import numpy as np
import pandas as pd
path=r"E:\北风\数据科学脚本\Python_book\5Preprocessing\teleco_camp_orig.csv"
o=open(path)
info=pd.read_csv(o)
print(info)
ID Suc_flag ARPU PromCnt12 PromCnt36 PromCntMsg12 \
0 12 1 50.0 6 10 2
1 53 0 NaN 5 9 1
2 67 1 25.0 6 11 2
3 71 1 80.0 7 10 2
4 142 1 15.0 6 11 2
5 159 1 60.0 8 13 2
6 186 0 NaN 7 13 2
7 210 0 NaN 5 10 2
8 220 1 40.0 7 10 2
9 250 1 75.0 6 11 2
10 257 0 NaN 3 7 1
11 263 1 50.0 8 13 2
12 268 1 165.0 7 12 2
13 282 1 100.0 4 7 1
14 325 0 NaN 6 10 2
15 341 1 125.0 7 12 2
16 364 1 50.0 6 11 2
17 368 1 50.0 6 11 2
18 383 0 NaN 6 11 2
19 387 1 25.0 3 8 1
20 397 1 80.0 4 8 1
21 403 1 175.0 6 9 2
22 427 1 50.0 4 9 1
23 441 1 50.0 7 12 2
24 458 0 NaN 4 9 1
25 511 1 60.0 6 10 2
26 527 0 NaN 6 12 2
27 539 0 NaN 5 9 2
28 542 1 50.0 3 7 1
29 544 0 NaN 7 12 2
... ... ... ... ... ... ...
9656 190840 1 125.0 3 7 1
9657 190842 0 NaN 3 8 1
9658 190859 1 50.0 5 10 2
9659 190863 0 NaN 3 7 1
9660 190891 0 NaN 4 9 1
9661 190902 0 NaN 3 7 1
9662 190920 1 100.0 4 8 1
9663 190976 1 100.0 4 7 1
9664 191007 0 NaN 6 10 2
9665 191056 1 235.0 5 10 1
9666 191064 1 15.0 2 4 1
9667 191134 1 25.0 4 9 1
9668 191138 1 75.0 2 4 1
9669 191155 0 NaN 3 5 1
9670 191233 0 NaN 3 8 1
9671 191248 0 NaN 4 6 1
9672 191296 1 45.0 4 9 1
9673 191297 1 25.0 2 5 1
9674 191316 0 NaN 4 9 1
9675 191465 0 NaN 3 7 1
9676 191475 0 NaN 3 8 1
9677 191482 0 NaN 4 9 1
9678 191524 0 NaN 4 9 1
9679 191528 1 100.0 3 7 1
9680 191531 1 65.0 3 8 1
9681 191547 1 75.0 4 9 2
9682 191649 0 NaN 3 8 1
9683 191663 1 50.0 2 7 1
9684 191672 0 NaN 4 8 1
9685 191779 1 750.0 3 5 1
PromCntMsg36 Class Age Gender HomeOwner AvgARPU AvgHomeValue \
0 3 4 57.0 M H 49.894904 33400
1 4 3 55.0 M H 48.574742 37600
2 4 1 57.0 F H 49.272646 100400
3 4 1 52.0 F H 47.334953 39900
4 4 1 NaN F U 47.827404 47500
5 4 2 58.0 M U 48.673449 53000
6 4 2 NaN F U 48.560389 91000
7 3 3 54.0 F H 49.644237 66300
8 3 1 44.0 M H 48.454812 55000
9 4 3 60.0 F U 48.724262 46900
10 1 3 48.0 F H 52.561329 49000
11 4 3 57.0 U U 50.282952 215600
12 4 2 40.0 F U 59.931475 176500
13 2 2 37.0 M H 51.024610 111700
14 4 2 40.0 F H 64.073400 543500
15 4 3 53.0 F U 49.071800 51700
16 4 4 50.0 F H 52.912850 310000
17 4 1 56.0 M H 50.273664 225700
18 2 2 NaN M U 62.063946 243900
19 3 1 NaN F U 49.613249 78900
20 1 4 57.0 F H 55.480759 185700
21 3 3 50.0 U U 64.311114 76500
22 3 2 NaN M U 48.799586 70900
23 4 1 49.0 F U 47.961826 55000
24 4 3 58.0 F U 50.774517 168800
25 3 3 58.0 F U 49.595220 102400
26 4 1 52.0 F H 48.854248 68400
27 2 3 56.0 M H 67.266602 200300
28 1 1 NaN M U 52.166583 53500
29 4 2 50.0 F U 48.614598 26600
... ... ... ... ... ... ... ...
9656 1 2 45.0 M H 61.245030 48500
9657 3 3 45.0 F U 50.652000 53100
9658 4 2 58.0 F U 50.019617 228800
9659 3 3 58.0 M U 51.371073 193000
9660 3 2 53.0 M U 49.514441 46200
9661 1 4 54.0 M U 55.786752 146300
9662 3 2 NaN M H 59.048710 91600
9663 3 4 NaN U U 49.217583 55800
9664 3 1 57.0 F U 48.018731 89600
9665 1 3 29.0 M U 72.475934 566700
9666 1 1 57.0 F H 49.318261 29700
9667 3 1 NaN F U 49.504599 43100
9668 1 4 48.0 F H 51.313396 57100
9669 1 2 NaN M U 60.536940 45500
9670 1 2 NaN M U 61.969402 165200
9671 2 2 43.0 F H 57.117598 79000
9672 3 2 46.0 F U 50.119001 102400
9673 2 2 NaN F U 49.511860 49500
9674 3 4 55.0 F U 55.201555 381300
9675 3 4 57.0 M U 49.286275 0
9676 3 2 59.0 U U 57.317004 28400
9677 3 3 44.0 M U 50.932958 88000
9678 1 2 48.0 U U 52.648915 50000
9679 1 2 49.0 F U 52.769421 81400
9680 3 2 50.0 U U 49.815856 107300
9681 3 2 58.0 F U 58.403995 31800
9682 3 1 55.0 F U 49.166689 38200
9683 3 2 54.0 M U 49.434637 53800
9684 3 1 59.0 F U 48.634900 36600
9685 2 2 54.0 M U 62.081260 143200
AvgIncome
0 39460
1 33545
2 42091
3 39313
4 0
5 49487
6 0
7 49047
8 43927
9 47256
10 40043
11 0
12 61523
13 89077
14 165543
15 0
16 44543
17 71260
18 0
19 53504
20 93770
21 60721
22 0
23 19203
24 0
25 42324
26 62414
27 105000
28 0
29 39250
... ...
9656 47256
9657 61313
9658 0
9659 59239
9660 0
9661 48209
9662 0
9663 0
9664 0
9665 52283
9666 25991
9667 42565
9668 44164
9669 0
9670 79842
9671 62804
9672 28268
9673 36364
9674 95837
9675 0
9676 0
9677 55290
9678 0
9679 54137
9680 0
9681 42358
9682 42373
9683 0
9684 44023
9685 79635
[9686 rows x 14 columns]
info_1=info["ARPU"]
info_11=info_1
print(info_11)
info_11nul=pd.isnull(info_11)#显示缺失值，缺失的为True
print(info_11nul)
info_11[info_11nul]#定位到null值得位置
len(info_11[info_11nul])# NAN值的个数0 50.0
1 NaN
2 25.0
3 80.0
4 15.0
5 60.0
6 NaN
7 NaN
8 40.0
9 75.0
10 NaN
11 50.0
12 165.0
13 100.0
14 NaN
15 125.0
16 50.0
17 50.0
18 NaN
19 25.0
20 80.0
21 175.0
22 50.0
23 50.0
24 NaN
25 60.0
26 NaN
27 NaN
28 50.0
29 NaN
...
9656 125.0
9657 NaN
9658 50.0
9659 NaN
9660 NaN
9661 NaN
9662 100.0
9663 100.0
9664 NaN
9665 235.0
9666 15.0
9667 25.0
9668 75.0
9669 NaN
9670 NaN
9671 NaN
9672 45.0
9673 25.0
9674 NaN
9675 NaN
9676 NaN
9677 NaN
9678 NaN
9679 100.0
9680 65.0
9681 75.0
9682 NaN
9683 50.0
9684 NaN
9685 750.0
Name: ARPU, Length: 9686, dtype: float64
0 False
1 True
2 False
3 False
4 False
5 False
6 True
7 True
8 False
9 False
10 True
11 False
12 False
13 False
14 True
15 False
16 False
17 False
18 True
19 False
20 False
21 False
22 False
23 False
24 True
25 False
26 True
27 True
28 False
29 True
...
9656 False
9657 True
9658 False
9659 True
9660 True
9661 True
9662 False
9663 False
9664 True
9665 False
9666 False
9667 False
9668 False
9669 True
9670 True
9671 True
9672 False
9673 False
9674 True
9675 True
9676 True
9677 True
9678 True
9679 False
9680 False
9681 False
9682 True
9683 False
9684 True
9685 False
Name: ARPU, Length: 9686, dtype: bool
4843
#求mean
print(sum(info["PromCntMsg12"])/ len(info["PromCntMsg12"]) )
1.0345860004129672
#求mean
print(sum(info["ARPU"])/ len(info["ARPU"]) ) #因为有缺失值 所以mean为 NANnan
ARPU_1=info["ARPU"]
#info_11nul=pd.isnull(info_11)#显示缺失值，缺失的为True
ARPU_11=ARPU_1[info_11nul==False]#去空置
ARPU_1.mean()
78.12172207309517
print(info)
ARPU_1=info["ARPU"]
#ARPU_1[info_11nul==True]==1
#sum(ARPU_1)
print(ARPU_1)
ID Suc_flag ARPU PromCnt12 PromCnt36 PromCntMsg12 \
0 12 1 50.0 6 10 2
1 53 0 0.0 5 9 1
2 67 1 25.0 6 11 2
3 71 1 80.0 7 10 2
4 142 1 15.0 6 11 2
5 159 1 60.0 8 13 2
6 186 0 0.0 7 13 2
7 210 0 0.0 5 10 2
8 220 1 40.0 7 10 2
9 250 1 75.0 6 11 2
10 257 0 0.0 3 7 1
11 263 1 50.0 8 13 2
12 268 1 165.0 7 12 2
13 282 1 100.0 4 7 1
14 325 0 0.0 6 10 2
15 341 1 125.0 7 12 2
16 364 1 50.0 6 11 2
17 368 1 50.0 6 11 2
18 383 0 0.0 6 11 2
19 387 1 25.0 3 8 1
20 397 1 80.0 4 8 1
21 403 1 175.0 6 9 2
22 427 1 50.0 4 9 1
23 441 1 50.0 7 12 2
24 458 0 0.0 4 9 1
25 511 1 60.0 6 10 2
26 527 0 0.0 6 12 2
27 539 0 0.0 5 9 2
28 542 1 50.0 3 7 1
29 544 0 0.0 7 12 2
... ... ... ... ... ... ...
9656 190840 1 125.0 3 7 1
9657 190842 0 0.0 3 8 1
9658 190859 1 50.0 5 10 2
9659 190863 0 0.0 3 7 1
9660 190891 0 0.0 4 9 1
9661 190902 0 0.0 3 7 1
9662 190920 1 100.0 4 8 1
9663 190976 1 100.0 4 7 1
9664 191007 0 0.0 6 10 2
9665 191056 1 235.0 5 10 1
9666 191064 1 15.0 2 4 1
9667 191134 1 25.0 4 9 1
9668 191138 1 75.0 2 4 1
9669 191155 0 0.0 3 5 1
9670 191233 0 0.0 3 8 1
9671 191248 0 0.0 4 6 1
9672 191296 1 45.0 4 9 1
9673 191297 1 25.0 2 5 1
9674 191316 0 0.0 4 9 1
9675 191465 0 0.0 3 7 1
9676 191475 0 0.0 3 8 1
9677 191482 0 0.0 4 9 1
9678 191524 0 0.0 4 9 1
9679 191528 1 100.0 3 7 1
9680 191531 1 65.0 3 8 1
9681 191547 1 75.0 4 9 2
9682 191649 0 0.0 3 8 1
9683 191663 1 50.0 2 7 1
9684 191672 0 0.0 4 8 1
9685 191779 1 750.0 3 5 1
PromCntMsg36 Class Age Gender HomeOwner AvgARPU AvgHomeValue \
0 3 4 57.0 M H 49.894904 33400
1 4 3 55.0 M H 48.574742 37600
2 4 1 57.0 F H 49.272646 100400
3 4 1 52.0 F H 47.334953 39900
4 4 1 NaN F U 47.827404 47500
5 4 2 58.0 M U 48.673449 53000
6 4 2 NaN F U 48.560389 91000
7 3 3 54.0 F H 49.644237 66300
8 3 1 44.0 M H 48.454812 55000
9 4 3 60.0 F U 48.724262 46900
10 1 3 48.0 F H 52.561329 49000
11 4 3 57.0 U U 50.282952 215600
12 4 2 40.0 F U 59.931475 176500
13 2 2 37.0 M H 51.024610 111700
14 4 2 40.0 F H 64.073400 543500
15 4 3 53.0 F U 49.071800 51700
16 4 4 50.0 F H 52.912850 310000
17 4 1 56.0 M H 50.273664 225700
18 2 2 NaN M U 62.063946 243900
19 3 1 NaN F U 49.613249 78900
20 1 4 57.0 F H 55.480759 185700
21 3 3 50.0 U U 64.311114 76500
22 3 2 NaN M U 48.799586 70900
23 4 1 49.0 F U 47.961826 55000
24 4 3 58.0 F U 50.774517 168800
25 3 3 58.0 F U 49.595220 102400
26 4 1 52.0 F H 48.854248 68400
27 2 3 56.0 M H 67.266602 200300
28 1 1 NaN M U 52.166583 53500
29 4 2 50.0 F U 48.614598 26600
... ... ... ... ... ... ... ...
9656 1 2 45.0 M H 61.245030 48500
9657 3 3 45.0 F U 50.652000 53100
9658 4 2 58.0 F U 50.019617 228800
9659 3 3 58.0 M U 51.371073 193000
9660 3 2 53.0 M U 49.514441 46200
9661 1 4 54.0 M U 55.786752 146300
9662 3 2 NaN M H 59.048710 91600
9663 3 4 NaN U U 49.217583 55800
9664 3 1 57.0 F U 48.018731 89600
9665 1 3 29.0 M U 72.475934 566700
9666 1 1 57.0 F H 49.318261 29700
9667 3 1 NaN F U 49.504599 43100
9668 1 4 48.0 F H 51.313396 57100
9669 1 2 NaN M U 60.536940 45500
9670 1 2 NaN M U 61.969402 165200
9671 2 2 43.0 F H 57.117598 79000
9672 3 2 46.0 F U 50.119001 102400
9673 2 2 NaN F U 49.511860 49500
9674 3 4 55.0 F U 55.201555 381300
9675 3 4 57.0 M U 49.286275 0
9676 3 2 59.0 U U 57.317004 28400
9677 3 3 44.0 M U 50.932958 88000
9678 1 2 48.0 U U 52.648915 50000
9679 1 2 49.0 F U 52.769421 81400
9680 3 2 50.0 U U 49.815856 107300
9681 3 2 58.0 F U 58.403995 31800
9682 3 1 55.0 F U 49.166689 38200
9683 3 2 54.0 M U 49.434637 53800
9684 3 1 59.0 F U 48.634900 36600
9685 2 2 54.0 M U 62.081260 143200
AvgIncome
0 39460
1 33545
2 42091
3 39313
4 0
5 49487
6 0
7 49047
8 43927
9 47256
10 40043
11 0
12 61523
13 89077
14 165543
15 0
16 44543
17 71260
18 0
19 53504
20 93770
21 60721
22 0
23 19203
24 0
25 42324
26 62414
27 105000
28 0
29 39250
... ...
9656 47256
9657 61313
9658 0
9659 59239
9660 0
9661 48209
9662 0
9663 0
9664 0
9665 52283
9666 25991
9667 42565
9668 44164
9669 0
9670 79842
9671 62804
9672 28268
9673 36364
9674 95837
9675 0
9676 0
9677 55290
9678 0
9679 54137
9680 0
9681 42358
9682 42373
9683 0
9684 44023
9685 79635
[9686 rows x 14 columns]
0 50.0
1 0.0
2 25.0
3 80.0
4 15.0
5 60.0
6 0.0
7 0.0
8 40.0
9 75.0
10 0.0
11 50.0
12 165.0
13 100.0
14 0.0
15 125.0
16 50.0
17 50.0
18 0.0
19 25.0
20 80.0
21 175.0
22 50.0
23 50.0
24 0.0
25 60.0
26 0.0
27 0.0
28 50.0
29 0.0
...
9656 125.0
9657 0.0
9658 50.0
9659 0.0
9660 0.0
9661 0.0
9662 100.0
9663 100.0
9664 0.0
9665 235.0
9666 15.0
9667 25.0
9668 75.0
9669 0.0
9670 0.0
9671 0.0
9672 45.0
9673 25.0
9674 0.0
9675 0.0
9676 0.0
9677 0.0
9678 0.0
9679 100.0
9680 65.0
9681 75.0
9682 0.0
9683 50.0
9684 0.0
9685 750.0
Name: ARPU, Length: 9686, dtype: float64
Cclass=[1,2,3,4]
proml={}
for a in Cclass:
info_row=info[info["Class"]==a]
fares=info_row["AvgIncome" ]
fares_a= fares.mean()
proml[a]=fares_a
#print(info_row)
print(info[info["Class"]==1])
print(proml)
ID Suc_flag ARPU PromCnt12 PromCnt36 PromCntMsg12 \
2 67 1 25.0 6 11 2
3 71 1 80.0 7 10 2
4 142 1 15.0 6 11 2
8 220 1 40.0 7 10 2
17 368 1 50.0 6 11 2
19 387 1 25.0 3 8 1
23 441 1 50.0 7 12 2
26 527 0 0.0 6 12 2
28 542 1 50.0 3 7 1
40 683 1 25.0 7 11 2
41 687 0 0.0 6 10 2
45 727 1 10.0 5 10 2
50 804 0 0.0 3 9 1
53 898 0 0.0 4 9 1
54 903 0 0.0 4 8 1
56 931 1 60.0 6 12 2
59 987 1 15.0 7 11 3
61 1007 0 0.0 6 11 2
65 1054 1 40.0 3 7 1
67 1068 1 25.0 5 10 1
72 1196 0 0.0 6 11 2
77 1293 1 10.0 4 9 1
86 1400 0 0.0 4 9 1
104 1765 0 0.0 3 7 1
113 1920 1 50.0 7 11 2
116 1928 0 0.0 3 8 1
120 1986 1 25.0 8 13 3
126 2087 0 0.0 7 11 2
133 2194 1 20.0 6 10 2
138 2262 1 62.5 6 11 2
... ... ... ... ... ... ...
9552 189508 1 35.0 3 8 1
9556 189564 0 0.0 8 14 2
9562 189711 1 50.0 1 3 1
9563 189715 1 125.0 1 2 1
9564 189722 1 30.0 1 3 1
9566 189725 1 25.0 1 3 1
9574 189902 1 30.0 3 8 1
9579 189958 1 25.0 2 6 1
9581 190025 0 0.0 1 2 0
9586 190108 1 25.0 1 3 1
9597 190291 0 0.0 1 3 0
9600 190313 1 35.0 1 2 0
9601 190326 1 50.0 1 3 1
9605 190382 0 0.0 4 9 1
9608 190408 1 35.0 1 3 1
9618 190534 1 30.0 4 8 1
9621 190560 1 25.0 5 10 2
9622 190562 1 50.0 2 6 1
9625 190579 1 20.0 5 11 2
9626 190597 1 60.0 4 9 1
9628 190614 1 15.0 1 3 1
9630 190619 1 15.0 4 9 2
9643 190727 1 55.0 3 7 1
9649 190778 1 100.0 2 5 1
9653 190799 1 20.0 3 7 1
9664 191007 0 0.0 6 10 2
9666 191064 1 15.0 2 4 1
9667 191134 1 25.0 4 9 1
9682 191649 0 0.0 3 8 1
9684 191672 0 0.0 4 8 1
PromCntMsg36 Class Age Gender HomeOwner AvgARPU AvgHomeValue \
2 4 1 57.0 F H 49.272646 100400
3 4 1 52.0 F H 47.334953 39900
4 4 1 NaN F U 47.827404 47500
8 3 1 44.0 M H 48.454812 55000
17 4 1 56.0 M H 50.273664 225700
19 3 1 NaN F U 49.613249 78900
23 4 1 49.0 F U 47.961826 55000
26 4 1 52.0 F H 48.854248 68400
28 1 1 NaN M U 52.166583 53500
40 4 1 57.0 F U 48.981318 225700
41 4 1 55.0 M H 49.371949 140700
45 4 1 55.0 F H 48.517754 24000
50 3 1 20.0 F H 50.134200 122700
53 3 1 57.0 F H 49.233658 38800
54 3 1 NaN F U 49.337121 52800
56 4 1 46.0 M H 48.522369 64600
59 4 1 48.0 F H 50.255308 214000
61 4 1 53.0 F H 47.812169 26600
65 2 1 NaN F U 49.792325 116100
67 3 1 53.0 F H 52.758229 352800
72 4 1 38.0 M U 49.332542 134800
77 3 1 56.0 F H 50.392768 91700
86 3 1 NaN M H 50.514782 180500
104 2 1 NaN M H 51.569514 226200
113 4 1 58.0 M H 48.525944 103200
116 3 1 58.0 F H 49.094017 110400
120 5 1 56.0 F U 47.491576 136800
126 4 1 NaN M U 47.431502 64900
133 4 1 40.0 M H 48.820141 100600
138 4 1 49.0 F H 49.321258 177800
... ... ... ... ... ... ... ...
9552 3 1 NaN M U 49.175197 68000
9556 4 1 45.0 F U 56.663899 132400
9562 1 1 NaN M U 48.683582 72700
9563 1 1 NaN F U 60.478072 570800
9564 1 1 NaN M U 47.929456 0
9566 1 1 NaN F U 48.762418 80300
9574 3 1 47.0 F H 50.007752 155300
9579 2 1 58.0 F U 48.484834 83800
9581 1 1 NaN F U 55.328011 70500
9586 1 1 NaN F U 48.677111 99500
9597 1 1 51.0 U U 48.824656 86300
9600 0 1 NaN M U 49.536740 100200
9601 1 1 NaN M U 48.448112 50000
9605 3 1 42.0 M U 49.780979 106700
9608 1 1 53.0 U U 48.669060 71300
9618 3 1 45.0 F U 50.219797 192200
9621 3 1 55.0 M U 48.919289 22000
9622 2 1 57.0 F U 49.585705 69500
9625 4 1 NaN M U 51.639889 320000
9626 3 1 46.0 F U 49.847102 73700
9628 1 1 NaN F U 49.131701 115900
9630 4 1 58.0 F U 49.385864 94300
9643 3 1 58.0 F U 49.500680 65500
9649 1 1 NaN M U 51.551344 87500
9653 3 1 53.0 F U 50.187331 84100
9664 3 1 57.0 F U 48.018731 89600
9666 1 1 57.0 F H 49.318261 29700
9667 3 1 NaN F U 49.504599 43100
9682 3 1 55.0 F U 49.166689 38200
9684 3 1 59.0 F U 48.634900 36600
AvgIncome
2 42091
3 39313
4 0
8 43927
17 71260
19 53504
23 19203
26 62414
28 0
40 0
41 72702
45 45515
50 37446
53 41000
54 38578
56 40909
59 100300
61 29327
65 0
67 92525
72 92295
77 80104
86 104341
104 61230
113 38463
116 59813
120 0
126 0
133 50524
138 42955
... ...
9552 0
9556 39695
9562 0
9563 0
9564 0
9566 0
9574 35841
9579 0
9581 0
9586 0
9597 0
9600 0
9601 0
9605 38755
9608 0
9618 41254
9621 45776
9622 34943
9625 54483
9626 53710
9628 0
9630 66796
9643 37481
9649 45666
9653 80172
9664 0
9666 25991
9667 42565
9682 42373
9684 44023
[2139 rows x 14 columns]
{1: 38757.43945769051, 2: 40692.939146230696, 3: 41409.17434063478, 4: 40984.98505231689}
#print(info[info["Class"]==1]["Age"])2 57.0
3 52.0
4 NaN
8 44.0
17 56.0
19 NaN
23 49.0
26 52.0
28 NaN
40 57.0
41 55.0
45 55.0
50 20.0
53 57.0
54 NaN
56 46.0
59 48.0
61 53.0
65 NaN
67 53.0
72 38.0
77 56.0
86 NaN
104 NaN
113 58.0
116 58.0
120 56.0
126 NaN
133 40.0
138 49.0
...
9552 NaN
9556 45.0
9562 NaN
9563 NaN
9564 NaN
9566 NaN
9574 47.0
9579 58.0
9581 NaN
9586 NaN
9597 51.0
9600 NaN
9601 NaN
9605 42.0
9608 53.0
9618 45.0
9621 55.0
9622 57.0
9625 NaN
9626 46.0
9628 NaN
9630 58.0
9643 58.0
9649 NaN
9653 53.0
9664 57.0
9666 57.0
9667 NaN
9682 55.0
9684 59.0
Name: Age, Length: 2139, dtype: float64