Overview

Dataset statistics

Number of variables47
Number of observations3230
Missing cells32943
Missing cells (%)21.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory404.0 B

Variable types

Categorical19
Text7
DateTime4
Unsupported4
Numeric11
Boolean2

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,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-17910/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (54.0%)Imbalance
사용시작지하층 is highly imbalanced (54.7%)Imbalance
사용끝지하층 is highly imbalanced (65.0%)Imbalance
조건부허가시작일자 is highly imbalanced (99.3%)Imbalance
조건부허가종료일자 is highly imbalanced (99.3%)Imbalance
건물소유구분명 is highly imbalanced (75.9%)Imbalance
인허가취소일자 has 3230 (100.0%) missing valuesMissing
폐업일자 has 1057 (32.7%) missing valuesMissing
휴업시작일자 has 3230 (100.0%) missing valuesMissing
휴업종료일자 has 3230 (100.0%) missing valuesMissing
재개업일자 has 3230 (100.0%) missing valuesMissing
전화번호 has 1135 (35.1%) missing valuesMissing
도로명주소 has 1237 (38.3%) missing valuesMissing
도로명우편번호 has 1257 (38.9%) missing valuesMissing
좌표정보(X) has 111 (3.4%) missing valuesMissing
좌표정보(Y) has 111 (3.4%) missing valuesMissing
건물지상층수 has 1193 (36.9%) missing valuesMissing
건물지하층수 has 1231 (38.1%) missing valuesMissing
사용시작지상층 has 1515 (46.9%) missing valuesMissing
사용끝지상층 has 2067 (64.0%) missing valuesMissing
발한실여부 has 539 (16.7%) missing valuesMissing
좌석수 has 597 (18.5%) missing valuesMissing
조건부허가신고사유 has 3226 (99.9%) missing valuesMissing
여성종사자수 has 2312 (71.6%) missing valuesMissing
침대수 has 1948 (60.3%) missing valuesMissing
다중이용업소여부 has 487 (15.1%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = 29.89388769)Skewed
좌석수 is highly skewed (γ1 = 44.99351053)Skewed
여성종사자수 is highly skewed (γ1 = 20.13335592)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
소재지면적 has 152 (4.7%) zerosZeros
건물지상층수 has 1731 (53.6%) zerosZeros
건물지하층수 has 1815 (56.2%) zerosZeros
사용시작지상층 has 913 (28.3%) zerosZeros
사용끝지상층 has 393 (12.2%) zerosZeros
좌석수 has 208 (6.4%) zerosZeros
여성종사자수 has 869 (26.9%) zerosZeros
침대수 has 1082 (33.5%) zerosZeros

Reproduction

Analysis started2024-04-06 11:39:29.019154
Analysis finished2024-04-06 11:39:31.562404
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
3080000
3230 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3080000
2nd row3080000
3rd row3080000
4th row3080000
5th row3080000

Common Values

ValueCountFrequency (%)
3080000 3230
100.0%

Length

2024-04-06T20:39:31.685697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:31.856348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 3230
100.0%

관리번호
Text

UNIQUE 

Distinct3230
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2024-04-06T20:39:32.135303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters71060
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

Unique3230 ?
Unique (%)100.0%

Sample

1st row3080000-204-1966-00669
2nd row3080000-204-1966-01257
3rd row3080000-204-1966-01258
4th row3080000-204-1969-00771
5th row3080000-204-1969-01337
ValueCountFrequency (%)
3080000-204-1966-00669 1
 
< 0.1%
3080000-211-2017-00012 1
 
< 0.1%
3080000-211-2017-00003 1
 
< 0.1%
3080000-211-2017-00025 1
 
< 0.1%
3080000-211-2017-00004 1
 
< 0.1%
3080000-211-2017-00005 1
 
< 0.1%
3080000-211-2017-00006 1
 
< 0.1%
3080000-211-2017-00007 1
 
< 0.1%
3080000-211-2017-00008 1
 
< 0.1%
3080000-211-2017-00009 1
 
< 0.1%
Other values (3220) 3220
99.7%
2024-04-06T20:39:32.778372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30481
42.9%
- 9690
 
13.6%
2 7558
 
10.6%
1 7041
 
9.9%
3 4433
 
6.2%
8 4282
 
6.0%
9 2438
 
3.4%
4 2156
 
3.0%
5 1136
 
1.6%
6 934
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61370
86.4%
Dash Punctuation 9690
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30481
49.7%
2 7558
 
12.3%
1 7041
 
11.5%
3 4433
 
7.2%
8 4282
 
7.0%
9 2438
 
4.0%
4 2156
 
3.5%
5 1136
 
1.9%
6 934
 
1.5%
7 911
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 9690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30481
42.9%
- 9690
 
13.6%
2 7558
 
10.6%
1 7041
 
9.9%
3 4433
 
6.2%
8 4282
 
6.0%
9 2438
 
3.4%
4 2156
 
3.0%
5 1136
 
1.6%
6 934
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30481
42.9%
- 9690
 
13.6%
2 7558
 
10.6%
1 7041
 
9.9%
3 4433
 
6.2%
8 4282
 
6.0%
9 2438
 
3.4%
4 2156
 
3.0%
5 1136
 
1.6%
6 934
 
1.3%
Distinct2507
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum1966-03-12 00:00:00
Maximum2024-04-03 00:00:00
2024-04-06T20:39:33.047772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:39:33.310306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3230
Missing (%)100.0%
Memory size28.5 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
3
2173 
1
1057 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 2173
67.3%
1 1057
32.7%

Length

2024-04-06T20:39:33.562902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:33.721839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2173
67.3%
1 1057
32.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
폐업
2173 
영업/정상
1057 

Length

Max length5
Median length2
Mean length2.9817337
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2173
67.3%
영업/정상 1057
32.7%

Length

2024-04-06T20:39:33.923662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:34.092426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2173
67.3%
영업/정상 1057
32.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2
2173 
1
1057 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 2173
67.3%
1 1057
32.7%

Length

2024-04-06T20:39:34.257566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:34.429348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2173
67.3%
1 1057
32.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
폐업
2173 
영업
1057 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2173
67.3%
영업 1057
32.7%

Length

2024-04-06T20:39:34.593057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:34.775091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2173
67.3%
영업 1057
32.7%

폐업일자
Date

MISSING 

Distinct1712
Distinct (%)78.8%
Missing1057
Missing (%)32.7%
Memory size25.4 KiB
Minimum1992-01-08 00:00:00
Maximum2024-04-04 00:00:00
2024-04-06T20:39:34.965251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:39:35.246660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3230
Missing (%)100.0%
Memory size28.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3230
Missing (%)100.0%
Memory size28.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3230
Missing (%)100.0%
Memory size28.5 KiB

전화번호
Text

MISSING 

Distinct1892
Distinct (%)90.3%
Missing1135
Missing (%)35.1%
Memory size25.4 KiB
2024-04-06T20:39:35.725586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.590453
Min length2

Characters and Unicode

Total characters22187
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1725 ?
Unique (%)82.3%

Sample

1st row02 9819584
2nd row02 9824012
3rd row0209814174
4th row0209941967
5th row0209848002
ValueCountFrequency (%)
02 1736
40.6%
070 48
 
1.1%
988 32
 
0.7%
980 27
 
0.6%
945 26
 
0.6%
900 23
 
0.5%
987 20
 
0.5%
999 19
 
0.4%
990 15
 
0.4%
989 14
 
0.3%
Other values (1915) 2314
54.1%
2024-04-06T20:39:36.398034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4049
18.2%
9 3386
15.3%
2 3070
13.8%
2934
13.2%
8 2060
9.3%
7 1168
 
5.3%
5 1135
 
5.1%
1 1116
 
5.0%
3 1100
 
5.0%
6 1085
 
4.9%
Other values (2) 1084
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19252
86.8%
Space Separator 2934
 
13.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4049
21.0%
9 3386
17.6%
2 3070
15.9%
8 2060
10.7%
7 1168
 
6.1%
5 1135
 
5.9%
1 1116
 
5.8%
3 1100
 
5.7%
6 1085
 
5.6%
4 1083
 
5.6%
Space Separator
ValueCountFrequency (%)
2934
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22187
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4049
18.2%
9 3386
15.3%
2 3070
13.8%
2934
13.2%
8 2060
9.3%
7 1168
 
5.3%
5 1135
 
5.1%
1 1116
 
5.0%
3 1100
 
5.0%
6 1085
 
4.9%
Other values (2) 1084
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4049
18.2%
9 3386
15.3%
2 3070
13.8%
2934
13.2%
8 2060
9.3%
7 1168
 
5.3%
5 1135
 
5.1%
1 1116
 
5.0%
3 1100
 
5.0%
6 1085
 
4.9%
Other values (2) 1084
 
4.9%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1383
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.247833
Minimum0
Maximum330
Zeros152
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:36.649043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117.1
median25
Q337.165
95-th percentile89.4855
Maximum330
Range330
Interquartile range (IQR)20.065

Descriptive statistics

Standard deviation30.307801
Coefficient of variation (CV)0.91157222
Kurtosis16.093883
Mean33.247833
Median Absolute Deviation (MAD)8.785
Skewness3.2610003
Sum107390.5
Variance918.5628
MonotonicityNot monotonic
2024-04-06T20:39:36.863741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 152
 
4.7%
33.0 118
 
3.7%
30.0 60
 
1.9%
24.0 54
 
1.7%
19.8 52
 
1.6%
20.0 51
 
1.6%
23.1 45
 
1.4%
16.5 41
 
1.3%
26.4 41
 
1.3%
27.0 39
 
1.2%
Other values (1373) 2577
79.8%
ValueCountFrequency (%)
0.0 152
4.7%
0.16 1
 
< 0.1%
2.0 1
 
< 0.1%
3.0 1
 
< 0.1%
3.3 2
 
0.1%
3.74 1
 
< 0.1%
3.9 3
 
0.1%
4.0 2
 
0.1%
4.28 1
 
< 0.1%
5.17 1
 
< 0.1%
ValueCountFrequency (%)
330.0 1
< 0.1%
314.96 1
< 0.1%
293.16 1
< 0.1%
259.2 1
< 0.1%
244.2 1
< 0.1%
242.0 1
< 0.1%
231.6 1
< 0.1%
218.13 1
< 0.1%
214.5 1
< 0.1%
205.0 2
0.1%
Distinct119
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2024-04-06T20:39:37.373729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1021672
Min length6

Characters and Unicode

Total characters19710
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

Unique14 ?
Unique (%)0.4%

Sample

1st row142100
2nd row142823
3rd row142823
4th row142868
5th row142816
ValueCountFrequency (%)
142876 138
 
4.3%
142878 133
 
4.1%
142804 129
 
4.0%
142100 120
 
3.7%
142872 112
 
3.5%
142810 111
 
3.4%
142864 110
 
3.4%
142805 99
 
3.1%
142877 89
 
2.8%
142874 82
 
2.5%
Other values (109) 2107
65.2%
2024-04-06T20:39:38.043097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4086
20.7%
2 3813
19.3%
4 3734
18.9%
8 3545
18.0%
0 1409
 
7.1%
7 1275
 
6.5%
6 734
 
3.7%
5 335
 
1.7%
- 330
 
1.7%
9 245
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19380
98.3%
Dash Punctuation 330
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4086
21.1%
2 3813
19.7%
4 3734
19.3%
8 3545
18.3%
0 1409
 
7.3%
7 1275
 
6.6%
6 734
 
3.8%
5 335
 
1.7%
9 245
 
1.3%
3 204
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4086
20.7%
2 3813
19.3%
4 3734
18.9%
8 3545
18.0%
0 1409
 
7.1%
7 1275
 
6.5%
6 734
 
3.7%
5 335
 
1.7%
- 330
 
1.7%
9 245
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4086
20.7%
2 3813
19.3%
4 3734
18.9%
8 3545
18.0%
0 1409
 
7.1%
7 1275
 
6.5%
6 734
 
3.7%
5 335
 
1.7%
- 330
 
1.7%
9 245
 
1.2%
Distinct2610
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2024-04-06T20:39:38.600199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length51
Mean length24.13808
Min length16

Characters and Unicode

Total characters77966
Distinct characters317
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2157 ?
Unique (%)66.8%

Sample

1st row서울특별시 강북구 미아동 111-0번지
2nd row서울특별시 강북구 미아동 776-37번지
3rd row서울특별시 강북구 미아동 762-67번지
4th row서울특별시 강북구 번동 464-14번지
5th row서울특별시 강북구 미아동 742-2번지
ValueCountFrequency (%)
서울특별시 3230
22.1%
강북구 3228
22.1%
미아동 1475
 
10.1%
수유동 1249
 
8.6%
번동 457
 
3.1%
1층 315
 
2.2%
2층 80
 
0.5%
우이동 49
 
0.3%
3층 34
 
0.2%
지상1층 31
 
0.2%
Other values (2739) 4460
30.5%
2024-04-06T20:39:39.399210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14031
18.0%
1 3509
 
4.5%
3336
 
4.3%
3262
 
4.2%
3248
 
4.2%
3239
 
4.2%
3237
 
4.2%
3231
 
4.1%
3230
 
4.1%
3230
 
4.1%
Other values (307) 34413
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43393
55.7%
Decimal Number 16930
 
21.7%
Space Separator 14031
 
18.0%
Dash Punctuation 3107
 
4.0%
Open Punctuation 168
 
0.2%
Close Punctuation 167
 
0.2%
Other Punctuation 76
 
0.1%
Uppercase Letter 72
 
0.1%
Lowercase Letter 21
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3336
 
7.7%
3262
 
7.5%
3248
 
7.5%
3239
 
7.5%
3237
 
7.5%
3231
 
7.4%
3230
 
7.4%
3230
 
7.4%
3230
 
7.4%
2529
 
5.8%
Other values (267) 11621
26.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
14.3%
s 3
14.3%
k 3
14.3%
p 2
9.5%
m 2
9.5%
a 2
9.5%
u 1
 
4.8%
n 1
 
4.8%
y 1
 
4.8%
i 1
 
4.8%
Other values (2) 2
9.5%
Decimal Number
ValueCountFrequency (%)
1 3509
20.7%
2 2350
13.9%
3 1918
11.3%
4 1903
11.2%
5 1404
8.3%
6 1343
 
7.9%
7 1259
 
7.4%
0 1233
 
7.3%
8 1070
 
6.3%
9 941
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 21
29.2%
A 21
29.2%
K 12
16.7%
S 11
15.3%
J 3
 
4.2%
H 2
 
2.8%
D 1
 
1.4%
T 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 62
81.6%
. 9
 
11.8%
@ 3
 
3.9%
! 1
 
1.3%
/ 1
 
1.3%
Space Separator
ValueCountFrequency (%)
14031
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Close Punctuation
ValueCountFrequency (%)
) 167
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43391
55.7%
Common 34480
44.2%
Latin 93
 
0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3336
 
7.7%
3262
 
7.5%
3248
 
7.5%
3239
 
7.5%
3237
 
7.5%
3231
 
7.4%
3230
 
7.4%
3230
 
7.4%
3230
 
7.4%
2529
 
5.8%
Other values (265) 11619
26.8%
Common
ValueCountFrequency (%)
14031
40.7%
1 3509
 
10.2%
- 3107
 
9.0%
2 2350
 
6.8%
3 1918
 
5.6%
4 1903
 
5.5%
5 1404
 
4.1%
6 1343
 
3.9%
7 1259
 
3.7%
0 1233
 
3.6%
Other values (10) 2423
 
7.0%
Latin
ValueCountFrequency (%)
B 21
22.6%
A 21
22.6%
K 12
12.9%
S 11
11.8%
e 3
 
3.2%
s 3
 
3.2%
J 3
 
3.2%
k 3
 
3.2%
H 2
 
2.2%
p 2
 
2.2%
Other values (10) 12
12.9%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43390
55.7%
ASCII 34573
44.3%
CJK 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14031
40.6%
1 3509
 
10.1%
- 3107
 
9.0%
2 2350
 
6.8%
3 1918
 
5.5%
4 1903
 
5.5%
5 1404
 
4.1%
6 1343
 
3.9%
7 1259
 
3.6%
0 1233
 
3.6%
Other values (30) 2516
 
7.3%
Hangul
ValueCountFrequency (%)
3336
 
7.7%
3262
 
7.5%
3248
 
7.5%
3239
 
7.5%
3237
 
7.5%
3231
 
7.4%
3230
 
7.4%
3230
 
7.4%
3230
 
7.4%
2529
 
5.8%
Other values (264) 11618
26.8%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1726
Distinct (%)86.6%
Missing1237
Missing (%)38.3%
Memory size25.4 KiB
2024-04-06T20:39:39.870795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length54
Mean length30.109885
Min length21

Characters and Unicode

Total characters60009
Distinct characters294
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1514 ?
Unique (%)76.0%

Sample

1st row서울특별시 강북구 삼양로123길 14 (수유동)
2nd row서울특별시 강북구 솔샘로 233 (미아동)
3rd row서울특별시 강북구 인수봉로55길 4 (수유동)
4th row서울특별시 강북구 숭인로 61-10 (미아동)
5th row서울특별시 강북구 덕릉로42길 8 (번동,(꽃샘길 6-2))
ValueCountFrequency (%)
서울특별시 1993
 
16.5%
강북구 1991
 
16.5%
미아동 823
 
6.8%
수유동 737
 
6.1%
1층 601
 
5.0%
번동 273
 
2.3%
도봉로 200
 
1.7%
2층 159
 
1.3%
삼양로 106
 
0.9%
3층 83
 
0.7%
Other values (1239) 5085
42.2%
2024-04-06T20:39:40.708316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10058
 
16.8%
1 2722
 
4.5%
2138
 
3.6%
) 2074
 
3.5%
( 2074
 
3.5%
2014
 
3.4%
2007
 
3.3%
2006
 
3.3%
1998
 
3.3%
1996
 
3.3%
Other values (284) 30922
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33898
56.5%
Decimal Number 10060
 
16.8%
Space Separator 10058
 
16.8%
Close Punctuation 2074
 
3.5%
Open Punctuation 2074
 
3.5%
Other Punctuation 1562
 
2.6%
Dash Punctuation 197
 
0.3%
Uppercase Letter 68
 
0.1%
Lowercase Letter 16
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2138
 
6.3%
2014
 
5.9%
2007
 
5.9%
2006
 
5.9%
1998
 
5.9%
1996
 
5.9%
1994
 
5.9%
1993
 
5.9%
1993
 
5.9%
1992
 
5.9%
Other values (245) 13767
40.6%
Lowercase Letter
ValueCountFrequency (%)
e 3
18.8%
m 2
12.5%
p 2
12.5%
s 1
 
6.2%
y 1
 
6.2%
i 1
 
6.2%
k 1
 
6.2%
a 1
 
6.2%
u 1
 
6.2%
n 1
 
6.2%
Other values (2) 2
12.5%
Decimal Number
ValueCountFrequency (%)
1 2722
27.1%
2 1403
13.9%
3 1266
12.6%
0 895
 
8.9%
4 820
 
8.2%
7 705
 
7.0%
5 696
 
6.9%
6 551
 
5.5%
8 527
 
5.2%
9 475
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 31
45.6%
A 15
22.1%
K 8
 
11.8%
S 7
 
10.3%
J 3
 
4.4%
H 2
 
2.9%
T 1
 
1.5%
D 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 1541
98.7%
. 16
 
1.0%
@ 4
 
0.3%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
10058
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2074
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2074
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 197
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33896
56.5%
Common 26027
43.4%
Latin 84
 
0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2138
 
6.3%
2014
 
5.9%
2007
 
5.9%
2006
 
5.9%
1998
 
5.9%
1996
 
5.9%
1994
 
5.9%
1993
 
5.9%
1993
 
5.9%
1992
 
5.9%
Other values (243) 13765
40.6%
Latin
ValueCountFrequency (%)
B 31
36.9%
A 15
17.9%
K 8
 
9.5%
S 7
 
8.3%
J 3
 
3.6%
e 3
 
3.6%
m 2
 
2.4%
H 2
 
2.4%
p 2
 
2.4%
s 1
 
1.2%
Other values (10) 10
 
11.9%
Common
ValueCountFrequency (%)
10058
38.6%
1 2722
 
10.5%
) 2074
 
8.0%
( 2074
 
8.0%
, 1541
 
5.9%
2 1403
 
5.4%
3 1266
 
4.9%
0 895
 
3.4%
4 820
 
3.2%
7 705
 
2.7%
Other values (9) 2469
 
9.5%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33895
56.5%
ASCII 26111
43.5%
CJK 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10058
38.5%
1 2722
 
10.4%
) 2074
 
7.9%
( 2074
 
7.9%
, 1541
 
5.9%
2 1403
 
5.4%
3 1266
 
4.8%
0 895
 
3.4%
4 820
 
3.1%
7 705
 
2.7%
Other values (29) 2553
 
9.8%
Hangul
ValueCountFrequency (%)
2138
 
6.3%
2014
 
5.9%
2007
 
5.9%
2006
 
5.9%
1998
 
5.9%
1996
 
5.9%
1994
 
5.9%
1993
 
5.9%
1993
 
5.9%
1992
 
5.9%
Other values (242) 13764
40.6%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct209
Distinct (%)10.6%
Missing1257
Missing (%)38.9%
Infinite0
Infinite (%)0.0%
Mean1122.4992
Minimum1001
Maximum6512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:41.034876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1031
Q11064
median1114
Q31177
95-th percentile1223
Maximum6512
Range5511
Interquartile range (IQR)113

Descriptive statistics

Standard deviation138.67074
Coefficient of variation (CV)0.12353749
Kurtosis1158.0995
Mean1122.4992
Median Absolute Deviation (MAD)56
Skewness29.893888
Sum2214691
Variance19229.574
MonotonicityNot monotonic
2024-04-06T20:39:41.277898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1073 62
 
1.9%
1054 44
 
1.4%
1072 41
 
1.3%
1064 41
 
1.3%
1157 37
 
1.1%
1125 36
 
1.1%
1118 33
 
1.0%
1170 30
 
0.9%
1233 29
 
0.9%
1041 27
 
0.8%
Other values (199) 1593
49.3%
(Missing) 1257
38.9%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 2
 
0.1%
1004 7
0.2%
1005 6
0.2%
1006 8
0.2%
1009 2
 
0.1%
1010 3
 
0.1%
1011 6
0.2%
1012 1
 
< 0.1%
1014 8
0.2%
ValueCountFrequency (%)
6512 1
 
< 0.1%
1882 1
 
< 0.1%
1237 15
0.5%
1236 4
 
0.1%
1234 7
 
0.2%
1233 29
0.9%
1232 2
 
0.1%
1231 2
 
0.1%
1230 6
 
0.2%
1229 2
 
0.1%
Distinct2705
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2024-04-06T20:39:41.831073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length5.7009288
Min length1

Characters and Unicode

Total characters18414
Distinct characters679
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2346 ?
Unique (%)72.6%

Sample

1st row정원
2nd row허순정미용실
3rd row까까머리
4th row쎄븐
5th row장승희미용실
ValueCountFrequency (%)
미용실 52
 
1.4%
헤어 42
 
1.1%
hair 38
 
1.0%
수유점 19
 
0.5%
네일 19
 
0.5%
nail 14
 
0.4%
에스테틱 11
 
0.3%
헤어샵 10
 
0.3%
머리사랑 10
 
0.3%
미아점 9
 
0.2%
Other values (2829) 3555
94.1%
2024-04-06T20:39:43.112419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1225
 
6.7%
1187
 
6.4%
774
 
4.2%
552
 
3.0%
534
 
2.9%
533
 
2.9%
432
 
2.3%
361
 
2.0%
352
 
1.9%
262
 
1.4%
Other values (669) 12202
66.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15491
84.1%
Lowercase Letter 838
 
4.6%
Uppercase Letter 763
 
4.1%
Space Separator 552
 
3.0%
Open Punctuation 222
 
1.2%
Close Punctuation 222
 
1.2%
Other Punctuation 160
 
0.9%
Decimal Number 152
 
0.8%
Dash Punctuation 9
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1225
 
7.9%
1187
 
7.7%
774
 
5.0%
534
 
3.4%
533
 
3.4%
432
 
2.8%
361
 
2.3%
352
 
2.3%
262
 
1.7%
249
 
1.6%
Other values (596) 9582
61.9%
Uppercase Letter
ValueCountFrequency (%)
A 76
 
10.0%
H 64
 
8.4%
N 56
 
7.3%
J 53
 
6.9%
M 53
 
6.9%
I 50
 
6.6%
S 47
 
6.2%
R 47
 
6.2%
O 39
 
5.1%
T 37
 
4.8%
Other values (15) 241
31.6%
Lowercase Letter
ValueCountFrequency (%)
a 120
14.3%
i 100
11.9%
e 94
11.2%
h 68
8.1%
r 67
8.0%
o 62
7.4%
n 58
 
6.9%
l 47
 
5.6%
y 39
 
4.7%
s 32
 
3.8%
Other values (14) 151
18.0%
Decimal Number
ValueCountFrequency (%)
2 41
27.0%
0 32
21.1%
1 28
18.4%
5 10
 
6.6%
4 10
 
6.6%
9 10
 
6.6%
3 9
 
5.9%
7 7
 
4.6%
8 3
 
2.0%
6 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
? 41
25.6%
& 39
24.4%
. 29
18.1%
# 24
15.0%
, 21
13.1%
: 3
 
1.9%
' 2
 
1.2%
! 1
 
0.6%
Space Separator
ValueCountFrequency (%)
552
100.0%
Open Punctuation
ValueCountFrequency (%)
( 222
100.0%
Close Punctuation
ValueCountFrequency (%)
) 222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15469
84.0%
Latin 1601
 
8.7%
Common 1322
 
7.2%
Han 22
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1225
 
7.9%
1187
 
7.7%
774
 
5.0%
534
 
3.5%
533
 
3.4%
432
 
2.8%
361
 
2.3%
352
 
2.3%
262
 
1.7%
249
 
1.6%
Other values (584) 9560
61.8%
Latin
ValueCountFrequency (%)
a 120
 
7.5%
i 100
 
6.2%
e 94
 
5.9%
A 76
 
4.7%
h 68
 
4.2%
r 67
 
4.2%
H 64
 
4.0%
o 62
 
3.9%
n 58
 
3.6%
N 56
 
3.5%
Other values (39) 836
52.2%
Common
ValueCountFrequency (%)
552
41.8%
( 222
16.8%
) 222
16.8%
2 41
 
3.1%
? 41
 
3.1%
& 39
 
3.0%
0 32
 
2.4%
. 29
 
2.2%
1 28
 
2.1%
# 24
 
1.8%
Other values (14) 92
 
7.0%
Han
ValueCountFrequency (%)
9
40.9%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (2) 2
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15469
84.0%
ASCII 2923
 
15.9%
CJK 21
 
0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1225
 
7.9%
1187
 
7.7%
774
 
5.0%
534
 
3.5%
533
 
3.4%
432
 
2.8%
361
 
2.3%
352
 
2.3%
262
 
1.7%
249
 
1.6%
Other values (584) 9560
61.8%
ASCII
ValueCountFrequency (%)
552
18.9%
( 222
 
7.6%
) 222
 
7.6%
a 120
 
4.1%
i 100
 
3.4%
e 94
 
3.2%
A 76
 
2.6%
h 68
 
2.3%
r 67
 
2.3%
H 64
 
2.2%
Other values (63) 1338
45.8%
CJK
ValueCountFrequency (%)
9
42.9%
2
 
9.5%
2
 
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Distinct2390
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum1999-01-07 00:00:00
Maximum2024-04-04 09:07:13
2024-04-06T20:39:43.560283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:39:44.094023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
I
2133 
U
1075 
D
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 2133
66.0%
U 1075
33.3%
D 22
 
0.7%

Length

2024-04-06T20:39:44.482991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:44.799908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2133
66.0%
u 1075
33.3%
d 22
 
0.7%
Distinct673
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-04-06T20:39:45.155801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:39:45.442856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
일반미용업
2541 
피부미용업
332 
네일아트업
276 
메이크업업
 
60
기타
 
21

Length

Max length5
Median length5
Mean length4.9804954
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2541
78.7%
피부미용업 332
 
10.3%
네일아트업 276
 
8.5%
메이크업업 60
 
1.9%
기타 21
 
0.7%

Length

2024-04-06T20:39:45.690825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:45.905532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2541
78.7%
피부미용업 332
 
10.3%
네일아트업 276
 
8.5%
메이크업업 60
 
1.9%
기타 21
 
0.7%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1678
Distinct (%)53.8%
Missing111
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean202042.13
Minimum200473.79
Maximum204454.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:46.179072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200473.79
5-th percentile201071.3
Q1201606.45
median202054.08
Q3202429.29
95-th percentile203002.81
Maximum204454.97
Range3981.1856
Interquartile range (IQR)822.8423

Descriptive statistics

Standard deviation601.1235
Coefficient of variation (CV)0.0029752384
Kurtosis0.2245726
Mean202042.13
Median Absolute Deviation (MAD)398.03903
Skewness0.31561364
Sum6.3016941 × 108
Variance361349.46
MonotonicityNot monotonic
2024-04-06T20:39:46.447835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200921.855251696 22
 
0.7%
201731.684416461 19
 
0.6%
201606.445832012 17
 
0.5%
201059.873232343 16
 
0.5%
202007.139170813 15
 
0.5%
202052.37985575 11
 
0.3%
202317.064607714 11
 
0.3%
202625.646264572 10
 
0.3%
202178.58911469 9
 
0.3%
202155.401317068 9
 
0.3%
Other values (1668) 2980
92.3%
(Missing) 111
 
3.4%
ValueCountFrequency (%)
200473.788511426 1
 
< 0.1%
200623.692487553 1
 
< 0.1%
200626.320844864 1
 
< 0.1%
200665.844382263 1
 
< 0.1%
200689.194615712 1
 
< 0.1%
200697.482832695 1
 
< 0.1%
200730.549356982 3
0.1%
200744.013301735 1
 
< 0.1%
200761.998619022 2
0.1%
200792.550015248 1
 
< 0.1%
ValueCountFrequency (%)
204454.974085477 1
 
< 0.1%
204157.843492258 3
0.1%
204083.177112537 6
0.2%
203981.35 4
0.1%
203932.991517795 4
0.1%
203920.882629874 1
 
< 0.1%
203895.660018622 1
 
< 0.1%
203812.72109062 1
 
< 0.1%
203791.242061767 1
 
< 0.1%
203789.411708636 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1678
Distinct (%)53.8%
Missing111
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean458727.63
Minimum444969.68
Maximum462248.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:46.696592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum444969.68
5-th percentile456824.55
Q1457744.35
median458815.98
Q3459670.2
95-th percentile460398.83
Maximum462248.26
Range17278.579
Interquartile range (IQR)1925.8471

Descriptive statistics

Standard deviation1204.2986
Coefficient of variation (CV)0.002625302
Kurtosis4.4650705
Mean458727.63
Median Absolute Deviation (MAD)959.56033
Skewness-0.50088393
Sum1.4307715 × 109
Variance1450335
MonotonicityNot monotonic
2024-04-06T20:39:47.018973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457533.770785984 22
 
0.7%
457219.335594234 19
 
0.6%
460001.632075829 17
 
0.5%
457514.212737818 16
 
0.5%
459396.553581885 15
 
0.5%
459491.573913047 11
 
0.3%
459590.573498344 11
 
0.3%
456875.973976242 10
 
0.3%
457840.702550761 9
 
0.3%
459411.940883021 9
 
0.3%
Other values (1668) 2980
92.3%
(Missing) 111
 
3.4%
ValueCountFrequency (%)
444969.683317754 1
< 0.1%
456417.052514595 1
< 0.1%
456434.500687631 1
< 0.1%
456449.379330053 2
0.1%
456473.886142136 2
0.1%
456485.91916084 1
< 0.1%
456507.83432035 1
< 0.1%
456528.977767312 2
0.1%
456533.819495897 1
< 0.1%
456551.090160874 1
< 0.1%
ValueCountFrequency (%)
462248.262541823 3
0.1%
462216.235709753 1
 
< 0.1%
462160.312358665 4
0.1%
462147.247975967 1
 
< 0.1%
462078.07721307 1
 
< 0.1%
462033.743870552 2
0.1%
461940.63368366 1
 
< 0.1%
461831.203426015 1
 
< 0.1%
461815.826106939 1
 
< 0.1%
461811.636263748 1
 
< 0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
미용업
1213 
일반미용업
1068 
<NA>
487 
피부미용업
204 
네일미용업
 
118
Other values (11)
140 

Length

Max length23
Median length16
Mean length4.3996904
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1213
37.6%
일반미용업 1068
33.1%
<NA> 487
15.1%
피부미용업 204
 
6.3%
네일미용업 118
 
3.7%
종합미용업 43
 
1.3%
피부미용업, 네일미용업 25
 
0.8%
네일미용업, 화장ㆍ분장 미용업 19
 
0.6%
화장ㆍ분장 미용업 14
 
0.4%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 10
 
0.3%
Other values (6) 29
 
0.9%

Length

2024-04-06T20:39:47.348698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1278
37.6%
일반미용업 1097
32.3%
na 487
 
14.3%
피부미용업 244
 
7.2%
네일미용업 183
 
5.4%
화장ㆍ분장 65
 
1.9%
종합미용업 43
 
1.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.7%
Missing1193
Missing (%)36.9%
Infinite0
Infinite (%)0.0%
Mean0.4658812
Minimum0
Maximum26
Zeros1731
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:47.679938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4805349
Coefficient of variation (CV)3.1779238
Kurtosis73.802132
Mean0.4658812
Median Absolute Deviation (MAD)0
Skewness6.5974506
Sum949
Variance2.1919837
MonotonicityNot monotonic
2024-04-06T20:39:47.965895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1731
53.6%
3 84
 
2.6%
2 79
 
2.4%
1 56
 
1.7%
4 48
 
1.5%
5 23
 
0.7%
6 4
 
0.1%
17 3
 
0.1%
8 2
 
0.1%
7 2
 
0.1%
Other values (5) 5
 
0.2%
(Missing) 1193
36.9%
ValueCountFrequency (%)
0 1731
53.6%
1 56
 
1.7%
2 79
 
2.4%
3 84
 
2.6%
4 48
 
1.5%
5 23
 
0.7%
6 4
 
0.1%
7 2
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
26 1
 
< 0.1%
17 3
 
0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
0.1%
7 2
 
0.1%
6 4
 
0.1%
5 23
0.7%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.4%
Missing1231
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean0.10755378
Minimum0
Maximum7
Zeros1815
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:48.224523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41486152
Coefficient of variation (CV)3.8572473
Kurtosis94.046255
Mean0.10755378
Median Absolute Deviation (MAD)0
Skewness7.7011761
Sum215
Variance0.17211008
MonotonicityNot monotonic
2024-04-06T20:39:48.435254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1815
56.2%
1 172
 
5.3%
2 6
 
0.2%
6 2
 
0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 1231
38.1%
ValueCountFrequency (%)
0 1815
56.2%
1 172
 
5.3%
2 6
 
0.2%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1 172
 
5.3%
0 1815
56.2%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.5%
Missing1515
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean0.62274052
Minimum0
Maximum10
Zeros913
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:48.636794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8785374
Coefficient of variation (CV)1.41076
Kurtosis17.974647
Mean0.62274052
Median Absolute Deviation (MAD)0
Skewness2.9411725
Sum1068
Variance0.77182796
MonotonicityNot monotonic
2024-04-06T20:39:48.837682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 913
28.3%
1 631
19.5%
2 119
 
3.7%
3 34
 
1.1%
5 8
 
0.2%
4 6
 
0.2%
7 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 1515
46.9%
ValueCountFrequency (%)
0 913
28.3%
1 631
19.5%
2 119
 
3.7%
3 34
 
1.1%
4 6
 
0.2%
5 8
 
0.2%
7 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
7 2
 
0.1%
5 8
 
0.2%
4 6
 
0.2%
3 34
 
1.1%
2 119
 
3.7%
1 631
19.5%
0 913
28.3%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.8%
Missing2067
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean0.8822012
Minimum0
Maximum10
Zeros393
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:49.023057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93822147
Coefficient of variation (CV)1.0635006
Kurtosis17.166173
Mean0.8822012
Median Absolute Deviation (MAD)0
Skewness2.8291431
Sum1026
Variance0.88025952
MonotonicityNot monotonic
2024-04-06T20:39:49.201200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 609
 
18.9%
0 393
 
12.2%
2 110
 
3.4%
3 32
 
1.0%
5 8
 
0.2%
4 7
 
0.2%
7 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
(Missing) 2067
64.0%
ValueCountFrequency (%)
0 393
12.2%
1 609
18.9%
2 110
 
3.4%
3 32
 
1.0%
4 7
 
0.2%
5 8
 
0.2%
7 2
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 1
 
< 0.1%
7 2
 
0.1%
5 8
 
0.2%
4 7
 
0.2%
3 32
 
1.0%
2 110
 
3.4%
1 609
18.9%
0 393
12.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
2032 
0
1153 
1
 
32
2
 
12
3
 
1

Length

Max length4
Median length4
Mean length2.8873065
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2032
62.9%
0 1153
35.7%
1 32
 
1.0%
2 12
 
0.4%
3 1
 
< 0.1%

Length

2024-04-06T20:39:49.400862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:49.649024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2032
62.9%
0 1153
35.7%
1 32
 
1.0%
2 12
 
0.4%
3 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
2570 
0
618 
1
 
30
2
 
11
3
 
1

Length

Max length4
Median length4
Mean length3.3869969
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2570
79.6%
0 618
 
19.1%
1 30
 
0.9%
2 11
 
0.3%
3 1
 
< 0.1%

Length

2024-04-06T20:39:49.854725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:50.058983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2570
79.6%
0 618
 
19.1%
1 30
 
0.9%
2 11
 
0.3%
3 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
1917 
<NA>
1313 

Length

Max length4
Median length1
Mean length2.2195046
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1917
59.3%
<NA> 1313
40.7%

Length

2024-04-06T20:39:50.273501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:50.481921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1917
59.3%
na 1313
40.7%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
1917 
<NA>
1313 

Length

Max length4
Median length1
Mean length2.2195046
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1917
59.3%
<NA> 1313
40.7%

Length

2024-04-06T20:39:50.688875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:50.871400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1917
59.3%
na 1313
40.7%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
0
1917 
<NA>
1313 

Length

Max length4
Median length1
Mean length2.2195046
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1917
59.3%
<NA> 1313
40.7%

Length

2024-04-06T20:39:51.047710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:51.323779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1917
59.3%
na 1313
40.7%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing539
Missing (%)16.7%
Memory size6.4 KiB
False
2691 
(Missing)
539 
ValueCountFrequency (%)
False 2691
83.3%
(Missing) 539
 
16.7%
2024-04-06T20:39:51.602201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19
Distinct (%)0.7%
Missing597
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.3133308
Minimum0
Maximum320
Zeros208
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:51.810693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum320
Range320
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4524359
Coefficient of variation (CV)1.9474168
Kurtosis2206.7481
Mean3.3133308
Median Absolute Deviation (MAD)1
Skewness44.993511
Sum8724
Variance41.633929
MonotonicityNot monotonic
2024-04-06T20:39:52.006607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1122
34.7%
2 501
15.5%
4 386
 
12.0%
0 208
 
6.4%
5 167
 
5.2%
6 88
 
2.7%
8 43
 
1.3%
1 39
 
1.2%
7 30
 
0.9%
10 15
 
0.5%
Other values (9) 34
 
1.1%
(Missing) 597
18.5%
ValueCountFrequency (%)
0 208
 
6.4%
1 39
 
1.2%
2 501
15.5%
3 1122
34.7%
4 386
 
12.0%
5 167
 
5.2%
6 88
 
2.7%
7 30
 
0.9%
8 43
 
1.3%
9 10
 
0.3%
ValueCountFrequency (%)
320 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
0.1%
14 4
 
0.1%
13 1
 
< 0.1%
12 8
0.2%
11 6
 
0.2%
10 15
0.5%
9 10
0.3%
Distinct3
Distinct (%)75.0%
Missing3226
Missing (%)99.9%
Memory size25.4 KiB
2024-04-06T20:39:52.231030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11.5
Mean length11.25
Min length8

Characters and Unicode

Total characters45
Distinct characters22
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

Unique2 ?
Unique (%)50.0%

Sample

1st row건축물임시사용승인 기간연장
2nd row건축물임시사용승인 기간연장
3rd row가사용승인기간까지
4th row대표자 체류기간
ValueCountFrequency (%)
건축물임시사용승인 2
28.6%
기간연장 2
28.6%
가사용승인기간까지 1
14.3%
대표자 1
14.3%
체류기간 1
14.3%
2024-04-06T20:39:52.710696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
 
8.9%
4
 
8.9%
3
 
6.7%
3
 
6.7%
3
 
6.7%
3
 
6.7%
3
 
6.7%
2
 
4.4%
2
 
4.4%
2
 
4.4%
Other values (12) 16
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
93.3%
Space Separator 3
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
9.5%
4
 
9.5%
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (11) 14
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
93.3%
Common 3
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
 
9.5%
4
 
9.5%
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (11) 14
33.3%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
93.3%
ASCII 3
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4
 
9.5%
4
 
9.5%
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (11) 14
33.3%
ASCII
ValueCountFrequency (%)
3
100.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
3226 
20030901
 
1
20040102
 
1
20180305
 
1
20170713
 
1

Length

Max length8
Median length4
Mean length4.0049536
Min length4

Unique

Unique4 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3226
99.9%
20030901 1
 
< 0.1%
20040102 1
 
< 0.1%
20180305 1
 
< 0.1%
20170713 1
 
< 0.1%

Length

2024-04-06T20:39:52.951221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:53.139350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3226
99.9%
20030901 1
 
< 0.1%
20040102 1
 
< 0.1%
20180305 1
 
< 0.1%
20170713 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
3226 
20040831
 
1
20041231
 
1
20191114
 
1
20190708
 
1

Length

Max length8
Median length4
Mean length4.0049536
Min length4

Unique

Unique4 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3226
99.9%
20040831 1
 
< 0.1%
20041231 1
 
< 0.1%
20191114 1
 
< 0.1%
20190708 1
 
< 0.1%

Length

2024-04-06T20:39:53.414192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:53.637133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3226
99.9%
20040831 1
 
< 0.1%
20041231 1
 
< 0.1%
20191114 1
 
< 0.1%
20190708 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
2996 
임대
 
231
자가
 
3

Length

Max length4
Median length4
Mean length3.8551084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2996
92.8%
임대 231
 
7.2%
자가 3
 
0.1%

Length

2024-04-06T20:39:53.862997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:54.092949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2996
92.8%
임대 231
 
7.2%
자가 3
 
0.1%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
1848 
0
1382 

Length

Max length4
Median length4
Mean length2.7164087
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1848
57.2%
0 1382
42.8%

Length

2024-04-06T20:39:54.282015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:54.473850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1848
57.2%
0 1382
42.8%

여성종사자수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)0.8%
Missing2312
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean0.091503268
Minimum0
Maximum19
Zeros869
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:54.611126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73122723
Coefficient of variation (CV)7.991269
Kurtosis495.71621
Mean0.091503268
Median Absolute Deviation (MAD)0
Skewness20.133356
Sum84
Variance0.53469327
MonotonicityNot monotonic
2024-04-06T20:39:54.792042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 869
 
26.9%
1 41
 
1.3%
3 3
 
0.1%
2 2
 
0.1%
6 1
 
< 0.1%
19 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 2312
71.6%
ValueCountFrequency (%)
0 869
26.9%
1 41
 
1.3%
2 2
 
0.1%
3 3
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 3
 
0.1%
2 2
 
0.1%
1 41
 
1.3%
0 869
26.9%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
2312 
0
913 
1
 
5

Length

Max length4
Median length4
Mean length3.1473684
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2312
71.6%
0 913
 
28.3%
1 5
 
0.2%

Length

2024-04-06T20:39:55.010383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:55.205582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2312
71.6%
0 913
 
28.3%
1 5
 
0.2%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
<NA>
1927 
0
1303 

Length

Max length4
Median length4
Mean length2.7897833
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1927
59.7%
0 1303
40.3%

Length

2024-04-06T20:39:55.385190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:39:55.561415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1927
59.7%
0 1303
40.3%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)1.0%
Missing1948
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean0.44305772
Minimum0
Maximum20
Zeros1082
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size28.5 KiB
2024-04-06T20:39:56.169471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3773973
Coefficient of variation (CV)3.108844
Kurtosis45.349688
Mean0.44305772
Median Absolute Deviation (MAD)0
Skewness5.3705578
Sum568
Variance1.8972234
MonotonicityNot monotonic
2024-04-06T20:39:56.408530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 1082
33.5%
2 69
 
2.1%
1 52
 
1.6%
3 35
 
1.1%
5 13
 
0.4%
4 11
 
0.3%
6 6
 
0.2%
8 5
 
0.2%
7 4
 
0.1%
9 2
 
0.1%
Other values (3) 3
 
0.1%
(Missing) 1948
60.3%
ValueCountFrequency (%)
0 1082
33.5%
1 52
 
1.6%
2 69
 
2.1%
3 35
 
1.1%
4 11
 
0.3%
5 13
 
0.4%
6 6
 
0.2%
7 4
 
0.1%
8 5
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
0.1%
8 5
 
0.2%
7 4
 
0.1%
6 6
 
0.2%
5 13
 
0.4%
4 11
 
0.3%
3 35
1.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing487
Missing (%)15.1%
Memory size6.4 KiB
False
2743 
(Missing)
487 
ValueCountFrequency (%)
False 2743
84.9%
(Missing) 487
 
15.1%
2024-04-06T20:39:56.727885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030800003080000-204-1966-0066919660623<NA>3폐업2폐업20020522<NA><NA><NA>02 981958413.44142100서울특별시 강북구 미아동 111-0번지<NA><NA>정원2002-05-24 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130800003080000-204-1966-0125719660312<NA>3폐업2폐업19960612<NA><NA><NA>02 982401216.2142823서울특별시 강북구 미아동 776-37번지<NA><NA>허순정미용실2001-09-26 00:00:00I2018-08-31 23:59:59.0일반미용업201522.030074457961.236895미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230800003080000-204-1966-0125819660623<NA>3폐업2폐업20021002<NA><NA><NA>020981417433.0142823서울특별시 강북구 미아동 762-67번지<NA><NA>까까머리2002-10-09 00:00:00I2018-08-31 23:59:59.0일반미용업201608.416667458071.032041미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330800003080000-204-1969-0077119690905<NA>3폐업2폐업20100518<NA><NA><NA>020994196731.23142868서울특별시 강북구 번동 464-14번지<NA><NA>쎄븐2003-07-02 00:00:00I2018-08-31 23:59:59.0일반미용업202541.248487459802.86057미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N6<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430800003080000-204-1969-0133719690808<NA>3폐업2폐업20090903<NA><NA><NA>020984800215.75142816서울특별시 강북구 미아동 742-2번지<NA><NA>장승희미용실2003-07-02 00:00:00I2018-08-31 23:59:59.0일반미용업201742.958929457803.097126미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530800003080000-204-1970-0062419700814<NA>3폐업2폐업19961118<NA><NA><NA>02 98455980.0142100서울특별시 강북구 미아동 773-36번지<NA><NA>덕성2002-07-09 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
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개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
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322530800003080000-226-2021-0000120210923<NA>3폐업2폐업20221024<NA><NA><NA><NA>27.0142810서울특별시 강북구 미아동 207-6 명화빌딩서울특별시 강북구 도봉로53길 8, 명화빌딩 1층 105호 (미아동)1125무드네일2022-10-24 10:30:55U2021-10-30 22:06:00.0네일아트업202157.200385458298.505425<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
322630800003080000-226-2021-000022021-01-15<NA>1영업/정상1영업<NA><NA><NA><NA><NA>57.1142-803서울특별시 강북구 미아동 165-37서울특별시 강북구 도봉로76길 24 (미아동)1132하이뷰티2024-02-27 17:20:04I2023-12-01 22:09:00.0메이크업업202140.197742458955.602366<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
322730800003080000-226-2023-000012023-01-27<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.17142-878서울특별시 강북구 수유동 229-49 경남아너스빌 111호서울특별시 강북구 노해로8길 22, 경남아너스빌 1층 111호 (수유동)1073유즈네일2024-01-18 15:39:58I2023-11-30 22:00:00.0네일아트업202071.310519459514.711298<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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