_ylabel(kwargs['ylabel'])

    if 'labels' in kwargs:
        legendProxies=[]
        for poly in polys:
            legendProxies.append(plt.Rectangle(0,0),1,1,
                                 fc=poly.get_factor()[0])

    axe.legend([legendProxies,kwargs.get('labels')])

    if 'title' in kwargs:
        plt.title(kwargs['title'])

    return fig

def income_composition(source):
    column=("Top 10% average income",
        "Top 5% average income",
        "Top 1% average income",
        "Top 0.5% average income",
        "Top 0.1% average income"
    )

    source=list(dataset(source))
    labels=("Salary","Dividends","Internet","Rent","Business")

    return stackedarea([timeseries(source,col)for col in column],labels=labels,title="U.S. Top 10% Income Composition",ylabel="Percentage")

income_composition(data_file)

四、本次案例输出结果展示 

这简直就是贫富差距拉大的佐证啊。