Overview

Dataset statistics

Number of variables9
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows17
Duplicate rows (%)8.5%
Total size in memory14.7 KiB
Average record size in memory75.7 B

Variable types

Categorical4
Text5

Alerts

Dataset has 17 (8.5%) duplicate rowsDuplicates
BRTC_NM is highly overall correlated with LA_DCMLPOINT_PRE_VALUE and 1 other fieldsHigh correlation
LA_DCMLPOINT_PRE_VALUE is highly overall correlated with BRTC_NMHigh correlation
LO_DCMLPOINT_PRE_VALUE is highly overall correlated with BRTC_NMHigh correlation

Reproduction

Analysis started2023-12-10 06:13:49.643012
Analysis finished2023-12-10 06:13:50.680127
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DATE
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
20201009
82 
20201006
44 
20201008
37 
20201007
36 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201006
2nd row20201006
3rd row20201006
4th row20201006
5th row20201006

Common Values

ValueCountFrequency (%)
20201009 82
41.2%
20201006 44
22.1%
20201008 37
18.6%
20201007 36
18.1%

Length

2023-12-10T15:13:50.795017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:50.961378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201009 82
41.2%
20201006 44
22.1%
20201008 37
18.6%
20201007 36
18.1%

INDUTY_NM
Text

Distinct111
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:13:51.347853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.1708543
Min length2

Characters and Unicode

Total characters830
Distinct characters186
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)39.7%

Sample

1st row카페
2nd row경찰서
3rd row경찰서
4th row경찰서
5th row경찰서
ValueCountFrequency (%)
한식 13
 
6.5%
부동산 12
 
6.0%
치킨 9
 
4.5%
카페 7
 
3.5%
치과 6
 
3.0%
경찰서 6
 
3.0%
지역자활센터 5
 
2.5%
중국음식 5
 
2.5%
차량연료소매(기타 4
 
2.0%
무역업(종합 4
 
2.0%
Other values (101) 128
64.3%
2023-12-10T15:13:51.969455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 28
 
3.4%
식 26
 
3.1%
매 21
 
2.5%
) 21
 
2.5%
( 21
 
2.5%
부 18
 
2.2%
동 16
 
1.9%
한 15
 
1.8%
치 15
 
1.8%
과 14
 
1.7%
Other values (176) 635
76.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 778
93.7%
Close Punctuation 21
 
2.5%
Open Punctuation 21
 
2.5%
Other Punctuation 10
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 28
 
3.6%
식 26
 
3.3%
매 21
 
2.7%
부 18
 
2.3%
동 16
 
2.1%
한 15
 
1.9%
치 15
 
1.9%
과 14
 
1.8%
소 14
 
1.8%
합 14
 
1.8%
Other values (173) 597
76.7%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 778
93.7%
Common 52
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 28
 
3.6%
식 26
 
3.3%
매 21
 
2.7%
부 18
 
2.3%
동 16
 
2.1%
한 15
 
1.9%
치 15
 
1.9%
과 14
 
1.8%
소 14
 
1.8%
합 14
 
1.8%
Other values (173) 597
76.7%
Common
ValueCountFrequency (%)
) 21
40.4%
( 21
40.4%
. 10
19.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 778
93.7%
ASCII 52
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 28
 
3.6%
식 26
 
3.3%
매 21
 
2.7%
부 18
 
2.3%
동 16
 
2.1%
한 15
 
1.9%
치 15
 
1.9%
과 14
 
1.8%
소 14
 
1.8%
합 14
 
1.8%
Other values (173) 597
76.7%
ASCII
ValueCountFrequency (%)
) 21
40.4%
( 21
40.4%
. 10
19.2%

BRTC_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울
87 
경기
45 
경남
11 
강원
 
8
경북
 
7
Other values (12)
41 

Length

Max length3
Median length2
Mean length2.0050251
Min length2

Unique

Unique4 ?
Unique (%)2.0%

Sample

1st row서울
2nd row서울
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 87
43.7%
경기 45
22.6%
경남 11
 
5.5%
강원 8
 
4.0%
경북 7
 
3.5%
대구 6
 
3.0%
전남 6
 
3.0%
충북 6
 
3.0%
부산 5
 
2.5%
대전 4
 
2.0%
Other values (7) 14
 
7.0%

Length

2023-12-10T15:13:52.177026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 87
43.7%
경기 45
22.6%
경남 11
 
5.5%
강원 8
 
4.0%
경북 7
 
3.5%
대구 6
 
3.0%
전남 6
 
3.0%
충북 6
 
3.0%
부산 5
 
2.5%
대전 4
 
2.0%
Other values (7) 14
 
7.0%

SIGNGU_NM
Text

Distinct90
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:13:52.609965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.4070352
Min length2

Characters and Unicode

Total characters877
Distinct characters105
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)26.1%

Sample

1st row양천구
2nd row마포구
3rd row마포구
4th row마포구
5th row마포구
ValueCountFrequency (%)
강남구 16
 
6.2%
종로구 11
 
4.3%
마포구 10
 
3.9%
중구 10
 
3.9%
남양주시 8
 
3.1%
송파구 7
 
2.7%
구로구 6
 
2.3%
영등포구 6
 
2.3%
수원시 6
 
2.3%
서초구 4
 
1.6%
Other values (111) 174
67.4%
2023-12-10T15:13:53.322962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 138
 
15.7%
96
 
10.9%
시 80
 
9.1%
남 34
 
3.9%
양 32
 
3.6%
주 26
 
3.0%
포 24
 
2.7%
강 18
 
2.1%
면 18
 
2.1%
읍 17
 
1.9%
Other values (95) 394
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 781
89.1%
Space Separator 96
 
10.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 138
 
17.7%
시 80
 
10.2%
남 34
 
4.4%
양 32
 
4.1%
주 26
 
3.3%
포 24
 
3.1%
강 18
 
2.3%
면 18
 
2.3%
읍 17
 
2.2%
로 17
 
2.2%
Other values (94) 377
48.3%
Space Separator
ValueCountFrequency (%)
96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 781
89.1%
Common 96
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 138
 
17.7%
시 80
 
10.2%
남 34
 
4.4%
양 32
 
4.1%
주 26
 
3.3%
포 24
 
3.1%
강 18
 
2.3%
면 18
 
2.3%
읍 17
 
2.2%
로 17
 
2.2%
Other values (94) 377
48.3%
Common
ValueCountFrequency (%)
96
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 781
89.1%
ASCII 96
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 138
 
17.7%
시 80
 
10.2%
남 34
 
4.4%
양 32
 
4.1%
주 26
 
3.3%
포 24
 
3.1%
강 18
 
2.3%
면 18
 
2.3%
읍 17
 
2.2%
로 17
 
2.2%
Other values (94) 377
48.3%
ASCII
ValueCountFrequency (%)
96
100.0%

EMD
Text

Distinct155
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:13:53.983125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2311558
Min length2

Characters and Unicode

Total characters643
Distinct characters143
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)64.8%

Sample

1st row신정동
2nd row아현동
3rd row아현동
4th row아현동
5th row아현동
ValueCountFrequency (%)
아현동 6
 
3.0%
금곡동 5
 
2.5%
구로동 5
 
2.5%
청담동 3
 
1.5%
내수동 3
 
1.5%
논현동 3
 
1.5%
옥암동 3
 
1.5%
중촌동 3
 
1.5%
방이동 3
 
1.5%
장교동 3
 
1.5%
Other values (145) 162
81.4%
2023-12-10T15:13:54.782146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 156
24.3%
리 40
 
6.2%
가 17
 
2.6%
로 12
 
1.9%
곡 12
 
1.9%
신 12
 
1.9%
산 12
 
1.9%
정 11
 
1.7%
현 10
 
1.6%
구 9
 
1.4%
Other values (133) 352
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 617
96.0%
Decimal Number 26
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 156
25.3%
리 40
 
6.5%
가 17
 
2.8%
로 12
 
1.9%
곡 12
 
1.9%
신 12
 
1.9%
산 12
 
1.9%
정 11
 
1.8%
현 10
 
1.6%
구 9
 
1.5%
Other values (127) 326
52.8%
Decimal Number
ValueCountFrequency (%)
1 8
30.8%
2 7
26.9%
3 4
15.4%
7 3
 
11.5%
6 3
 
11.5%
5 1
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 617
96.0%
Common 26
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 156
25.3%
리 40
 
6.5%
가 17
 
2.8%
로 12
 
1.9%
곡 12
 
1.9%
신 12
 
1.9%
산 12
 
1.9%
정 11
 
1.8%
현 10
 
1.6%
구 9
 
1.5%
Other values (127) 326
52.8%
Common
ValueCountFrequency (%)
1 8
30.8%
2 7
26.9%
3 4
15.4%
7 3
 
11.5%
6 3
 
11.5%
5 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 617
96.0%
ASCII 26
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 156
25.3%
리 40
 
6.5%
가 17
 
2.8%
로 12
 
1.9%
곡 12
 
1.9%
신 12
 
1.9%
산 12
 
1.9%
정 11
 
1.8%
현 10
 
1.6%
구 9
 
1.5%
Other values (127) 326
52.8%
ASCII
ValueCountFrequency (%)
1 8
30.8%
2 7
26.9%
3 4
15.4%
7 3
 
11.5%
6 3
 
11.5%
5 1
 
3.8%

LA_DCMLPOINT_PRE_VALUE
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
37
144 
35
30 
36
17 
34
 
7
38
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row37
2nd row37
3rd row37
4th row37
5th row37

Common Values

ValueCountFrequency (%)
37 144
72.4%
35 30
 
15.1%
36 17
 
8.5%
34 7
 
3.5%
38 1
 
0.5%

Length

2023-12-10T15:13:54.990646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:55.161164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
37 144
72.4%
35 30
 
15.1%
36 17
 
8.5%
34 7
 
3.5%
38 1
 
0.5%

LA_DCMLPOINT_RR_VALUE
Text

Distinct141
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:13:55.637856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1194
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)53.8%

Sample

1st row522***
2nd row550***
3rd row550***
4th row550***
5th row550***
ValueCountFrequency (%)
483 7
 
3.5%
550 6
 
3.0%
633 5
 
2.5%
567 4
 
2.0%
510 4
 
2.0%
572 3
 
1.5%
570 3
 
1.5%
288 3
 
1.5%
810 3
 
1.5%
573 3
 
1.5%
Other values (131) 158
79.4%
2023-12-10T15:13:56.328549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 597
50.0%
5 112
 
9.4%
3 66
 
5.5%
6 62
 
5.2%
8 61
 
5.1%
0 57
 
4.8%
4 53
 
4.4%
7 52
 
4.4%
2 51
 
4.3%
1 49
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 597
50.0%
Decimal Number 597
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 112
18.8%
3 66
11.1%
6 62
10.4%
8 61
10.2%
0 57
9.5%
4 53
8.9%
7 52
8.7%
2 51
8.5%
1 49
8.2%
9 34
 
5.7%
Other Punctuation
ValueCountFrequency (%)
* 597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 597
50.0%
5 112
 
9.4%
3 66
 
5.5%
6 62
 
5.2%
8 61
 
5.1%
0 57
 
4.8%
4 53
 
4.4%
7 52
 
4.4%
2 51
 
4.3%
1 49
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 597
50.0%
5 112
 
9.4%
3 66
 
5.5%
6 62
 
5.2%
8 61
 
5.1%
0 57
 
4.8%
4 53
 
4.4%
7 52
 
4.4%
2 51
 
4.3%
1 49
 
4.1%

LO_DCMLPOINT_PRE_VALUE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
127
89 
126
80 
128
19 
129
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row126
2nd row126
3rd row126
4th row126
5th row126

Common Values

ValueCountFrequency (%)
127 89
44.7%
126 80
40.2%
128 19
 
9.5%
129 11
 
5.5%

Length

2023-12-10T15:13:56.549205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:56.705437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
127 89
44.7%
126 80
40.2%
128 19
 
9.5%
129 11
 
5.5%

LO_DCMLPOINT_RR_VALUE
Text

Distinct147
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:13:57.164537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1194
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)57.3%

Sample

1st row863***
2nd row954***
3rd row954***
4th row954***
5th row954***
ValueCountFrequency (%)
954 6
 
3.0%
972 5
 
2.5%
207 5
 
2.5%
892 4
 
2.0%
769 3
 
1.5%
452 3
 
1.5%
973 3
 
1.5%
986 3
 
1.5%
037 3
 
1.5%
412 3
 
1.5%
Other values (137) 161
80.9%
2023-12-10T15:13:57.796224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 74
 
6.2%
8 71
 
5.9%
4 68
 
5.7%
2 66
 
5.5%
1 61
 
5.1%
7 59
 
4.9%
5 43
 
3.6%
3 41
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 597
50.0%
Decimal Number 597
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 84
14.1%
0 74
12.4%
8 71
11.9%
4 68
11.4%
2 66
11.1%
1 61
10.2%
7 59
9.9%
5 43
7.2%
3 41
6.9%
6 30
 
5.0%
Other Punctuation
ValueCountFrequency (%)
* 597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 74
 
6.2%
8 71
 
5.9%
4 68
 
5.7%
2 66
 
5.5%
1 61
 
5.1%
7 59
 
4.9%
5 43
 
3.6%
3 41
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 597
50.0%
9 84
 
7.0%
0 74
 
6.2%
8 71
 
5.9%
4 68
 
5.7%
2 66
 
5.5%
1 61
 
5.1%
7 59
 
4.9%
5 43
 
3.6%
3 41
 
3.4%

Correlations

2023-12-10T15:13:57.937053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DATEBRTC_NMSIGNGU_NMLA_DCMLPOINT_PRE_VALUELO_DCMLPOINT_PRE_VALUE
DATE1.0000.6970.9020.2970.486
BRTC_NM0.6971.0000.9910.9030.855
SIGNGU_NM0.9020.9911.0000.9980.989
LA_DCMLPOINT_PRE_VALUE0.2970.9030.9981.0000.535
LO_DCMLPOINT_PRE_VALUE0.4860.8550.9890.5351.000
2023-12-10T15:13:58.064655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BRTC_NMLO_DCMLPOINT_PRE_VALUELA_DCMLPOINT_PRE_VALUEDATE
BRTC_NM1.0000.6490.7230.453
LO_DCMLPOINT_PRE_VALUE0.6491.0000.4610.206
LA_DCMLPOINT_PRE_VALUE0.7230.4611.0000.245
DATE0.4530.2060.2451.000
2023-12-10T15:13:58.187580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DATEBRTC_NMLA_DCMLPOINT_PRE_VALUELO_DCMLPOINT_PRE_VALUE
DATE1.0000.4530.2450.206
BRTC_NM0.4531.0000.7230.649
LA_DCMLPOINT_PRE_VALUE0.2450.7231.0000.461
LO_DCMLPOINT_PRE_VALUE0.2060.6490.4611.000

Missing values

2023-12-10T15:13:50.386065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:13:50.599666image/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.

Sample

DATEINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE
020201006카페서울양천구신정동37522***126863***
120201006경찰서서울마포구아현동37550***126954***
220201006경찰서서울마포구아현동37550***126954***
320201006경찰서서울마포구아현동37550***126954***
420201006경찰서서울마포구아현동37550***126954***
520201006경찰서서울마포구아현동37550***126954***
620201006경찰서서울마포구아현동37550***126954***
720201006곰탕서울서초구양재2동37463***127034***
820201006치과서울서대문구남가좌동37569***126915***
920201006산부인과서울영등포구신길동37505***126911***
DATEINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE
18920201009차량연료소매(기타)서울구로구구로동37483***126892***
19020201009치킨서울관악구봉천동37479***126947***
19120201009체형미교실경기광명시일직동37422***126884***
19220201009치킨경기광명시소하동37447***126882***
19320201009치킨경기광명시소하동37447***126882***
19420201009기타서울성북구정릉동37609***127003***
19520201009한식서울노원구상계동37661***127072***
19620201009치킨서울노원구중계동37660***127074***
19720201009일식서울성북구보문동6가37582***127017***
19820201009스포츠마케팅서울노원구공릉동37628***127070***

Duplicate rows

Most frequently occurring

DATEINDUTY_NMBRTC_NMSIGNGU_NMEMDLA_DCMLPOINT_PRE_VALUELA_DCMLPOINT_RR_VALUELO_DCMLPOINT_PRE_VALUELO_DCMLPOINT_RR_VALUE# duplicates
020201006경찰서서울마포구아현동37550***126954***6
120201006지역자활센터경기남양주시금곡동37633***127207***5
1320201009차량연료소매(기타)서울구로구구로동37483***126892***4
420201007중학교전남목포시옥암동34810***126452***3
820201008인쇄업대전중구중촌동36337***127412***3
920201009무역업(종합)서울종로구신문로1가37570***126972***3
1420201009치과서울중구장교동37567***126986***3
220201007부동산경기수원시 팔달구화서2동37288***126984***2
320201007소방기구판매경기고양시 덕양구화정동37631***126831***2
520201007콘도강원춘천시 동내면거두리37860***127769***2