需求：

  • 导入文件，查看原始数据

  • 将人口数据和各州简称数据进行合并

  • 将合并的数据中重复的abbreviation列进行删除

  • 查看存在缺失数据的列

  • 找到有哪些state/region使得state的值为NaN，进行去重操作

  • 为找到的这些state/region的state项补上正确的值，从而去除掉state这一列的所有NaN

  • 合并各州面积数据areas

  • 我们会发现area(sq.mi)这一列有缺失数据，找出是哪些行

  • 去除含有缺失数据的行

  • 找出2010年的全民人口数据

  • 计算各州的人口密度

  • 排序，并找出人口密度最高的州

import numpy as np
import pandas as pd
from pandas import DataFrame

 

1、导入文件，查看原始数据

​
abb = pd.read_csv('../data/state-abbrevs.csv') #state(州的全称)abbreviation（州的简称）
abb

    state   abbreviation
0   Alabama AL
1   Alaska  AK
2   Arizona AZ
3   Arkansas    AR
4   California  CA
5   Colorado    CO
6   Connecticut CT
7   Delaware    DE
8   District of Columbia    DC
9   Florida FL
10  Georgia GA
11  Hawaii  HI
12  Idaho   ID
13  Illinois    IL
14  Indiana IN
15  Iowa    IA
16  Kansas  KS
17  Kentucky    KY
18  Louisiana   LA
19  Maine   ME
20  Montana MT
21  Nebraska    NE
22  Nevada  NV
23  New Hampshire   NH
24  New Jersey  NJ
25  New Mexico  NM
26  New York    NY
27  North Carolina  NC
28  North Dakota    ND
29  Ohio    OH
30  Oklahoma    OK
31  Oregon  OR
32  Maryland    MD
33  Massachusetts   MA
34  Michigan    MI
35  Minnesota   MN
36  Mississippi MS
37  Missouri    MO
38  Pennsylvania    PA
39  Rhode Island    RI
40  South Carolina  SC
41  South Dakota    SD
42  Tennessee   TN
43  Texas   TX
44  Utah    UT
45  Vermont VT
46  Virginia    VA
47  Washington  WA
48  West Virginia   WV
49  Wisconsin   WI
50  Wyoming WY

View Code

 

area = pd.read_csv('../data/state-areas.csv') #state州的全称，area (sq. mi)州的面积
area

​
state   area (sq. mi)
0   Alabama 52423
1   Alaska  656425
2   Arizona 114006
3   Arkansas    53182
4   California  163707
5   Colorado    104100
6   Connecticut 5544
7   Delaware    1954
8   Florida 65758
9   Georgia 59441
10  Hawaii  10932
11  Idaho   83574
12  Illinois    57918
13  Indiana 36420
14  Iowa    56276
15  Kansas  82282
16  Kentucky    40411
17  Louisiana   51843
18  Maine   35387
19  Maryland    12407
20  Massachusetts   10555
21  Michigan    96810
22  Minnesota   86943
23  Mississippi 48434
24  Missouri    69709
25  Montana 147046
26  Nebraska    77358
27  Nevada  110567
28  New Hampshire   9351
29  New Jersey  8722
30  New Mexico  121593
31  New York    54475
32  North Carolina  53821
33  North Dakota    70704
34  Ohio    44828
35  Oklahoma    69903
36  Oregon  98386
37  Pennsylvania    46058
38  Rhode Island    1545
39  South Carolina  32007
40  South Dakota    77121
41  Tennessee   42146
42  Texas   268601
43  Utah    84904
44  Vermont 9615
45  Virginia    42769
46  Washington  71303
47  West Virginia   24231
48  Wisconsin   65503
49  Wyoming 97818
50  District of Columbia    68
51  Puerto Rico 3515

View Code

 

pop = pd.read_csv('../data/state-population.csv')#state/region简称，ages年龄，year时间，population人口数量
pop

state/region ages year  population
0   AL  under18 2012    1117489.0
1   AL  total   2012    4817528.0
2   AL  under18 2010    1130966.0
3   AL  total   2010    4785570.0
4   AL  under18 2011    1125763.0
... ... ... ... ...
2539    USA total   2010    309326295.0
2540    USA under18 2011    73902222.0
2541    USA total   2011    311582564.0
2542    USA under18 2012    73708179.0
2543    USA total   2012    313873685.0
2544 rows × 4 columns

View Code

 

2、将人口数据和各州简称数据进行合并

​
abb_pop = pd.merge(abb,pop,left_on='abbreviation',right_on='state/region',how='outer')
abb_pop.head()

​
state   abbreviation    state/region    ages    year    population
0   Alabama AL  AL  under18 2012    1117489.0
1   Alabama AL  AL  total   2012    4817528.0
2   Alabama AL  AL  under18 2010    1130966.0
3   Alabama AL  AL  total   2010    4785570.0
4   Alabama AL  AL  under18 2011    1125763.0

View Code

 

#将合并的数据中重复的abbreviation列进行删除
abb_pop.drop(labels='abbreviation',axis=1,inplace=True)
abb_pop.head()

    state   state/region    ages    year    population
0   Alabama AL  under18 2012    1117489.0
1   Alabama AL  total   2012    4817528.0
2   Alabama AL  under18 2010    1130966.0
3   Alabama AL  total   2010    4785570.0
4   Alabama AL  under18 2011    1125763.0

View Code

 

3、查看存在缺失数据的列

#方式1：isnull，notll，any，all
abb_pop.isnull().any(axis=0) #state,population这两列中是存在空值

state            True
state/region    False
ages            False
year            False
population       True
dtype: bool

View Code 

#方式2：
abb_pop.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2544 entries, 0 to 2543
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   state         2448 non-null   object 
 1   state/region  2544 non-null   object 
 2   ages          2544 non-null   object 
 3   year          2544 non-null   int64  
 4   population    2524 non-null   float64
dtypes: float64(1), int64(1), object(3)
memory usage: 119.2+ KB

View Code

 

4、找到有哪些state/region使得state的值为NaN，进行去重操作

# (将state中的空值对应的简称找到，且对简称进行去重)
abb_pop.head()

    state   state/region    ages    year    population
0   Alabama AL  under18 2012    1117489.0
1   Alabama AL  total   2012    4817528.0
2   Alabama AL  under18 2010    1130966.0
3   Alabama AL  total   2010    4785570.0
4   Alabama AL  under18 2011    1125763.0

View Code

 

思路：可以将state这一列中的空值对应的行数据取出，从该行数据中就可以取出简称的值

#1.将state中的空值定位到
abb_pop['state'].isnull()

0       False
1       False
2       False
3       False
4       False
        ...  
2539     True
2540     True
2541     True
2542     True
2543     True
Name: state, Length: 2544, dtype: bool

View Code

 

#2.将上述的布尔值作为源数据的行索引
abb_pop.loc[abb_pop['state'].isnull()]#将state中空对应的行数据取出

​
      state state/region    ages    year    population
2448    NaN PR  under18 1990    NaN
2449    NaN PR  total   1990    NaN
2450    NaN PR  total   1991    NaN
2451    NaN PR  under18 1991    NaN
2452    NaN PR  total   1993    NaN
... ... ... ... ... ...
2539    NaN USA total   2010    309326295.0
2540    NaN USA under18 2011    73902222.0
2541    NaN USA total   2011    311582564.0
2542    NaN USA under18 2012    73708179.0
2543    NaN USA total   2012    313873685.0
96 rows × 5 columns

View Code

 

#3.将简称取出
abb_pop.loc[abb_pop['state'].isnull()]['state/region']

2448     PR
2449     PR
2450     PR
2451     PR
2452     PR
       ... 
2539    USA
2540    USA
2541    USA
2542    USA
2543    USA
Name: state/region, Length: 96, dtype: object

View Code

 

#4.对简称去重
abb_pop.loc[abb_pop['state'].isnull()]['state/region'].unique()
​
#结论：只有PR和USA对应的全称数据为空值

array(['PR', 'USA'], dtype=object)

View Code

 

5、找到的这些state/region的state项补上正确的值，从而去除掉state这一列的所有NaN

#思考：填充该需求中的空值可不可以使用fillna？

  • 不可以。fillna可以使用空的紧邻值做填充。fillna(value=’xxx’)使用指定的值填充空值

  • 使用给元素赋值的方式进行填充！

#思路

1.先给USA的全称对应的空值进行批量赋值

1.1将USA对应的行数据找出（行数据中就存在state的空值）
​
abb_pop['state/region'] == 'USA'
abb_pop.loc[abb_pop['state/region'] == 'USA']#将usa对应的行数据取出
​

      state state/region    ages    year    population
2496    NaN USA under18 1990    64218512.0
2497    NaN USA total   1990    249622814.0
2498    NaN USA total   1991    252980942.0
2499    NaN USA under18 1991    65313018.0
2500    NaN USA under18 1992    66509177.0
2501    NaN USA total   1992    256514231.0
2502    NaN USA total   1993    259918595.0
2503    NaN USA under18 1993    67594938.0
2504    NaN USA under18 1994    68640936.0
2505    NaN USA total   1994    263125826.0
2506    NaN USA under18 1995    69473140.0
2507    NaN USA under18 1996    70233512.0
2508    NaN USA total   1995    266278403.0
2509    NaN USA total   1996    269394291.0
2510    NaN USA total   1997    272646932.0
2511    NaN USA under18 1997    70920738.0
2512    NaN USA under18 1998    71431406.0
2513    NaN USA total   1998    275854116.0
2514    NaN USA under18 1999    71946051.0
2515    NaN USA total   2000    282162411.0
2516    NaN USA under18 2000    72376189.0
2517    NaN USA total   1999    279040181.0
2518    NaN USA total   2001    284968955.0
2519    NaN USA under18 2001    72671175.0
2520    NaN USA total   2002    287625193.0
2521    NaN USA under18 2002    72936457.0
2522    NaN USA total   2003    290107933.0
2523    NaN USA under18 2003    73100758.0
2524    NaN USA total   2004    292805298.0
2525    NaN USA under18 2004    73297735.0
2526    NaN USA total   2005    295516599.0
2527    NaN USA under18 2005    73523669.0
2528    NaN USA total   2006    298379912.0
2529    NaN USA under18 2006    73757714.0
2530    NaN USA total   2007    301231207.0
2531    NaN USA under18 2007    74019405.0
2532    NaN USA total   2008    304093966.0
2533    NaN USA under18 2008    74104602.0
2534    NaN USA under18 2013    73585872.0
2535    NaN USA total   2013    316128839.0
2536    NaN USA total   2009    306771529.0
2537    NaN USA under18 2009    74134167.0
2538    NaN USA under18 2010    74119556.0
2539    NaN USA total   2010    309326295.0
2540    NaN USA under18 2011    73902222.0
2541    NaN USA total   2011    311582564.0
2542    NaN USA under18 2012    73708179.0
2543    NaN USA total   2012    313873685.0

View Code

 

1.2将USA对应的全称空对应的行索引取出
indexs = abb_pop.loc[abb_pop['state/region'] == 'USA'].index

Int64Index([2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506,
            2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517,
            2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528,
            2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539,
            2540, 2541, 2542, 2543],
           dtype='int64')

View Code

 

abb_pop.iloc[indexs] # 找出索引对应的数据
abb_pop.loc[indexs,'state'] = 'United States' # 赋值为United States

      state   state/region  ages    year    population
2496    United States   USA under18 1990    64218512.0
2497    United States   USA total   1990    249622814.0
2498    United States   USA total   1991    252980942.0
2499    United States   USA under18 1991    65313018.0
2500    United States   USA under18 1992    66509177.0
2501    United States   USA total   1992    256514231.0
2502    United States   USA total   1993    259918595.0
2503    United States   USA under18 1993    67594938.0
2504    United States   USA under18 1994    68640936.0
2505    United States   USA total   1994    263125826.0
2506    United States   USA under18 1995    69473140.0
2507    United States   USA under18 1996    70233512.0
2508    United States   USA total   1995    266278403.0
2509    United States   USA total   1996    269394291.0
2510    United States   USA total   1997    272646932.0
2511    United States   USA under18 1997    70920738.0
2512    United States   USA under18 1998    71431406.0
2513    United States   USA total   1998    275854116.0
2514    United States   USA under18 1999    71946051.0
2515    United States   USA total   2000    282162411.0
2516    United States   USA under18 2000    72376189.0
2517    United States   USA total   1999    279040181.0
2518    United States   USA total   2001    284968955.0
2519    United States   USA under18 2001    72671175.0
2520    United States   USA total   2002    287625193.0
2521    United States   USA under18 2002    72936457.0
2522    United States   USA total   2003    290107933.0
2523    United States   USA under18 2003    73100758.0
2524    United States   USA total   2004    292805298.0
2525    United States   USA under18 2004    73297735.0
2526    United States   USA total   2005    295516599.0
2527    United States   USA under18 2005    73523669.0
2528    United States   USA total   2006    298379912.0
2529    United States   USA under18 2006    73757714.0
2530    United States   USA total   2007    301231207.0
2531    United States   USA under18 2007    74019405.0
2532    United States   USA total   2008    304093966.0
2533    United States   USA under18 2008    74104602.0
2534    United States   USA under18 2013    73585872.0
2535    United States   USA total   2013    316128839.0
2536    United States   USA total   2009    306771529.0
2537    United States   USA under18 2009    74134167.0
2538    United States   USA under18 2010    74119556.0
2539    United States   USA total   2010    309326295.0
2540    United States   USA under18 2011    73902222.0
2541    United States   USA total   2011    311582564.0
2542    United States   USA under18 2012    73708179.0
2543    United States   USA total   2012    313873685.0

View Code

 

2.可以将PR的全称进行赋值（同上）

abb_pop['state/region'] == 'PR'
abb_pop.loc[abb_pop['state/region'] == 'PR'] #PR对应的行数据
indexs = abb_pop.loc[abb_pop['state/region'] == 'PR'].index#行索引
abb_pop.loc[indexs,'state'] = 'PPPRRR'

 

6、合并各州面积数据areas

abb_pop_area = pd.merge(abb_pop,area,how='outer')
​

   state state/region ages  year population area (sq. mi)
0   Alabama AL  under18 2012.0  1117489.0   52423.0
1   Alabama AL  total   2012.0  4817528.0   52423.0
2   Alabama AL  under18 2010.0  1130966.0   52423.0
3   Alabama AL  total   2010.0  4785570.0   52423.0
4   Alabama AL  under18 2011.0  1125763.0   52423.0
... ... ... ... ... ... ...
2540    United States   USA under18 2011.0  73902222.0  NaN
2541    United States   USA total   2011.0  311582564.0 NaN
2542    United States   USA under18 2012.0  73708179.0  NaN
2543    United States   USA total   2012.0  313873685.0 NaN
2544    Puerto Rico NaN NaN NaN NaN 3515.0

View Code

 

7、我们会发现area(sq.mi)这一列有缺失数据，找出是哪些行

abb_pop_area['area (sq. mi)'].isnull()
abb_pop_area.loc[abb_pop_area['area (sq. mi)'].isnull()] #空对应的行数据
indexs = abb_pop_area.loc[abb_pop_area['area (sq. mi)'].isnull()].index# 拿索引

 

8、去除含有缺失数据的行

abb_pop_area.drop(labels=indexs,axis=0,inplace=True)

 

9、找出2010年的全民人口数据(基于df做条件查询)

abb_pop_area.query('ages == "total" & year == 2010')

    state   state/region    ages    year    population  area (sq. mi)
3   Alabama AL  total   2010.0  4785570.0   52423.0
91  Alaska  AK  total   2010.0  713868.0    656425.0
101 Arizona AZ  total   2010.0  6408790.0   114006.0
189 Arkansas    AR  total   2010.0  2922280.0   53182.0
197 California  CA  total   2010.0  37333601.0  163707.0
283 Colorado    CO  total   2010.0  5048196.0   104100.0
293 Connecticut CT  total   2010.0  3579210.0   5544.0
379 Delaware    DE  total   2010.0  899711.0    1954.0
389 District of Columbia    DC  total   2010.0  605125.0    68.0
475 Florida FL  total   2010.0  18846054.0  65758.0
485 Georgia GA  total   2010.0  9713248.0   59441.0
570 Hawaii  HI  total   2010.0  1363731.0   10932.0
581 Idaho   ID  total   2010.0  1570718.0   83574.0
666 Illinois    IL  total   2010.0  12839695.0  57918.0
677 Indiana IN  total   2010.0  6489965.0   36420.0
762 Iowa    IA  total   2010.0  3050314.0   56276.0
773 Kansas  KS  total   2010.0  2858910.0   82282.0
858 Kentucky    KY  total   2010.0  4347698.0   40411.0
869 Louisiana   LA  total   2010.0  4545392.0   51843.0
954 Maine   ME  total   2010.0  1327366.0   35387.0
965 Montana MT  total   2010.0  990527.0    147046.0
1050    Nebraska    NE  total   2010.0  1829838.0   77358.0
1061    Nevada  NV  total   2010.0  2703230.0   110567.0
1146    New Hampshire   NH  total   2010.0  1316614.0   9351.0
1157    New Jersey  NJ  total   2010.0  8802707.0   8722.0
1242    New Mexico  NM  total   2010.0  2064982.0   121593.0
1253    New York    NY  total   2010.0  19398228.0  54475.0
1338    North Carolina  NC  total   2010.0  9559533.0   53821.0
1349    North Dakota    ND  total   2010.0  674344.0    70704.0
1434    Ohio    OH  total   2010.0  11545435.0  44828.0
1445    Oklahoma    OK  total   2010.0  3759263.0   69903.0
1530    Oregon  OR  total   2010.0  3837208.0   98386.0
1541    Maryland    MD  total   2010.0  5787193.0   12407.0
1626    Massachusetts   MA  total   2010.0  6563263.0   10555.0
1637    Michigan    MI  total   2010.0  9876149.0   96810.0
1722    Minnesota   MN  total   2010.0  5310337.0   86943.0
1733    Mississippi MS  total   2010.0  2970047.0   48434.0
1818    Missouri    MO  total   2010.0  5996063.0   69709.0
1829    Pennsylvania    PA  total   2010.0  12710472.0  46058.0
1914    Rhode Island    RI  total   2010.0  1052669.0   1545.0
1925    South Carolina  SC  total   2010.0  4636361.0   32007.0
2010    South Dakota    SD  total   2010.0  816211.0    77121.0
2021    Tennessee   TN  total   2010.0  6356683.0   42146.0
2106    Texas   TX  total   2010.0  25245178.0  268601.0
2117    Utah    UT  total   2010.0  2774424.0   84904.0
2202    Vermont VT  total   2010.0  625793.0    9615.0
2213    Virginia    VA  total   2010.0  8024417.0   42769.0
2298    Washington  WA  total   2010.0  6742256.0   71303.0
2309    West Virginia   WV  total   2010.0  1854146.0   24231.0
2394    Wisconsin   WI  total   2010.0  5689060.0   65503.0
2405    Wyoming WY  total   2010.0  564222.0    97818.0

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10、计算各州的人口密度(人口除以面积)，并把结果汇总到原始数据中

abb_pop_area['midu'] = abb_pop_area['population'] / abb_pop_area['area (sq. mi)']
abb_pop_area

    state   state/region    ages    year    population  area (sq. mi)   midu
0   Alabama AL  under18 2012.0  1117489.0   52423.0 21.316769
1   Alabama AL  total   2012.0  4817528.0   52423.0 91.897221
2   Alabama AL  under18 2010.0  1130966.0   52423.0 21.573851
3   Alabama AL  total   2010.0  4785570.0   52423.0 91.287603
4   Alabama AL  under18 2011.0  1125763.0   52423.0 21.474601
5   Alabama AL  total   2011.0  4801627.0   52423.0 91.593900
6   Alabama AL  total   2009.0  4757938.0   52423.0 90.760506
7   Alabama AL  under18 2009.0  1134192.0   52423.0 21.635389
8   Alabama AL  under18 2013.0  1111481.0   52423.0 21.202163
9   Alabama AL  total   2013.0  4833722.0   52423.0 92.206131
10  Alabama AL  total   2007.0  4672840.0   52423.0 89.137211
11  Alabama AL  under18 2007.0  1132296.0   52423.0 21.599222
12  Alabama AL  total   2008.0  4718206.0   52423.0 90.002594
13  Alabama AL  under18 2008.0  1134927.0   52423.0 21.649410
14  Alabama AL  total   2005.0  4569805.0   52423.0 87.171757
15  Alabama AL  under18 2005.0  1117229.0   52423.0 21.311810
16  Alabama AL  total   2006.0  4628981.0   52423.0 88.300574
17  Alabama AL  under18 2006.0  1126798.0   52423.0 21.494344
18  Alabama AL  total   2004.0  4530729.0   52423.0 86.426359
19  Alabama AL  under18 2004.0  1113662.0   52423.0 21.243767
20  Alabama AL  total   2003.0  4503491.0   52423.0 85.906778
21  Alabama AL  under18 2003.0  1113083.0   52423.0 21.232722
22  Alabama AL  total   2001.0  4467634.0   52423.0 85.222784
23  Alabama AL  under18 2001.0  1120409.0   52423.0 21.372470
24  Alabama AL  total   2002.0  4480089.0   52423.0 85.460370
25  Alabama AL  under18 2002.0  1116590.0   52423.0 21.299620
26  Alabama AL  under18 1999.0  1121287.0   52423.0 21.389218
27  Alabama AL  total   1999.0  4430141.0   52423.0 84.507583
28  Alabama AL  total   2000.0  4452173.0   52423.0 84.927856
29  Alabama AL  under18 2000.0  1122273.0   52423.0 21.408027
... ... ... ... ... ... ... ...
2419    Wyoming WY  under18 2003.0  124182.0    97818.0 1.269521
2420    Wyoming WY  total   2004.0  509106.0    97818.0 5.204625
2421    Wyoming WY  under18 2004.0  123974.0    97818.0 1.267395
2422    Wyoming WY  total   2002.0  500017.0    97818.0 5.111707
2423    Wyoming WY  under18 2002.0  125495.0    97818.0 1.282944
2424    Wyoming WY  total   2001.0  494657.0    97818.0 5.056912
2425    Wyoming WY  under18 2001.0  126212.0    97818.0 1.290274
2426    Wyoming WY  total   2000.0  494300.0    97818.0 5.053262
2427    Wyoming WY  under18 2000.0  128774.0    97818.0 1.316465
2428    Wyoming WY  total   1999.0  491780.0    97818.0 5.027500
2429    Wyoming WY  under18 1999.0  130793.0    97818.0 1.337106
2430    Wyoming WY  total   1997.0  489452.0    97818.0 5.003701
2431    Wyoming WY  under18 1997.0  134328.0    97818.0 1.373244
2432    Wyoming WY  under18 1998.0  132602.0    97818.0 1.355599
2433    Wyoming WY  total   1998.0  490787.0    97818.0 5.017349
2434    Wyoming WY  under18 1996.0  135698.0    97818.0 1.387250
2435    Wyoming WY  total   1996.0  488167.0    97818.0 4.990564
2436    Wyoming WY  total   1995.0  485160.0    97818.0 4.959823
2437    Wyoming WY  under18 1995.0  136785.0    97818.0 1.398362
2438    Wyoming WY  under18 1994.0  137733.0    97818.0 1.408054
2439    Wyoming WY  total   1994.0  480283.0    97818.0 4.909965
2440    Wyoming WY  under18 1992.0  137308.0    97818.0 1.403709
2441    Wyoming WY  total   1992.0  466251.0    97818.0 4.766515
2442    Wyoming WY  total   1993.0  473081.0    97818.0 4.836339
2443    Wyoming WY  under18 1993.0  137458.0    97818.0 1.405242
2444    Wyoming WY  total   1991.0  459260.0    97818.0 4.695046
2445    Wyoming WY  under18 1991.0  136720.0    97818.0 1.397698
2446    Wyoming WY  under18 1990.0  136078.0    97818.0 1.391135
2447    Wyoming WY  total   1990.0  453690.0    97818.0 4.638103
2544    Puerto Rico NaN NaN NaN NaN 3515.0  NaN
2449 rows × 7 columns

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11、排序，并找出人口密度最高的州

abb_pop_area.sort_values(by='midu',axis=0,ascending=False).iloc[0]['state']

'District of Columbia'

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原文地址：http://www.cnblogs.com/erhuoyuan/p/16928790.html

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