6. 综合练习(1)

6. 综合练习

import numpy as np
import pandas as pd
# 不用print，直接显示结果
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

# 显示所有列
pd.set_option('display.max_columns', 600)

一、2002 年-2018 年上海机动车牌照拍卖

(1) 哪一次拍卖的中标率首次小于 5%？

(考察点：创建列，索引)

(2) 按年统计拍卖最低价的下列统计量：最大值、均值、 0.75 分位数，要求显示在同一张表上。

建议先完成第三问，再做第二问
(考察点：创建列，索引，分组，统计量，分列)

(3) 将第一列时间列拆分成两个列，一列为年份（格式为 20××），另一列为月份（英语缩写），添加到列表作为第一第二列，并将原表第一列删除，其他列依次向后顺延。

(考察点：拆分列)

(4) 现在将表格行索引设为多级索引，外层为年份，内层为原表格第二至第五列的变量名，列索引为月份。

(考察点：多级索引)

(5) 一般而言某个月最低价与上月最低价的差额，会与该月均值与上月均值的差额具有相同的正负号，哪些拍卖时间不具有这个特点？

(考察点：统计量，分组，合并，索引排序)

(6) 将某一个月牌照发行量与其前两个月发行量均值的差额定义为发行增益，最初的两个月用 0 填充，求发行增益极值出现的时间。

包含极大值极小值
(考察点：统计量，索引，排序，差分)

# MVL = Motor Vehicle License
MVL = pd.read_csv('./2002年-2018年上海机动车拍照拍卖.csv')
MVL.head()
# MVL.info()

	Date	Total number of license issued	lowest price	avg price	Total number of applicants
0	2-Jan	1400	13600	14735	3718
1	2-Feb	1800	13100	14057	4590
2	2-Mar	2000	14300	14662	5190
3	2-Apr	2300	16000	16334	4806
4	2-May	2350	17800	18357	4665

(1) 哪一次拍卖的中标率首次小于 5%？

(考察点：创建列，索引)

MVL[(MVL['Total number of license issued']/MVL['Total number of applicants'])<=0.05]

		Total number of license issued	lowest price	avg price	Total number of applicants
年份	月份
2001	May	7482	79000	79099	156007
Jun	7441	80000	80020	172205
Jul	7531	83100	83171	166302
Aug	7454	82600	82642	166939
Oct	7763	85300	85424	170995
Nov	7514	84600	84703	169159
Dec	7698	84500	84572	179133
Feb	8363	83200	83244	196470
Mar	8310	83100	83148	221109
Apr	11829	85100	85127	256897
May	11598	85000	85058	277889
Jun	11546	84400	84483	275438
Jul	11475	87200	87235	240750
Aug	11549	86900	86946	251188
Feb	10157	88200	88240	251717
Mar	10356	87800	87916	262010
Apr	12196	89800	89850	252273
May	10316	90100	90209	270197
Jun	10312	89400	89532	244349
Jul	10325	92200	92250	269189
Aug	10558	91600	91629	256083
Sep	12413	91300	91415	250566
Oct	11388	93500	93540	244868
Nov	11002	93100	93130	226911
Mar	9855	88100	88176	217056

(2) 按年统计拍卖最低价的下列统计量：最大值、均值、 0.75 分位数，要求显示在同一张表上。

建议先完成第三问，再做第二问
(考察点：创建列，索引，分组，统计量，分列)

# for name,group in MVL.groupby('年份'):
    
MVL.groupby('年份')['lowest price '].agg(['max','mean','quantile'])

	max	mean	quantile
年份
2001	93500	71640.740741	75750.0
2002	30800	20316.666667	19700.0
2003	38500	31983.333333	33600.0
2004	44200	29408.333333	28650.0
2005	37900	31908.333333	33000.0
2006	39900	37058.333333	37750.0
2007	53800	45691.666667	45850.0
2008	37300	29945.454545	32600.0
2009	36900	31333.333333	31050.0

(3) 将第一列时间列拆分成两个列，一列为年份（格式为 20××），另一列为月份（英语缩写），添加到列表作为第一第二列，并将原表第一列删除，其他列依次向后顺延。

(考察点：拆分列)

MVL['年份']=MVL['Date'].apply(lambda x:int(str(x)[:-4])+2000)
MVL['月份']=MVL['Date'].apply(lambda x:str(x)[-3:])
MVL.drop(columns='Date',inplace=True)
MVL=MVL[['年份','月份','Total number of license issued','lowest price ','avg price','Total number of applicants']]
# MVL.reorder_levels(['年份','月份','Total number of license issued','lowest price','avg price','Total number of applicants'],axis=0).head()
# MVL=MVL.set_index(['年份','月份'])
MVL.head()

	年份	月份	Total number of license issued	lowest price	avg price	Total number of applicants
0	2002	Jan	1400	13600	14735	3718
1	2002	Feb	1800	13100	14057	4590
2	2002	Mar	2000	14300	14662	5190
3	2002	Apr	2300	16000	16334	4806
4	2002	May	2350	17800	18357	4665

(4) 现在将表格行索引设为多级索引，外层为年份，内层为原表格第二至第五列的变量名，列索引为月份。

(考察点：多级索引)

# MVL.set_index(['年份','Total number of license issued','lowest price ','avg price','Total number of applicants'])
# pd.crosstab(index=MVL['年份','Total number of license issued','lowest price ','avg price','Total number of applicants'],columns=MVL['月份'])
# MVL.pivot_table(index=['年份','Total number of license issued','lowest price ','avg price','Total number of applicants'],columns='月份',values=1).head()
MVL_m=MVL.melt(id_vars=['年份','月份'],value_vars=['Total number of license issued','lowest price ','avg price','Total number of applicants'])
MVL_m=MVL_m.set_index(['年份','variable'])

pd.pivot_table(MVL_m,index=['年份','variable'],columns='月份',values='value')

	月份	Apr	Aug	Dec	Feb	Jan	Jul	Jun	Mar	May	Nov	Oct	Sep
年份	variable
2002	Total number of applicants	4806.0	4640.0	3525.0	4590.0	3718.0	3774.0	4502.0	5190.0	4665.0	4021.0	4661.0	4393.0
Total number of license issued	2300.0	3000.0	3600.0	1800.0	1400.0	3000.0	2800.0	2000.0	2350.0	3200.0	3200.0	3200.0
avg price	16334.0	21601.0	27848.0	14057.0	14735.0	20904.0	20178.0	14662.0	18357.0	31721.0	27040.0	24040.0
lowest price	16000.0	21000.0	27800.0	13100.0	13600.0	19800.0	19600.0	14300.0	17800.0	30800.0	26400.0	23600.0
2003	Total number of applicants	8794.0	9315.0	10491.0	12030.0	9442.0	11929.0	15507.0	11219.0	14634.0	9849.0	9383.0	8532.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017	lowest price	89800.0	91600.0	92800.0	88200.0	87600.0	92200.0	89400.0	87800.0	90100.0	93100.0	93500.0	91300.0
2018	Total number of applicants	204980.0	192755.0	165442.0	220831.0	226316.0	202337.0	209672.0	217056.0	198627.0	177355.0	181861.0	189142.0
Total number of license issued	11916.0	10402.0	12850.0	11098.0	12183.0	10395.0	10775.0	9855.0	10216.0	11766.0	10728.0	12712.0
avg price	87089.0	88365.0	87508.0	87660.0	87936.0	88380.0	87900.0	88176.0	89018.0	87374.0	88070.0	87410.0
lowest price	86900.0	88300.0	87400.0	87600.0	87900.0	88300.0	87800.0	88100.0	89000.0	87300.0	88000.0	87300.0

68 rows × 12 columns

(5) 一般而言某个月最低价与上月最低价的差额，会与该月均值与上月均值的差额具有相同的正负号，哪些拍卖时间不具有这个特点？

(考察点：统计量，分组，合并，索引排序)

MVL['LOW_Difference']=MVL[['lowest price ']].diff()
MVL['AVG_Difference']=MVL[['avg price']].diff()
MVL[MVL['LOW_Difference']*MVL['AVG_Difference']<0]
# df.diff(2)

	年份	月份	Total number of license issued	lowest price	avg price	Total number of applicants	LOW_Difference	AVG_Difference
21	2003	Oct	4500	32800	34842	9383	4000.0	-3886.0
22	2003	Nov	5042	33100	34284	9849	300.0	-558.0
29	2004	Jun	6233	17800	21001	19233	7000.0	-13225.0
36	2005	Jan	5500	28500	32520	6208	-800.0	2238.0
37	2005	Feb	3800	31700	32425	8949	3200.0	-95.0
44	2005	Sep	6700	26500	28927	10972	1500.0	-6978.0
52	2006	May	4500	37700	38139	8301	200.0	-187.0
56	2006	Sep	6500	37000	41601	7064	-2900.0	1142.0
60	2007	Jan	6000	38500	40974	6587	-1300.0	456.0
61	2007	Feb	3500	39100	40473	5056	600.0	-501.0
71	2007	Dec	7500	50000	56042	10356	-3800.0	1725.0
128	2012	Oct	9500	65200	66708	19921	-500.0	283.0

(6) 将某一个月牌照发行量与其前两个月发行量均值的差额定义为发行增益，最初的两个月用 0 填充，求发行增益极值出现的时间。

包含极大值极小值
(考察点：统计量，索引，排序，差分)

MVL.sort_index()
MVL['m1']=MVL['Total number of license issued'].diff()
MVL['m2']=MVL['Total number of license issued'].diff(2)
MVL['re']=(MVL['m1']+MVL['m2'])/2
MVL
MVL['re'].idxmax()
MVL.loc[72]
MVL[MVL['re']==MVL['re'].min()]

	年份	月份	Total number of license issued	lowest price	avg price	Total number of applicants	LOW_Difference	AVG_Difference	m1	m2	re
0	2002	Jan	1400	13600	14735	3718	NaN	NaN	NaN	NaN	NaN
1	2002	Feb	1800	13100	14057	4590	-500.0	-678.0	400.0	NaN	NaN
2	2002	Mar	2000	14300	14662	5190	1200.0	605.0	200.0	600.0	400.0
3	2002	Apr	2300	16000	16334	4806	1700.0	1672.0	300.0	500.0	400.0
4	2002	May	2350	17800	18357	4665	1800.0	2023.0	50.0	350.0	200.0
...	...	...	...	...	...	...	...	...	...	...	...
198	2018	Aug	10402	88300	88365	192755	0.0	-15.0	7.0	-373.0	-183.0
199	2018	Sep	12712	87300	87410	189142	-1000.0	-955.0	2310.0	2317.0	2313.5
200	2018	Oct	10728	88000	88070	181861	700.0	660.0	-1984.0	326.0	-829.0
201	2018	Nov	11766	87300	87374	177355	-700.0	-696.0	1038.0	-946.0	46.0
202	2018	Dec	12850	87400	87508	165442	100.0	134.0	1084.0	2122.0	1603.0

203 rows × 11 columns

	年份	月份	Total number of license issued	lowest price	avg price	Total number of applicants	LOW_Difference	AVG_Difference	m1	m2	re
0	2002	Jan	1400	13600	14735	3718	NaN	NaN	NaN	NaN	NaN
1	2002	Feb	1800	13100	14057	4590	-500.0	-678.0	400.0	NaN	NaN
2	2002	Mar	2000	14300	14662	5190	1200.0	605.0	200.0	600.0	400.0
3	2002	Apr	2300	16000	16334	4806	1700.0	1672.0	300.0	500.0	400.0
4	2002	May	2350	17800	18357	4665	1800.0	2023.0	50.0	350.0	200.0
...	...	...	...	...	...	...	...	...	...	...	...
198	2018	Aug	10402	88300	88365	192755	0.0	-15.0	7.0	-373.0	-183.0
199	2018	Sep	12712	87300	87410	189142	-1000.0	-955.0	2310.0	2317.0	2313.5
200	2018	Oct	10728	88000	88070	181861	700.0	660.0	-1984.0	326.0	-829.0
201	2018	Nov	11766	87300	87374	177355	-700.0	-696.0	1038.0	-946.0	46.0
202	2018	Dec	12850	87400	87508	165442	100.0	134.0	1084.0	2122.0	1603.0

203 rows × 11 columns

72






年份                                 2008
月份                                  Jan
Total number of license issued    16000
lowest price                       8100
avg price                         23370
Total number of applicants        20539
LOW_Difference                   -41900
AVG_Difference                   -32672
m1                                 8500
m2                                 8500
re                                 8500
Name: 72, dtype: object

	年份	月份	Total number of license issued	lowest price	avg price	Total number of applicants	LOW_Difference	AVG_Difference	m1	m2	re
74	2008	Apr	9000	37300	37659	37072	6000.0	5490.0	-300.0	-7000.0	-3650.0

二、2007 年-2019 年俄罗斯机场货运航班运载量

(1) 求每年货运航班总运量。

(考察点：统计量，分组)

(2) 每年记录的机场都是相同的吗？

(考察点：分组，查看类别值)

(3) 按年计算 2010 年-2015 年全年货运量记录为 0 的机场航班比例。

(考察点：分组，统计量，筛选)

(4) 若某机场至少存在 5 年或以上满足所有月运量记录都为 0，则将其所有年份的记录信息从表中删除，并返回处理后的表格

(考察点：数据删除，分组，筛选，索引)

(5) 采用一种合理的方式将所有机场划分为东南西北四个分区，并给出 2017年-2019 年货运总量最大的区域。

提示：最后一列 →_→
(考察点：分组)

(6) 在统计学中常常用秩代表排名，现在规定某个机场某年某个月的秩为该机场该月在当年所有月份中货运量的排名（例如 *** 机场 19 年 1 月运量在整个 19 年 12 个月中排名第一，则秩为 1），那么判断某月运量情况的相对大小的秩方法为将所有机场在该月的秩排名相加，并将这个量定义为每一个月的秩综合指数，请根据上述定义计算 2016 年 12 个月的秩综合指数。

(考察点：分组，合并，排序)

# RAS = Russian airport shipping
RAS = pd.read_csv('./2007年-2019年俄罗斯货运航班运载量.csv')
RAS.head()
RAS.info()

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates
0	Abakan	2019	44.70	66.21	72.7	75.82	100.34	78.38	63.88	73.06	66.74	75.44	110.5	89.8	917.57	(Decimal('91.399735'), Decimal('53.751351'))
1	Aikhal	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('111.543324'), Decimal('65.957161'))
2	Loss	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('125.398355'), Decimal('58.602489'))
3	Amderma	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('61.577429'), Decimal('69.759076'))
4	Anadyr (Carbon)	2019	81.63	143.01	260.9	304.36	122.00	106.87	84.99	130.00	102.00	118.00	94.0	199.0	1746.76	(Decimal('177.738273'), Decimal('64.713433'))

RangeIndex: 3711 entries, 0 to 3710
Data columns (total 16 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Airport name         3711 non-null   object 
 1   Year                 3711 non-null   int64  
 2   January              3711 non-null   float64
 3   February             3711 non-null   float64
 4   March                3711 non-null   float64
 5   April                3711 non-null   float64
 6   May                  3711 non-null   float64
 7   June                 3711 non-null   float64
 8   July                 3711 non-null   float64
 9   August               3711 non-null   float64
 10  September            3711 non-null   float64
 11  October              3711 non-null   float64
 12  November             3711 non-null   float64
 13  December             3711 non-null   float64
 14  Whole year           3711 non-null   float64
 15  Airport coordinates  3711 non-null   object 
dtypes: float64(13), int64(1), object(2)
memory usage: 464.0+ KB

(1) 求每年货运航班总运量。

(考察点：统计量，分组)

RAS.groupby('Year')['Whole year'].sum()

Year
2007    659438.23
2008    664682.46
2009    560809.77
2010    693033.98
2011    818691.71
2012    846388.03
2013    792337.08
2014    729457.12
2015    630208.97
2016    679370.15
2017    773662.28
2018    767095.28
2019    764606.27
Name: Whole year, dtype: float64

(2) 每年记录的机场都是相同的吗？

(考察点：分组，查看类别值)

temp=pd.DataFrame()
for name ,group in RAS.groupby('Year'):
    print(name,temp.equals(pd.DataFrame(group['Airport name'].value_counts())))
    temp=pd.DataFrame(group['Airport name'].value_counts())
#     display(pd.DataFrame(group['Airport name'].value_counts()))
#     print(name )
#     display(group['Airport name'].value_counts())
temp

2007 False
2008 True
2009 True
2010 True
2011 True
2012 True
2013 True
2014 True
2015 True
2016 True
2017 True
2018 False
2019 False

	Airport name
Usinsk	2
Nyagan	2
Vorkuta	2
Ust-Tsilma	2
Nerungri (Chulman)	1
...	...
Keperveem	1
Blagoveshchensk (Ignatevo)	1
Nogliki	1
Sovetskaya Gavan	1
Мотыгино	1

247 rows × 1 columns

(3) 按年计算 2010 年-2015 年全年货运量记录为 0 的机场航班比例。

(考察点：分组，统计量，筛选)

RAS_10_15=RAS[RAS['Year'].isin([2010,2011,2012,2013,2014,2015])]
RAS_10_15.head()
for name ,group in RAS_10_15.groupby('Year'):
    print(name,group[group['Whole year']==0].count()/group.count())

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates
1083	Abakan	2015	37.70	47.97	54.67	82.12	68.81	112.95	55.83	95.20	137.79	72.13	63.67	78.30	907.14	(Decimal('91.399735'), Decimal('53.751351'))
1084	Aikhal	2015	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	(Decimal('111.543324'), Decimal('65.957161'))
1085	Loss	2015	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	(Decimal('125.398355'), Decimal('58.602489'))
1086	Amderma	2015	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	(Decimal('61.577429'), Decimal('69.759076'))
1087	Anadyr	2015	124.31	254.19	340.37	286.06	156.55	124.95	89.05	72.29	118.16	92.89	158.39	316.94	2134.15	(Decimal('177.738273'), Decimal('64.713433'))

2010 Airport name           0.767123
Year                   0.767123
January                0.767123
February               0.767123
March                  0.767123
April                  0.767123
May                    0.767123
June                   0.767123
July                   0.767123
August                 0.767123
September              0.767123
October                0.767123
November               0.767123
December               0.767123
Whole year             0.767123
Airport coordinates    0.767123
dtype: float64
2011 Airport name           0.770548
Year                   0.770548
January                0.770548
February               0.770548
March                  0.770548
April                  0.770548
May                    0.770548
June                   0.770548
July                   0.770548
August                 0.770548
September              0.770548
October                0.770548
November               0.770548
December               0.770548
Whole year             0.770548
Airport coordinates    0.770548
dtype: float64
2012 Airport name           0.770548
Year                   0.770548
January                0.770548
February               0.770548
March                  0.770548
April                  0.770548
May                    0.770548
June                   0.770548
July                   0.770548
August                 0.770548
September              0.770548
October                0.770548
November               0.770548
December               0.770548
Whole year             0.770548
Airport coordinates    0.770548
dtype: float64
2013 Airport name           0.770548
Year                   0.770548
January                0.770548
February               0.770548
March                  0.770548
April                  0.770548
May                    0.770548
June                   0.770548
July                   0.770548
August                 0.770548
September              0.770548
October                0.770548
November               0.770548
December               0.770548
Whole year             0.770548
Airport coordinates    0.770548
dtype: float64
2014 Airport name           0.770548
Year                   0.770548
January                0.770548
February               0.770548
March                  0.770548
April                  0.770548
May                    0.770548
June                   0.770548
July                   0.770548
August                 0.770548
September              0.770548
October                0.770548
November               0.770548
December               0.770548
Whole year             0.770548
Airport coordinates    0.770548
dtype: float64
2015 Airport name           0.770548
Year                   0.770548
January                0.770548
February               0.770548
March                  0.770548
April                  0.770548
May                    0.770548
June                   0.770548
July                   0.770548
August                 0.770548
September              0.770548
October                0.770548
November               0.770548
December               0.770548
Whole year             0.770548
Airport coordinates    0.770548
dtype: float64

(4) 若某机场至少存在 5 年或以上满足所有月运量记录都为 0，则将其所有年份的记录信息从表中删除，并返回处理后的表格

(考察点：数据删除，分组，筛选，索引)

RAS_0=RAS[RAS['Whole year']==0]
RAS_0.head()
RAS_0=RAS_0[['Airport name','Whole year','Year']]
temp=RAS_0.groupby('Airport name')['Year'].count()>=5
type(temp)
print('temp',temp)
RAS[~RAS['Airport name'].isin(temp.index)]#== False 
print(RAS_0['Airport name'].isin(temp.index) )
# RAS_0.loc[temp.values]['Airport name']
# RAS_0.groupby('Airport name').head(1)#.count()

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates
1	Aikhal	2019	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	(Decimal('111.543324'), Decimal('65.957161'))
2	Loss	2019	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	(Decimal('125.398355'), Decimal('58.602489'))
3	Amderma	2019	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	(Decimal('61.577429'), Decimal('69.759076'))
6	Apatite (Khibiny)	2019	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	(Decimal('33.581999'), Decimal('67.459641'))
7	Arkhangelsk (Vaskovo)	2019	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	(Decimal('40.706789'), Decimal('64.592645'))

pandas.core.series.Series



temp Airport name
Achinsk              True
Aikhal               True
Amderma              True
Antypayuta           True
Apatite (Khibiny)    True
                     ... 
Лешуконское          True
Мотыгино             True
Нюрба                True
Среднеколымск        True
Таксимо              True
Name: Year, Length: 230, dtype: bool

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates
0	Abakan	2019	44.70	66.21	72.70	75.82	100.34	78.38	63.88	73.06	66.74	75.44	110.50	89.80	917.57	(Decimal('91.399735'), Decimal('53.751351'))
4	Anadyr (Carbon)	2019	81.63	143.01	260.90	304.36	122.00	106.87	84.99	130.00	102.00	118.00	94.00	199.00	1746.76	(Decimal('177.738273'), Decimal('64.713433'))
5	Anapa (Vitjazevo)	2019	45.92	53.15	54.00	54.72	52.00	67.45	172.31	72.57	70.00	63.00	69.00	82.10	856.22	(Decimal('37.341511'), Decimal('45.003748'))
8	Arkhangelsk (Talagy)	2019	85.61	118.70	131.39	144.82	137.95	140.18	128.56	135.68	124.75	139.60	210.27	307.10	1804.61	(Decimal('40.714892'), Decimal('64.596138'))
9	Astrakhan (Narimanovo)	2019	51.75	61.08	65.60	71.84	71.38	63.95	164.86	79.46	85.21	87.23	79.06	99.16	980.58	(Decimal('47.999896'), Decimal('46.287344'))
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3693	Reads (tub)	2007	55.96	80.09	85.90	154.54	162.71	107.51	80.14	138.71	133.19	188.97	228.84	184.00	1600.56	(Decimal('113.306492'), Decimal('52.020464'))
3705	Yuzhno-(Khomutovo)	2007	710.80	970.00	1330.30	1352.30	1324.40	1613.00	1450.70	1815.60	1902.30	1903.20	1666.10	1632.10	17670.80	(Decimal('142.723677'), Decimal('46.886967'))
3706	Yakutsk	2007	583.70	707.80	851.80	1018.00	950.80	900.00	1154.90	1137.84	1485.50	1382.50	1488.00	1916.60	13577.44	(Decimal('129.750225'), Decimal('62.086594'))
3708	Yamburg	2007	3.55	0.16	3.37	5.32	4.31	6.30	6.88	3.60	4.13	4.93	4.17	8.87	55.59	(Decimal('75.097783'), Decimal('67.980026'))
3709	Yaroslavl (Tunoshna)	2007	847.00	1482.90	1325.40	1235.97	629.00	838.00	1211.30	915.00	1249.60	1650.50	1822.60	2055.60	15262.87	(Decimal('40.170054'), Decimal('57.56231'))

795 rows × 16 columns

1       True
2       True
3       True
6       True
7       True
        ... 
3702    True
3703    True
3704    True
3707    True
3710    True
Name: Airport name, Length: 2807, dtype: bool

(5) 采用一种合理的方式将所有机场划分为东南西北四个分区，并给出 2017年-2019 年货运总量最大的区域。

提示：最后一列 →_→
(考察点：分组)

RAS['x']=RAS['Airport coordinates'].apply(lambda x:str(x).split(',')[0][10:-2] if str(x)!='Not found'else 'Not found')
RAS['y']=RAS['Airport coordinates'].apply(lambda x:str(x).split(',')[1][10:-3] if str(x)!='Not found'else 'Not found')
RAS=RAS[(RAS['x']!='Not found')&(RAS['x']!='Abakan')]
RAS.head()

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates	x	y
0	Abakan	2019	44.70	66.21	72.7	75.82	100.34	78.38	63.88	73.06	66.74	75.44	110.5	89.8	917.57	(Decimal('91.399735'), Decimal('53.751351'))	91.399735	53.751351
1	Aikhal	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('111.543324'), Decimal('65.957161'))	111.543324	65.957161
2	Loss	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('125.398355'), Decimal('58.602489'))	125.398355	58.602489
3	Amderma	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('61.577429'), Decimal('69.759076'))	61.577429	69.759076
4	Anadyr (Carbon)	2019	81.63	143.01	260.9	304.36	122.00	106.87	84.99	130.00	102.00	118.00	94.0	199.0	1746.76	(Decimal('177.738273'), Decimal('64.713433'))	177.738273	64.713433

RAS['x']=RAS['Airport coordinates'].apply(lambda x:str(x).split(',')[0][10:-2] if str(x)!='Not found'else 'Not found')
# RAS['x']=RAS['Airport coordinates'].apply(lambda x:print(str(x).split(',')) )
RAS['y']=RAS['Airport coordinates'].apply(lambda x:str(x).split(',')[1][10:-3] if str(x)!='Not found'else 'Not found')
RAS=RAS[(RAS['x']!='Not found')&(RAS['x']!='Abakan')]
RAS=RAS[RAS['y']!='Not found']#&(RAS['y']!='Abakan')
# RAS['X']=pd.cut(RAS['x'],bins=[RAS['x'].min(),RAS['x'].mean(),RAS['x'].max()])
# RAS['Y']=pd.cut(RAS['y'],bins=[RAS['y'].min(),RAS['y'].mean(),RAS['y'].max()])
# RAS['region']=RAS['X']+RAS['Y']
RAS.head()
# RAS[RAS['x']=='Abakan']
# RAS['x'].astype(np.int64).mean()

	Airport name	Year	January	February	March	April	May	June	July	August	September	October	November	December	Whole year	Airport coordinates	x	y
0	Abakan	2019	44.70	66.21	72.7	75.82	100.34	78.38	63.88	73.06	66.74	75.44	110.5	89.8	917.57	(Decimal('91.399735'), Decimal('53.751351'))	91.399735	53.751351
1	Aikhal	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('111.543324'), Decimal('65.957161'))	111.543324	65.957161
2	Loss	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('125.398355'), Decimal('58.602489'))	125.398355	58.602489
3	Amderma	2019	0.00	0.00	0.0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.0	0.0	0.00	(Decimal('61.577429'), Decimal('69.759076'))	61.577429	69.759076
4	Anadyr (Carbon)	2019	81.63	143.01	260.9	304.36	122.00	106.87	84.99	130.00	102.00	118.00	94.0	199.0	1746.76	(Decimal('177.738273'), Decimal('64.713433'))	177.738273	64.713433

(6) 在统计学中常常用秩代表排名，现在规定某个机场某年某个月的秩为该机场该月在当年所有月份中货运量的排名（例如 *** 机场 19 年 1 月运量在整个 19 年 12 个月中排名第一，则秩为 1），那么判断某月运量情况的相对大小的秩方法为将所有机场在该月的秩排名相加，并将这个量定义为每一个月的秩综合指数，请根据上述定义计算 2016 年 12 个月的秩综合指数。

(考察点：分组，合并，排序)

RAS_6=RAS[RAS.Year==2016]
RAS_6=RAS_6.melt(id_vars=['Airport name'],value_vars=['January','February','March','April','May','June','July','August','September','October','November','December'],value_name='month')
RAS_6=RAS_6.join(RAS_6.groupby('Airport name').rank(method='min'),rsuffix='_rank').sort_values(by=['Airport name','month'])
RAS_6
pd.pivot_table(RAS_6,columns='variable',values='month_rank',aggfunc='sum')#'value_rank'

	Airport name	variable	month	month_rank
0	Abakan	January	34.10	1.0
292	Abakan	February	45.41	2.0
584	Abakan	March	58.97	3.0
1752	Abakan	July	64.31	4.0
876	Abakan	April	72.71	5.0
...	...	...	...	...
2265	Таксимо	August	0.00	1.0
2557	Таксимо	September	0.00	1.0
2849	Таксимо	October	0.00	1.0
3141	Таксимо	November	0.00	1.0
3433	Таксимо	December	0.00	1.0

3504 rows × 4 columns

variable	April	August	December	February	January	July	June	March	May	November	October	September
month_rank	701.0	703.0	905.0	507.0	402.0	603.0	633.0	628.0	631.0	824.0	771.0	736.0

三、在美国的传播

(1) 用 corr() 函数计算县（每行都是一个县）人口与表中最后一天记录日期死亡数的相关系数。

(考察点：corr函数)

(2) 截止到 4 月 1 日，统计每个州零感染县的比例。

(考察点：分组，筛选，创建列)

(3) 请找出最早出确证病例的三个县。

(考察点：筛选，转换，索引)

(4) 按州统计单日死亡增加数，并给出哪个州在哪一天确诊数增加最大（这里指的是在所有州和所有天两个指标一起算，不是分别算）。

(考察点：分组，索引，差分，转换，筛选)

(5) 现需对每个州编制确证与死亡表，第一列为时间，并且起始时间为该州开始出现死亡比例的那一天，第二列和第三列分别为确证数和死亡数, 每个州需要保存为一个单独的 csv 文件，文件名为“州名.csv”。

(考察点：分组，索引，转换，循环，文件写入输出)

(6) 现需对 4 月 1 日至 4 月 10 日编制新增确证数与新增死亡数表，第一列为州名，第二列和第三列分别为新增确证数和新增死亡数，分别保存为十个单独的 csv 文件，文件名为“日期.csv”。

(考察点：分组，索引，转换，循环，文件写入输出)

# USCOV = COVID-19 in US
USCOV_diagnose = pd.read_csv('./美国确证数.csv')
USCOV_diagnose.head()
USCOV_diagnose.info()

USCOV_death = pd.read_csv('./美国死亡数.csv')
USCOV_death.head()
USCOV_death.info()

	UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key	2020/1/22	2020/1/23	2020/1/24	2020/1/25	2020/1/26	2020/1/27	2020/1/28	2020/1/29	2020/1/30	2020/1/31	2020/2/1	2020/2/2	2020/2/3	2020/2/4	2020/2/5	2020/2/6	2020/2/7	2020/2/8	2020/2/9	2020/2/10	2020/2/11	2020/2/12	2020/2/13	2020/2/14	2020/2/15	2020/2/16	2020/2/17	2020/2/18	2020/2/19	2020/2/20	2020/2/21	2020/2/22	2020/2/23	2020/2/24	2020/2/25	2020/2/26	2020/2/27	2020/2/28	2020/2/29	2020/3/1	2020/3/2	2020/3/3	2020/3/4	2020/3/5	2020/3/6	2020/3/7	2020/3/8	2020/3/9	2020/3/10	2020/3/11	2020/3/12	2020/3/13	2020/3/14	2020/3/15	2020/3/16	2020/3/17	2020/3/18	2020/3/19	2020/3/20	2020/3/21	2020/3/22	2020/3/23	2020/3/24	2020/3/25	2020/3/26	2020/3/27	2020/3/28	2020/3/29	2020/3/30	2020/3/31	2020/4/1	2020/4/2	2020/4/3	2020/4/4	2020/4/5	2020/4/6	2020/4/7	2020/4/8	2020/4/9	2020/4/10	2020/4/11	2020/4/12	2020/4/13	2020/4/14	2020/4/15	2020/4/16	2020/4/17	2020/4/18	2020/4/19	2020/4/20	2020/4/21	2020/4/22	2020/4/23	2020/4/24	2020/4/25	2020/4/26
0	84001001	US	USA	840	1001	Autauga	Alabama	US	32.539527	-86.644082	Autauga, Alabama, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	4	6	6	6	6	6	7	8	10	12	12	12	12	12	12	15	17	19	19	19	23	24	26	26	25	26	28	30	32	33	36	36	37
1	84001003	US	USA	840	1003	Baldwin	Alabama	US	30.727750	-87.722071	Baldwin, Alabama, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	2	2	2	3	4	4	5	5	10	15	18	19	20	24	28	29	29	38	42	44	56	59	66	71	72	87	91	101	103	109	112	117	123	132	143	147	147	161
2	84001005	US	USA	840	1005	Barbour	Alabama	US	31.868263	-85.387129	Barbour, Alabama, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	2	2	3	3	4	9	9	10	10	11	12	14	15	18	20	22	28	29	30	32	32	33
3	84001007	US	USA	840	1007	Bibb	Alabama	US	32.996421	-87.125115	Bibb, Alabama, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	3	3	4	4	4	5	7	8	9	9	11	13	16	17	17	18	22	24	26	28	32	32	34	33	34	34	38
4	84001009	US	USA	840	1009	Blount	Alabama	US	33.982109	-86.567906	Blount, Alabama, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	4	5	5	5	5	5	6	9	10	10	10	10	10	11	12	12	13	14	16	17	18	20	20	21	22	26	29	31	31	31	34

RangeIndex: 3142 entries, 0 to 3141
Columns: 107 entries, UID to 2020/4/26
dtypes: float64(2), int64(99), object(6)
memory usage: 2.6+ MB

	UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key	Population	2020/1/22	2020/1/23	2020/1/24	2020/1/25	2020/1/26	2020/1/27	2020/1/28	2020/1/29	2020/1/30	2020/1/31	2020/2/1	2020/2/2	2020/2/3	2020/2/4	2020/2/5	2020/2/6	2020/2/7	2020/2/8	2020/2/9	2020/2/10	2020/2/11	2020/2/12	2020/2/13	2020/2/14	2020/2/15	2020/2/16	2020/2/17	2020/2/18	2020/2/19	2020/2/20	2020/2/21	2020/2/22	2020/2/23	2020/2/24	2020/2/25	2020/2/26	2020/2/27	2020/2/28	2020/2/29	2020/3/1	2020/3/2	2020/3/3	2020/3/4	2020/3/5	2020/3/6	2020/3/7	2020/3/8	2020/3/9	2020/3/10	2020/3/11	2020/3/12	2020/3/13	2020/3/14	2020/3/15	2020/3/16	2020/3/17	2020/3/18	2020/3/19	2020/3/20	2020/3/21	2020/3/22	2020/3/23	2020/3/24	2020/3/25	2020/3/26	2020/3/27	2020/3/28	2020/3/29	2020/3/30	2020/3/31	2020/4/1	2020/4/2	2020/4/3	2020/4/4	2020/4/5	2020/4/6	2020/4/7	2020/4/8	2020/4/9	2020/4/10	2020/4/11	2020/4/12	2020/4/13	2020/4/14	2020/4/15	2020/4/16	2020/4/17	2020/4/18	2020/4/19	2020/4/20	2020/4/21	2020/4/22	2020/4/23	2020/4/24	2020/4/25	2020/4/26
0	84001001	US	USA	840	1001	Autauga	Alabama	US	32.539527	-86.644082	Autauga, Alabama, US	55869	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	2	2	2	1	1	2	2	2	2	2
1	84001003	US	USA	840	1003	Baldwin	Alabama	US	30.727750	-87.722071	Baldwin, Alabama, US	223234	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	2	3	3	3	3	3	3	3
2	84001005	US	USA	840	1005	Barbour	Alabama	US	31.868263	-85.387129	Barbour, Alabama, US	24686	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	84001007	US	USA	840	1007	Bibb	Alabama	US	32.996421	-87.125115	Bibb, Alabama, US	22394	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	84001009	US	USA	840	1009	Blount	Alabama	US	33.982109	-86.567906	Blount, Alabama, US	57826	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

RangeIndex: 3142 entries, 0 to 3141
Columns: 108 entries, UID to 2020/4/26
dtypes: float64(2), int64(100), object(6)
memory usage: 2.6+ MB

(1) 用 corr() 函数计算县（每行都是一个县）人口与表中最后一天记录日期死亡数的相关系数。

(考察点：corr函数)

• data.corr() #相关系数矩阵，即给出了任意两个变量之间的相关系数
• data.corr()[u’A’] #只显示“A”与其他间的相关系数
• data[u’A’].corr(data[u’B’]) #A与B两者的相关系数

USCOV_death[u'Population'].corr(USCOV_death[u'2020/4/26'])

0.4038441973480701

(2) 截止到 4 月 1 日，统计每个州零感染县的比例。

(考察点：分组，筛选，创建列)

# for name ,group in USCOV_diagnose.groupby('Province_State'):
#     temp=[]
# #     display( group.loc[:,'2020/1/22':'2020/4/1'])
#     for i in group.loc[:,'2020/1/22':'2020/4/1']:
#         display(i)
# #     print(group[group['sum']==0].count()/group.count())
(USCOV_diagnose[USCOV_diagnose['2020/4/1']==0].groupby('Province_State')['UID'].count()/USCOV_diagnose.groupby('Province_State')['UID'].count()).fillna(0)

Province_State
Alabama                 0.119403
Alaska                  0.793103
Arizona                 0.000000
Arkansas                0.293333
California              0.137931
Colorado                0.218750
Connecticut             0.000000
Delaware                0.000000
District of Columbia    0.000000
Florida                 0.164179
Georgia                 0.125786
Hawaii                  0.200000
Idaho                   0.386364
Illinois                0.480392
Indiana                 0.108696
Iowa                    0.404040
Kansas                  0.609524
Kentucky                0.441667
Louisiana               0.062500
Maine                   0.250000
Maryland                0.041667
Massachusetts           0.142857
Michigan                0.192771
Minnesota               0.367816
Mississippi             0.060976
Missouri                0.391304
Montana                 0.625000
Nebraska                0.752688
Nevada                  0.470588
New Hampshire           0.100000
New Jersey              0.000000
New Mexico              0.424242
New York                0.080645
North Carolina          0.180000
North Dakota            0.547170
Ohio                    0.181818
Oklahoma                0.376623
Oregon                  0.277778
Pennsylvania            0.104478
Rhode Island            0.000000
South Carolina          0.065217
South Dakota            0.560606
Tennessee               0.115789
Texas                   0.452756
Utah                    0.482759
Vermont                 0.142857
Virginia                0.270677
Washington              0.128205
West Virginia           0.472727
Wisconsin               0.319444
Wyoming                 0.347826
Name: UID, dtype: float64

(3) 请找出最早出确证病例的三个县。

(考察点：筛选，转换，索引)

USCOV_diagnose.sort_values(by=['2020/1/22','2020/1/23','2020/1/24','2020/1/25','2020/1/26'],ascending=False)

	UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key	2020/1/22	2020/1/23	2020/1/24	2020/1/25	2020/1/26	2020/1/27	2020/1/28	2020/1/29	2020/1/30	2020/1/31	2020/2/1	2020/2/2	2020/2/3	2020/2/4	2020/2/5	2020/2/6	2020/2/7	2020/2/8	2020/2/9	2020/2/10	2020/2/11	2020/2/12	2020/2/13	2020/2/14	2020/2/15	2020/2/16	2020/2/17	2020/2/18	2020/2/19	2020/2/20	2020/2/21	2020/2/22	2020/2/23	2020/2/24	2020/2/25	2020/2/26	2020/2/27	2020/2/28	2020/2/29	2020/3/1	2020/3/2	2020/3/3	2020/3/4	2020/3/5	2020/3/6	2020/3/7	2020/3/8	2020/3/9	2020/3/10	2020/3/11	2020/3/12	2020/3/13	2020/3/14	2020/3/15	2020/3/16	2020/3/17	2020/3/18	2020/3/19	2020/3/20	2020/3/21	2020/3/22	2020/3/23	2020/3/24	2020/3/25	2020/3/26	2020/3/27	2020/3/28	2020/3/29	2020/3/30	2020/3/31	2020/4/1	2020/4/2	2020/4/3	2020/4/4	2020/4/5	2020/4/6	2020/4/7	2020/4/8	2020/4/9	2020/4/10	2020/4/11	2020/4/12	2020/4/13	2020/4/14	2020/4/15	2020/4/16	2020/4/17	2020/4/18	2020/4/19	2020/4/20	2020/4/21	2020/4/22	2020/4/23	2020/4/24	2020/4/25	2020/4/26
2969	84053033	US	USA	840	53033	King	Washington	US	47.491379	-121.834613	King, Washington, US	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	6	9	14	21	31	51	58	71	83	83	116	190	270	328	387	387	488	569	562	693	793	934	1040	1170	1170	1359	1577	1577	2077	2159	2161	2330	2330	2656	2787	2898	3167	3331	3486	3688	3886	4117	4262	4426	4426	4549	4620	4697	4902	4902	5174	5174	5293	5379	5532	5637	5739	5863
610	84017031	US	USA	840	17031	Cook	Illinois	US	41.841448	-87.816588	Cook, Illinois, US	0	0	1	1	1	1	1	1	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	3	4	4	4	5	5	6	7	7	11	22	27	40	50	50	62	107	178	278	278	548	805	922	1194	1418	1418	2239	2613	3445	3727	4496	5152	5575	6111	7439	8034	8728	9509	10520	11415	12472	13417	14585	15474	16323	17306	18087	19391	20395	21272	22101	23181	24546	25811	27616	29058	30574
103	84004013	US	USA	840	4013	Maricopa	Arizona	US	33.348359	-112.491815	Maricopa, Arizona, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	3	3	3	4	4	8	9	11	22	34	49	81	139	199	251	299	399	454	545	690	788	871	961	1049	1171	1326	1433	1495	1559	1689	1741	1891	1960	2020	2056	2146	2264	2404	2491	2589	2636	2738	2846	2970	3116	3234	3359
204	84006037	US	USA	840	6037	Los Angeles	California	US	34.308284	-118.228241	Los Angeles, California, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	7	11	13	14	14	14	20	27	32	40	53	53	94	144	190	231	292	292	407	536	662	812	1229	1465	1465	1829	2474	3019	3518	4045	4566	4605	5955	6377	6936	7559	7955	8443	8453	8894	9433	10047	10517	10854	11400	12021	12341	13823	15153	16447	17537	18545	19133	19567
215	84006059	US	USA	840	6059	Orange	California	US	33.701475	-117.764600	Orange, California, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	3	3	3	3	3	4	5	5	6	9	14	14	17	22	29	53	65	78	95	125	152	187	256	321	403	431	464	502	606	656	711	786	834	882	931	1016	1079	1138	1221	1277	1283	1299	1376	1425	1501	1556	1636	1676	1691	1753	1827	1845	1969	2074
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3137	84056037	US	USA	840	56037	Sweetwater	Wyoming	US	41.659439	-108.882788	Sweetwater, Wyoming, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	2	2	3	3	4	5	5	5	6	6	6	7	7	9	9	10	10	10	10	10	10	16	16	16	16	16	16
3138	84056039	US	USA	840	56039	Teton	Wyoming	US	43.935225	-110.589080	Teton, Wyoming, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2	2	2	2	2	5	7	10	13	14	16	20	26	29	32	36	39	40	41	44	45	50	53	56	56	57	58	59	61	62	62	62	92	93	93	95	95	95
3139	84056041	US	USA	840	56041	Uinta	Wyoming	US	41.287818	-110.547578	Uinta, Wyoming, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	2	2	3	3	3	3	4	4	4	4	4	4	4	6	6	6	6	7	7	7	7	7	7
3140	84056043	US	USA	840	56043	Washakie	Wyoming	US	43.904516	-107.680187	Washakie, Wyoming, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	2	2	4	4	4	5	5	5	5	5	5	5	5	6	5	5	5	5	8	8	8	8	8	8
3141	84056045	US	USA	840	56045	Weston	Wyoming	US	43.839612	-104.567488	Weston, Wyoming, US	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

3142 rows × 107 columns

for col in USCOV_diagnose.loc[:,'2020/1/22':'2020/4/26'].columns: 
    if USCOV_diagnose[USCOV_diagnose[col] != 0][col].count() >= 3:
        USCOV_diagnose[USCOV_diagnose[col] != 0]
        break    

	UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key	2020/1/22	2020/1/23	2020/1/24	2020/1/25	2020/1/26	2020/1/27	2020/1/28	2020/1/29	2020/1/30	2020/1/31	2020/2/1	2020/2/2	2020/2/3	2020/2/4	2020/2/5	2020/2/6	2020/2/7	2020/2/8	2020/2/9	2020/2/10	2020/2/11	2020/2/12	2020/2/13	2020/2/14	2020/2/15	2020/2/16	2020/2/17	2020/2/18	2020/2/19	2020/2/20	2020/2/21	2020/2/22	2020/2/23	2020/2/24	2020/2/25	2020/2/26	2020/2/27	2020/2/28	2020/2/29	2020/3/1	2020/3/2	2020/3/3	2020/3/4	2020/3/5	2020/3/6	2020/3/7	2020/3/8	2020/3/9	2020/3/10	2020/3/11	2020/3/12	2020/3/13	2020/3/14	2020/3/15	2020/3/16	2020/3/17	2020/3/18	2020/3/19	2020/3/20	2020/3/21	2020/3/22	2020/3/23	2020/3/24	2020/3/25	2020/3/26	2020/3/27	2020/3/28	2020/3/29	2020/3/30	2020/3/31	2020/4/1	2020/4/2	2020/4/3	2020/4/4	2020/4/5	2020/4/6	2020/4/7	2020/4/8	2020/4/9	2020/4/10	2020/4/11	2020/4/12	2020/4/13	2020/4/14	2020/4/15	2020/4/16	2020/4/17	2020/4/18	2020/4/19	2020/4/20	2020/4/21	2020/4/22	2020/4/23	2020/4/24	2020/4/25	2020/4/26
103	84004013	US	USA	840	4013	Maricopa	Arizona	US	33.348359	-112.491815	Maricopa, Arizona, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	2	2	2	2	3	3	3	4	4	8	9	11	22	34	49	81	139	199	251	299	399	454	545	690	788	871	961	1049	1171	1326	1433	1495	1559	1689	1741	1891	1960	2020	2056	2146	2264	2404	2491	2589	2636	2738	2846	2970	3116	3234	3359
204	84006037	US	USA	840	6037	Los Angeles	California	US	34.308284	-118.228241	Los Angeles, California, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	7	11	13	14	14	14	20	27	32	40	53	53	94	144	190	231	292	292	407	536	662	812	1229	1465	1465	1829	2474	3019	3518	4045	4566	4605	5955	6377	6936	7559	7955	8443	8453	8894	9433	10047	10517	10854	11400	12021	12341	13823	15153	16447	17537	18545	19133	19567
215	84006059	US	USA	840	6059	Orange	California	US	33.701475	-117.764600	Orange, California, US	0	0	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	3	3	3	3	3	4	5	5	6	9	14	14	17	22	29	53	65	78	95	125	152	187	256	321	403	431	464	502	606	656	711	786	834	882	931	1016	1079	1138	1221	1277	1283	1299	1376	1425	1501	1556	1636	1676	1691	1753	1827	1845	1969	2074
610	84017031	US	USA	840	17031	Cook	Illinois	US	41.841448	-87.816588	Cook, Illinois, US	0	0	1	1	1	1	1	1	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	3	4	4	4	5	5	6	7	7	11	22	27	40	50	50	62	107	178	278	278	548	805	922	1194	1418	1418	2239	2613	3445	3727	4496	5152	5575	6111	7439	8034	8728	9509	10520	11415	12472	13417	14585	15474	16323	17306	18087	19391	20395	21272	22101	23181	24546	25811	27616	29058	30574
2969	84053033	US	USA	840	53033	King	Washington	US	47.491379	-121.834613	King, Washington, US	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	6	9	14	21	31	51	58	71	83	83	116	190	270	328	387	387	488	569	562	693	793	934	1040	1170	1170	1359	1577	1577	2077	2159	2161	2330	2330	2656	2787	2898	3167	3331	3486	3688	3886	4117	4262	4426	4426	4549	4620	4697	4902	4902	5174	5174	5293	5379	5532	5637	5739	5863

(4) 按州统计单日死亡增加数，并给出哪个州在哪一天确诊数增加最大（这里指的是在所有州和所有天两个指标一起算，不是分别算）。

(考察点：分组，索引，差分，转换，筛选)

USCOV_death.groupby('Province_State')[:,'2020/1/22':'2020/4/1']