Overview

Dataset statistics

Number of variables44
Number of observations10000
Missing cells135696
Missing cells (%)30.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory382.0 B

Variable types

Numeric13
Text8
DateTime4
Unsupported6
Categorical12
Boolean1

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-16078/S/1/datasetView.do

Alerts

업태구분명 is highly imbalanced (65.9%)Imbalance
위생업태명 is highly imbalanced (56.0%)Imbalance
영업장주변구분명 is highly imbalanced (64.2%)Imbalance
등급구분명 is highly imbalanced (64.6%)Imbalance
급수시설구분명 is highly imbalanced (57.9%)Imbalance
총인원 is highly imbalanced (54.7%)Imbalance
다중이용업소여부 is highly imbalanced (99.8%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 1346 (13.5%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
전화번호 has 2663 (26.6%) missing valuesMissing
소재지면적 has 1026 (10.3%) missing valuesMissing
도로명주소 has 4801 (48.0%) missing valuesMissing
도로명우편번호 has 4895 (48.9%) missing valuesMissing
좌표정보(X) has 328 (3.3%) missing valuesMissing
좌표정보(Y) has 328 (3.3%) missing valuesMissing
남성종사자수 has 7517 (75.2%) missing valuesMissing
여성종사자수 has 7552 (75.5%) missing valuesMissing
본사종업원수 has 4392 (43.9%) missing valuesMissing
공장사무직종업원수 has 4359 (43.6%) missing valuesMissing
공장판매직종업원수 has 4392 (43.9%) missing valuesMissing
공장생산직종업원수 has 4253 (42.5%) missing valuesMissing
보증액 has 7957 (79.6%) missing valuesMissing
월세액 has 7968 (79.7%) missing valuesMissing
다중이용업소여부 has 943 (9.4%) missing valuesMissing
시설총규모 has 943 (9.4%) missing valuesMissing
전통업소지정번호 has 10000 (100.0%) missing valuesMissing
전통업소주된음식 has 10000 (100.0%) missing valuesMissing
홈페이지 has 9987 (99.9%) missing valuesMissing
본사종업원수 is highly skewed (γ1 = 26.94939708)Skewed
공장사무직종업원수 is highly skewed (γ1 = 48.34944648)Skewed
공장판매직종업원수 is highly skewed (γ1 = 37.85376039)Skewed
보증액 is highly skewed (γ1 = 35.86976914)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
남성종사자수 has 1753 (17.5%) zerosZeros
여성종사자수 has 1856 (18.6%) zerosZeros
본사종업원수 has 5544 (55.4%) zerosZeros
공장사무직종업원수 has 5485 (54.9%) zerosZeros
공장판매직종업원수 has 5519 (55.2%) zerosZeros
공장생산직종업원수 has 5226 (52.3%) zerosZeros
보증액 has 1880 (18.8%) zerosZeros
월세액 has 1883 (18.8%) zerosZeros
시설총규모 has 7965 (79.7%) zerosZeros

Reproduction

Analysis started2024-05-11 04:23:00.273011
Analysis finished2024-05-11 04:23:06.098403
Duration5.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3141145
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:06.387678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3020000
Q13070000
median3150000
Q33210000
95-th percentile3230000
Maximum3240000
Range240000
Interquartile range (IQR)140000

Descriptive statistics

Standard deviation74980.005
Coefficient of variation (CV)0.023870278
Kurtosis-1.2630864
Mean3141145
Median Absolute Deviation (MAD)70000
Skewness-0.32558517
Sum3.141145 × 1010
Variance5.6220012 × 109
MonotonicityNot monotonic
2024-05-11T04:23:07.027637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3230000 1220
 
12.2%
3220000 815
 
8.2%
3030000 571
 
5.7%
3050000 560
 
5.6%
3180000 520
 
5.2%
3210000 517
 
5.2%
3130000 474
 
4.7%
3240000 455
 
4.5%
3170000 442
 
4.4%
3150000 382
 
3.8%
Other values (15) 4044
40.4%
ValueCountFrequency (%)
3000000 184
 
1.8%
3010000 270
2.7%
3020000 206
 
2.1%
3030000 571
5.7%
3040000 289
2.9%
3050000 560
5.6%
3060000 342
3.4%
3070000 236
2.4%
3080000 222
 
2.2%
3090000 215
 
2.1%
ValueCountFrequency (%)
3240000 455
 
4.5%
3230000 1220
12.2%
3220000 815
8.2%
3210000 517
5.2%
3200000 285
 
2.9%
3190000 294
 
2.9%
3180000 520
5.2%
3170000 442
 
4.4%
3160000 362
 
3.6%
3150000 382
 
3.8%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:23:07.635628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters220000
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row3150000-106-2014-00001
2nd row3230000-106-2001-03290
3rd row3180000-106-1997-00371
4th row3120000-106-2005-00004
5th row3160000-106-1997-00052
ValueCountFrequency (%)
3150000-106-2014-00001 1
 
< 0.1%
3140000-106-2022-00005 1
 
< 0.1%
3230000-106-1999-01776 1
 
< 0.1%
3030000-106-2014-00022 1
 
< 0.1%
3140000-106-2005-00024 1
 
< 0.1%
3010000-106-2014-00004 1
 
< 0.1%
3050000-106-2000-01263 1
 
< 0.1%
3010000-106-2012-00008 1
 
< 0.1%
3220000-106-2022-00011 1
 
< 0.1%
3080000-106-2019-00007 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T04:23:08.934286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 99494
45.2%
- 30000
 
13.6%
1 25740
 
11.7%
2 17195
 
7.8%
3 15348
 
7.0%
6 12960
 
5.9%
9 6024
 
2.7%
4 3867
 
1.8%
5 3474
 
1.6%
8 3011
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190000
86.4%
Dash Punctuation 30000
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99494
52.4%
1 25740
 
13.5%
2 17195
 
9.0%
3 15348
 
8.1%
6 12960
 
6.8%
9 6024
 
3.2%
4 3867
 
2.0%
5 3474
 
1.8%
8 3011
 
1.6%
7 2887
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99494
45.2%
- 30000
 
13.6%
1 25740
 
11.7%
2 17195
 
7.8%
3 15348
 
7.0%
6 12960
 
5.9%
9 6024
 
2.7%
4 3867
 
1.8%
5 3474
 
1.6%
8 3011
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99494
45.2%
- 30000
 
13.6%
1 25740
 
11.7%
2 17195
 
7.8%
3 15348
 
7.0%
6 12960
 
5.9%
9 6024
 
2.7%
4 3867
 
1.8%
5 3474
 
1.6%
8 3011
 
1.4%
Distinct5369
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1963-02-12 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T04:23:09.510183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:23:10.175003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
8654 
1
1346 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 8654
86.5%
1 1346
 
13.5%

Length

2024-05-11T04:23:10.929776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:11.364973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 8654
86.5%
1 1346
 
13.5%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
8654 
영업/정상
1346 

Length

Max length5
Median length2
Mean length2.4038
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row폐업
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 8654
86.5%
영업/정상 1346
 
13.5%

Length

2024-05-11T04:23:11.769303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:12.115682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 8654
86.5%
영업/정상 1346
 
13.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
8654 
1
1346 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8654
86.5%
1 1346
 
13.5%

Length

2024-05-11T04:23:12.556446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:13.098203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8654
86.5%
1 1346
 
13.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
8654 
영업
1346 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row폐업
3rd row폐업
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 8654
86.5%
영업 1346
 
13.5%

Length

2024-05-11T04:23:13.645746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:14.118416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 8654
86.5%
영업 1346
 
13.5%

폐업일자
Date

MISSING 

Distinct4590
Distinct (%)53.0%
Missing1346
Missing (%)13.5%
Memory size156.2 KiB
Minimum1991-02-02 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T04:23:14.579877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:23:15.249445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

전화번호
Text

MISSING 

Distinct6618
Distinct (%)90.2%
Missing2663
Missing (%)26.6%
Memory size156.2 KiB
2024-05-11T04:23:16.390785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.13357
Min length2

Characters and Unicode

Total characters74350
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6260 ?
Unique (%)85.3%

Sample

1st row20931899
2nd row0226363105
3rd row0231431354
4th row02 7558261
5th row02 4860741
ValueCountFrequency (%)
02 5032
36.9%
070 257
 
1.9%
407 56
 
0.4%
0 43
 
0.3%
031 17
 
0.1%
960 13
 
0.1%
512 11
 
0.1%
423 11
 
0.1%
515 11
 
0.1%
488 10
 
0.1%
Other values (6965) 8181
60.0%
2024-05-11T04:23:18.002261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12874
17.3%
2 12651
17.0%
8152
11.0%
4 5838
7.9%
3 5547
7.5%
5 5150
6.9%
8 5051
 
6.8%
6 4993
 
6.7%
7 4992
 
6.7%
9 4587
 
6.2%
Other values (3) 4515
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66189
89.0%
Space Separator 8152
 
11.0%
Other Punctuation 7
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12874
19.5%
2 12651
19.1%
4 5838
8.8%
3 5547
8.4%
5 5150
7.8%
8 5051
 
7.6%
6 4993
 
7.5%
7 4992
 
7.5%
9 4587
 
6.9%
1 4506
 
6.8%
Space Separator
ValueCountFrequency (%)
8152
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12874
17.3%
2 12651
17.0%
8152
11.0%
4 5838
7.9%
3 5547
7.5%
5 5150
6.9%
8 5051
 
6.8%
6 4993
 
6.7%
7 4992
 
6.7%
9 4587
 
6.2%
Other values (3) 4515
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12874
17.3%
2 12651
17.0%
8152
11.0%
4 5838
7.9%
3 5547
7.5%
5 5150
6.9%
8 5051
 
6.8%
6 4993
 
6.7%
7 4992
 
6.7%
9 4587
 
6.2%
Other values (3) 4515
 
6.1%

소재지면적
Text

MISSING 

Distinct4803
Distinct (%)53.5%
Missing1026
Missing (%)10.3%
Memory size156.2 KiB
2024-05-11T04:23:19.026541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.1854246
Min length3

Characters and Unicode

Total characters46534
Distinct characters12
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

Unique3570 ?
Unique (%)39.8%

Sample

1st row135.08
2nd row30.00
3rd row16,354.67
4th row99.66
5th row529.70
ValueCountFrequency (%)
00 165
 
1.8%
33.00 146
 
1.6%
30.00 90
 
1.0%
66.00 81
 
0.9%
20.00 61
 
0.7%
60.00 55
 
0.6%
40.00 54
 
0.6%
50.00 51
 
0.6%
15.00 49
 
0.5%
99.00 48
 
0.5%
Other values (4793) 8174
91.1%
2024-05-11T04:23:20.656736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9067
19.5%
. 8974
19.3%
1 4733
10.2%
2 3899
8.4%
3 3330
 
7.2%
5 3151
 
6.8%
6 3046
 
6.5%
4 2968
 
6.4%
8 2584
 
5.6%
9 2452
 
5.3%
Other values (2) 2330
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37546
80.7%
Other Punctuation 8988
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9067
24.1%
1 4733
12.6%
2 3899
10.4%
3 3330
 
8.9%
5 3151
 
8.4%
6 3046
 
8.1%
4 2968
 
7.9%
8 2584
 
6.9%
9 2452
 
6.5%
7 2316
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 8974
99.8%
, 14
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 46534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9067
19.5%
. 8974
19.3%
1 4733
10.2%
2 3899
8.4%
3 3330
 
7.2%
5 3151
 
6.8%
6 3046
 
6.5%
4 2968
 
6.4%
8 2584
 
5.6%
9 2452
 
5.3%
Other values (2) 2330
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9067
19.5%
. 8974
19.3%
1 4733
10.2%
2 3899
8.4%
3 3330
 
7.2%
5 3151
 
6.8%
6 3046
 
6.5%
4 2968
 
6.4%
8 2584
 
5.6%
9 2452
 
5.3%
Other values (2) 2330
 
5.0%
Distinct2443
Distinct (%)24.5%
Missing23
Missing (%)0.2%
Memory size156.2 KiB
2024-05-11T04:23:21.601377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0618422
Min length6

Characters and Unicode

Total characters60479
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

Unique900 ?
Unique (%)9.0%

Sample

1st row157-779
2nd row138881
3rd row150834
4th row120827
5th row152887
ValueCountFrequency (%)
138881 180
 
1.8%
130864 153
 
1.5%
138200 95
 
1.0%
153803 83
 
0.8%
100869 60
 
0.6%
133832 51
 
0.5%
153801 49
 
0.5%
133833 39
 
0.4%
138806 38
 
0.4%
153802 37
 
0.4%
Other values (2433) 9192
92.1%
2024-05-11T04:23:22.995428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13814
22.8%
8 11115
18.4%
3 8589
14.2%
0 5823
9.6%
5 5351
 
8.8%
2 4452
 
7.4%
4 3240
 
5.4%
7 2554
 
4.2%
6 2507
 
4.1%
9 2417
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59862
99.0%
Dash Punctuation 617
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13814
23.1%
8 11115
18.6%
3 8589
14.3%
0 5823
9.7%
5 5351
 
8.9%
2 4452
 
7.4%
4 3240
 
5.4%
7 2554
 
4.3%
6 2507
 
4.2%
9 2417
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13814
22.8%
8 11115
18.4%
3 8589
14.2%
0 5823
9.6%
5 5351
 
8.8%
2 4452
 
7.4%
4 3240
 
5.4%
7 2554
 
4.2%
6 2507
 
4.1%
9 2417
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13814
22.8%
8 11115
18.4%
3 8589
14.2%
0 5823
9.6%
5 5351
 
8.8%
2 4452
 
7.4%
4 3240
 
5.4%
7 2554
 
4.2%
6 2507
 
4.1%
9 2417
 
4.0%
Distinct9474
Distinct (%)95.0%
Missing23
Missing (%)0.2%
Memory size156.2 KiB
2024-05-11T04:23:23.758230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length58
Mean length26.112759
Min length14

Characters and Unicode

Total characters260527
Distinct characters548
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9076 ?
Unique (%)91.0%

Sample

1st row서울특별시 강서구 염창동 240-21 비-408 우림블루나인
2nd row서울특별시 송파구 가락동 600-0번지 (청과관련2동19-1)
3rd row서울특별시 영등포구 문래동3가 9-0번지
4th row서울특별시 서대문구 연희동 715-2번지
5th row서울특별시 구로구 신도림동 306-0번지
ValueCountFrequency (%)
서울특별시 9976
 
20.9%
송파구 1220
 
2.6%
1층 970
 
2.0%
강남구 813
 
1.7%
지하1층 765
 
1.6%
성동구 571
 
1.2%
동대문구 560
 
1.2%
서초구 517
 
1.1%
영등포구 513
 
1.1%
마포구 472
 
1.0%
Other values (10673) 31310
65.7%
2024-05-11T04:23:24.982472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46310
 
17.8%
1 12495
 
4.8%
12329
 
4.7%
11527
 
4.4%
10641
 
4.1%
10598
 
4.1%
10256
 
3.9%
10032
 
3.9%
9983
 
3.8%
9976
 
3.8%
Other values (538) 116380
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150439
57.7%
Decimal Number 51642
 
19.8%
Space Separator 46310
 
17.8%
Dash Punctuation 8933
 
3.4%
Open Punctuation 1085
 
0.4%
Close Punctuation 1083
 
0.4%
Uppercase Letter 547
 
0.2%
Other Punctuation 361
 
0.1%
Lowercase Letter 85
 
< 0.1%
Math Symbol 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12329
 
8.2%
11527
 
7.7%
10641
 
7.1%
10598
 
7.0%
10256
 
6.8%
10032
 
6.7%
9983
 
6.6%
9976
 
6.6%
8336
 
5.5%
3503
 
2.3%
Other values (471) 53258
35.4%
Uppercase Letter
ValueCountFrequency (%)
B 186
34.0%
A 57
 
10.4%
S 43
 
7.9%
T 39
 
7.1%
K 34
 
6.2%
I 26
 
4.8%
C 19
 
3.5%
V 15
 
2.7%
F 14
 
2.6%
E 14
 
2.6%
Other values (15) 100
18.3%
Lowercase Letter
ValueCountFrequency (%)
e 21
24.7%
n 12
14.1%
r 11
12.9%
c 10
11.8%
t 9
10.6%
b 5
 
5.9%
i 4
 
4.7%
a 3
 
3.5%
s 2
 
2.4%
o 2
 
2.4%
Other values (4) 6
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 12495
24.2%
2 7029
13.6%
3 5405
10.5%
4 4568
 
8.8%
0 4557
 
8.8%
5 4067
 
7.9%
6 3974
 
7.7%
7 3389
 
6.6%
9 3084
 
6.0%
8 3074
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 303
83.9%
/ 23
 
6.4%
. 20
 
5.5%
@ 12
 
3.3%
? 2
 
0.6%
& 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 29
90.6%
+ 1
 
3.1%
< 1
 
3.1%
> 1
 
3.1%
Open Punctuation
ValueCountFrequency (%)
( 1017
93.7%
[ 68
 
6.3%
Close Punctuation
ValueCountFrequency (%)
) 1015
93.7%
] 68
 
6.3%
Letter Number
ValueCountFrequency (%)
7
70.0%
3
30.0%
Space Separator
ValueCountFrequency (%)
46310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8933
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150439
57.7%
Common 109446
42.0%
Latin 642
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12329
 
8.2%
11527
 
7.7%
10641
 
7.1%
10598
 
7.0%
10256
 
6.8%
10032
 
6.7%
9983
 
6.6%
9976
 
6.6%
8336
 
5.5%
3503
 
2.3%
Other values (471) 53258
35.4%
Latin
ValueCountFrequency (%)
B 186
29.0%
A 57
 
8.9%
S 43
 
6.7%
T 39
 
6.1%
K 34
 
5.3%
I 26
 
4.0%
e 21
 
3.3%
C 19
 
3.0%
V 15
 
2.3%
F 14
 
2.2%
Other values (31) 188
29.3%
Common
ValueCountFrequency (%)
46310
42.3%
1 12495
 
11.4%
- 8933
 
8.2%
2 7029
 
6.4%
3 5405
 
4.9%
4 4568
 
4.2%
0 4557
 
4.2%
5 4067
 
3.7%
6 3974
 
3.6%
7 3389
 
3.1%
Other values (16) 8719
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150439
57.7%
ASCII 110078
42.3%
Number Forms 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46310
42.1%
1 12495
 
11.4%
- 8933
 
8.1%
2 7029
 
6.4%
3 5405
 
4.9%
4 4568
 
4.1%
0 4557
 
4.1%
5 4067
 
3.7%
6 3974
 
3.6%
7 3389
 
3.1%
Other values (55) 9351
 
8.5%
Hangul
ValueCountFrequency (%)
12329
 
8.2%
11527
 
7.7%
10641
 
7.1%
10598
 
7.0%
10256
 
6.8%
10032
 
6.7%
9983
 
6.6%
9976
 
6.6%
8336
 
5.5%
3503
 
2.3%
Other values (471) 53258
35.4%
Number Forms
ValueCountFrequency (%)
7
70.0%
3
30.0%

도로명주소
Text

MISSING 

Distinct5090
Distinct (%)97.9%
Missing4801
Missing (%)48.0%
Memory size156.2 KiB
2024-05-11T04:23:25.849131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length68
Mean length33.442585
Min length21

Characters and Unicode

Total characters173868
Distinct characters531
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4992 ?
Unique (%)96.0%

Sample

1st row서울특별시 강서구 양천로 583 (염창동, 비-408 우림블루나인)
2nd row서울특별시 송파구 양재대로 932 (가락동,(청과관련2동19-1))
3rd row서울특별시 영등포구 신길로 87 (문래동3가)
4th row서울특별시 서대문구 홍연길 76 (연희동)
5th row서울특별시 강동구 천호대로175길 52 (길동,4층)
ValueCountFrequency (%)
서울특별시 5198
 
15.6%
1층 1181
 
3.6%
지하1층 670
 
2.0%
송파구 619
 
1.9%
강남구 429
 
1.3%
2층 390
 
1.2%
성동구 359
 
1.1%
동대문구 322
 
1.0%
지상1층 317
 
1.0%
영등포구 305
 
0.9%
Other values (6383) 23425
70.5%
2024-05-11T04:23:27.386671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28027
 
16.1%
1 8407
 
4.8%
7058
 
4.1%
6252
 
3.6%
5596
 
3.2%
5496
 
3.2%
5403
 
3.1%
) 5394
 
3.1%
( 5393
 
3.1%
, 5359
 
3.1%
Other values (521) 91483
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99304
57.1%
Decimal Number 28384
 
16.3%
Space Separator 28027
 
16.1%
Close Punctuation 5410
 
3.1%
Open Punctuation 5409
 
3.1%
Other Punctuation 5382
 
3.1%
Dash Punctuation 1174
 
0.7%
Uppercase Letter 627
 
0.4%
Lowercase Letter 87
 
0.1%
Math Symbol 55
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7058
 
7.1%
6252
 
6.3%
5596
 
5.6%
5496
 
5.5%
5403
 
5.4%
5275
 
5.3%
5200
 
5.2%
5199
 
5.2%
3721
 
3.7%
3309
 
3.3%
Other values (458) 46795
47.1%
Uppercase Letter
ValueCountFrequency (%)
B 289
46.1%
A 53
 
8.5%
S 44
 
7.0%
K 37
 
5.9%
T 30
 
4.8%
I 25
 
4.0%
C 21
 
3.3%
F 17
 
2.7%
V 16
 
2.6%
E 15
 
2.4%
Other values (15) 80
 
12.8%
Lowercase Letter
ValueCountFrequency (%)
e 21
24.1%
n 12
13.8%
c 11
12.6%
r 11
12.6%
t 9
10.3%
b 4
 
4.6%
i 4
 
4.6%
s 3
 
3.4%
w 2
 
2.3%
k 2
 
2.3%
Other values (4) 8
 
9.2%
Decimal Number
ValueCountFrequency (%)
1 8407
29.6%
2 4443
15.7%
3 3008
 
10.6%
0 2362
 
8.3%
4 2154
 
7.6%
5 1844
 
6.5%
6 1760
 
6.2%
7 1564
 
5.5%
8 1434
 
5.1%
9 1408
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 5359
99.6%
/ 10
 
0.2%
. 8
 
0.1%
? 3
 
0.1%
@ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 5394
99.7%
] 16
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 5393
99.7%
[ 16
 
0.3%
Letter Number
ValueCountFrequency (%)
6
66.7%
3
33.3%
Space Separator
ValueCountFrequency (%)
28027
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1174
100.0%
Math Symbol
ValueCountFrequency (%)
~ 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99304
57.1%
Common 73841
42.5%
Latin 723
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7058
 
7.1%
6252
 
6.3%
5596
 
5.6%
5496
 
5.5%
5403
 
5.4%
5275
 
5.3%
5200
 
5.2%
5199
 
5.2%
3721
 
3.7%
3309
 
3.3%
Other values (458) 46795
47.1%
Latin
ValueCountFrequency (%)
B 289
40.0%
A 53
 
7.3%
S 44
 
6.1%
K 37
 
5.1%
T 30
 
4.1%
I 25
 
3.5%
e 21
 
2.9%
C 21
 
2.9%
F 17
 
2.4%
V 16
 
2.2%
Other values (31) 170
23.5%
Common
ValueCountFrequency (%)
28027
38.0%
1 8407
 
11.4%
) 5394
 
7.3%
( 5393
 
7.3%
, 5359
 
7.3%
2 4443
 
6.0%
3 3008
 
4.1%
0 2362
 
3.2%
4 2154
 
2.9%
5 1844
 
2.5%
Other values (12) 7450
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99304
57.1%
ASCII 74555
42.9%
Number Forms 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28027
37.6%
1 8407
 
11.3%
) 5394
 
7.2%
( 5393
 
7.2%
, 5359
 
7.2%
2 4443
 
6.0%
3 3008
 
4.0%
0 2362
 
3.2%
4 2154
 
2.9%
5 1844
 
2.5%
Other values (51) 8164
 
11.0%
Hangul
ValueCountFrequency (%)
7058
 
7.1%
6252
 
6.3%
5596
 
5.6%
5496
 
5.5%
5403
 
5.4%
5275
 
5.3%
5200
 
5.2%
5199
 
5.2%
3721
 
3.7%
3309
 
3.3%
Other values (458) 46795
47.1%
Number Forms
ValueCountFrequency (%)
6
66.7%
3
33.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct2360
Distinct (%)46.2%
Missing4895
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean5298.514
Minimum1004
Maximum10301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:27.857145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1004
5-th percentile1767.2
Q13927
median5559
Q36945
95-th percentile8584
Maximum10301
Range9297
Interquartile range (IQR)3018

Descriptive statistics

Standard deviation2064.4402
Coefficient of variation (CV)0.38962626
Kurtosis-0.85713005
Mean5298.514
Median Absolute Deviation (MAD)1541
Skewness-0.15000465
Sum27048914
Variance4261913.3
MonotonicityNot monotonic
2024-05-11T04:23:28.322237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5699 161
 
1.6%
2570 65
 
0.7%
4576 43
 
0.4%
2478 36
 
0.4%
2569 28
 
0.3%
4799 24
 
0.2%
4793 21
 
0.2%
8589 20
 
0.2%
4778 20
 
0.2%
2571 19
 
0.2%
Other values (2350) 4668
46.7%
(Missing) 4895
48.9%
ValueCountFrequency (%)
1004 1
< 0.1%
1006 1
< 0.1%
1012 2
< 0.1%
1014 1
< 0.1%
1019 2
< 0.1%
1021 2
< 0.1%
1027 1
< 0.1%
1029 2
< 0.1%
1033 1
< 0.1%
1035 2
< 0.1%
ValueCountFrequency (%)
10301 1
 
< 0.1%
8865 1
 
< 0.1%
8863 1
 
< 0.1%
8861 2
< 0.1%
8857 3
< 0.1%
8856 1
 
< 0.1%
8854 1
 
< 0.1%
8849 1
 
< 0.1%
8848 1
 
< 0.1%
8846 3
< 0.1%
Distinct8497
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T04:23:29.218703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length6.3873
Min length1

Characters and Unicode

Total characters63873
Distinct characters986
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7529 ?
Unique (%)75.3%

Sample

1st row주식회사 콘테일
2nd row정훈상회
3rd row대선제분(주)
4th row(주)조은세상에프엔씨
5th row조흥화학공업주식회사
ValueCountFrequency (%)
주식회사 323
 
2.8%
커피 54
 
0.5%
coffee 50
 
0.4%
홍삼나라 23
 
0.2%
식품 20
 
0.2%
roasters 20
 
0.2%
로스터스 19
 
0.2%
한양식품 19
 
0.2%
제일식품 18
 
0.2%
우리식품 18
 
0.2%
Other values (9121) 11092
95.2%
2024-05-11T04:23:30.704431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2650
 
4.1%
2356
 
3.7%
) 2339
 
3.7%
( 2329
 
3.6%
2187
 
3.4%
1660
 
2.6%
1477
 
2.3%
1379
 
2.2%
1010
 
1.6%
978
 
1.5%
Other values (976) 45508
71.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53441
83.7%
Close Punctuation 2340
 
3.7%
Open Punctuation 2330
 
3.6%
Uppercase Letter 1856
 
2.9%
Lowercase Letter 1768
 
2.8%
Space Separator 1660
 
2.6%
Decimal Number 259
 
0.4%
Other Punctuation 199
 
0.3%
Dash Punctuation 19
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2650
 
5.0%
2356
 
4.4%
2187
 
4.1%
1477
 
2.8%
1379
 
2.6%
1010
 
1.9%
978
 
1.8%
766
 
1.4%
757
 
1.4%
729
 
1.4%
Other values (899) 39152
73.3%
Uppercase Letter
ValueCountFrequency (%)
C 184
 
9.9%
F 171
 
9.2%
E 154
 
8.3%
O 150
 
8.1%
A 138
 
7.4%
S 124
 
6.7%
B 115
 
6.2%
R 98
 
5.3%
T 96
 
5.2%
N 81
 
4.4%
Other values (16) 545
29.4%
Lowercase Letter
ValueCountFrequency (%)
e 295
16.7%
o 229
13.0%
a 166
9.4%
r 121
 
6.8%
f 117
 
6.6%
s 105
 
5.9%
n 101
 
5.7%
i 89
 
5.0%
t 84
 
4.8%
c 69
 
3.9%
Other values (15) 392
22.2%
Decimal Number
ValueCountFrequency (%)
2 62
23.9%
1 59
22.8%
3 34
13.1%
0 23
 
8.9%
4 20
 
7.7%
5 17
 
6.6%
9 16
 
6.2%
8 10
 
3.9%
7 9
 
3.5%
6 9
 
3.5%
Other Punctuation
ValueCountFrequency (%)
& 78
39.2%
. 64
32.2%
? 23
 
11.6%
' 19
 
9.5%
, 10
 
5.0%
/ 2
 
1.0%
! 1
 
0.5%
1
 
0.5%
: 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 2339
> 99.9%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2329
> 99.9%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1660
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53430
83.7%
Common 6807
 
10.7%
Latin 3624
 
5.7%
Han 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2650
 
5.0%
2356
 
4.4%
2187
 
4.1%
1477
 
2.8%
1379
 
2.6%
1010
 
1.9%
978
 
1.8%
766
 
1.4%
757
 
1.4%
729
 
1.4%
Other values (892) 39141
73.3%
Latin
ValueCountFrequency (%)
e 295
 
8.1%
o 229
 
6.3%
C 184
 
5.1%
F 171
 
4.7%
a 166
 
4.6%
E 154
 
4.2%
O 150
 
4.1%
A 138
 
3.8%
S 124
 
3.4%
r 121
 
3.3%
Other values (41) 1892
52.2%
Common
ValueCountFrequency (%)
) 2339
34.4%
( 2329
34.2%
1660
24.4%
& 78
 
1.1%
. 64
 
0.9%
2 62
 
0.9%
1 59
 
0.9%
3 34
 
0.5%
? 23
 
0.3%
0 23
 
0.3%
Other values (15) 136
 
2.0%
Han
ValueCountFrequency (%)
3
25.0%
2
16.7%
2
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53428
83.6%
ASCII 10428
 
16.3%
CJK 10
 
< 0.1%
None 4
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2650
 
5.0%
2356
 
4.4%
2187
 
4.1%
1477
 
2.8%
1379
 
2.6%
1010
 
1.9%
978
 
1.8%
766
 
1.4%
757
 
1.4%
729
 
1.4%
Other values (890) 39139
73.3%
ASCII
ValueCountFrequency (%)
) 2339
22.4%
( 2329
22.3%
1660
15.9%
e 295
 
2.8%
o 229
 
2.2%
C 184
 
1.8%
F 171
 
1.6%
a 166
 
1.6%
E 154
 
1.5%
O 150
 
1.4%
Other values (63) 2751
26.4%
CJK
ValueCountFrequency (%)
3
30.0%
2
20.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
2
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct7687
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1999-01-05 00:00:00
Maximum2024-05-09 15:06:44
2024-05-11T04:23:31.367218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:23:32.240966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7846 
U
2154 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowI
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 7846
78.5%
U 2154
 
21.5%

Length

2024-05-11T04:23:32.820826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:33.199365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7846
78.5%
u 2154
 
21.5%
Distinct1214
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T04:23:33.682758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:23:34.122837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식품제조가공업
7951 
기타 식품제조가공업
1960 
<NA>
 
69
도시락제조업
 
17
PB제품 제조업체
 
3

Length

Max length10
Median length7
Mean length7.5662
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품제조가공업 7951
79.5%
기타 식품제조가공업 1960
 
19.6%
<NA> 69
 
0.7%
도시락제조업 17
 
0.2%
PB제품 제조업체 3
 
< 0.1%

Length

2024-05-11T04:23:34.594195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:34.982461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 9911
82.8%
기타 1960
 
16.4%
na 69
 
0.6%
도시락제조업 17
 
0.1%
pb제품 3
 
< 0.1%
제조업체 3
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct7840
Distinct (%)81.1%
Missing328
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean200082.15
Minimum181309.92
Maximum215383.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:35.414824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181309.92
5-th percentile186598.68
Q1192757.74
median201918.73
Q3206024.48
95-th percentile211749.79
Maximum215383.03
Range34073.11
Interquartile range (IQR)13266.736

Descriptive statistics

Standard deviation7973.0692
Coefficient of variation (CV)0.039848977
Kurtosis-1.0506484
Mean200082.15
Median Absolute Deviation (MAD)6640.2006
Skewness-0.23292538
Sum1.9351946 × 109
Variance63569832
MonotonicityNot monotonic
2024-05-11T04:23:35.877061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209790.959909032 199
 
2.0%
189575.815287955 13
 
0.1%
204461.776750011 13
 
0.1%
194807.845295028 13
 
0.1%
205707.089399978 13
 
0.1%
205214.696074247 11
 
0.1%
189369.53962474 11
 
0.1%
190081.372341474 10
 
0.1%
191226.287379467 8
 
0.1%
195509.580070948 8
 
0.1%
Other values (7830) 9373
93.7%
(Missing) 328
 
3.3%
ValueCountFrequency (%)
181309.923908659 1
< 0.1%
182734.07539408 1
< 0.1%
182742.537963411 1
< 0.1%
182830.370717987 1
< 0.1%
182865.612372488 1
< 0.1%
182876.367858149 1
< 0.1%
182879.610355768 1
< 0.1%
182897.251870492 1
< 0.1%
182956.589087267 1
< 0.1%
182967.067219996 1
< 0.1%
ValueCountFrequency (%)
215383.034106 1
 
< 0.1%
215203.880915796 1
 
< 0.1%
215203.799066155 1
 
< 0.1%
215199.030430497 1
 
< 0.1%
215195.884311721 3
< 0.1%
215184.811228094 1
 
< 0.1%
215178.170771585 1
 
< 0.1%
215177.804033096 1
 
< 0.1%
215165.55837609 1
 
< 0.1%
215162.624131128 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct7840
Distinct (%)81.1%
Missing328
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean448446.15
Minimum436946.36
Maximum465136.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:36.329928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum436946.36
5-th percentile441516.5
Q1444049.63
median448055.12
Q3451740.73
95-th percentile458768.95
Maximum465136.86
Range28190.502
Interquartile range (IQR)7691.1033

Descriptive statistics

Standard deviation5273.8717
Coefficient of variation (CV)0.011760323
Kurtosis-0.069990664
Mean448446.15
Median Absolute Deviation (MAD)3881.1581
Skewness0.62844926
Sum4.3373711 × 109
Variance27813723
MonotonicityNot monotonic
2024-05-11T04:23:37.097396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
443481.212174317 199
 
2.0%
441503.181731081 13
 
0.1%
449524.558350945 13
 
0.1%
445901.413432497 13
 
0.1%
443914.194133105 13
 
0.1%
449326.791808157 11
 
0.1%
441629.361414684 11
 
0.1%
441019.186737744 10
 
0.1%
437914.06299827 8
 
0.1%
449388.269219974 8
 
0.1%
Other values (7830) 9373
93.7%
(Missing) 328
 
3.3%
ValueCountFrequency (%)
436946.358720615 1
 
< 0.1%
436997.075839023 1
 
< 0.1%
437099.172901614 1
 
< 0.1%
437546.055336785 1
 
< 0.1%
437562.242368734 1
 
< 0.1%
437607.039565128 5
0.1%
437680.1128998 2
 
< 0.1%
437777.824339474 1
 
< 0.1%
437816.239800826 1
 
< 0.1%
437914.06299827 8
0.1%
ValueCountFrequency (%)
465136.8608906 1
< 0.1%
465025.246646886 1
< 0.1%
464907.336540817 1
< 0.1%
464814.717432497 2
< 0.1%
464751.482559612 1
< 0.1%
464674.288790614 2
< 0.1%
464203.913871704 1
< 0.1%
464134.681988189 2
< 0.1%
463993.614523962 1
< 0.1%
463884.895275557 2
< 0.1%

위생업태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
식품제조가공업
7686 
기타 식품제조가공업
1288 
<NA>
1012 
도시락제조업
 
12
PB제품 제조업체
 
2

Length

Max length10
Median length7
Mean length7.082
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row식품제조가공업
3rd row식품제조가공업
4th row식품제조가공업
5th row식품제조가공업

Common Values

ValueCountFrequency (%)
식품제조가공업 7686
76.9%
기타 식품제조가공업 1288
 
12.9%
<NA> 1012
 
10.1%
도시락제조업 12
 
0.1%
PB제품 제조업체 2
 
< 0.1%

Length

2024-05-11T04:23:37.681039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:38.196117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품제조가공업 8974
79.5%
기타 1288
 
11.4%
na 1012
 
9.0%
도시락제조업 12
 
0.1%
pb제품 2
 
< 0.1%
제조업체 2
 
< 0.1%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)0.8%
Missing7517
Missing (%)75.2%
Infinite0
Infinite (%)0.0%
Mean0.64881192
Minimum0
Maximum59
Zeros1753
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:38.687959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum59
Range59
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4101722
Coefficient of variation (CV)3.7147471
Kurtosis268.36075
Mean0.64881192
Median Absolute Deviation (MAD)0
Skewness14.112649
Sum1611
Variance5.8089299
MonotonicityNot monotonic
2024-05-11T04:23:39.116754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1753
 
17.5%
1 426
 
4.3%
2 176
 
1.8%
3 60
 
0.6%
4 19
 
0.2%
5 15
 
0.1%
6 6
 
0.1%
12 5
 
0.1%
10 5
 
0.1%
7 5
 
0.1%
Other values (11) 13
 
0.1%
(Missing) 7517
75.2%
ValueCountFrequency (%)
0 1753
17.5%
1 426
 
4.3%
2 176
 
1.8%
3 60
 
0.6%
4 19
 
0.2%
5 15
 
0.1%
6 6
 
0.1%
7 5
 
0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
59 1
 
< 0.1%
50 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
19 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 5
0.1%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.7%
Missing7552
Missing (%)75.5%
Infinite0
Infinite (%)0.0%
Mean0.51552288
Minimum0
Maximum60
Zeros1856
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:39.496600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1719513
Coefficient of variation (CV)4.2131036
Kurtosis445.09377
Mean0.51552288
Median Absolute Deviation (MAD)0
Skewness18.114549
Sum1262
Variance4.7173723
MonotonicityNot monotonic
2024-05-11T04:23:40.004173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1856
 
18.6%
1 334
 
3.3%
2 156
 
1.6%
3 55
 
0.5%
4 23
 
0.2%
5 5
 
0.1%
20 4
 
< 0.1%
6 4
 
< 0.1%
10 2
 
< 0.1%
16 2
 
< 0.1%
Other values (6) 7
 
0.1%
(Missing) 7552
75.5%
ValueCountFrequency (%)
0 1856
18.6%
1 334
 
3.3%
2 156
 
1.6%
3 55
 
0.5%
4 23
 
0.2%
5 5
 
0.1%
6 4
 
< 0.1%
7 2
 
< 0.1%
10 2
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
60 1
 
< 0.1%
58 1
 
< 0.1%
20 4
< 0.1%
19 1
 
< 0.1%
16 2
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
10 2
< 0.1%
7 2
< 0.1%
6 4
< 0.1%

영업장주변구분명
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7857 
기타
1131 
주택가주변
942 
아파트지역
 
58
유흥업소밀집지역
 
8
Other values (2)
 
4

Length

Max length8
Median length4
Mean length3.8785
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row기타
3rd row기타
4th row<NA>
5th row기타

Common Values

ValueCountFrequency (%)
<NA> 7857
78.6%
기타 1131
 
11.3%
주택가주변 942
 
9.4%
아파트지역 58
 
0.6%
유흥업소밀집지역 8
 
0.1%
학교정화(상대) 3
 
< 0.1%
결혼예식장주변 1
 
< 0.1%

Length

2024-05-11T04:23:40.570680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:41.003314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7857
78.6%
기타 1131
 
11.3%
주택가주변 942
 
9.4%
아파트지역 58
 
0.6%
유흥업소밀집지역 8
 
0.1%
학교정화(상대 3
 
< 0.1%
결혼예식장주변 1
 
< 0.1%

등급구분명
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7857 
기타
1355 
자율
 
553
우수
 
133
관리
 
56
Other values (3)
 
46

Length

Max length4
Median length4
Mean length3.5678
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row기타
3rd row기타
4th row<NA>
5th row기타

Common Values

ValueCountFrequency (%)
<NA> 7857
78.6%
기타 1355
 
13.6%
자율 553
 
5.5%
우수 133
 
1.3%
관리 56
 
0.6%
23
 
0.2%
13
 
0.1%
지도 10
 
0.1%

Length

2024-05-11T04:23:41.648369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:42.142186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7857
78.6%
기타 1355
 
13.6%
자율 553
 
5.5%
우수 133
 
1.3%
관리 56
 
0.6%
23
 
0.2%
13
 
0.1%
지도 10
 
0.1%

급수시설구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6196 
상수도전용
3786 
상수도(음용)지하수(주방용)겸용
 
8
지하수전용
 
7
간이상수도
 
3

Length

Max length17
Median length4
Mean length4.39
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row상수도전용

Common Values

ValueCountFrequency (%)
<NA> 6196
62.0%
상수도전용 3786
37.9%
상수도(음용)지하수(주방용)겸용 8
 
0.1%
지하수전용 7
 
0.1%
간이상수도 3
 
< 0.1%

Length

2024-05-11T04:23:42.535924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:42.903661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6196
62.0%
상수도전용 3786
37.9%
상수도(음용)지하수(주방용)겸용 8
 
0.1%
지하수전용 7
 
0.1%
간이상수도 3
 
< 0.1%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9051 
0
949 

Length

Max length4
Median length4
Mean length3.7153
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9051
90.5%
0 949
 
9.5%

Length

2024-05-11T04:23:43.296031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:43.652756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9051
90.5%
0 949
 
9.5%

본사종업원수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)0.1%
Missing4392
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean0.029243937
Minimum0
Maximum15
Zeros5544
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:43.990771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41832554
Coefficient of variation (CV)14.304693
Kurtosis897.39734
Mean0.029243937
Median Absolute Deviation (MAD)0
Skewness26.949397
Sum164
Variance0.17499625
MonotonicityNot monotonic
2024-05-11T04:23:44.483504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5544
55.4%
1 28
 
0.3%
2 18
 
0.2%
3 8
 
0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
15 3
 
< 0.1%
(Missing) 4392
43.9%
ValueCountFrequency (%)
0 5544
55.4%
1 28
 
0.3%
2 18
 
0.2%
3 8
 
0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
15 3
 
< 0.1%
ValueCountFrequency (%)
15 3
 
< 0.1%
5 3
 
< 0.1%
4 4
 
< 0.1%
3 8
 
0.1%
2 18
 
0.2%
1 28
 
0.3%
0 5544
55.4%

공장사무직종업원수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9
Distinct (%)0.2%
Missing4359
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean0.043432016
Minimum0
Maximum33
Zeros5485
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:45.034444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51405066
Coefficient of variation (CV)11.835754
Kurtosis3009.9058
Mean0.043432016
Median Absolute Deviation (MAD)0
Skewness48.349446
Sum245
Variance0.26424808
MonotonicityNot monotonic
2024-05-11T04:23:45.560163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 5485
54.9%
1 120
 
1.2%
2 24
 
0.2%
3 6
 
0.1%
4 2
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
33 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 4359
43.6%
ValueCountFrequency (%)
0 5485
54.9%
1 120
 
1.2%
2 24
 
0.2%
3 6
 
0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
33 1
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 6
 
0.1%
2 24
 
0.2%
1 120
 
1.2%
0 5485
54.9%

공장판매직종업원수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct11
Distinct (%)0.2%
Missing4392
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean0.035663338
Minimum0
Maximum30
Zeros5519
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:45.899637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55912543
Coefficient of variation (CV)15.677877
Kurtosis1785.5125
Mean0.035663338
Median Absolute Deviation (MAD)0
Skewness37.85376
Sum200
Variance0.31262125
MonotonicityNot monotonic
2024-05-11T04:23:46.240961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 5519
55.2%
1 63
 
0.6%
2 9
 
0.1%
3 6
 
0.1%
5 5
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
4 1
 
< 0.1%
30 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 4392
43.9%
ValueCountFrequency (%)
0 5519
55.2%
1 63
 
0.6%
2 9
 
0.1%
3 6
 
0.1%
4 1
 
< 0.1%
5 5
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
20 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 5
 
0.1%
4 1
 
< 0.1%
3 6
 
0.1%
2 9
 
0.1%
1 63
0.6%

공장생산직종업원수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)0.3%
Missing4253
Missing (%)42.5%
Infinite0
Infinite (%)0.0%
Mean0.22463894
Minimum0
Maximum30
Zeros5226
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:46.689261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1338379
Coefficient of variation (CV)5.0473792
Kurtosis183.90868
Mean0.22463894
Median Absolute Deviation (MAD)0
Skewness11.138729
Sum1291
Variance1.2855884
MonotonicityNot monotonic
2024-05-11T04:23:47.168750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5226
52.3%
1 262
 
2.6%
2 122
 
1.2%
3 40
 
0.4%
4 35
 
0.4%
5 21
 
0.2%
6 11
 
0.1%
7 8
 
0.1%
10 4
 
< 0.1%
18 3
 
< 0.1%
Other values (7) 15
 
0.1%
(Missing) 4253
42.5%
ValueCountFrequency (%)
0 5226
52.3%
1 262
 
2.6%
2 122
 
1.2%
3 40
 
0.4%
4 35
 
0.4%
5 21
 
0.2%
6 11
 
0.1%
7 8
 
0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
20 3
 
< 0.1%
18 3
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 4
< 0.1%
9 2
 
< 0.1%
8 3
 
< 0.1%
7 8
0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5305 
임대
2992 
자가
1703 

Length

Max length4
Median length4
Mean length3.061
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row임대
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5305
53.0%
임대 2992
29.9%
자가 1703
 
17.0%

Length

2024-05-11T04:23:47.883643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:23:48.386806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5305
53.0%
임대 2992
29.9%
자가 1703
 
17.0%

보증액
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct33
Distinct (%)1.6%
Missing7957
Missing (%)79.6%
Infinite0
Infinite (%)0.0%
Mean2692121.9
Minimum0
Maximum1.2 × 109
Zeros1880
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:49.173843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10000000
Maximum1.2 × 109
Range1.2 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28772362
Coefficient of variation (CV)10.687615
Kurtosis1472.4869
Mean2692121.9
Median Absolute Deviation (MAD)0
Skewness35.869769
Sum5.5000051 × 109
Variance8.2784883 × 1014
MonotonicityNot monotonic
2024-05-11T04:23:50.076721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 1880
 
18.8%
10000000 40
 
0.4%
20000000 28
 
0.3%
30000000 19
 
0.2%
5000000 13
 
0.1%
15000000 10
 
0.1%
40000000 8
 
0.1%
50000000 5
 
0.1%
100000000 4
 
< 0.1%
45000000 4
 
< 0.1%
Other values (23) 32
 
0.3%
(Missing) 7957
79.6%
ValueCountFrequency (%)
0 1880
18.8%
90 1
 
< 0.1%
500 1
 
< 0.1%
4500 1
 
< 0.1%
2000000 2
 
< 0.1%
3000000 2
 
< 0.1%
4000000 1
 
< 0.1%
5000000 13
 
0.1%
6000000 2
 
< 0.1%
7000000 2
 
< 0.1%
ValueCountFrequency (%)
1200000000 1
 
< 0.1%
200000000 1
 
< 0.1%
170000000 1
 
< 0.1%
150000000 1
 
< 0.1%
140000000 1
 
< 0.1%
100000000 4
< 0.1%
90000000 1
 
< 0.1%
80000000 2
< 0.1%
70000000 3
< 0.1%
60000000 2
< 0.1%

월세액
Real number (ℝ)

MISSING  ZEROS 

Distinct61
Distinct (%)3.0%
Missing7968
Missing (%)79.7%
Infinite0
Infinite (%)0.0%
Mean104598.58
Minimum0
Maximum12000000
Zeros1883
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:50.863916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile622500
Maximum12000000
Range12000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation541606.12
Coefficient of variation (CV)5.1779492
Kurtosis153.02148
Mean104598.58
Median Absolute Deviation (MAD)0
Skewness9.9323022
Sum2.1254431 × 108
Variance2.9333719 × 1011
MonotonicityNot monotonic
2024-05-11T04:23:51.672630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1883
 
18.8%
1000000 17
 
0.2%
600000 11
 
0.1%
500000 10
 
0.1%
2000000 7
 
0.1%
700000 6
 
0.1%
300000 6
 
0.1%
4000000 5
 
0.1%
3000000 5
 
0.1%
900000 4
 
< 0.1%
Other values (51) 78
 
0.8%
(Missing) 7968
79.7%
ValueCountFrequency (%)
0 1883
18.8%
18 1
 
< 0.1%
50 1
 
< 0.1%
90 1
 
< 0.1%
150 1
 
< 0.1%
4000 1
 
< 0.1%
130000 1
 
< 0.1%
150000 1
 
< 0.1%
200000 1
 
< 0.1%
240000 1
 
< 0.1%
ValueCountFrequency (%)
12000000 1
 
< 0.1%
7940000 1
 
< 0.1%
4500000 2
 
< 0.1%
4100000 1
 
< 0.1%
4000000 5
0.1%
3800000 1
 
< 0.1%
3500000 1
 
< 0.1%
3000000 5
0.1%
2700000 1
 
< 0.1%
2600000 1
 
< 0.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing943
Missing (%)9.4%
Memory size97.7 KiB
False
9056 
True
 
1
(Missing)
943 
ValueCountFrequency (%)
False 9056
90.6%
True 1
 
< 0.1%
(Missing) 943
 
9.4%
2024-05-11T04:23:52.196276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING  ZEROS 

Distinct742
Distinct (%)8.2%
Missing943
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean4.4237562
Minimum0
Maximum670.65
Zeros7965
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T04:23:52.675625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.246
Maximum670.65
Range670.65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.811422
Coefficient of variation (CV)5.3826253
Kurtosis172.11711
Mean4.4237562
Median Absolute Deviation (MAD)0
Skewness10.722676
Sum40065.96
Variance566.98382
MonotonicityNot monotonic
2024-05-11T04:23:53.282389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7965
79.7%
3.0 23
 
0.2%
2.0 17
 
0.2%
0.72 17
 
0.2%
1.0 17
 
0.2%
33.0 16
 
0.2%
0.25 15
 
0.1%
5.0 14
 
0.1%
1.5 14
 
0.1%
3.3 11
 
0.1%
Other values (732) 948
 
9.5%
(Missing) 943
 
9.4%
ValueCountFrequency (%)
0.0 7965
79.7%
0.11 1
 
< 0.1%
0.25 15
 
0.1%
0.28 1
 
< 0.1%
0.3 2
 
< 0.1%
0.33 1
 
< 0.1%
0.35 1
 
< 0.1%
0.36 3
 
< 0.1%
0.39 1
 
< 0.1%
0.4 1
 
< 0.1%
ValueCountFrequency (%)
670.65 1
< 0.1%
512.11 1
< 0.1%
450.0 1
< 0.1%
448.2 1
< 0.1%
428.0 1
< 0.1%
368.02 1
< 0.1%
325.77 1
< 0.1%
292.0 1
< 0.1%
285.15 1
< 0.1%
283.25 1
< 0.1%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

홈페이지
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing9987
Missing (%)99.9%
Memory size156.2 KiB
2024-05-11T04:23:53.911510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.538462
Min length13

Characters and Unicode

Total characters241
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st rowsung9284@hanmail.net
2nd rowlokum@yenicheri.co.kr
3rd rowwww.starcollege.or.kr
4th rowwww.nanumfood.co.kr
5th rowssdd2230@naver.com
ValueCountFrequency (%)
sung9284@hanmail.net 1
 
7.7%
lokum@yenicheri.co.kr 1
 
7.7%
www.starcollege.or.kr 1
 
7.7%
www.nanumfood.co.kr 1
 
7.7%
ssdd2230@naver.com 1
 
7.7%
jo8409031@naver.com 1
 
7.7%
esperecoffee@naver.com 1
 
7.7%
yuns1313@naver.com 1
 
7.7%
perry6611@nate.com 1
 
7.7%
070-4276-4200 1
 
7.7%
Other values (3) 3
23.1%
2024-05-11T04:23:55.240842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22
 
9.1%
o 18
 
7.5%
. 18
 
7.5%
r 16
 
6.6%
n 15
 
6.2%
a 13
 
5.4%
c 12
 
5.0%
m 11
 
4.6%
w 9
 
3.7%
@ 9
 
3.7%
Other values (24) 98
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 174
72.2%
Decimal Number 38
 
15.8%
Other Punctuation 27
 
11.2%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22
12.6%
o 18
 
10.3%
r 16
 
9.2%
n 15
 
8.6%
a 13
 
7.5%
c 12
 
6.9%
m 11
 
6.3%
w 9
 
5.2%
k 6
 
3.4%
s 6
 
3.4%
Other values (12) 46
26.4%
Decimal Number
ValueCountFrequency (%)
0 8
21.1%
1 6
15.8%
2 5
13.2%
7 4
10.5%
3 4
10.5%
4 4
10.5%
6 3
 
7.9%
8 2
 
5.3%
9 2
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 18
66.7%
@ 9
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174
72.2%
Common 67
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22
12.6%
o 18
 
10.3%
r 16
 
9.2%
n 15
 
8.6%
a 13
 
7.5%
c 12
 
6.9%
m 11
 
6.3%
w 9
 
5.2%
k 6
 
3.4%
s 6
 
3.4%
Other values (12) 46
26.4%
Common
ValueCountFrequency (%)
. 18
26.9%
@ 9
13.4%
0 8
11.9%
1 6
 
9.0%
2 5
 
7.5%
7 4
 
6.0%
3 4
 
6.0%
4 4
 
6.0%
6 3
 
4.5%
8 2
 
3.0%
Other values (2) 4
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22
 
9.1%
o 18
 
7.5%
. 18
 
7.5%
r 16
 
6.6%
n 15
 
6.2%
a 13
 
5.4%
c 12
 
5.0%
m 11
 
4.6%
w 9
 
3.7%
@ 9
 
3.7%
Other values (24) 98
40.7%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
514031500003150000-106-2014-000012014-01-14<NA>1영업/정상1영업<NA><NA><NA><NA>20931899135.08157-779서울특별시 강서구 염창동 240-21 비-408 우림블루나인서울특별시 강서구 양천로 583 (염창동, 비-408 우림블루나인)7547주식회사 콘테일2023-10-04 11:34:37U2022-10-31 00:06:00.0식품제조가공업187952.560028450562.020226<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
905832300003230000-106-2001-0329020011203<NA>3폐업2폐업20161223<NA><NA><NA><NA>30.00138881서울특별시 송파구 가락동 600-0번지 (청과관련2동19-1)서울특별시 송파구 양재대로 932 (가락동,(청과관련2동19-1))5699정훈상회2016-12-21 16:20:48I2018-08-31 23:59:59.0식품제조가공업209790.959909443481.212174식품제조가공업<NA><NA>기타기타<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
660631800003180000-106-1997-0037119970417<NA>3폐업2폐업20140418<NA><NA><NA>022636310516,354.67150834서울특별시 영등포구 문래동3가 9-0번지서울특별시 영등포구 신길로 87 (문래동3가)<NA>대선제분(주)2002-05-22 00:00:00I2018-08-31 23:59:59.0식품제조가공업191207.828725445969.291108식품제조가공업00기타기타<NA>00000<NA>00N0.0<NA><NA><NA>
401831200003120000-106-2005-0000420050126<NA>3폐업2폐업20130213<NA><NA><NA>023143135499.66120827서울특별시 서대문구 연희동 715-2번지서울특별시 서대문구 홍연길 76 (연희동)3695(주)조은세상에프엔씨2012-11-22 17:05:47I2018-08-31 23:59:59.0식품제조가공업193819.617385452676.355021식품제조가공업<NA><NA><NA><NA><NA><NA>0000임대<NA><NA>N0.0<NA><NA><NA>
559631600003160000-106-1997-0005219970425<NA>3폐업2폐업19971106<NA><NA><NA>02 7558261529.70152887서울특별시 구로구 신도림동 306-0번지<NA><NA>조흥화학공업주식회사2001-09-26 00:00:00I2018-08-31 23:59:59.0식품제조가공업<NA><NA>식품제조가공업00기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
1013032400003240000-106-2001-0116120011108<NA>3폐업2폐업20201110<NA><NA><NA>02 486074149.50134814서울특별시 강동구 길동 414-2 4층서울특별시 강동구 천호대로175길 52 (길동,4층)5353동원건강식품2020-11-10 09:54:23U2020-11-12 02:40:00.0식품제조가공업212093.609201448309.197385식품제조가공업<NA><NA>유흥업소밀집지역기타<NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
1029332400003240000-106-2009-0001220091228<NA>3폐업2폐업20130114<NA><NA><NA>02 444 760242.35134861서울특별시 강동구 천호동 19-1번지 우성상가101호<NA><NA>설악식품2012-03-02 15:53:03I2018-08-31 23:59:59.0식품제조가공업212197.189535449731.44017식품제조가공업<NA><NA><NA><NA>상수도전용<NA>0000임대<NA><NA>N0.0<NA><NA><NA>
217730500003050000-106-2001-0151320011127<NA>3폐업2폐업20190320<NA><NA><NA>02 22484990145.92130875서울특별시 동대문구 휘경동 72-7번지 (4층)서울특별시 동대문구 망우로 117 (휘경동, 4층)2436유준푸드2019-03-20 10:54:16U2019-03-22 02:40:00.0식품제조가공업205754.942836454349.694318식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
543131500003150000-106-2004-0000220040224<NA>3폐업2폐업20050422<NA><NA><NA>022666115423.00157829서울특별시 강서구 내발산동 678번지<NA><NA>짱구도시락2004-02-24 00:00:00I2018-08-31 23:59:59.0식품제조가공업185455.545472450233.085942식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
148530300003030000-106-2008-0001020080805<NA>3폐업2폐업20130114<NA><NA><NA>022295173861.80133812서울특별시 성동구 마장동 465-6번지 지상1층서울특별시 성동구 청계천로10가길 72-17, 1층 (마장동, 465-6번지)4704온누리미트2012-11-15 13:59:42I2018-08-31 23:59:59.0식품제조가공업203140.276864452116.187204식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N15.6<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
32930500003050000-106-2015-000252015-12-02<NA>3폐업2폐업2024-01-25<NA><NA><NA>02 2274118632.13130-823서울특별시 동대문구 용두동 252-159서울특별시 동대문구 천호대로 52 (용두동, 신일빌딩1층)2587(유)순희네에프엔비2024-01-25 16:42:01U2023-11-30 22:07:00.0기타 식품제조가공업202491.159883452378.673834<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
917532300003230000-106-1998-0017719980306<NA>3폐업2폐업20031029<NA><NA><NA>023401850096.72138817서울특별시 송파구 마천동 10-10번지<NA><NA>다물농산2003-10-30 00:00:00I2018-08-31 23:59:59.0식품제조가공업212785.915247444301.862964식품제조가공업<NA><NA>주택가주변우수상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
334630900003090000-106-1982-0000119820703<NA>3폐업2폐업19971203<NA><NA><NA>02 9921191<NA>132854서울특별시 도봉구 방학동 720번지<NA><NA>서울미원(주)2002-02-27 00:00:00I2018-08-31 23:59:59.0식품제조가공업204086.050996462924.421741식품제조가공업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
70130100003010000-106-2021-0000120210324<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.50100272서울특별시 중구 필동2가 98-1 낙원빌딩 1층서울특별시 중구 필동로 32, 낙원빌딩 1층 (필동2가)4626hebe coffee(헤베 커피)2021-03-24 16:21:40I2021-03-26 00:22:59.0기타 식품제조가공업199581.368047450732.918935기타 식품제조가공업00<NA><NA><NA>00000임대00N0.0<NA><NA><NA>
932132300003230000-106-2001-0277520010119<NA>3폐업2폐업20070117<NA><NA><NA>02 402888530.00138881서울특별시 송파구 가락동 600번지 건고추매장 22-1<NA><NA>(주)한강앤드서울2006-09-08 00:00:00I2018-08-31 23:59:59.0식품제조가공업209790.959909443481.212174식품제조가공업<NA><NA>기타기타<NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
646031800003180000-106-2004-0000920040414<NA>3폐업2폐업20060802<NA><NA><NA>02 8313603<NA>150822서울특별시 영등포구 대림동 965-5번지<NA><NA>자연제과2004-12-03 00:00:00I2018-08-31 23:59:59.0식품제조가공업191695.825239443228.349007식품제조가공업00<NA><NA><NA>00000자가00N0.0<NA><NA><NA>
734932000003200000-106-2001-0080620010725<NA>3폐업2폐업20020321<NA><NA><NA>8891160<NA>151833서울특별시 관악구 봉천동 1563-19번지<NA><NA>고추랜드2001-12-07 00:00:00I2018-08-31 23:59:59.0식품제조가공업194953.657616441947.52533식품제조가공업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA>
322530800003080000-106-1998-0014819981104<NA>3폐업2폐업20020325<NA><NA><NA>02 987729828.95142886서울특별시 강북구 수유동 472-498번지<NA><NA>하나로식품2002-07-30 00:00:00I2018-08-31 23:59:59.0식품제조가공업201499.51277458955.428739식품제조가공업11주택가주변기타상수도전용<NA>0000<NA><NA><NA>N0.0<NA><NA><NA>
549831600003160000-106-2015-0001820150123<NA>1영업/정상1영업<NA><NA><NA><NA>031 444336084.66152816서울특별시 구로구 개봉동 403-92서울특별시 구로구 개봉로 17-1, 1층 101호 (개봉동)8352보리마을2022-07-08 14:12:43U2021-12-06 23:02:00.0식품제조가공업187201.373986442802.399122<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
997632400003240000-106-2006-0001020060711<NA>3폐업2폐업20120305<NA><NA><NA><NA>89.45134822서울특별시 강동구 둔촌동 490-12번지<NA><NA>행복한시간2011-03-14 14:19:47I2018-08-31 23:59:59.0식품제조가공업212246.369633447581.449698식품제조가공업<NA><NA><NA><NA>상수도전용<NA>0000임대00N0.0<NA><NA><NA>