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)
(考察点:创建列,索引)
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)
(考察点:拆分列)
(考察点:多级索引)
(考察点:统计量,分组,合并,索引排序)
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 |
(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 |
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 |
(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 |
(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
(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 |
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 |
(考察点:统计量,分组)
(考察点:分组,查看类别值)
(考察点:分组,统计量,筛选)
(考察点:数据删除,分组,筛选,索引)
提示:最后一列 →_→
(考察点:分组)
(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
(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
(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
(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
(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
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 |
(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 |
(考察点:corr函数)
(考察点:分组,筛选,创建列)
(考察点:筛选,转换,索引)
(考察点:分组,索引,差分,转换,筛选)
(考察点:分组,索引,转换,循环,文件写入输出)
(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
(考察点:corr函数)
USCOV_death[u'Population'].corr(USCOV_death[u'2020/4/26'])
0.4038441973480701
(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
(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 |
(考察点:分组,索引,差分,转换,筛选)
USCOV_death.groupby('Province_State')[:,'2020/1/22':'2020/4/1']