In [1]:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

In [2]:

#读取excel数据并转化为csv格式

data_xls=pd.read_excel('地市级党委书记数据库（2000-10）.xls','中国人民共和国地市级党委书记数据库（2000-10）',index_col = 0)

data_xls.to_csv('test_csv.csv', encoding='utf-8')

In [3]:

#读取csv格式的文件

data=pd.read_csv('test_csv.csv')

data.head(5)

Out[3]:

	省级政区代码	省级政区名称	地市级政区代码	地市级政区名称	年份	党委书记姓名	出生年份	出生月份	籍贯省份代码	籍贯省份名称	...	民族	教育	是否是党校教育（是=1，否=0）	专业：人文	专业：社科	专业：理工	专业：农科	专业：医科	入党年份	工作年份
0	130000	河北省	130100	石家庄市	2000	陈来立	NaN	NaN	NaN	NaN	...	NaN	硕士	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	130000	河北省	130100	石家庄市	2001	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	0.0	0.0	0.0	1.0	0.0	0.0	NaN	NaN
2	130000	河北省	130100	石家庄市	2002	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	0.0	0.0	0.0	1.0	0.0	0.0	NaN	NaN
3	130000	河北省	130100	石家庄市	2003	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	0.0	0.0	0.0	1.0	0.0	0.0	NaN	NaN
4	130000	河北省	130100	石家庄市	2004	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	0.0	0.0	0.0	1.0	0.0	0.0	NaN	NaN

5 rows × 23 columns

In [4]:

#把列名转化长一个列表

filed=data.columns.tolist()

print(filed)

print(data.columns)

['省级政区代码', '省级政区名称', '地市级政区代码', '地市级政区名称', '年份', '党委书记姓名', '出生年份', '出生月份', '籍贯省份代码', '籍贯省份名称', '籍贯地市代码', '籍贯地市名称', '性别', '民族', '教育', '是否是党校教育（是=1，否=0）', '专业：人文', '专业：社科', '专业：理工', '专业：农科', '专业：医科', '入党年份', '工作年份']

Index(['省级政区代码', '省级政区名称', '地市级政区代码', '地市级政区名称', '年份', '党委书记姓名', '出生年份',

       '出生月份', '籍贯省份代码', '籍贯省份名称', '籍贯地市代码', '籍贯地市名称', '性别', '民族', '教育',

       '是否是党校教育（是=1，否=0）', '专业：人文', '专业：社科', '专业：理工', '专业：农科', '专业：医科', '入党年份',

       '工作年份'],

      dtype='object')

In [5]:

print(data.describe(include=[np.number]))

print('----------')

# .describe()返回基本数据信息

# .describe(include=[np.number])只统计数值类型

# ()中没有任何参数时，会默认只统计数值类型的字段内容，包括：计数，平均数，方差，最小值，最大值，四分位数，若其中有字符串数据会报错

print(data.describe(include=[np.object]))

# 这里代表只统计字符串类型的字段内容：计数，唯一值数量，出现频率最高的内容，最高出现频率

              省级政区代码        地市级政区代码           年份         出生年份         出生月份  \

count    3663.000000    3663.000000  3663.000000  2676.000000  2645.000000

mean   403393.393393  404456.756757  2005.000000  1953.622571     6.790548

std    148176.721620  148485.810327     3.162709     4.416316     3.614664

min    130000.000000  130100.000000  2000.000000  1941.000000     1.000000

25%    330000.000000  330100.000000  2002.000000  1951.000000     3.000000

50%    420000.000000  420200.000000  2005.000000  1954.000000     7.000000

75%    510000.000000  513400.000000  2008.000000  1956.000000    10.000000

max    650000.000000  654300.000000  2010.000000  1966.000000    14.000000   

              籍贯省份代码         籍贯地市代码  是否是党校教育（是=1，否=0）        专业：人文  \

count    2624.000000    2615.000000       2493.000000  2370.000000

mean   364428.353659  365742.332696          0.430405     0.275527

std    126267.485520  125961.993399          0.576136     0.446874

min    110000.000000  120000.000000          0.000000     0.000000

25%    320000.000000  320700.000000          0.000000     0.000000

50%    370000.000000  370700.000000          0.000000     0.000000

75%    430000.000000  431300.000000          1.000000     1.000000

max    640000.000000  640500.000000          9.000000     1.000000   

             专业：社科        专业：理工        专业：农科        专业：医科         入党年份  \

count  2376.000000  2371.000000  2369.000000  2370.000000  2384.000000

mean      0.627525     0.256854     0.067539     0.009705  1976.906879

std       0.483566     0.436990     0.251006     0.098054     5.310080

min       0.000000     0.000000     0.000000     0.000000  1961.000000

25%       0.000000     0.000000     0.000000     0.000000  1973.000000

50%       1.000000     0.000000     0.000000     0.000000  1976.000000

75%       1.000000     1.000000     0.000000     0.000000  1981.000000

max       1.000000     1.000000     1.000000     1.000000  1994.000000   

              工作年份

count  2568.000000

mean   1973.129673

std       4.856564

min    1958.000000

25%    1970.000000

50%    1972.500000

75%    1976.000000

max    1990.000000

----------

       省级政区名称 地市级政区名称 党委书记姓名 籍贯省份名称 籍贯地市名称    性别    民族    教育

count    3663    3663   3021   2624   2615  2708  2517  2550

unique     27     333    901     29    240     2     2     7

top       广东省     白山市    焉荣竹    山东省    威海市     男    汉族    硕士

freq      231      11     11    313     58  2633  2351  1381

In [6]:

#取出性别这一列

data_gender=data['性别']

data_gender.head()

Out[6]:

0    NaN

1    NaN

2    NaN

3    NaN

4    NaN

Name: 性别, dtype: object

In [7]:

#过来吃掉空值

data_gender_re=data_gender[data_gender.notnull()]

data_gender_re.head()

Out[7]:

121    男

122    男

123    男

124    男

125    男

Name: 性别, dtype: object

In [8]:

#查看性别这一列都有那些数值

data_gender_re.unique()

Out[8]:

array(['男', '女'], dtype=object)

In [9]:

#统计总数

count_total=data_gender_re.count()

count_total

Out[9]:

In [10]:

#分别统计男女占比

count_m=data_gender_re[data_gender_re=='男'].count()

count_m

Out[10]:

In [11]:

#统计女的数量

count_f=data_gender_re[data_gender_re=='女'].count()

count_f

Out[11]:

In [12]:

#分别查看男女数量

data_gender_re.value_counts()

Out[12]:

男    2633

女      75

Name: 性别, dtype: int64

In [13]:

#查看男占比

count_m/count_total

Out[13]:

0.9723042836041359

In [14]:

#查看女占比

count_f/count_total

Out[14]:

0.027695716395864108

按省份分析市委书记的女性比例

In [15]:

#取出省份和性别这两列，过滤掉性别为空的

data_gender2 = data[['省级政区名称','性别']]

data_gender2_re = data_gender2[data_gender2['性别'].notnull()]

data_gender2_re.head()

Out[15]:

	省级政区名称	性别
121	山西省	男
122	山西省	男
123	山西省	男
124	山西省	男
125	山西省	男

In [16]:

#按省份分组，统计性别的频数

pt=pd.crosstab(data_gender2_re['省级政区名称'],data_gender2_re['性别'])

pt.head()

Out[16]:

性别	女	男
省级政区名称
云南省	2	73
内蒙古自治区	0	86
吉林省	4	72
四川省	8	155
宁夏回族自治区	0	49

In [17]:

#给pt1新增一列女性占比，按男性占比后赋值给pt2

pt['女性占比']=pt['女']/(pt['男']+pt['女'])

pt2=pt.sort_values(by=['女性占比'],ascending=False)

pt2.head()

Out[17]:

性别	女	男	女性占比
省级政区名称
辽宁省	13	121	0.097015
陕西省	9	93	0.088235
吉林省	4	72	0.052632
山西省	6	112	0.050847
四川省	8	155	0.049080

In [18]:

#根据上面结构绘图

#创建一张8*4的图标

fig_q1_1=plt.figure(figsize=(8,4))

#把省份作为横轴，取钱10个

index=pt2.index[:10]

plt.bar(range(10),  # 横坐标

        pt2['女性占比'][:10], # 纵坐标

        tick_label=index, # 横轴标签

        color = 'red' )  # 颜色

plt.title('不同省份女性市委书记占比')

plt.xlabel('省份')

plt.ylabel('女性占比')

plt.show()

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" alt="" />

In [19]:

##图标2：女性视为书籍的占比结构

fig_q1_2=plt.figure(figsize=(4,4))

plt.boxplot(pt2['女性占比'],#值

           vert=True,#纵向

           showmeans=True)#显示均值

plt.title('女性市委书记占比')

plt.xticks([])

plt.ylabel('女性占比')

plt.show()

aaarticlea/png;base64,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" alt="" />

年龄情况，专业情况

In [20]:

#年龄情况，整体年龄情况/入职年龄情况/退休年龄情况

data_age=data[['出生年份','党委书记姓名','年份']]

data_age.head()

Out[20]:

	出生年份	党委书记姓名	年份
0	NaN	陈来立	2000
1	NaN	吴振华	2001
2	NaN	吴振华	2002
3	NaN	吴振华	2003
4	NaN	吴振华	2004

In [21]:

#过滤掉出生年份为空的

data_age_re=data_age[data_age['出生年份'].notnull()]

data_age_re.head()

Out[21]:

	出生年份	党委书记姓名	年份
121	1945.0	侯伍杰	2000
122	1945.0	侯伍杰	2001
123	1950.0	云公民	2002
124	1950.0	云公民	2003
125	1950.0	云公民	2004

In [23]:

#查看出生年份的组成值

data_age_re['出生年份'].unique()

Out[23]:

array([1945., 1950., 1956., 1949., 1952., 1957., 1953., 1960., 1955.,

       1951., 1954., 1948., 1947., 1946., 1944., 1962., 1964., 1942.,

       1963., 1958., 1965., 1943., 1961., 1959., 1941., 1966.])

In [24]:

#查看出生年份的描述

data_age_re.describe()

Out[24]:

	出生年份	年份
count	2676.000000	2676.000000
mean	1953.622571	2005.214499
std	4.416316	3.046486
min	1941.000000	2000.000000
25%	1951.000000	2003.000000
50%	1954.000000	2005.000000
75%	1956.000000	2008.000000
max	1966.000000	2010.000000

In [31]:

#计算出整体年龄数据

df1=2017-data_age_re['出生年份']

df1.head()

Out[31]:

121    72.0

122    72.0

123    67.0

124    67.0

125    67.0

Name: 出生年份, dtype: float64

In [37]:

df1.describe()

Out[37]:

count    2676.000000

mean       63.377429

std         4.416316

min        51.000000

25%        61.000000

50%        63.000000

75%        66.000000

max        76.000000

Name: 出生年份, dtype: float64

In [53]:

#计算入职年龄(先计算每个人入职年龄的最小值)，再看每个年份的入职人数

df_yearmin = data_age_re[['党委书记姓名','年份']].groupby(data_age_re['党委书记姓名']).min()

df2=df_yearmin['年份'].groupby(df_yearmin['年份']).count()

df2

Out[53]:

年份

2000    190

2001     69

2002     65

2003     88

2004     51

2005     55

2006     50

2007     59

2008     99

2009     23

Name: 年份, dtype: int64

In [55]:

#查看卸任年龄

df_yearmax = data_age_re[['党委书记姓名','年份']].groupby(data_age_re['党委书记姓名']).max()

df3=df_yearmax.groupby(df_yearmax['年份']).count()

df3

Out[55]:

	党委书记姓名
年份
2000	47
2001	44
2002	71
2003	38
2004	48
2005	49
2006	58
2007	105
2008	25
2009	41
2010	223

In [64]:

##专业情况  专业结构 / 专业整体情况 / 专业大类分布

data_major=data[['党委书记姓名','专业：人文','专业：社科','专业：理工','专业：农科','专业：医科']]

data_major_re=data[data_major['专业：人文'].notnull()]

data_major_re.head()

Out[64]:

	省级政区代码	省级政区名称	地市级政区代码	地市级政区名称	年份	党委书记姓名	出生年份	出生月份	籍贯省份代码	籍贯省份名称	...	民族	教育	专业：理工	入党年份	工作年份
1	130000	河北省	130100	石家庄市	2001	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	1.0	NaN	NaN
2	130000	河北省	130100	石家庄市	2002	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	1.0	NaN	NaN
3	130000	河北省	130100	石家庄市	2003	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	1.0	NaN	NaN
4	130000	河北省	130100	石家庄市	2004	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	1.0	NaN	NaN
5	130000	河北省	130100	石家庄市	2005	吴振华	NaN	NaN	NaN	NaN	...	NaN	本科	1.0	NaN	NaN

5 rows × 23 columns

In [66]:

data_major_re.mean()

Out[66]:

省级政区代码              388046.413502

地市级政区代码             388781.772152

年份                    2005.479747

出生年份                  1954.297048

出生月份                     6.704082

籍贯省份代码              369873.772791

籍贯地市代码              371312.159624

是否是党校教育（是=1，否=0）         0.406809

专业：人文                    0.275527

专业：社科                    0.626582

专业：理工                    0.256540

专业：农科                    0.067539

专业：医科                    0.009705

入党年份                  1977.430507

工作年份                  1973.622338

dtype: float64

In [67]:

data_major_re.describe()

Out[67]:

	省级政区代码	地市级政区代码	年份	出生年份	出生月份	籍贯省份代码	籍贯地市代码	是否是党校教育（是=1，否=0）	专业：人文	专业：社科	专业：理工	专业：农科	专业：医科	入党年份	工作年份
count	2370.000000	2370.000000	2370.000000	2168.000000	2156.000000	2139.000000	2130.000000	2350.000000	2370.000000	2370.000000	2370.000000	2369.000000	2370.000000	1993.000000	2113.000000
mean	388046.413502	388781.772152	2005.479747	1954.297048	6.704082	369873.772791	371312.159624	0.406809	0.275527	0.626582	0.256540	0.067539	0.009705	1977.430507	1973.622338
std	137507.595852	137533.425865	3.040290	4.250503	3.618283	126451.656681	126006.184835	0.491343	0.446874	0.483814	0.436815	0.251006	0.098054	5.265569	4.857468
min	130000.000000	130100.000000	2000.000000	1941.000000	1.000000	110000.000000	120000.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1964.000000	1960.000000
25%	320000.000000	321300.000000	2003.000000	1952.000000	3.000000	320000.000000	320900.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1974.000000	1970.000000
50%	410000.000000	410600.000000	2006.000000	1954.000000	7.000000	370000.000000	370900.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1976.000000	1973.000000
75%	450000.000000	451175.000000	2008.000000	1957.000000	10.000000	440000.000000	440500.000000	1.000000	1.000000	1.000000	1.000000	0.000000	0.000000	1982.000000	1976.000000
max	650000.000000	650200.000000	2010.000000	1966.000000	14.000000	640000.000000	640500.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1994.000000	1990.000000

In [86]:

#统计每个人的专业

data_major_re['专业']=data_major_re[['专业：人文', '专业：社科', '专业：理工', '专业：农科', '专业：医科']].idxmax(axis=1)

data_major_re.head()

C:\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning:

A value is trying to be set on a copy of a slice from a DataFrame.

Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

Out[86]:

	省级政区代码	省级政区名称	地市级政区代码	地市级政区名称	年份	党委书记姓名	出生年份	出生月份	籍贯省份代码	籍贯省份名称	...	教育	专业：理工	入党年份	工作年份	专业
1	130000	河北省	130100	石家庄市	2001	吴振华	NaN	NaN	NaN	NaN	...	本科	1.0	NaN	NaN	专业：理工
2	130000	河北省	130100	石家庄市	2002	吴振华	NaN	NaN	NaN	NaN	...	本科	1.0	NaN	NaN	专业：理工
3	130000	河北省	130100	石家庄市	2003	吴振华	NaN	NaN	NaN	NaN	...	本科	1.0	NaN	NaN	专业：理工
4	130000	河北省	130100	石家庄市	2004	吴振华	NaN	NaN	NaN	NaN	...	本科	1.0	NaN	NaN	专业：理工
5	130000	河北省	130100	石家庄市	2005	吴振华	NaN	NaN	NaN	NaN	...	本科	1.0	NaN	NaN	专业：理工

5 rows × 24 columns

In [90]:

#去重

data_major_st=data_major_re[['党委书记姓名','专业']].drop_duplicates()

data_major_st.head()

Out[90]:

	党委书记姓名	专业
1	吴振华	专业：理工
7	吴显国	专业：社科
9	车俊	专业：社科
10	孙瑞彬	专业：社科
11	白润璋	专业：理工

In [93]:

#统计专业结构

df4=data_major_st['专业'].groupby(data_major_st['专业']).count()

df4

Out[93]:

专业

专业：人文    187

专业：农科     21

专业：医科      5

专业：理工    127

专业：社科    346

Name: 专业, dtype: int64

In [96]:

#计算每年专业的整体数据情况(这是未去重的)

df5=pd.crosstab(data_major_re['年份'],data_major_re['专业'])

df5

Out[96]:

专业	专业：人文	专业：农科	专业：医科	专业：理工	专业：社科
年份
2000	33	7	2	43	53
2001	42	6	3	36	78
2002	56	5	4	35	90
2003	67	7	2	25	106
2004	68	7	2	28	118
2005	63	7	2	31	123
2006	67	9	2	34	123
2007	69	8	2	34	128
2008	68	8	0	44	130
2009	67	8	0	42	131
2010	71	7	0	38	131

In [97]:

# 计算每年专业大类分布数据

df5['社科比例'] = df5['专业：社科'] / (df5['专业：理工'] + df5['专业：医科'] + df5['专业：社科'] + df5['专业：农科'] + df5['专业：人文'])

df5['人文比例'] = df5['专业：人文'] / (df5['专业：理工'] + df5['专业：医科'] + df5['专业：社科'] + df5['专业：农科'] + df5['专业：人文'])

df5['理工农医比例'] = (df5['专业：理工'] + df5['专业：医科'] + df5['专业：农科'])/ (df5['专业：理工'] + df5['专业：医科'] + df5['专业：社科'] + df5['专业：农科'] + df5['专业：人文'])

print(df5[['社科比例','人文比例','理工农医比例']])

专业        社科比例      人文比例    理工农医比例

年份

2000  0.384058  0.239130  0.376812

2001  0.472727  0.254545  0.272727

2002  0.473684  0.294737  0.231579

2003  0.512077  0.323671  0.164251

2004  0.529148  0.304933  0.165919

2005  0.544248  0.278761  0.176991

2006  0.523404  0.285106  0.191489

2007  0.531120  0.286307  0.182573

2008  0.520000  0.272000  0.208000

2009  0.528226  0.270161  0.201613

2010  0.530364  0.287449  0.182186

年龄情况的图标绘制

In [120]:

fig_q2=plt.figure(figsize=(12,8))

ax1=fig_q2.add_subplot(2,3,1)

ax2=fig_q2.add_subplot(2,3,2)

ax3=fig_q2.add_subplot(2,3,3)

ax4=fig_q2.add_subplot(2,3,4)

ax5=fig_q2.add_subplot(2,3,5)

ax6=fig_q2.add_subplot(2,3,6)

#亿创建的图标划分成2*3#的表格矩阵

ax1.hist(df1,bins=11,color='gray',alpha=0.9)

ax1.set_title('整体年龄分布')

ax1.grid(True)

ax2.plot(df2,color='r',marker='o',alpha=0.9)

ax2.set_title('入职年龄分布')

ax2.set_xticks(range(2000,2011,2))

ax2.grid(True)

ax3.plot(df3,color='g',marker='o',alpha=0.9)

ax3.set_title('卸任年龄分布')

ax3.set_xticks(range(2000,2011,2))

ax3.grid(True)

ax4.bar(range(len(df4)),df4,color='y')

ax4.set_xticklabels(['人文','农科','医科','理工','社科'])

ax4.grid(True)

ax4.set_title('专业结构')

ax5.plot(df5.index,df5[['专业：人文','专业：农科','专业：医科','专业：理工','专业：社科']])

ax5.grid(True)

ax5.set_title('专业整体情况')

ax6.bar(df5.index,df5['社科比例'],color = 'darkred',alpha=0.7)

ax6.bar(df5.index,df5['人文比例'],color = 'darkred',bottom = df5['社科比例'],alpha=0.5)

ax6.bar(df5.index,df5['理工农医比例'],color = 'darkred',bottom = df5['人文比例'] + df5['社科比例'],alpha=0.3)

ax6.grid(True)

ax6.set_title('专业大类分布：社科、人文、理工农医')

plt.show()

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" 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任期

In [128]:

#出生年份与任期的关系

# 新建变量data_term，赋值包括年份、姓名、出生年份字段内容

# 清除缺失值

data_term=data[['年份','党委书记姓名','出生年份']]

data_term_re=data_term[data_term['出生年份'].notnull()]

data_term_re.head()

Out[128]:

	年份	党委书记姓名	出生年份
121	2000	侯伍杰	1945.0
122	2001	侯伍杰	1945.0
123	2002	云公民	1950.0
124	2003	云公民	1950.0
125	2004	云公民	1950.0

In [129]:

year_max = data_term_re[['出生年份','年份']].groupby(data_term_re['党委书记姓名']).max()

year_max.rename(columns={'年份':'年份max'}, inplace = True)

year_max['姓名'] = year_max.index

# 统计每个党委书记任期年份最大值，且更改列明

# 将index提取出字段内容

In [130]:

year_min = data_term_re[['出生年份','年份']].groupby(data_term_re['党委书记姓名']).min()

year_min.rename(columns={'年份':'年份min'}, inplace = True)

year_min['姓名'] = year_min.index

# 统计每个党委书记任期年份最小值，且更改列明

# 将index提取出字段内容

In [131]:

data_term_fin = pd.merge(year_max,year_min)

print(data_term_fin.head())

print(data_term_fin.dtypes)

# 合并表格，默认重叠重复列明

# .dtypes查看字段类型 → 年份均为int

     出生年份  年份max   姓名  年份min

0  1951.0   2009  丁海中   2003

1  1948.0   2003  丁耀民   2000

2  1951.0   2007  丁解民   2001

3  1964.0   2007  万庆良   2005

4  1957.0   2010  丰立祥   2008

出生年份     float64

年份max      int64

姓名        object

年份min      int64

dtype: object

In [132]:

data_term_fin['任期'] = data_term_fin['年份max'] - data_term_fin['年份min']

print(data_term_fin.head())

# 计算任期

     出生年份  年份max   姓名  年份min  任期

0  1951.0   2009  丁海中   2003   6

1  1948.0   2003  丁耀民   2000   3

2  1951.0   2007  丁解民   2001   6

3  1964.0   2007  万庆良   2005   2

4  1957.0   2010  丰立祥   2008   2

In [133]:

# 绘制图表1：任期与出生年份关系

fig_q3_1 = plt.figure(figsize = (8,4))

# 创建一个图表，大小为8*4

plt.scatter(data_term_fin['出生年份'],data_term_fin['任期'],color = 'black', alpha=0.2, s = 10)

plt.title('任期与出生年份关系')

plt.xlabel('出身年份')

plt.ylabel('任期（年）')

plt.grid(True)

plt.show()

# 创建散点图，aplha代表透明度 → 点颜色叠加，s代表点大小，

# 参数添加，grid添加网格

# plt.show()：显示图表

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ORSIReRktLC5s3b6alpSVUfGtrK8ePH6e1tTVUfDweZ8+ePcTj8VDx+dgGo70dRQp5PW6R161kMklbWxuxWIxUKsWSJUsKej3jRCLBzp07KS0tZWBggLvuuovZs2cHWkZLSwtr166lpKSExx9/HIDbbrst5/jW1lYeeOAB3v/+97Nt2zY2btzI0qVLc46Px+Ns2bKFsrIy+vv7WbNmTaDreudjG4z2dhQBjbhFCqK3t5dYLMb06dOJxWL09vYWtP2uri5KS0u56qqrKC0tpaurK/Ay2tvbKSkpobq6mpKSEtrb2wPFHzlyhNLSUiZMmEBpaSlHjhwJFN/R0UFZWRlz586lrKyMjo6OQPH52AajvR1FQIVbpCAqKipIpVIcP36cVCpFRUVFQduvqqpiYGCAzs5OBgYGqKqqCryMmpoaBgcH6ejoYHBwkJqamkDx8+bNY2BggDNnzjAwMMC8efMCxVdXV9Pf38/zzz9Pf38/1dXVgeLzsQ1GezuKgE6VixREZWUlS5Ysobe3l4qKioKfXp09ezZ33XUXXV1dVFVVBT5NDq+eFm9vb6empibQaXKApUuXsnHjRrq7uwOfJgdYuHAha9asoaOjg+rq6kCnySE/22C0t6MIqHCLFExlZeWoHuhnz54dqmAPddtttwUu2EMtXbqUvXv3Bi7aWQsXLgxcsIfKxzYY7e0oolPlIiIiRUSFW0REpIiocIuIiBQRFW4REZEiosItIiJSRFS4RUREiogKt4iISBFR4RYRESkiKtwiIiJFRIVbRESkiKhwi4iIFBEVbhERkSKiwi0iIlJECl64zazBzH6t0O2KiIiMBwUt3Ga2DJjl7l8rZLsSXTKZpLOzk2QyOSrx+ZBIJGhrayORSISOP3XqVOj41tZWtm/fTmtra+j2o/Y/Snw+ltHS0kJ3dzctLS2j0n48HmfPnj3E4/FQ8fnow1jYF0abchBNwa7HbWZlwF8DzWb2Lnf/SqHalmiSySRtbW3EYjFSqRRLliwJdD3iqPH5kEgk2LlzJ6WlpQwMDHDXXXcFujZ1Nv7qq69m586dgeNbW1t54IEHzra/cePGQNekzlf/w8bnYxktLS2sXbuWVatWsWnTJoBA1/aO2n48HmfLli2UlZXR39/PmjVrAl/bO2ofxsK+MNqUg+jM3QvTkNk9wK8Cq4E/BLrd/bNDHr8XuBdg5syZ1+3atWtY/MmTJ5k8eXJB+jpehc1hX18fp0+fPnuwmjhxIuXl5QWLz4dTp05x4sQJysvL6evrY8qUKUyaNClw/MSJEzl9+nTg+OPHj9PT08OECRM4c+YM06ZNY/r06QXvf9j4fCyju7ubnp4eZsyYwY9//GOmTZvGrFmzCtZ+MpkkmUye3QaVlZWBC0bUPuRrXyjm4+FYOB7A2Mzh8uXLn3H32ovNV8jC/ZfA1939CTP7OeBhd3/3SPPW1tb6oUOHhk3bu3cvdXV1l76j41jYHGrEPXzE/f3vf18j7ogj7scee4xHHnlEI+6Q+0IxHw/HwvEAxmYOzWzMFe4/BvrcvcHMfge41t0/PNK8KtyXRpQcJpNJent7qaioCLWTRY3Ph0QiQVdXF1VVVYGLVjb+2WefZf78+aHiW1tbOXLkCPPmzQtUtIe2H7X/UeLzsYyWlhZefvllpk6dGqho56v9eDxOR0cH1dXVgYt2vvqQj32h2I+HY+F4MBZzmLfCbWaTgfuBamAwM/kf3f0bATs0BfgbYCZQBrzH3Y+NNK8K96WhHEanHEanHEanHEY3FnOYa+G+4IfTzOwtwKeBDe7+H5lpE4GPmNkt7v7RXDvk7ieAO3KdX0RERF7rYl8Huxu4O1u0Adz9tLs/DBwxs3DnmkRERCSUC4643X39BR77m/x3R0RERC4k8A+wmFnMzD5wKTojIiIiF3bRwm1m/5L5vzozyYHfuJSdEhERkZHlMuJ+JfP/XQCe/hh66pL1SERERM4rl8KdLdL9Q6YV5svfIiIiMkwuv1VeZWZPAddk/htwtZkdACa5+4JL2kMRERE5K5fC3eXuv25mX3P3XwMYeltEREQK57ynys1srpn9M1AxZJqZWQydKhcRERkVF3qP+0XgS8BbzGwN8EPgSeCbheiYiIiIvNZ5C7e797n7DuAXgF8GXnb3Xwb+N+nfGhcREZECu+h73O7+EzP7VeCK7CTgK5e0VyIiIjKiXD6chrsPAInM7RTwV5eyUyIiIjKyC36P28y2mtmIF0s1s9W6yEjxSCQSnDp1ikQiESo+mUzS2dlJMpksyvh8LKOlpYXu7m5aWlpCxScSCdra2kJvg6amJtasWUNTU1Oo+Pr6em6//Xbq6+tDxeejD01NTRw7dix0fEtLC5s3bw69DeLxOHv27CEej4eKHwtGe1/O9iHKc1miudiI+7PA582s3t0PwNnraq8DBt29eJ/9ryOJRIKdO3dy9dVXs3PnTu666y5mz56dc3wymaStrY1YLEYqlWLJkiVUVo74em5MxudjGS0tLaxdu5ZVq1axadMmAG677bac47PboLS0lIGBgcDbIFswY7HY2aJ3xx25XyW3vr6eT37yk5gZ3/rWtwD46EdzvipvXvqQjf/IRz7Co48+Gjg+uw1KSkp4/PHHgWDbIB6Ps2XLFsrKyujv72fNmjUsXFhcY4/R3peH9iHsc1miu+CI2907gd8BlprZ35rZF4B64Al3f6AQHZTourq6KC0tpby8nNLSUrq6ugLF9/b2EovFmD59OrFYjN7e3qKKz8cy2tvbKSkpYcKECZSUlNDe3h4oPrsNrrrqqlDb4ODBg8RiMebMmUMsFuPgwYOB4ltbWzEz3vjGN2JmtLa2BorPRx+y8eXl5aHis9uguro61Dbo6OigrKyMuXPnUlZWRkdHR6D4sWC09+WhfQj7XJboLvqTp+7+irvXu/vvuvs97v4hd//XQnRO8qOqqoqBgQH6+voYGBigqqoqUHxFRQWpVIrjx4+TSqWoqKi4eNAYis/HMmpqahgcHOTMmTMMDg5SU1MTKD67DTo7O0Ntg0WLFpFKpTh69CipVIpFixYFil+6dCnuzk9+8hPcnaVLlwaKz0cfsvF9fX2h4rPboKOjI9Q2qK6upr+/n+eff57+/n6qq6sDxY8Fo70vD+1D2OeyRGfpa4aMLbW1tX7o0KFh0/bu3UtdXd3odGgcSCQSPPvss8yfPz/Uaa1kMklvby8VFRWBT62Nhfh8LKOlpYWXX36ZqVOnBjpFm5VIJOjq6qKqqirUNmhqauLgwYMsWrQo0CnmrPr6elpbW1m6dGng0+T56kNTUxN9fX2Ul5eHim9paaG9vZ2amppQ2yAej9PR0UF1dXXRnSbPGu19OduHKM/lsWAs1hQze8bday86nwr364dyGJ1yGJ1yGJ1yGN1YzGGuhTuXq4OJiIjIGKHCLSIiUkRUuEVERIrIBb/HbWbvBPrO8/AgcMDdT+W9VyIiIjKiXH6AZQdgwF3APwOXA53Am4B3AX90KTsoIiIir7pY4X7B3R8EMLM60oV8urs/ZWaXo6uEiYiIFNTFCve1ZnaA9BXBqoEvAyfM7NtAo7v/4FJ3UERERF51sZ88rQQ+7+5L3H2Gu88HvggcBHaZ2b2F6KSIiIikXezDac8AbzGzu4dMfjPwQeBZ4BuXsG8iIiJyjot9HWwx8F13vxE4A3wBOAC8BHzA3XVNNxERkQK6WOGuA6aa2f8CKoGrM/9PuHv/Je6biIiInONihft/AdOBpcCXgF7gjcCNZvZlMwt+aRkREREJ7YLvcbv7A2b2HXf/tpmVAHcCf+XunzOzVy4WPxIzm0n6et6/EK7LIiIir18XHHGb2fuAnwC4+yCw0t3/MvPwB939pRBt1gOXhYiTCBKJBKdOnSKRCPexhGQySWdnJ8lkMs89K5x4PM6ePXuIx+Oh4ltaWuju7qalpSV0/ObNm0PHNzU1sWbNGpqamkLFNzY2ct9999HY2BgqHmD9+vUsW7aM9evXh4rfuHEjP/jBD9i4cWOo+IaGBlasWEFDQ8OoxEN6X2prawu9L0WNTyaT9PX1hd4X87EvR12H0RY1h6PtYiPmMuAG4D8y94deA/SVoI2Z2S8Bp4DuoLESXiKRYOfOnVx99dXs3LmTu+66K9A1dJPJJG1tbcRiMVKpFEuWLAl9Hd/REo/H2bJlC2VlZfT397NmzZpA12NuaWlh7dq1rFq1ik2bNgEEuh50Nr6kpITHH388cHy2aMdisbOFO8j1rBsbG1m7di2xWIw9e/YAsHLlypzjIV20t27dCsCBAwcAzuYiFxs3buShhx5i8+bNPPTQQwA88MADOcc3NDSwfv16YrEY//zP/wzA6tWrCxYPr+5LpaWlDAwMBN6XosZn98X+/n7a2toC74v52JejrsNoi5rDseCC1+M2s2uAJ4AXSP/s6duB9sztt7r7lTk3ZFZO+utjvwH8k7vXnfP4vcC9ADNnzrxu165dw+JPnjzJ5MmTc21Ohjh16hQnTpxg4sSJnD59milTpjBp0qSc4/v6+jh9+vTZHXXixImUl5dfwh7nXzKZJJlMMmHCBM6cOUNlZWWgnbW7u5uenh5mzJjBj3/8Y6ZNm8asWbMCx2fbDxp/7Ngxenp6KC8vp6+vj2nTpnHFFVfkHJ8dYWVfuFRWVnLVVVflHA/w3HPPcerUKUpKShgcHGTSpElcc801Ocf/4Ac/4MSJE1x55ZW8+OKLTJkyhbe+9a05x7/wwgu8/PLLZ9dh6tSpvPnNby5YPLy6L2W3Q9B9KWp8dl/MCrov5mNfjroOoy1qDi+l5cuX53Q97ouNuH9A+vvav0n6tPo3gF/J3P56wD59HGhw96SZveZBd98ObAeora31cy9wPhYvel4sho64v//970cacZsZ119/fdG9Qs3HiHvTpk2sWrWKxx57jEceeSTQ8zEbny16QeObmpp49NFHz46UtmzZEii+sbGRhx9++Gx80PYBvvnNb54dcQN87GMf40Mf+lDO8fv27Ts74l63bh2f+MQn+OAHP5hz/JEjR/jUpz51dh02bdoUaB2ixsPYGnGXlZUF3hfzsS+PpxF3mByOBRcccQOY2dfc/dcyt7/t7r+Uub333FHzRZbzr0Aqc7cG+JK7f2CkeWtra/3QoUPDpqlwR5NIJHj22WeZP39+qJ0smUzS29tLRUVF0T3Js+LxOB0dHVRXVwcq2lktLS28/PLLTJ06NdBp7qHx7e3t1NTUhIpvamri4MGDLFq0KNBp8qzGxkYOHDjA4sWLA58mz1q/fj379u1j2bJlgU6TZ23cuJFZs2bR3d0d6DR5VkNDA/v37+emm24KfJo7H/GQ3pe6urqoqqoKtS9FjU8mkzz99NOhC04+9uWo6zDaoubwUjGznEbc5y3clh4W/ybwn+7+PTMrA/a4+zszj7/P3XeNGHzxzl2w6KtwXxrKYXTKYXTKYXTKYXRjMYe5Fu4LnSqfDPwS8Mdm9t3MtBfM7C8yt0vMrM7d7wvauSAjdREREXnVeQu3u58AVme+d72F9PW3/wToGxKry3qKiIgU0EV/QMXd/y9wl5n9NvCiu//3pe+WiIiIjCTnXz5z98cvZUdERETk4i72W+UiIiIyhqhwi4iIFBEVbhERkSKiwi0iIlJEVLhFRESKiAq3iIhIEVHhFhERKSIq3CIiIkVEhVtERKSIqHCLiIgUERXuIpBMJuns7CSZTEZaRl9fX+hltLa2sn37dlpbW0PFJxIJ2traSCQSo9I+pK9Hfd9999HY2BgqvqGhgRdeeIGGhoZQ8evXr2fZsmWsX78+VPzq1atZuHBh6OtINzQ0sGLFitD9B7jzzjuZO3cud955Z6j49evX89xzz4XOwcaNG7n11lvZuHFjqPimpibWrFlDU1NTqHhIX1d98+bNtLS0hIqPx+Ps2bOHeDweKr61tZXjx4+H3hfycTzJx/4YRdTjSSKR4NSpU6HjR527j7m/6667zs/1ne985zXTXg96enq8ubnZn3jiCW9ubvaenp7Qy/jKV74Sahn79u3zuro6v/nmm72urs737dsXKP7o0aO+YcMGf/jhh33Dhg1+9OjRgrbv7r5jxw6fMWOGz5o1y2fMmOE7duwIFL9t2zafOnWqf+Yzn/GpU6f6tm3bAsWhYrtiAAAaB0lEQVSvW7fOS0tLz/6tW7cuUPyqVascOPu3atWqQPHZ/k+bNi1U/93dV6xYMawPK1asCBSfzcGjjz4aKgcPPvigl5eX+4QJE7y8vNwffPDBQPG7d+/2OXPm+Ny5c33OnDm+e/fuQPHu7s3Nzb5gwQKvqanxBQsWeHNzc6D4w4cP+4oVK3zlypW+YsUKP3z4cKD47L6wffv2UPtCPo4n+dgfo4h6PMnGP/7446HiLyXgkOdQIzXiHuN6e3uJxWJMnz6dWCxGb29v6GWUlpaGWsaRI0coLS3lLW95C6WlpRw5ciRQfFdXF6WlpVx11VWUlpbS1dVV0PYBDhw4QCwW48orryQWi3HgwIFA8fv37ycWi1FWVkYsFmP//v2B4vft2wdAZWXlsPu5yo5sJk+ePOx+rrL9nzVrVqj+Azz11FMATJgwYdj9XGXXuaSkZNj9IPFmxhve8AbMLHD8wYMHicVizJkzh1gsxsGDBwPFA7S3t1NSUkJ1dTUlJSW0t7cHiu/o6KCsrIy5c+dSVlZGR0dHoPjsvjBhwoRQ+0I+jif52B+jiHo8ycaXl5eHih8LVLjHuIqKClKpFMePHyeVSlFRURF6GQMDA6GWMW/ePAYGBvjhD3/IwMAA8+bNCxRfVVXFwMAAnZ2dDAwMUFVVVdD2ARYvXkwqleLFF18klUqxePHiQPE33XQTqVSK/v5+UqkUN910U6D4ZcuWAZw9PZm9n6ulS5cCcPLkyWH3c5Xtf3d3d6j+A9x4440AnDlzZtj9XGXXeXBwcNj9IPHuzksvvYS7B45ftGgRqVSKo0ePkkqlWLRoUaB4gJqaGgYHB+no6GBwcJCamppA8dXV1fT39/P888/T399PdXV1oPjsvnDmzJlQ+0I+jif52B+jiHo8ycb39fWFih8LLD06H1tqa2v90KFDw6bt3buXurq60enQKEsmk/T29lJRUXF2xBZmGU8//TTXX399qGW0trZy5MgR5s2bF7hoQPo9pa6uLqqqqpg9e3bB24f0e9wHDhxg8eLFrFy5MnB8Q0MDl112Ga+88kqo95nXr1/Pvn37WLZsGZs2bQocv3r1alpbW1m6dGmo96kbGhrYv38/N910U+j3ye+8806eeuopbrzxRr74xS8Gjl+/fj2zZ88mkUiEysHGjRvP5vCBBx4IHN/U1MTBgwdZtGgRd9xxR+B4SL/H3d7eTk1NDbfddlvg+Hg8TkdHB9XV1SxcuDBwfGtrK93d3cyaNSvUvpCP40k+9scooh5PEokEzz77LPPnzw8Vf6mY2TPuXnvR+VS4Xz+Uw+iUw+iUw+iUw+jGYg5zLdw6VS4iIlJEVLhFRESKiAq3iIhIEVHhFhERKSIq3CIiIkVEhVtERKSIqHCLiIgUERVuERGRIqLCLSIiUkRUuEVERIqICreIiEgRUeEWEREpIircIiIiRaS0UA2Z2VRgF1ACnALe6+59hWpfRERkPCjkiHsF8KfufivQDfxKAduOJJFI0NbWRiKRCBWfTCbp7OwkmUyGim9tbWX79u20traGiof0dYiPHTtGU1NTqPj6+npuv/126uvrQ8W3tLSwefNmWlpaQsWvX7+eZcuWsX79+lDx+VhGS0sL3d3dodehoaGBFStWhLqWNqS34Zo1a0Jvw1tvvZVp06Zx6623hooHWLx4MZdddhmLFy8OFb9gwQKeeeYZFixYECr+nnvu4W1vexv33HNPqPh85GC094V4PE4ymSQej4eKj9p/SF/b/r777qOxsTFUfDweZ8+ePaHXIR/HxKLm7gX/A74ELD7f49ddd52f6zvf+c5rphXC0aNHfcOGDf7www/7hg0b/OjRo4Hie3p6vLm52Z944glvbm72np6eQPH79u3zuro6v/nmm72urs737dsXKN7dfffu3T5nzhz/7Gc/63PmzPHdu3cHit+6datffvnlPmnSJL/88st969atgeKbm5t9wYIFXlNT4wsWLPDm5uZA8evWrfPS0tKzf+vWrQsUn49lZNehoaEh1Dps27bNp06d6tOmTfOpU6f6tm3bAsVnt+HcuXNDbcNbbrnFgbN/t9xyS6B4d/cbbrhh2DJuuOGGQPHz5893wOvr6x3w+fPnB4q/++67h7V/9913B4rPRw5Ge184fPiwr1ixwnfs2OErVqzww4cPB4qP2n939x07dviMGTN81qxZPmPGDN+xY0eg+Ow6rFy5MtQ65OOY6D56NeVCgEOeQw219LyFY2ZLgIfc/ZfPmX4vcC/AzJkzr9u1a9ewuJMnTzJ58uSC9TPr1KlTnDhxgvLycvr6+pgyZQqTJk3KOb6vr4/Tp09TWlrKwMAAEydOpLy8POf448eP09PTw4QJEzhz5gzTpk1j+vTpgdbh2LFj9PT0MGvWLLq7u5k2bRpXXHFFzvEdHR309vaeXYeKigqqq6tzju/u7n7NOsyaNSvn+Oeee45Tp05RUlLC4OAgkyZN4pprrsk5Ph/LyK7DjBkz+PGPfxx4HV544QVefvllysrK6O/vZ+rUqbz5zW/OOT67DbPPw6DbsL29ncHBwbP3S0pKqKmpyTke4Lvf/S5DjxdmxrXXXptz/DPPPAPAlVdeyYsvvgjAddddl3P8f/7nf3L69GlisRipVIqJEyfy8z//8znH5yMHo70vJJNJkskkFRUV9Pb2UllZSWVlZcH6D5w9e5h9LldWVnLVVVcFXodsDoKuQz6OiTB6NeVCli9f/oy71150xlyqe77+gDcAh4DZF5pPI+5XacStEbe7RtzuGnG7a8TtrhG3uxeucAPlwL8At1xs3rFUuN3Txfupp54KXLSzenp6PJFIBC7aWfv27fPPfe5zoZ+g7ukD/xe/+MXAB/ysrVu3+rve9a5QO7p7+oC1adOmwAeqrHXr1vnSpUtDFe18LaO5udkff/zx0Ouwbds2f//73x+4aGft3r3bP/axj4XehrfccotXVlaGKlhZN9xwg0+cODFw0c6aP3++19fXBy7aWXfffbdfc801gYt2Vj5yMNr7wuHDh33Pnj2BC15W1P67p4v3hz70ocBFO+vw4cP+5S9/OfQ65OOYWMyFu2Cnys1sFbAJOJyZ9Ji7/+NI89bW1vqhQ4eGTdu7dy91dXWXtI/jnXIYnXIYnXIYnXIY3VjMoZnldKq8YF8Hc/fHgMcK1Z6IiMh4pB9gERERKSIq3CIiIkVEhVtERKSIqHCLiIgUERVuERGRIqLCLSIiUkRUuEVERIqICreIiEgRUeEWEREpIircIiIiRUSFW0REpIiocIuIiBQRFW4REZEi8roo3IlEgra2NhKJRKj4eDzOnj17iMfjoeKTySSdnZ0kk8lQ8U1NTaxZs4ampqZQ8ZBeh2QyGXodovYhag7zkYOGhgZWrFhBQ0ND6PgXXnghdHxLSwubN2+mpaUlVPz999/PokWLuP/++0PF19fXc/vtt1NfXx8qPh99aGxspLOzk8bGxlDx99xzD29729u45557QsWvXr2ahQsXsnr16lDxEP252Nrayvbt22ltbQ0VH3Vfjno8gvR2vO+++0Jvx6h9iHpMj2q027/oBbtH4++66657zQXGw170/OjRo75hwwZ/+OGHfcOGDX706NFA8YcPH/YVK1b4ypUrfcWKFYEv/N7T0+PNzc3+xBNPeHNzs/f09ASK3717t8+ZM8fnzp3rc+bM8d27dweKd391HXbs2BFqHaL2IWoO85GDbdu2+dSpU33atGk+depU37ZtW6j4z3zmM6Him5ubfcGCBV5TU+MLFizw5ubmQPEf/vCHPRaLnf378Ic/HCh+69atfvnll/ukSZP88ssv961btwaKz0cfduzY4TNmzPA///M/9xkzZviOHTsCxd99990OnP27++67A8WvWrVqWPyqVasCxbtHfy7u27fP6+rq/Oabb/a6ujrft29foPio+3LU45H7q9tx1qxZobZj1D5EPaZnjVZNuRDgkOdQI8f9iLurq4vS0lKuuuoqSktL6erqChTf0dFBWVkZc+fOpaysjI6OjkDxvb29xGIxpk+fTiwWo7e3N1D8wYMHicVizJkzh1gsxsGDBwPFw6vrMGHChFDrELUPUXOYjxzs37+fWCzGrFmziMVi7N+/P1R8WVlZqPj29nZKSkqorq6mpKSE9vb2QPH79u0DoKKiYtj9XLW2tmJmvPGNb8TMQo32ovbhwIEDw3J44MCBQPHZnF922WXD7ucqu86TJ08edj+IqM/FI0eOUFpaylve8hZKS0s5cuRIoPio+3LU4xG8uh2vvPLKUNsxah+iHtOjGu324XVwqryqqoqBgQE6OzsZGBigqqoqUHx1dTX9/f08//zz9Pf3U11dHSi+oqKCVCrF8ePHSaVSZw96uVq0aBGpVIqjR4+SSqVYtGhRoHh4dR3OnDkTah2i9iFqDvORg5tuuolUKkV3dzepVIqbbropVHx/f3+o+JqaGgYHB+no6GBwcJCamppA8cuWLQM4e5DL3s/V0qVLcXd+8pOf4O4sXbo0UHw++rB48eJhOVy8eHGg+GzOX3nllWH3c5Vd55MnTw67H0TU5+K8efMYGBjghz/8IQMDA8ybNy9QfNR9OerxCF7dji+++GKo7Ri1D1GP6VGNdvsAlh6djy21tbV+6NChYdP27t1LXV1dqOUlEgm6urqoqqpi9uzZgePj8TgdHR1UV1ezcOHCwPHJZJLe3l4qKiqorKwMHN/U1MTBgwdZtGgRd9xxR+B4SK/D888/z9y5c0OtQ9Q+RM1hPnLQ0NDA/v37uemmm0K9x9nQ0MBll13GK6+8Eiq+paWF9vZ2ampquO222wLH33///ezbt49ly5bxp3/6p4Hj6+vraW1tZenSpXz0ox8NHJ+PPjQ2NhKLxUilUqxcuTJw/D333HN2G37hC18IHL969eqzOQj7WYWoz8XW1laOHDnCvHnzQr14iLovRz0eQXo7HjhwgMWLF4fajlH7EPWYDqNbU87HzJ5x99qLzvd6KNySphxGpxxGpxxGpxxGNxZzmGvhHvenykVERMYTFW4REZEiosItIiJSRFS4RUREiogKt4iISBFR4RYRESkiKtwiIiJFRIVbRESkiKhwi4iIFBEVbhERkSKiwi0iIlJEVLhFRESKiAq3iIhIESlo4TazL5hZm5l9opDttra2sn37dlpbW0PFJxIJ2traSCQSoeKbmppYs2YNTU1NoeJbWlrYvHkzLS0toeKzfTh27FjoPjQ2NnLffffR2NgYKj4ej7Nnzx7i8Xio+HzkIB/rkEwmQ69D1BxEfR7mI4dR16G+vp6Ojg7q6+tDxUfNQdT4fC0jimQySV9fH8lkMnR8Z2dn6PjxIGoOR1tpoRoys3cDJe6+xMz+xsze6u4/uNTttra28sADD1BaWsrAwAAbN24MdA3cRCLBzp07z8bfddddga6/mi3asVjsbNEMcg3flpYW1q5dS0lJCY8//jhA4Gs5Z/vwkY98hEcffTRwHxobG1m7di2xWIw9e/YABLoGbzweZ8uWLZSVldHf38+aNWsCXUc4HznI1zrcfPPNbNmyJfA6RM1B1OdhPnIYdR3q6+v55Cc/yUMPPcQnP/lJgEDXBY+ag6jx+VpGFMlkkra2Nvr7+2lra2PJkiWBrmedjc9eEz1o/HgQNYdjQcGux21mfwE84e7NZvY+4DJ33zHk8XuBewFmzpx53a5du4bFnzx5ksmTJwdu9/jx4/T09DBhwgTOnDnDtGnTmD59es7xp06d4sSJE5SXl9PX18eUKVOYNGlSzvHHjh2jp6fnbPy0adO44oorco7v7u5+Tf9nzZqVc/zQPsyaNYvu7u7Afci+Os8esCsrK7nqqqtyjk8mkySTybPrUFlZGWhHyUcO8rUOFRUV9Pb2Bl6HqDmI+jzMRw6jrkNHRwe9vb1cccUVHDt2jIqKCqqrq3OOj5qDqPH5WkYUfX19nD59+uz9iRMnUl5eHjg++8IjaPx4EDWHl9Ly5ctzuh53wUbcwCTgWOb2S8C1Qx909+3AdoDa2lo/9wLnYS963trayrZt20Z1xP3oo4+efYW7ZcuWQOvR0tLCpk2bKCkpYXBwkEceeSRwHrJ9yI64g/ahsbGRhx9++Ow6BO1DPkbcUXOQr3W4+eabefLJJ4tyxB01h1HX4dChQ2dH3J/4xCfYsGFDoD5oxD18tFhWVsb1118fesRtZoHjx4OoORwLCjni/nPgcXc/kDlt/jZ33zTSvLW1tX7o0KFh08IWbkgX7yNHjjBv3rxARTsrkUjQ1dVFVVVVqJ20qamJgwcPsmjRokCnqLNaWlpob2+npqYm8OnNoX3o6+ujvLw8VB8aGxs5cOAAixcvDnSKOSsej9PR0UF1dXWgg31WPnKQj3V4/vnnmTt3bqh1iJqDqM/DfOQw6jrU19czbdo0enp6Ap0mz4qag6jx+VpGFMlkkqeffjp0wUkmk/T29lJRUVF0BStfoubwUjGznEbchSzcvwvMcPd6M9sAPOfu/zDSvPku3JKmHEanHEanHEanHEY3FnOYa+Eu5KnyfwL2mVkVcBuwuIBti4iIjAsF+zqYu/cCdcABYLm7v1yotkVERMaLQo64cfceYHch2xQRERlP9MtpIiIiRUSFW0REpIiocIuIiBSRgn0dLAgz+wlw7g8BTweOj0J3xhPlMDrlMDrlMDrlMLqxmMPZ7v7Gi800Jgv3SMzsUC7fb5PzUw6jUw6jUw6jUw6jK+Yc6lS5iIhIEVHhFhERKSLFVLi3j3YHxgHlMDrlMDrlMDrlMLqizWHRvMctIiIixTXiFhERed1T4RYRESkiY6Jwm9lMM9uXuX2tmT1pZvvN7CPnzDffzL6VuV1mZl/LzHf3aPR7LAmZwyvM7EUz25v5u+j3B8e7i+XxfDkzsy+YWZuZfWI0+z8WhMmhmZWaWeeQaQtGdy1GV4D9+WtmVpO5rWPiECFzWBzHRHcf1T9gGvAE8N3M/f3AmwADngLenJluwDeBvZn79wOfytxuBqaM9roUYQ7fDawa7f6Plb9c8jhSzjLTGjO3/wZ462ivSxHm8FrgkdHu/1j4C7A/rwD+bEicjonRc1gUx8SxMOIeBN4L9Gbuv8Hd/9vTWfwfoCIz/feA7wyJq+PVK439K1CUX6TPk7A5XAx8wMy+a2abCtbbsSuXPI6UszpefS5+E1hauC6POWFzuBh4p5k9nTl7UdArF44xF82hmb0BeBToMbPlmfnq0DExK2wOi+KYOOo7h6ev042ZZSftN7M/AF4C5gBxM/sZ4E7gf2f+ACYBxzK3XwJmFqjLY06EHLYAG4GfAk+a2UJ3jxew62NKLnkE3sA5OeO1z8VrC9frsSVCDg8CN7v7j8zsb4F3AF8tbO/Hhhxz+CDQBHwO2GxmU9Ax8awIOSyKY+JYGHGf60PAfwF/QPrUmQOfBta5e/+Q+U4Cl2VuT2ZsrstoyTWHT7n7CXcfBP4deGvhuzqmjZTHkXKm5+L55ZrDuLv/KBNzCD0Xhxoph78AbHP3btKj7Dr0PLyQXHNYFMfEMbdhMwl7LnP37zP/fxF4xMz2AjVm9hDwDK+eknw7cLSA3RzTAuTwG2b2s2Z2OXAr8GzBOzuGnSePI+VMz8XzCJDDvzOzt5tZCXA7cLjwvR2bzpPDHwJzM7drSV+USc/D8wiQw6I4Jo76qfLzeAhYm3lVhLtfnX3AzPa6+yfMbDbQbGbLgHnAv41OV8esXHK4nPR73n3AX7n7cyMv6nVtWB6BDZyTMzP7EbDPzKqA20i/TyavyiWHDwL/QPrDQ1919ydHp6tj1rk53AJ83sz+hPRp3XeTfgtCx8TzyyWHcYrgmFjUv5yWOVAuBb7h7i+Pdn/k9cvMpgG3AP+aOfUmUnA6Jr4+FHXhFhEReb0Zc+9xi4iIyPmpcIuIiBQRFW6R1yEzW25m593/zWyCmVUXsk8ikhsVbpFxwMyuGvK76R80s3syt2eY2cwh89Wa2fuARuAdZvZOM7tlhEW+H/hsDu1uziwzZma/aGbVZvaBvKyUiIxIhVtkfLgDWJK53U/6Jx8h/fW0Xx0y38eBy4HfHzJt89AFZV4APARMNrOvm9kzmf/NZvadIfNNJP0J5n/PtH0n0An8Vt7WSkReY6x+j1tEghkEUueZbkPuDwB3A6eHTLs8e8PMykj/QMVn3f3TmWkH3P2dIyz7Q8D/7+6DZraK9C9S9ZvZc2Z2o7s/FW2VRGQkKtwi40M56d9b/igwC8DMVpL+verPnDPvg0DXkPsNZvaLpH/H+QRwAJhlZl/PPH6NmTWTPl581d3/0szeBHwU+ELmh3xS7v4fmfk/BXzZzN7p7ifyvJ4ir3sq3CLjQ6e7L4CzBRt3b8xcZesd58y7lHSRzpoNfAxY7e6dwP8xs28Dt7r7QGbEfe4ylgFbgWrSp9qPmVkP6Z8qfRPwn6TfJ/9cHtdRRFDhFhkvVgG7RphuwP9nZglgJ+kR+NWkf9LRSF9RqhS4PVOkSzJxI/4yU+aT6Obu/2BmS0n/zOYvZZb3VXd/h5mtAw65+7fyt3oikqXCLTI+nO8nEB1wdz8M1GQnmtkS0leM2wrc5+4DmYfuBd4FvAL8U+ayiNcMOW1eAuwBtp9twP2nZraIVy/IMIH0bz+LyCWgwi0yPrwpc+U3GP4et5H+lDlm9gbSVzx6L+nrNr/X3bszHywDwN0fAx4bumAz+7fzfDgtBsTMrJz0+9ofzUx/I6DfyRa5RFS4RcYBdx/xx1Iy73F/K/Nhsi8B/wjc4+4vmdkkM3sa+NFIsUNMPs/0CaQ/FPco8A/u/j0zawSmAd8PsRoikgNdZETkdczM3uDuL118zgsuw1wHEpGCUeEWEREpIvrlNBERkSKiwi0iIlJEVLhFRESKiAq3iIhIEVHhFhERKSL/Dy4Ts+tMYmPPAAAAAElFTkSuQmCC" alt="" />

In [135]:

# 绘制图表2：任期与出生年份关系 - 热图

fig_q3_2 = plt.figure(figsize = (8,4))

# 创建一个图表，大小为8*4

df = pd.crosstab(data_term_fin['任期'], data_term_fin['出生年份'])

print(df.head())

print('----------')

# 整合数据

ax = fig_q3_2.add_subplot(111)

cax = ax.pcolor(df, cmap='Blues')

#cax = ax.matshow(df, cmap='Blues_r')

fig_q3_2.colorbar(cax)

plt.title('任期与出生年份关系 - 热图\n')

ax.set_xticklabels(data_term_fin['出生年份'].tolist())

plt.show()

# 创建热图，横坐标为出生年份，纵坐标为任期，

出生年份  1941.0  1942.0  1943.0  1944.0  1945.0  1946.0  1947.0  1948.0  1949.0  \

任期

0          4       2       6       5       6       7       3       5       7

1          2       3       1       6       3       6       3       6       8

2          0       2       1       4       6       9       5      11      11

3          0       0       0       1       1       4       2       4       7

4          0       0       0       0       2       1       0       0       3   

出生年份  1950.0   ...    1957.0  1958.0  1959.0  1960.0  1961.0  1962.0  1963.0  \

任期             ...

0          3   ...         2       2       0       1       1       0       0

1          8   ...        13       6       4       3       1       7       1

2          5   ...        19       9       5       3       9       7       3

3          8   ...         6       5       3       4       1       4       0

4          5   ...         8       2       2       1       1       2       0   

出生年份  1964.0  1965.0  1966.0

任期

0          0       1       0

1          2       0       2

2          4       0       0

3          2       2       0

4          0       1       0  

[5 rows x 26 columns]

----------

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" 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