Overview

Dataset statistics

Number of variables14
Number of observations46
Missing cells141
Missing cells (%)21.9%
Duplicate rows1
Duplicate rows (%)2.2%
Total size in memory5.2 KiB
Average record size in memory114.9 B

Variable types

Unsupported8
Text4
Categorical2

Dataset

Description영등포구 관내 공영주차장(노상/노외) 현황에 대한 데이터(2014)
Author영등포구시설관리공단
URLhttps://www.data.go.kr/data/15044353/fileData.do

Alerts

Dataset has 1 (2.2%) duplicate rowsDuplicates
Unnamed: 9 is highly overall correlated with Unnamed: 10High correlation
Unnamed: 10 is highly overall correlated with Unnamed: 9High correlation
영등포구 공영 노상주차장 일반현황 has 3 (6.5%) missing valuesMissing
Unnamed: 1 has 4 (8.7%) missing valuesMissing
Unnamed: 2 has 3 (6.5%) missing valuesMissing
Unnamed: 3 has 5 (10.9%) missing valuesMissing
Unnamed: 4 has 4 (8.7%) missing valuesMissing
Unnamed: 5 has 4 (8.7%) missing valuesMissing
Unnamed: 6 has 2 (4.3%) missing valuesMissing
Unnamed: 7 has 4 (8.7%) missing valuesMissing
Unnamed: 8 has 4 (8.7%) missing valuesMissing
Unnamed: 11 has 31 (67.4%) missing valuesMissing
Unnamed: 12 has 34 (73.9%) missing valuesMissing
Unnamed: 13 has 43 (93.5%) missing valuesMissing
영등포구 공영 노상주차장 일반현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-13 00:17:41.634302
Analysis finished2023-12-13 00:17:42.391299
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영등포구 공영 노상주차장 일반현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)6.5%
Memory size500.0 B

Unnamed: 1
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing4
Missing (%)8.7%
Memory size500.0 B
2023-12-13T09:17:42.519564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.9047619
Min length4

Characters and Unicode

Total characters248
Distinct characters115
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row주차장명
2nd row가든예식장앞
3rd row경남아파트앞
4th row경도상가앞
5th row경호빌딩앞
ValueCountFrequency (%)
도림고가 1
 
2.4%
양남사거리앞 1
 
2.4%
여의도공원앞2 1
 
2.4%
여의도공원앞3 1
 
2.4%
여의도백화점앞 1
 
2.4%
영등포중앙시장 1
 
2.4%
중마루공원앞 1
 
2.4%
중소기업중앙회앞 1
 
2.4%
진주아파트옆 1
 
2.4%
태양빌딩앞 1
 
2.4%
Other values (32) 32
76.2%
2023-12-13T09:17:42.805136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
앞 31
 
12.5%
도 7
 
2.8%
딩 7
 
2.8%
빌 7
 
2.8%
원 5
 
2.0%
K 5
 
2.0%
B 5
 
2.0%
S 5
 
2.0%
장 4
 
1.6%
보 4
 
1.6%
Other values (105) 168
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
91.9%
Uppercase Letter 17
 
6.9%
Decimal Number 3
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
Uppercase Letter
ValueCountFrequency (%)
K 5
29.4%
B 5
29.4%
S 5
29.4%
C 1
 
5.9%
M 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
91.9%
Latin 17
 
6.9%
Common 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
Latin
ValueCountFrequency (%)
K 5
29.4%
B 5
29.4%
S 5
29.4%
C 1
 
5.9%
M 1
 
5.9%
Common
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
91.9%
ASCII 20
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
앞 31
 
13.6%
도 7
 
3.1%
딩 7
 
3.1%
빌 7
 
3.1%
원 5
 
2.2%
장 4
 
1.8%
보 4
 
1.8%
의 4
 
1.8%
여 4
 
1.8%
중 4
 
1.8%
Other values (97) 151
66.2%
ASCII
ValueCountFrequency (%)
K 5
25.0%
B 5
25.0%
S 5
25.0%
3 1
 
5.0%
C 1
 
5.0%
M 1
 
5.0%
2 1
 
5.0%
1 1
 
5.0%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)6.5%
Memory size500.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)10.9%
Memory size500.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)8.7%
Memory size500.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)8.7%
Memory size500.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)4.3%
Memory size500.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)8.7%
Memory size500.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)8.7%
Memory size500.0 B

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size500.0 B
2005.1.1.
23 
2006.12.4
7 
2004.7.1
5 
<NA>
4 
2012.4.1
4 
Other values (3)
3 

Length

Max length10
Median length9
Mean length8.2826087
Min length4

Unique

Unique3 ?
Unique (%)6.5%

Sample

1st row<NA>
2nd row인수현황
3rd row<NA>
4th row<NA>
5th row2004.10.1

Common Values

ValueCountFrequency (%)
2005.1.1. 23
50.0%
2006.12.4 7
 
15.2%
2004.7.1 5
 
10.9%
<NA> 4
 
8.7%
2012.4.1 4
 
8.7%
인수현황 1
 
2.2%
2004.10.1 1
 
2.2%
2005.11.1. 1
 
2.2%

Length

2023-12-13T09:17:42.911614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:17:42.997071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2005.1.1 23
50.0%
2006.12.4 7
 
15.2%
2004.7.1 5
 
10.9%
na 4
 
8.7%
2012.4.1 4
 
8.7%
인수현황 1
 
2.2%
2004.10.1 1
 
2.2%
2005.11.1 1
 
2.2%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
<NA>
35 
거주자
10 
야간 거주자 (10개소 327면)
 
1

Length

Max length18
Median length4
Mean length4.0869565
Min length3

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row야간 거주자 (10개소 327면)
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 35
76.1%
거주자 10
 
21.7%
야간 거주자 (10개소 327면) 1
 
2.2%

Length

2023-12-13T09:17:43.107586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:17:43.200444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
71.4%
거주자 11
 
22.4%
야간 1
 
2.0%
10개소 1
 
2.0%
327면 1
 
2.0%

Unnamed: 11
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing31
Missing (%)67.4%
Memory size500.0 B
2023-12-13T09:17:43.344464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.0666667
Min length3

Characters and Unicode

Total characters76
Distinct characters65
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row기존명칭
2nd row대우아파트앞
3rd row장기신용은행
4th row맨하탄호텔뒤
5th row제일은행
ValueCountFrequency (%)
기존명칭 1
 
6.2%
대우아파트앞 1
 
6.2%
장기신용은행 1
 
6.2%
맨하탄호텔뒤 1
 
6.2%
제일은행 1
 
6.2%
문래고가 1
 
6.2%
바이더웨이 1
 
6.2%
조흥화학 1
 
6.2%
서울방송 1
 
6.2%
선명회 1
 
6.2%
Other values (6) 6
37.5%
2023-12-13T09:17:43.596324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 2
 
2.6%
앞 2
 
2.6%
래 2
 
2.6%
가 2
 
2.6%
행 2
 
2.6%
은 2
 
2.6%
이 2
 
2.6%
장 2
 
2.6%
화 2
 
2.6%
명 2
 
2.6%
Other values (55) 56
73.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71
93.4%
Uppercase Letter 3
 
3.9%
Decimal Number 1
 
1.3%
Control 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
Uppercase Letter
ValueCountFrequency (%)
I 1
33.3%
B 1
33.3%
M 1
33.3%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71
93.4%
Latin 3
 
3.9%
Common 2
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
Latin
ValueCountFrequency (%)
I 1
33.3%
B 1
33.3%
M 1
33.3%
Common
ValueCountFrequency (%)
7 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71
93.4%
ASCII 5
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 2
 
2.8%
앞 2
 
2.8%
래 2
 
2.8%
가 2
 
2.8%
행 2
 
2.8%
은 2
 
2.8%
이 2
 
2.8%
장 2
 
2.8%
화 2
 
2.8%
명 2
 
2.8%
Other values (50) 51
71.8%
ASCII
ValueCountFrequency (%)
7 1
20.0%
I 1
20.0%
B 1
20.0%
M 1
20.0%
1
20.0%

Unnamed: 12
Text

MISSING 

Distinct10
Distinct (%)83.3%
Missing34
Missing (%)73.9%
Memory size500.0 B
2023-12-13T09:17:43.732201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length18.5
Mean length15.583333
Min length3

Characters and Unicode

Total characters187
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)75.0%

Sample

1st row비 고
2nd row2010.5.3 신설(6면)
3rd row2012.4.1 신설
4th row2012.4.1 신설
5th row2012.4.1 신설
ValueCountFrequency (%)
신설 5
13.9%
5
13.9%
2012.4.1 4
 
11.1%
2012.11.5 3
 
8.3%
상인회위탁 2
 
5.6%
신설(6면 1
 
2.8%
추가신설 1
 
2.8%
관리반장 1
 
2.8%
8명 1
 
2.8%
대직인력 1
 
2.8%
Other values (12) 12
33.3%
2023-12-13T09:17:43.960315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 25
13.4%
22
 
11.8%
. 20
 
10.7%
2 18
 
9.6%
0 13
 
7.0%
신 8
 
4.3%
설 8
 
4.3%
4 5
 
2.7%
면 5
 
2.7%
3 4
 
2.1%
Other values (37) 59
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
40.1%
Other Letter 56
29.9%
Other Punctuation 23
 
12.3%
Space Separator 22
 
11.8%
Open Punctuation 3
 
1.6%
Control 3
 
1.6%
Close Punctuation 3
 
1.6%
Dash Punctuation 2
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%
Decimal Number
ValueCountFrequency (%)
1 25
33.3%
2 18
24.0%
0 13
17.3%
4 5
 
6.7%
3 4
 
5.3%
5 4
 
5.3%
7 2
 
2.7%
6 2
 
2.7%
8 1
 
1.3%
9 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 20
87.0%
/ 3
 
13.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 131
70.1%
Hangul 56
29.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%
Common
ValueCountFrequency (%)
1 25
19.1%
22
16.8%
. 20
15.3%
2 18
13.7%
0 13
9.9%
4 5
 
3.8%
3 4
 
3.1%
5 4
 
3.1%
( 3
 
2.3%
3
 
2.3%
Other values (7) 14
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131
70.1%
Hangul 56
29.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25
19.1%
22
16.8%
. 20
15.3%
2 18
13.7%
0 13
9.9%
4 5
 
3.8%
3 4
 
3.1%
5 4
 
3.1%
( 3
 
2.3%
3
 
2.3%
Other values (7) 14
10.7%
Hangul
ValueCountFrequency (%)
신 8
 
14.3%
설 8
 
14.3%
면 5
 
8.9%
인 3
 
5.4%
명 3
 
5.4%
탁 2
 
3.6%
회 2
 
3.6%
위 2
 
3.6%
상 2
 
3.6%
센 1
 
1.8%
Other values (20) 20
35.7%

Unnamed: 13
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing43
Missing (%)93.5%
Memory size500.0 B
2023-12-13T09:17:44.056659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3333333
Min length2

Characters and Unicode

Total characters10
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st row비고
2nd row민간위탁
3rd row민간위탁
ValueCountFrequency (%)
민간위탁 2
66.7%
비고 1
33.3%
2023-12-13T09:17:44.278114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
민 2
20.0%
간 2
20.0%
위 2
20.0%
탁 2
20.0%
비 1
10.0%
고 1
10.0%

Correlations

2023-12-13T09:17:44.344337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
Unnamed: 11.0001.0001.0001.0001.0001.000
Unnamed: 91.0001.0001.0001.0001.0000.000
Unnamed: 101.0001.0001.0001.0001.000NaN
Unnamed: 111.0001.0001.0001.0001.0001.000
Unnamed: 121.0001.0001.0001.0001.0001.000
Unnamed: 131.0000.000NaN1.0001.0001.000
2023-12-13T09:17:44.429248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 10Unnamed: 9
Unnamed: 101.0000.816
Unnamed: 90.8161.000
2023-12-13T09:17:44.504467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 9Unnamed: 10
Unnamed: 91.0000.816
Unnamed: 100.8161.000

Missing values

2023-12-13T09:17:41.980100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:17:42.126356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T09:17:42.268856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

영등포구 공영 노상주차장 일반현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0NaN<NA>NaNNaNNaNNaNNaNNaNNaN<NA><NA><NA><NA><NA>
1연번주차장명주차면인력급지반주 차 요 금NaNNaN인수현황야간 거주자 (10개소 327면)기존명칭비 고비고
2NaN<NA>NaNNaNNaNNaN시간권일주차권월정기권<NA><NA><NA><NA><NA>
3NaN<NA>NaNNaNNaNNaN5분당NaNNaN<NA><NA><NA><NA><NA>
41가든예식장앞7112500200002000002004.10.1<NA><NA><NA><NA>
52경남아파트앞262431006000500002005.1.1.거주자<NA><NA><NA>
63경도상가앞17212500200002000002005.1.1.<NA><NA><NA><NA>
74경호빌딩앞10111500200002000002005.1.1.<NA><NA><NA><NA>
85교보생명앞35123250150001000002005.1.1.거주자대우아파트앞2010.5.3 신설(6면)<NA>
96교보생명옆13123250150001000002012.4.1거주자<NA>2012.4.1 신설<NA>
영등포구 공영 노상주차장 일반현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
3633호성빌딩앞22212500200002000002004.7.1<NA><NA><NA><NA>
3734홍우빌딩앞11112500200002000002005.1.1.<NA><NA><NA><NA>
3835화재보험협회앞16212500200002000002006.12.4<NA><NA><NA><NA>
3936KBS별관뒤26212500200002000002005.1.1.<NA><NA><NA><NA>
4037KBS별관옆21212500200002000002005.1.1.<NA><NA><NA><NA>
4138KBS본관앞69611500200002000002005.1.1.<NA><NA>2012.11.5 / 16면 신설<NA>
4239KBS연구관앞59211500200002000002005.1.1.<NA><NA><NA><NA>
4340MBC방송국뒤63212500200002000002005.1.1.거주자<NA><NA><NA>
4441SK리더스뷰도로변30123250150001000002005.1.1.거주자홈플러스뒤문래동주민센터 (13면)<NA>
45계<NA>93360NaNNaNNaNNaNNaN<NA><NA><NA>대직인력 8명 관리반장 3명(총74명)<NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13# duplicates
0<NA><NA><NA><NA><NA><NA>3