Overview

Dataset statistics

Number of variables51
Number of observations10000
Missing cells109706
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory450.0 B

Variable types

Numeric16
Categorical19
Text6
Unsupported7
DateTime1
Boolean2

Dataset

Description2021-05-01
Author지방행정인허가공개데이터
URLhttps://bigdata.busan.go.kr/data/bigDataDetailView.do?menuCode=M00000000007&hdfs_file_sn=20230901050101123088

Alerts

개방서비스명 has constant value ""Constant
개방서비스id has constant value ""Constant
발한실여부 has constant value ""Constant
사용시작지하층 is highly imbalanced (52.6%)Imbalance
사용끝지하층 is highly imbalanced (57.1%)Imbalance
조건부허가종료일자 is highly imbalanced (99.6%)Imbalance
남성종사자수 is highly imbalanced (58.9%)Imbalance
다중이용업소여부 is highly imbalanced (99.7%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4917 (49.2%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
소재지전화 has 3035 (30.3%) missing valuesMissing
소재지우편번호 has 102 (1.0%) missing valuesMissing
도로명전체주소 has 2981 (29.8%) missing valuesMissing
도로명우편번호 has 3045 (30.4%) missing valuesMissing
좌표정보(x) has 273 (2.7%) missing valuesMissing
좌표정보(y) has 273 (2.7%) missing valuesMissing
건물지상층수 has 2104 (21.0%) missing valuesMissing
건물지하층수 has 2961 (29.6%) missing valuesMissing
사용시작지상층 has 2702 (27.0%) missing valuesMissing
사용끝지상층 has 4177 (41.8%) missing valuesMissing
발한실여부 has 138 (1.4%) missing valuesMissing
의자수 has 756 (7.6%) missing valuesMissing
조건부허가신고사유 has 10000 (100.0%) missing valuesMissing
조건부허가시작일자 has 10000 (100.0%) missing valuesMissing
여성종사자수 has 7537 (75.4%) missing valuesMissing
침대수 has 4650 (46.5%) missing valuesMissing
Unnamed: 50 has 10000 (100.0%) missing valuesMissing
인허가일자 is highly skewed (γ1 = -33.31869892)Skewed
폐업일자 is highly skewed (γ1 = -64.3336144)Skewed
사용시작지상층 is highly skewed (γ1 = 71.36752611)Skewed
사용끝지상층 is highly skewed (γ1 = 66.42599971)Skewed
번호 has unique valuesUnique
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
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 50 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 2967 (29.7%) zerosZeros
건물지하층수 has 5132 (51.3%) zerosZeros
사용시작지상층 has 1502 (15.0%) zerosZeros
사용끝지상층 has 1050 (10.5%) zerosZeros
의자수 has 1402 (14.0%) zerosZeros
여성종사자수 has 2313 (23.1%) zerosZeros
침대수 has 3636 (36.4%) zerosZeros

Reproduction

Analysis started2024-04-17 03:26:36.826019
Analysis finished2024-04-17 03:26:38.845867
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11605.053
Minimum1
Maximum23122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:38.899101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1202.95
Q15901.5
median11613.5
Q317362.25
95-th percentile21935.25
Maximum23122
Range23121
Interquartile range (IQR)11460.75

Descriptive statistics

Standard deviation6648.6547
Coefficient of variation (CV)0.57291032
Kurtosis-1.1893715
Mean11605.053
Median Absolute Deviation (MAD)5733
Skewness-0.0056794183
Sum1.1605053 × 108
Variance44204610
MonotonicityNot monotonic
2024-04-17T12:26:38.996513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20533 1
 
< 0.1%
9193 1
 
< 0.1%
788 1
 
< 0.1%
7029 1
 
< 0.1%
20472 1
 
< 0.1%
6086 1
 
< 0.1%
11295 1
 
< 0.1%
22992 1
 
< 0.1%
15701 1
 
< 0.1%
1239 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
ValueCountFrequency (%)
23122 1
< 0.1%
23120 1
< 0.1%
23117 1
< 0.1%
23116 1
< 0.1%
23114 1
< 0.1%
23113 1
< 0.1%
23111 1
< 0.1%
23110 1
< 0.1%
23106 1
< 0.1%
23105 1
< 0.1%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 10000
100.0%

Length

2024-04-17T12:26:39.087992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:39.153085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 10000
100.0%

개방서비스id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
05_18_01_P
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05_18_01_P
2nd row05_18_01_P
3rd row05_18_01_P
4th row05_18_01_P
5th row05_18_01_P

Common Values

ValueCountFrequency (%)
05_18_01_P 10000
100.0%

Length

2024-04-17T12:26:39.223825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:39.289833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
05_18_01_p 10000
100.0%

개방자치단체코드
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3325658
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:39.358057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13300000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation37899.745
Coefficient of variation (CV)0.011396164
Kurtosis-0.77094034
Mean3325658
Median Absolute Deviation (MAD)30000
Skewness0.024096894
Sum3.325658 × 1010
Variance1.4363907 × 109
MonotonicityNot monotonic
2024-04-17T12:26:39.444737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3330000 1277
12.8%
3290000 1237
12.4%
3340000 979
9.8%
3300000 851
8.5%
3370000 777
7.8%
3310000 755
7.5%
3350000 725
7.2%
3380000 721
7.2%
3320000 703
7.0%
3390000 428
 
4.3%
Other values (6) 1547
15.5%
ValueCountFrequency (%)
3250000 345
 
3.5%
3260000 275
 
2.8%
3270000 290
 
2.9%
3280000 324
 
3.2%
3290000 1237
12.4%
3300000 851
8.5%
3310000 755
7.5%
3320000 703
7.0%
3330000 1277
12.8%
3340000 979
9.8%
ValueCountFrequency (%)
3400000 192
 
1.9%
3390000 428
 
4.3%
3380000 721
7.2%
3370000 777
7.8%
3360000 121
 
1.2%
3350000 725
7.2%
3340000 979
9.8%
3330000 1277
12.8%
3320000 703
7.0%
3310000 755
7.5%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:26:39.617200image/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 row3380000-204-1994-00016
2nd row3290000-204-2001-01054
3rd row3400000-211-2015-00009
4th row3380000-211-2000-00022
5th row3350000-204-2012-00001
ValueCountFrequency (%)
3380000-204-1994-00016 1
 
< 0.1%
3320000-204-1999-00628 1
 
< 0.1%
3330000-204-2004-00055 1
 
< 0.1%
3250000-204-2003-00004 1
 
< 0.1%
3350000-215-2019-00001 1
 
< 0.1%
3300000-211-2014-00007 1
 
< 0.1%
3380000-212-2016-00014 1
 
< 0.1%
3330000-212-2012-00010 1
 
< 0.1%
3360000-211-2006-00004 1
 
< 0.1%
3300000-215-2020-00004 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-17T12:26:39.893641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 87802
39.9%
- 30000
 
13.6%
2 26365
 
12.0%
1 22093
 
10.0%
3 21988
 
10.0%
9 8740
 
4.0%
4 7875
 
3.6%
8 4384
 
2.0%
5 4193
 
1.9%
7 3648
 
1.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87802
46.2%
2 26365
 
13.9%
1 22093
 
11.6%
3 21988
 
11.6%
9 8740
 
4.6%
4 7875
 
4.1%
8 4384
 
2.3%
5 4193
 
2.2%
7 3648
 
1.9%
6 2912
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 87802
39.9%
- 30000
 
13.6%
2 26365
 
12.0%
1 22093
 
10.0%
3 21988
 
10.0%
9 8740
 
4.0%
4 7875
 
3.6%
8 4384
 
2.0%
5 4193
 
1.9%
7 3648
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 87802
39.9%
- 30000
 
13.6%
2 26365
 
12.0%
1 22093
 
10.0%
3 21988
 
10.0%
9 8740
 
4.0%
4 7875
 
3.6%
8 4384
 
2.0%
5 4193
 
1.9%
7 3648
 
1.7%

인허가일자
Real number (ℝ)

SKEWED 

Distinct5755
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20054361
Minimum9970925
Maximum20210331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:40.009873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9970925
5-th percentile19840407
Q119980930
median20080624
Q320151201
95-th percentile20200210
Maximum20210331
Range10239406
Interquartile range (IQR)170271.25

Descriptive statistics

Standard deviation183442.82
Coefficient of variation (CV)0.0091472784
Kurtosis1817.4064
Mean20054361
Median Absolute Deviation (MAD)80185.5
Skewness-33.318699
Sum2.0054361 × 1011
Variance3.3651269 × 1010
MonotonicityNot monotonic
2024-04-17T12:26:40.122748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000712 26
 
0.3%
20000415 18
 
0.2%
20030225 13
 
0.1%
20030224 12
 
0.1%
20020508 10
 
0.1%
20001114 9
 
0.1%
20170116 9
 
0.1%
20170403 9
 
0.1%
20200102 8
 
0.1%
19980930 8
 
0.1%
Other values (5745) 9878
98.8%
ValueCountFrequency (%)
9970925 1
 
< 0.1%
9990827 1
 
< 0.1%
19630110 2
< 0.1%
19630130 1
 
< 0.1%
19630206 1
 
< 0.1%
19630520 1
 
< 0.1%
19631111 1
 
< 0.1%
19650426 1
 
< 0.1%
19650427 1
 
< 0.1%
19660301 4
< 0.1%
ValueCountFrequency (%)
20210331 6
0.1%
20210330 3
< 0.1%
20210329 1
 
< 0.1%
20210326 1
 
< 0.1%
20210325 1
 
< 0.1%
20210324 1
 
< 0.1%
20210323 4
< 0.1%
20210322 2
 
< 0.1%
20210319 1
 
< 0.1%
20210318 2
 
< 0.1%

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
5083 
1
4917 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 5083
50.8%
1 4917
49.2%

Length

2024-04-17T12:26:40.216464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:40.285134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5083
50.8%
1 4917
49.2%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5083 
영업/정상
4917 

Length

Max length5
Median length2
Mean length3.4751
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5083
50.8%
영업/정상 4917
49.2%

Length

2024-04-17T12:26:40.362594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:40.433750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5083
50.8%
영업/정상 4917
49.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5083 
1
4917 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5083
50.8%
1 4917
49.2%

Length

2024-04-17T12:26:40.706531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:40.780738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5083
50.8%
1 4917
49.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5083 
영업
4917 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5083
50.8%
영업 4917
49.2%

Length

2024-04-17T12:26:40.850166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:40.920675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5083
50.8%
영업 4917
49.2%

폐업일자
Real number (ℝ)

MISSING  SKEWED 

Distinct2930
Distinct (%)57.6%
Missing4917
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean20093364
Minimum2013102
Maximum20210331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:41.000780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013102
5-th percentile19990937
Q120040304
median20091231
Q320160222
95-th percentile20200429
Maximum20210331
Range18197229
Interquartile range (IQR)119918.5

Descriptive statistics

Standard deviation262481.81
Coefficient of variation (CV)0.013063109
Kurtosis4432.0724
Mean20093364
Median Absolute Deviation (MAD)59993
Skewness-64.333614
Sum1.0213457 × 1011
Variance6.8896699 × 1010
MonotonicityNot monotonic
2024-04-17T12:26:41.107635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030227 163
 
1.6%
20050117 57
 
0.6%
20030226 45
 
0.4%
20050510 33
 
0.3%
20010321 29
 
0.3%
20000712 25
 
0.2%
20000531 25
 
0.2%
20030606 25
 
0.2%
20210322 23
 
0.2%
20051222 19
 
0.2%
Other values (2920) 4639
46.4%
(Missing) 4917
49.2%
ValueCountFrequency (%)
2013102 1
< 0.1%
19821109 1
< 0.1%
19891116 1
< 0.1%
19900615 1
< 0.1%
19910205 1
< 0.1%
19921012 1
< 0.1%
19930209 1
< 0.1%
19930308 1
< 0.1%
19930806 1
< 0.1%
19930924 1
< 0.1%
ValueCountFrequency (%)
20210331 6
 
0.1%
20210330 3
 
< 0.1%
20210326 1
 
< 0.1%
20210324 2
 
< 0.1%
20210322 23
0.2%
20210315 1
 
< 0.1%
20210303 4
 
< 0.1%
20210302 2
 
< 0.1%
20210226 1
 
< 0.1%
20210224 1
 
< 0.1%

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

소재지전화
Text

MISSING 

Distinct6214
Distinct (%)89.2%
Missing3035
Missing (%)30.3%
Memory size156.2 KiB
2024-04-17T12:26:41.418769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.664465
Min length1

Characters and Unicode

Total characters74278
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

Unique6025 ?
Unique (%)86.5%

Sample

1st row051 7589294
2nd row051 8977388
3rd row051 918 8782
4th row051 583 2838
5th row051 6287969
ValueCountFrequency (%)
051 6520
41.8%
070 125
 
0.8%
868 36
 
0.2%
852 30
 
0.2%
727 29
 
0.2%
853 26
 
0.2%
851 26
 
0.2%
747 25
 
0.2%
203 24
 
0.2%
337 23
 
0.1%
Other values (6154) 8718
55.9%
2024-04-17T12:26:41.815477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12250
16.5%
0 11141
15.0%
1 10938
14.7%
8664
11.7%
2 5790
7.8%
7 4833
 
6.5%
3 4718
 
6.4%
6 4465
 
6.0%
8 4385
 
5.9%
4 4226
 
5.7%
Other values (2) 2868
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65613
88.3%
Space Separator 8664
 
11.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12250
18.7%
0 11141
17.0%
1 10938
16.7%
2 5790
8.8%
7 4833
 
7.4%
3 4718
 
7.2%
6 4465
 
6.8%
8 4385
 
6.7%
4 4226
 
6.4%
9 2867
 
4.4%
Space Separator
ValueCountFrequency (%)
8664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74278
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12250
16.5%
0 11141
15.0%
1 10938
14.7%
8664
11.7%
2 5790
7.8%
7 4833
 
6.5%
3 4718
 
6.4%
6 4465
 
6.0%
8 4385
 
5.9%
4 4226
 
5.7%
Other values (2) 2868
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12250
16.5%
0 11141
15.0%
1 10938
14.7%
8664
11.7%
2 5790
7.8%
7 4833
 
6.5%
3 4718
 
6.4%
6 4465
 
6.0%
8 4385
 
5.9%
4 4226
 
5.7%
Other values (2) 2868
 
3.9%
Distinct4072
Distinct (%)40.9%
Missing41
Missing (%)0.4%
Memory size156.2 KiB
2024-04-17T12:26:42.115256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9112361
Min length3

Characters and Unicode

Total characters48911
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

Unique2555 ?
Unique (%)25.7%

Sample

1st row16.48
2nd row46.80
3rd row57.31
4th row12.50
5th row23.45
ValueCountFrequency (%)
00 770
 
7.7%
33.00 129
 
1.3%
24.00 59
 
0.6%
20.00 55
 
0.6%
30.00 55
 
0.6%
26.40 48
 
0.5%
16.50 46
 
0.5%
28.00 41
 
0.4%
18.00 40
 
0.4%
23.00 39
 
0.4%
Other values (4062) 8677
87.1%
2024-04-17T12:26:42.505593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9959
20.4%
0 8790
18.0%
2 5008
10.2%
1 4881
10.0%
3 3717
 
7.6%
4 3306
 
6.8%
5 3133
 
6.4%
6 3027
 
6.2%
8 2622
 
5.4%
7 2239
 
4.6%
Other values (2) 2229
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38949
79.6%
Other Punctuation 9962
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8790
22.6%
2 5008
12.9%
1 4881
12.5%
3 3717
9.5%
4 3306
 
8.5%
5 3133
 
8.0%
6 3027
 
7.8%
8 2622
 
6.7%
7 2239
 
5.7%
9 2226
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 9959
> 99.9%
, 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 48911
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9959
20.4%
0 8790
18.0%
2 5008
10.2%
1 4881
10.0%
3 3717
 
7.6%
4 3306
 
6.8%
5 3133
 
6.4%
6 3027
 
6.2%
8 2622
 
5.4%
7 2239
 
4.6%
Other values (2) 2229
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9959
20.4%
0 8790
18.0%
2 5008
10.2%
1 4881
10.0%
3 3717
 
7.6%
4 3306
 
6.8%
5 3133
 
6.4%
6 3027
 
6.2%
8 2622
 
5.4%
7 2239
 
4.6%
Other values (2) 2229
 
4.6%

소재지우편번호
Real number (ℝ)

MISSING 

Distinct855
Distinct (%)8.6%
Missing102
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean610666.54
Minimum361856
Maximum619953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:42.624482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum361856
5-th percentile601817
Q1607813
median611819
Q3614805
95-th percentile617814
Maximum619953
Range258097
Interquartile range (IQR)6992

Descriptive statistics

Standard deviation5397.7225
Coefficient of variation (CV)0.008839067
Kurtosis454.88492
Mean610666.54
Median Absolute Deviation (MAD)3031
Skewness-10.153137
Sum6.0443774 × 109
Variance29135408
MonotonicityNot monotonic
2024-04-17T12:26:42.733391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
609839 113
 
1.1%
614847 86
 
0.9%
612824 80
 
0.8%
608805 78
 
0.8%
604851 74
 
0.7%
616852 68
 
0.7%
614845 65
 
0.7%
612842 65
 
0.7%
608832 61
 
0.6%
612847 58
 
0.6%
Other values (845) 9150
91.5%
(Missing) 102
 
1.0%
ValueCountFrequency (%)
361856 1
 
< 0.1%
600012 2
 
< 0.1%
600013 4
< 0.1%
600015 1
 
< 0.1%
600016 6
0.1%
600017 6
0.1%
600022 3
 
< 0.1%
600023 2
 
< 0.1%
600024 3
 
< 0.1%
600025 8
0.1%
ValueCountFrequency (%)
619953 2
 
< 0.1%
619952 5
 
0.1%
619951 3
 
< 0.1%
619913 3
 
< 0.1%
619912 8
 
0.1%
619911 2
 
< 0.1%
619906 3
 
< 0.1%
619905 30
0.3%
619904 2
 
< 0.1%
619903 25
0.2%
Distinct9264
Distinct (%)92.8%
Missing14
Missing (%)0.1%
Memory size156.2 KiB
2024-04-17T12:26:42.977067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length49
Mean length24.844382
Min length14

Characters and Unicode

Total characters248096
Distinct characters513
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8656 ?
Unique (%)86.7%

Sample

1st row부산광역시 수영구 망미동 774-265번지 연수골목 10
2nd row부산광역시 부산진구 당감동 289-8
3rd row부산광역시 기장군 정관읍 모전리 730번지 이지더원3차 323동 118호
4th row부산광역시 수영구 광안동 76-19번지
5th row부산광역시 금정구 구서동 252-32번지
ValueCountFrequency (%)
부산광역시 9985
 
21.3%
해운대구 1277
 
2.7%
부산진구 1229
 
2.6%
사하구 977
 
2.1%
t통b반 862
 
1.8%
동래구 851
 
1.8%
연제구 772
 
1.6%
남구 755
 
1.6%
금정구 725
 
1.5%
수영구 721
 
1.5%
Other values (10119) 28676
61.2%
2024-04-17T12:26:43.346545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36851
 
14.9%
12149
 
4.9%
12094
 
4.9%
1 12052
 
4.9%
12039
 
4.9%
10365
 
4.2%
10187
 
4.1%
10142
 
4.1%
10003
 
4.0%
- 8949
 
3.6%
Other values (503) 113265
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 146706
59.1%
Decimal Number 52321
 
21.1%
Space Separator 36851
 
14.9%
Dash Punctuation 8949
 
3.6%
Uppercase Letter 2202
 
0.9%
Open Punctuation 361
 
0.1%
Close Punctuation 359
 
0.1%
Other Punctuation 266
 
0.1%
Lowercase Letter 72
 
< 0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12149
 
8.3%
12094
 
8.2%
12039
 
8.2%
10365
 
7.1%
10187
 
6.9%
10142
 
6.9%
10003
 
6.8%
8892
 
6.1%
8424
 
5.7%
2335
 
1.6%
Other values (443) 50076
34.1%
Uppercase Letter
ValueCountFrequency (%)
B 922
41.9%
T 869
39.5%
A 71
 
3.2%
S 64
 
2.9%
K 49
 
2.2%
I 31
 
1.4%
G 27
 
1.2%
E 22
 
1.0%
H 21
 
1.0%
L 19
 
0.9%
Other values (13) 107
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
l 20
27.8%
s 13
18.1%
e 13
18.1%
i 11
15.3%
t 3
 
4.2%
b 2
 
2.8%
c 2
 
2.8%
h 1
 
1.4%
w 1
 
1.4%
v 1
 
1.4%
Other values (5) 5
 
6.9%
Decimal Number
ValueCountFrequency (%)
1 12052
23.0%
2 7305
14.0%
3 5849
11.2%
4 4794
 
9.2%
0 4490
 
8.6%
5 4313
 
8.2%
6 3734
 
7.1%
7 3485
 
6.7%
8 3272
 
6.3%
9 3027
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 191
71.8%
@ 47
 
17.7%
. 13
 
4.9%
/ 10
 
3.8%
· 3
 
1.1%
: 1
 
0.4%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
36851
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8949
100.0%
Open Punctuation
ValueCountFrequency (%)
( 361
100.0%
Close Punctuation
ValueCountFrequency (%)
) 359
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 146706
59.1%
Common 99116
40.0%
Latin 2274
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12149
 
8.3%
12094
 
8.2%
12039
 
8.2%
10365
 
7.1%
10187
 
6.9%
10142
 
6.9%
10003
 
6.8%
8892
 
6.1%
8424
 
5.7%
2335
 
1.6%
Other values (443) 50076
34.1%
Latin
ValueCountFrequency (%)
B 922
40.5%
T 869
38.2%
A 71
 
3.1%
S 64
 
2.8%
K 49
 
2.2%
I 31
 
1.4%
G 27
 
1.2%
E 22
 
1.0%
H 21
 
0.9%
l 20
 
0.9%
Other values (28) 178
 
7.8%
Common
ValueCountFrequency (%)
36851
37.2%
1 12052
 
12.2%
- 8949
 
9.0%
2 7305
 
7.4%
3 5849
 
5.9%
4 4794
 
4.8%
0 4490
 
4.5%
5 4313
 
4.4%
6 3734
 
3.8%
7 3485
 
3.5%
Other values (12) 7294
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 146706
59.1%
ASCII 101387
40.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36851
36.3%
1 12052
 
11.9%
- 8949
 
8.8%
2 7305
 
7.2%
3 5849
 
5.8%
4 4794
 
4.7%
0 4490
 
4.4%
5 4313
 
4.3%
6 3734
 
3.7%
7 3485
 
3.4%
Other values (49) 9565
 
9.4%
Hangul
ValueCountFrequency (%)
12149
 
8.3%
12094
 
8.2%
12039
 
8.2%
10365
 
7.1%
10187
 
6.9%
10142
 
6.9%
10003
 
6.8%
8892
 
6.1%
8424
 
5.7%
2335
 
1.6%
Other values (443) 50076
34.1%
None
ValueCountFrequency (%)
· 3
100.0%

도로명전체주소
Text

MISSING 

Distinct6798
Distinct (%)96.9%
Missing2981
Missing (%)29.8%
Memory size156.2 KiB
2024-04-17T12:26:43.650611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length56
Mean length32.068671
Min length17

Characters and Unicode

Total characters225090
Distinct characters539
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6588 ?
Unique (%)93.9%

Sample

1st row부산광역시 부산진구 동평로 93-1 (당감동)
2nd row부산광역시 기장군 정관읍 정관로 350, 323동 118호 (이지더원3차)
3rd row부산광역시 수영구 무학로21번길 106, 1층 (광안동)
4th row부산광역시 금정구 중앙대로1841번길 65 (구서동)
5th row부산광역시 수영구 광남로 185-1, 2층 (광안동)
ValueCountFrequency (%)
부산광역시 7018
 
16.1%
1층 1714
 
3.9%
부산진구 984
 
2.3%
2층 924
 
2.1%
해운대구 867
 
2.0%
사하구 619
 
1.4%
동래구 597
 
1.4%
수영구 527
 
1.2%
남구 525
 
1.2%
금정구 512
 
1.2%
Other values (5706) 29340
67.3%
2024-04-17T12:26:44.066035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36611
 
16.3%
9688
 
4.3%
1 9511
 
4.2%
8911
 
4.0%
8787
 
3.9%
7575
 
3.4%
7472
 
3.3%
7214
 
3.2%
7034
 
3.1%
) 6986
 
3.1%
Other values (529) 115301
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130085
57.8%
Space Separator 36611
 
16.3%
Decimal Number 36567
 
16.2%
Close Punctuation 6986
 
3.1%
Open Punctuation 6986
 
3.1%
Other Punctuation 6112
 
2.7%
Dash Punctuation 1134
 
0.5%
Uppercase Letter 504
 
0.2%
Lowercase Letter 75
 
< 0.1%
Math Symbol 30
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9688
 
7.4%
8911
 
6.9%
8787
 
6.8%
7575
 
5.8%
7472
 
5.7%
7214
 
5.5%
7034
 
5.4%
6899
 
5.3%
3510
 
2.7%
3479
 
2.7%
Other values (469) 59516
45.8%
Uppercase Letter
ValueCountFrequency (%)
A 91
18.1%
B 84
16.7%
S 64
12.7%
K 49
9.7%
I 27
 
5.4%
E 25
 
5.0%
H 23
 
4.6%
C 22
 
4.4%
V 18
 
3.6%
W 17
 
3.4%
Other values (13) 84
16.7%
Lowercase Letter
ValueCountFrequency (%)
l 20
26.7%
e 16
21.3%
s 13
17.3%
i 11
14.7%
t 3
 
4.0%
k 3
 
4.0%
c 2
 
2.7%
v 1
 
1.3%
w 1
 
1.3%
h 1
 
1.3%
Other values (4) 4
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 9511
26.0%
2 6239
17.1%
3 4025
11.0%
0 3548
 
9.7%
4 2962
 
8.1%
5 2520
 
6.9%
6 2184
 
6.0%
7 1995
 
5.5%
8 1898
 
5.2%
9 1685
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 6067
99.3%
@ 34
 
0.6%
/ 3
 
< 0.1%
. 3
 
< 0.1%
· 3
 
< 0.1%
& 1
 
< 0.1%
: 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 29
96.7%
1
 
3.3%
Space Separator
ValueCountFrequency (%)
36611
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6986
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6986
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 130085
57.8%
Common 94426
42.0%
Latin 579
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9688
 
7.4%
8911
 
6.9%
8787
 
6.8%
7575
 
5.8%
7472
 
5.7%
7214
 
5.5%
7034
 
5.4%
6899
 
5.3%
3510
 
2.7%
3479
 
2.7%
Other values (469) 59516
45.8%
Latin
ValueCountFrequency (%)
A 91
15.7%
B 84
14.5%
S 64
11.1%
K 49
 
8.5%
I 27
 
4.7%
E 25
 
4.3%
H 23
 
4.0%
C 22
 
3.8%
l 20
 
3.5%
V 18
 
3.1%
Other values (27) 156
26.9%
Common
ValueCountFrequency (%)
36611
38.8%
1 9511
 
10.1%
) 6986
 
7.4%
( 6986
 
7.4%
2 6239
 
6.6%
, 6067
 
6.4%
3 4025
 
4.3%
0 3548
 
3.8%
4 2962
 
3.1%
5 2520
 
2.7%
Other values (13) 8971
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130085
57.8%
ASCII 95001
42.2%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36611
38.5%
1 9511
 
10.0%
) 6986
 
7.4%
( 6986
 
7.4%
2 6239
 
6.6%
, 6067
 
6.4%
3 4025
 
4.2%
0 3548
 
3.7%
4 2962
 
3.1%
5 2520
 
2.7%
Other values (48) 9546
 
10.0%
Hangul
ValueCountFrequency (%)
9688
 
7.4%
8911
 
6.9%
8787
 
6.8%
7575
 
5.8%
7472
 
5.7%
7214
 
5.5%
7034
 
5.4%
6899
 
5.3%
3510
 
2.7%
3479
 
2.7%
Other values (469) 59516
45.8%
None
ValueCountFrequency (%)
· 3
75.0%
1
 
25.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct1594
Distinct (%)22.9%
Missing3045
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean47807.656
Minimum28465
Maximum49525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:44.183822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28465
5-th percentile46245
Q147147
median47843
Q348485
95-th percentile49387.3
Maximum49525
Range21060
Interquartile range (IQR)1338

Descriptive statistics

Standard deviation983.54655
Coefficient of variation (CV)0.020572992
Kurtosis20.414739
Mean47807.656
Median Absolute Deviation (MAD)660
Skewness-1.1025764
Sum3.3250225 × 108
Variance967363.81
MonotonicityNot monotonic
2024-04-17T12:26:44.286584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46726 50
 
0.5%
48059 43
 
0.4%
48111 41
 
0.4%
48498 33
 
0.3%
46291 33
 
0.3%
46526 31
 
0.3%
48060 31
 
0.3%
48119 30
 
0.3%
48947 30
 
0.3%
48110 29
 
0.3%
Other values (1584) 6604
66.0%
(Missing) 3045
30.4%
ValueCountFrequency (%)
28465 1
 
< 0.1%
46007 8
0.1%
46008 11
0.1%
46009 1
 
< 0.1%
46010 2
 
< 0.1%
46012 4
 
< 0.1%
46013 7
0.1%
46014 2
 
< 0.1%
46015 9
0.1%
46016 3
 
< 0.1%
ValueCountFrequency (%)
49525 1
 
< 0.1%
49524 4
 
< 0.1%
49523 2
 
< 0.1%
49522 3
 
< 0.1%
49521 2
 
< 0.1%
49520 14
0.1%
49519 6
 
0.1%
49518 15
0.1%
49516 1
 
< 0.1%
49515 6
 
0.1%
Distinct8219
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T12:26:44.582174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length5.595
Min length1

Characters and Unicode

Total characters55950
Distinct characters890
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7297 ?
Unique (%)73.0%

Sample

1st row숙미용실
2nd row티아라
3rd row브이엠(VM)헤어
4th row황금손헤어
5th row코이네일
ValueCountFrequency (%)
미용실 286
 
2.3%
헤어 240
 
1.9%
에스테틱 110
 
0.9%
네일 104
 
0.8%
헤어샵 89
 
0.7%
hair 65
 
0.5%
nail 56
 
0.4%
뷰티 42
 
0.3%
40
 
0.3%
34
 
0.3%
Other values (8234) 11551
91.6%
2024-04-17T12:26:45.003302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3536
 
6.3%
3434
 
6.1%
2621
 
4.7%
1841
 
3.3%
1384
 
2.5%
1186
 
2.1%
1109
 
2.0%
1108
 
2.0%
1058
 
1.9%
862
 
1.5%
Other values (880) 37811
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47111
84.2%
Space Separator 2621
 
4.7%
Lowercase Letter 2459
 
4.4%
Uppercase Letter 1958
 
3.5%
Close Punctuation 559
 
1.0%
Open Punctuation 559
 
1.0%
Other Punctuation 358
 
0.6%
Decimal Number 279
 
0.5%
Dash Punctuation 37
 
0.1%
Math Symbol 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3536
 
7.5%
3434
 
7.3%
1841
 
3.9%
1384
 
2.9%
1186
 
2.5%
1109
 
2.4%
1108
 
2.4%
1058
 
2.2%
862
 
1.8%
796
 
1.7%
Other values (794) 30797
65.4%
Lowercase Letter
ValueCountFrequency (%)
a 328
13.3%
i 271
11.0%
e 267
10.9%
n 205
 
8.3%
o 197
 
8.0%
l 173
 
7.0%
r 159
 
6.5%
y 121
 
4.9%
h 120
 
4.9%
u 92
 
3.7%
Other values (16) 526
21.4%
Uppercase Letter
ValueCountFrequency (%)
A 173
 
8.8%
N 171
 
8.7%
S 161
 
8.2%
H 139
 
7.1%
I 132
 
6.7%
B 109
 
5.6%
M 102
 
5.2%
J 102
 
5.2%
L 101
 
5.2%
E 96
 
4.9%
Other values (16) 672
34.3%
Other Punctuation
ValueCountFrequency (%)
& 123
34.4%
. 87
24.3%
# 49
 
13.7%
, 41
 
11.5%
' 28
 
7.8%
· 8
 
2.2%
; 5
 
1.4%
5
 
1.4%
" 4
 
1.1%
: 3
 
0.8%
Other values (4) 5
 
1.4%
Decimal Number
ValueCountFrequency (%)
1 67
24.0%
2 58
20.8%
0 51
18.3%
3 31
11.1%
4 18
 
6.5%
7 13
 
4.7%
5 13
 
4.7%
9 11
 
3.9%
8 9
 
3.2%
6 8
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 557
99.6%
] 2
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 557
99.6%
[ 2
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 4
80.0%
1
 
20.0%
Space Separator
ValueCountFrequency (%)
2621
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47078
84.1%
Common 4422
 
7.9%
Latin 4417
 
7.9%
Han 33
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3536
 
7.5%
3434
 
7.3%
1841
 
3.9%
1384
 
2.9%
1186
 
2.5%
1109
 
2.4%
1108
 
2.4%
1058
 
2.2%
862
 
1.8%
796
 
1.7%
Other values (776) 30764
65.3%
Latin
ValueCountFrequency (%)
a 328
 
7.4%
i 271
 
6.1%
e 267
 
6.0%
n 205
 
4.6%
o 197
 
4.5%
A 173
 
3.9%
l 173
 
3.9%
N 171
 
3.9%
S 161
 
3.6%
r 159
 
3.6%
Other values (42) 2312
52.3%
Common
ValueCountFrequency (%)
2621
59.3%
) 557
 
12.6%
( 557
 
12.6%
& 123
 
2.8%
. 87
 
2.0%
1 67
 
1.5%
2 58
 
1.3%
0 51
 
1.2%
# 49
 
1.1%
, 41
 
0.9%
Other values (24) 211
 
4.8%
Han
ValueCountFrequency (%)
13
39.4%
2
 
6.1%
2
 
6.1%
2
 
6.1%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
Other values (8) 8
24.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47076
84.1%
ASCII 8823
 
15.8%
CJK 33
 
0.1%
None 15
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3536
 
7.5%
3434
 
7.3%
1841
 
3.9%
1384
 
2.9%
1186
 
2.5%
1109
 
2.4%
1108
 
2.4%
1058
 
2.2%
862
 
1.8%
796
 
1.7%
Other values (774) 30762
65.3%
ASCII
ValueCountFrequency (%)
2621
29.7%
) 557
 
6.3%
( 557
 
6.3%
a 328
 
3.7%
i 271
 
3.1%
e 267
 
3.0%
n 205
 
2.3%
o 197
 
2.2%
A 173
 
2.0%
l 173
 
2.0%
Other values (72) 3474
39.4%
CJK
ValueCountFrequency (%)
13
39.4%
2
 
6.1%
2
 
6.1%
2
 
6.1%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
Other values (8) 8
24.2%
None
ValueCountFrequency (%)
· 8
53.3%
5
33.3%
2
 
13.3%
Compat Jamo
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

최종수정시점
Real number (ℝ)

Distinct8143
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0131177 × 1013
Minimum1.9990125 × 1013
Maximum2.0210331 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:45.119988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990125 × 1013
5-th percentile2.0010927 × 1013
Q12.006101 × 1013
median2.0151006 × 1013
Q32.0191105 × 1013
95-th percentile2.0201223 × 1013
Maximum2.0210331 × 1013
Range2.2020617 × 1011
Interquartile range (IQR)1.300951 × 1011

Descriptive statistics

Standard deviation6.9036064 × 1010
Coefficient of variation (CV)0.0034293109
Kurtosis-1.109499
Mean2.0131177 × 1013
Median Absolute Deviation (MAD)4.9421502 × 1010
Skewness-0.58838665
Sum2.0131177 × 1017
Variance4.7659782 × 1021
MonotonicityNot monotonic
2024-04-17T12:26:45.224077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030402000000 108
 
1.1%
20001209000000 38
 
0.4%
20040827000000 36
 
0.4%
20040719000000 34
 
0.3%
20020823000000 33
 
0.3%
20030627000000 31
 
0.3%
20061113000000 29
 
0.3%
20030403000000 29
 
0.3%
19990429000000 26
 
0.3%
20031023000000 25
 
0.2%
Other values (8133) 9611
96.1%
ValueCountFrequency (%)
19990125000000 4
 
< 0.1%
19990126000000 7
0.1%
19990222000000 1
 
< 0.1%
19990223000000 3
 
< 0.1%
19990224000000 3
 
< 0.1%
19990225000000 10
0.1%
19990226000000 1
 
< 0.1%
19990303000000 4
 
< 0.1%
19990304000000 10
0.1%
19990305000000 12
0.1%
ValueCountFrequency (%)
20210331173419 1
< 0.1%
20210331163757 1
< 0.1%
20210331163604 1
< 0.1%
20210331162309 1
< 0.1%
20210331161928 1
< 0.1%
20210331155112 1
< 0.1%
20210331144147 1
< 0.1%
20210331144055 1
< 0.1%
20210331143319 1
< 0.1%
20210331133445 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7046 
U
2954 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 7046
70.5%
U 2954
29.5%

Length

2024-04-17T12:26:45.313730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:45.396997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7046
70.5%
u 2954
29.5%
Distinct938
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2021-04-02 02:40:00
2024-04-17T12:26:45.505095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T12:26:45.617253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반미용업
6961 
피부미용업
1860 
네일아트업
899 
메이크업업
 
188
기타
 
90

Length

Max length6
Median length5
Mean length4.9732
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 6961
69.6%
피부미용업 1860
 
18.6%
네일아트업 899
 
9.0%
메이크업업 188
 
1.9%
기타 90
 
0.9%
미용업 기타 2
 
< 0.1%

Length

2024-04-17T12:26:45.727571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:45.816384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 6961
69.6%
피부미용업 1860
 
18.6%
네일아트업 899
 
9.0%
메이크업업 188
 
1.9%
기타 92
 
0.9%
미용업 2
 
< 0.1%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct7518
Distinct (%)77.3%
Missing273
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean388263.05
Minimum241128.92
Maximum407824.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:45.909221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241128.92
5-th percentile379710.92
Q1384468.79
median388693.59
Q3391798.02
95-th percentile397616.88
Maximum407824.89
Range166695.96
Interquartile range (IQR)7329.2343

Descriptive statistics

Standard deviation5506.1056
Coefficient of variation (CV)0.014181379
Kurtosis52.139419
Mean388263.05
Median Absolute Deviation (MAD)3564.2681
Skewness-2.02823
Sum3.7766347 × 109
Variance30317199
MonotonicityNot monotonic
2024-04-17T12:26:46.009693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
392474.578116018 21
 
0.2%
395388.715069604 20
 
0.2%
393239.237933586 19
 
0.2%
395560.220499924 18
 
0.2%
380398.062015138 18
 
0.2%
398237.363461482 17
 
0.2%
398330.516530402 14
 
0.1%
398491.352089522 13
 
0.1%
394112.524871033 11
 
0.1%
383412.190468328 11
 
0.1%
Other values (7508) 9565
95.7%
(Missing) 273
 
2.7%
ValueCountFrequency (%)
241128.922467 1
< 0.1%
366931.435995074 1
< 0.1%
367047.233040251 1
< 0.1%
367051.926419356 2
< 0.1%
367108.112280274 1
< 0.1%
367145.447260979 1
< 0.1%
367179.004823381 1
< 0.1%
367193.583454597 1
< 0.1%
367195.063042763 2
< 0.1%
367226.95953767 1
< 0.1%
ValueCountFrequency (%)
407824.887002439 1
< 0.1%
407739.046710947 1
< 0.1%
407663.462947301 1
< 0.1%
407652.048377874 1
< 0.1%
407556.4753504 1
< 0.1%
407037.787059465 1
< 0.1%
406982.053033795 1
< 0.1%
405370.623777615 1
< 0.1%
405351.525157834 1
< 0.1%
404952.299376958 1
< 0.1%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct7520
Distinct (%)77.3%
Missing273
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean186983.42
Minimum173942.79
Maximum349970.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:46.110590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173942.79
5-th percentile178307.62
Q1183179.72
median187277.73
Q3190764.45
95-th percentile195642.22
Maximum349970.06
Range176027.27
Interquartile range (IQR)7584.7352

Descriptive statistics

Standard deviation5758.5398
Coefficient of variation (CV)0.030797061
Kurtosis65.672895
Mean186983.42
Median Absolute Deviation (MAD)3643.7847
Skewness2.488083
Sum1.8187878 × 109
Variance33160781
MonotonicityNot monotonic
2024-04-17T12:26:46.215302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183052.21115244 21
 
0.2%
186268.853282623 20
 
0.2%
188619.149645081 19
 
0.2%
175314.286676535 18
 
0.2%
186273.769084383 18
 
0.2%
187720.511056894 17
 
0.2%
187771.511373596 14
 
0.1%
187644.220019205 13
 
0.1%
191421.760662067 11
 
0.1%
187887.931705979 11
 
0.1%
Other values (7510) 9565
95.7%
(Missing) 273
 
2.7%
ValueCountFrequency (%)
173942.787360397 1
 
< 0.1%
173961.914773076 2
< 0.1%
173969.719902491 1
 
< 0.1%
173994.386578688 1
 
< 0.1%
174016.551235181 2
< 0.1%
174031.935803657 2
< 0.1%
174035.700224564 1
 
< 0.1%
174101.406639044 3
< 0.1%
174106.09009853 2
< 0.1%
174156.617297535 1
 
< 0.1%
ValueCountFrequency (%)
349970.057043 1
 
< 0.1%
210001.947526977 1
 
< 0.1%
206512.517255249 1
 
< 0.1%
206353.855586145 1
 
< 0.1%
206298.919203021 1
 
< 0.1%
206242.308287228 1
 
< 0.1%
206184.609573703 7
0.1%
206175.365141713 1
 
< 0.1%
206174.795860476 1
 
< 0.1%
206145.8644854 1
 
< 0.1%

위생업태명
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미용업
3888 
미용업(일반)
2805 
미용업(피부)
959 
일반미용업
580 
미용업(손톱ㆍ발톱)
 
364
Other values (26)
1404 

Length

Max length31
Median length28
Mean length6.1358
Min length3

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row미용업
2nd row미용업
3rd row미용업(일반)
4th row미용업(일반)
5th row미용업

Common Values

ValueCountFrequency (%)
미용업 3888
38.9%
미용업(일반) 2805
28.1%
미용업(피부) 959
 
9.6%
일반미용업 580
 
5.8%
미용업(손톱ㆍ발톱) 364
 
3.6%
미용업(종합) 306
 
3.1%
피부미용업 217
 
2.2%
네일미용업 137
 
1.4%
미용업(피부), 미용업(손톱ㆍ발톱) 78
 
0.8%
종합미용업 77
 
0.8%
Other values (21) 589
 
5.9%

Length

2024-04-17T12:26:46.319425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 4050
37.3%
미용업(일반 2963
27.3%
미용업(피부 1164
 
10.7%
미용업(손톱ㆍ발톱 642
 
5.9%
일반미용업 636
 
5.9%
미용업(화장ㆍ분장 316
 
2.9%
미용업(종합 306
 
2.8%
피부미용업 292
 
2.7%
네일미용업 244
 
2.2%
화장ㆍ분장 162
 
1.5%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct39
Distinct (%)0.5%
Missing2104
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean2.5652229
Minimum0
Maximum63
Zeros2967
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:46.406075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum63
Range63
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8979771
Coefficient of variation (CV)1.5195471
Kurtosis41.3473
Mean2.5652229
Median Absolute Deviation (MAD)2
Skewness5.0260504
Sum20255
Variance15.194225
MonotonicityNot monotonic
2024-04-17T12:26:46.500999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 2967
29.7%
2 1273
12.7%
3 1076
 
10.8%
4 979
 
9.8%
5 509
 
5.1%
1 423
 
4.2%
6 203
 
2.0%
7 86
 
0.9%
8 67
 
0.7%
9 63
 
0.6%
Other values (29) 250
 
2.5%
(Missing) 2104
21.0%
ValueCountFrequency (%)
0 2967
29.7%
1 423
 
4.2%
2 1273
12.7%
3 1076
 
10.8%
4 979
 
9.8%
5 509
 
5.1%
6 203
 
2.0%
7 86
 
0.9%
8 67
 
0.7%
9 63
 
0.6%
ValueCountFrequency (%)
63 1
 
< 0.1%
51 1
 
< 0.1%
49 2
 
< 0.1%
47 1
 
< 0.1%
43 2
 
< 0.1%
42 4
< 0.1%
39 2
 
< 0.1%
38 4
< 0.1%
37 4
< 0.1%
30 8
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.2%
Missing2961
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean0.39948856
Minimum0
Maximum18
Zeros5132
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:46.594342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94391142
Coefficient of variation (CV)2.3627996
Kurtosis51.878861
Mean0.39948856
Median Absolute Deviation (MAD)0
Skewness5.4160482
Sum2812
Variance0.89096876
MonotonicityNot monotonic
2024-04-17T12:26:46.913864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5132
51.3%
1 1532
 
15.3%
2 172
 
1.7%
3 72
 
0.7%
5 53
 
0.5%
4 37
 
0.4%
6 24
 
0.2%
7 6
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 2961
29.6%
ValueCountFrequency (%)
0 5132
51.3%
1 1532
 
15.3%
2 172
 
1.7%
3 72
 
0.7%
4 37
 
0.4%
5 53
 
0.5%
6 24
 
0.2%
7 6
 
0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 6
 
0.1%
6 24
0.2%
5 53
0.5%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct18
Distinct (%)0.2%
Missing2702
Missing (%)27.0%
Infinite0
Infinite (%)0.0%
Mean1.3773637
Minimum0
Maximum325
Zeros1502
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:46.998780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum325
Range325
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.026443
Coefficient of variation (CV)2.923297
Kurtosis5721.8341
Mean1.3773637
Median Absolute Deviation (MAD)1
Skewness71.367526
Sum10052
Variance16.212243
MonotonicityNot monotonic
2024-04-17T12:26:47.081396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 3555
35.5%
0 1502
15.0%
2 1412
 
14.1%
3 493
 
4.9%
4 147
 
1.5%
5 70
 
0.7%
6 49
 
0.5%
7 28
 
0.3%
8 10
 
0.1%
9 9
 
0.1%
Other values (8) 23
 
0.2%
(Missing) 2702
27.0%
ValueCountFrequency (%)
0 1502
15.0%
1 3555
35.5%
2 1412
 
14.1%
3 493
 
4.9%
4 147
 
1.5%
5 70
 
0.7%
6 49
 
0.5%
7 28
 
0.3%
8 10
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
325 1
 
< 0.1%
37 1
 
< 0.1%
19 1
 
< 0.1%
15 1
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 5
0.1%
10 8
0.1%
9 9
0.1%
8 10
0.1%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct18
Distinct (%)0.3%
Missing4177
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean1.421604
Minimum0
Maximum326
Zeros1050
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:47.168754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum326
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.4577863
Coefficient of variation (CV)3.1357441
Kurtosis4829.8372
Mean1.421604
Median Absolute Deviation (MAD)0
Skewness66.426
Sum8278
Variance19.871859
MonotonicityNot monotonic
2024-04-17T12:26:47.252070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 2950
29.5%
2 1152
 
11.5%
0 1050
 
10.5%
3 405
 
4.0%
4 117
 
1.2%
5 56
 
0.6%
6 40
 
0.4%
7 19
 
0.2%
10 12
 
0.1%
9 6
 
0.1%
Other values (8) 16
 
0.2%
(Missing) 4177
41.8%
ValueCountFrequency (%)
0 1050
 
10.5%
1 2950
29.5%
2 1152
 
11.5%
3 405
 
4.0%
4 117
 
1.2%
5 56
 
0.6%
6 40
 
0.4%
7 19
 
0.2%
8 5
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
326 1
 
< 0.1%
21 2
 
< 0.1%
19 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 1
 
< 0.1%
10 12
0.1%
9 6
0.1%
8 5
0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5715 
0
4109 
1
 
154
2
 
20
3
 
2

Length

Max length4
Median length4
Mean length2.7145
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 5715
57.1%
0 4109
41.1%
1 154
 
1.5%
2 20
 
0.2%
3 2
 
< 0.1%

Length

2024-04-17T12:26:47.349995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:47.429868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5715
57.1%
0 4109
41.1%
1 154
 
1.5%
2 20
 
0.2%
3 2
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6801 
0
3059 
1
 
121
2
 
17
3
 
2

Length

Max length4
Median length4
Mean length3.0403
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 6801
68.0%
0 3059
30.6%
1 121
 
1.2%
2 17
 
0.2%
3 2
 
< 0.1%

Length

2024-04-17T12:26:47.517482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:47.612338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6801
68.0%
0 3059
30.6%
1 121
 
1.2%
2 17
 
0.2%
3 2
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6689 
<NA>
3311 

Length

Max length4
Median length1
Mean length1.9933
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6689
66.9%
<NA> 3311
33.1%

Length

2024-04-17T12:26:47.701066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:47.777176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6689
66.9%
na 3311
33.1%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6689 
<NA>
3311 

Length

Max length4
Median length1
Mean length1.9933
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6689
66.9%
<NA> 3311
33.1%

Length

2024-04-17T12:26:47.856579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:47.932024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6689
66.9%
na 3311
33.1%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6689 
<NA>
3311 

Length

Max length4
Median length1
Mean length1.9933
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6689
66.9%
<NA> 3311
33.1%

Length

2024-04-17T12:26:48.010786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:48.087136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6689
66.9%
na 3311
33.1%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing138
Missing (%)1.4%
Memory size97.7 KiB
False
9862 
(Missing)
 
138
ValueCountFrequency (%)
False 9862
98.6%
(Missing) 138
 
1.4%
2024-04-17T12:26:48.152576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

의자수
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)0.3%
Missing756
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean3.3062527
Minimum0
Maximum31
Zeros1402
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:48.222720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8252505
Coefficient of variation (CV)0.85451741
Kurtosis14.44845
Mean3.3062527
Median Absolute Deviation (MAD)1
Skewness2.8014295
Sum30563
Variance7.9820403
MonotonicityNot monotonic
2024-04-17T12:26:48.316091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 3096
31.0%
2 1491
14.9%
4 1415
14.1%
0 1402
14.0%
5 530
 
5.3%
6 372
 
3.7%
1 225
 
2.2%
8 172
 
1.7%
7 132
 
1.3%
10 97
 
1.0%
Other values (21) 312
 
3.1%
(Missing) 756
 
7.6%
ValueCountFrequency (%)
0 1402
14.0%
1 225
 
2.2%
2 1491
14.9%
3 3096
31.0%
4 1415
14.1%
5 530
 
5.3%
6 372
 
3.7%
7 132
 
1.3%
8 172
 
1.7%
9 72
 
0.7%
ValueCountFrequency (%)
31 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 2
< 0.1%
27 3
< 0.1%
26 2
< 0.1%
24 3
< 0.1%
23 4
< 0.1%
22 4
< 0.1%
21 4
< 0.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
2
 
3

Length

Max length4
Median length4
Mean length3.9991
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> 9997
> 99.9%
2 3
 
< 0.1%

Length

2024-04-17T12:26:48.413935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:48.485649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
2 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7661 
임대
2260 
자가
 
79

Length

Max length4
Median length4
Mean length3.5322
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> 7661
76.6%
임대 2260
 
22.6%
자가 79
 
0.8%

Length

2024-04-17T12:26:48.563386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:48.646407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7661
76.6%
임대 2260
 
22.6%
자가 79
 
0.8%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5615 
<NA>
4385 

Length

Max length4
Median length1
Mean length2.3155
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 5615
56.1%
<NA> 4385
43.9%

Length

2024-04-17T12:26:48.724452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:48.801751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5615
56.1%
na 4385
43.9%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.3%
Missing7537
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean0.078359724
Minimum0
Maximum8
Zeros2313
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:48.867521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38444394
Coefficient of variation (CV)4.9061421
Kurtosis140.34328
Mean0.078359724
Median Absolute Deviation (MAD)0
Skewness9.4742295
Sum193
Variance0.14779715
MonotonicityNot monotonic
2024-04-17T12:26:48.948918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 2313
 
23.1%
1 128
 
1.3%
2 13
 
0.1%
3 5
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 7537
75.4%
ValueCountFrequency (%)
0 2313
23.1%
1 128
 
1.3%
2 13
 
0.1%
3 5
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.1%
2 13
 
0.1%
1 128
 
1.3%
0 2313
23.1%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7548 
0
2431 
1
 
20
2
 
1

Length

Max length4
Median length4
Mean length3.2644
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 7548
75.5%
0 2431
 
24.3%
1 20
 
0.2%
2 1
 
< 0.1%

Length

2024-04-17T12:26:49.069445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:49.171333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7548
75.5%
0 2431
 
24.3%
1 20
 
0.2%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5375 
<NA>
4625 

Length

Max length4
Median length1
Mean length2.3875
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 5375
53.8%
<NA> 4625
46.2%

Length

2024-04-17T12:26:49.275693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T12:26:49.347337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5375
53.8%
na 4625
46.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)0.3%
Missing4650
Missing (%)46.5%
Infinite0
Infinite (%)0.0%
Mean0.95140187
Minimum0
Maximum25
Zeros3636
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T12:26:49.413952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8535543
Coefficient of variation (CV)1.9482349
Kurtosis15.422867
Mean0.95140187
Median Absolute Deviation (MAD)0
Skewness3.0329451
Sum5090
Variance3.4356636
MonotonicityNot monotonic
2024-04-17T12:26:49.501104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3636
36.4%
2 499
 
5.0%
1 426
 
4.3%
3 302
 
3.0%
4 175
 
1.8%
5 116
 
1.2%
6 81
 
0.8%
7 54
 
0.5%
8 22
 
0.2%
10 12
 
0.1%
Other values (8) 27
 
0.3%
(Missing) 4650
46.5%
ValueCountFrequency (%)
0 3636
36.4%
1 426
 
4.3%
2 499
 
5.0%
3 302
 
3.0%
4 175
 
1.8%
5 116
 
1.2%
6 81
 
0.8%
7 54
 
0.5%
8 22
 
0.2%
9 12
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
12 5
 
0.1%
11 4
 
< 0.1%
10 12
0.1%
9 12
0.1%
8 22
0.2%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9998 
True
 
2
ValueCountFrequency (%)
False 9998
> 99.9%
True 2
 
< 0.1%
2024-04-17T12:26:49.576397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Unnamed: 50
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

Sample

번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
2053220533미용업05_18_01_P33800003380000-204-1994-0001619941129<NA>3폐업2폐업20100203<NA><NA><NA>051 758929416.48613825부산광역시 수영구 망미동 774-265번지 연수골목 10<NA><NA>숙미용실20080701161310I2018-08-31 23:59:59.0일반미용업391017.944922187992.203231미용업4<NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2189521896미용업05_18_01_P32900003290000-204-2001-0105420011204<NA>3폐업2폐업20201028<NA><NA><NA>051 897738846.80614817부산광역시 부산진구 당감동 289-8부산광역시 부산진구 동평로 93-1 (당감동)47144티아라20201028143452U2020-10-30 02:40:00.0일반미용업385776.999406187077.576769미용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
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51385139미용업05_18_01_P33800003380000-211-2000-0002220000926<NA>1영업/정상1영업<NA><NA><NA><NA>051 918 878212.50613800부산광역시 수영구 광안동 76-19번지부산광역시 수영구 무학로21번길 106, 1층 (광안동)48266황금손헤어20160322153434I2018-08-31 23:59:59.0일반미용업392832.120053187292.320095미용업(일반)101100000N2<NA><NA><NA><NA>0<NA><NA>00N<NA>
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47244725미용업05_18_01_P32800003280000-211-1993-0000219931123<NA>1영업/정상1영업<NA><NA><NA><NA>051 404042327.52606080부산광역시 영도구 동삼동 1123번지 동삼주공영구임대아파트 상가동 106호부산광역시 영도구 상리로 1, 상가동 106호 (동삼동, 동삼주공영구임대아파트)49089동현20200406183056U2020-04-08 02:40:00.0일반미용업388577.66451178279.998453미용업(일반)<NA><NA><NA><NA><NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1820118202미용업05_18_01_P33400003340000-204-1988-0104719881124<NA>3폐업2폐업20030227<NA><NA><NA>051 2616796.00604845부산광역시 사하구 장림동 615-246번지<NA><NA>솔잎미용실20040719000000I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부의자수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부Unnamed: 50
45044505미용업05_18_01_P33200003320000-215-2017-0000720170529<NA>1영업/정상1영업<NA><NA><NA><NA><NA>79.79616852부산광역시 북구 화명동 2272-5번지부산광역시 북구 화명대로 31, 3층 306호 (화명동, 현호타워)46525렛미인20170529171421I2018-08-31 23:59:59.0네일아트업383050.676657194733.824694미용업(손톱ㆍ발톱)8133<NA><NA>000N4<NA><NA><NA><NA>00000N<NA>
31203121미용업05_18_01_P33800003380000-222-2019-0000120190401<NA>1영업/정상1영업<NA><NA><NA><NA><NA>49.72613806부산광역시 수영구 광안동 776-19번지부산광역시 수영구 수영로 523, 3층 (광안동)48262리미샵20190614131619U2019-06-16 02:40:00.0네일아트업392232.821816187891.118233미용업(손톱ㆍ발톱), 미용업(화장ㆍ분장)303300000N3<NA><NA><NA><NA>00002N<NA>
97219722미용업05_18_01_P33500003350000-215-2020-0000820200504<NA>1영업/정상1영업<NA><NA><NA><NA><NA>14.00609822부산광역시 금정구 부곡동 276-9번지부산광역시 금정구 동부곡로15번길 83, 1층 (부곡동)46271네일빵20200504112647I2020-05-06 00:23:19.0네일아트업390544.866844194246.850312미용업(손톱ㆍ발톱)0011<NA><NA>000N3<NA><NA><NA><NA>01000N<NA>
1624516246미용업05_18_01_P33500003350000-204-1989-0021219890201<NA>3폐업2폐업20071122<NA><NA><NA>051 5149686145.00609839부산광역시 금정구 장전동 417-34번지<NA><NA>대학로20060221000000I2018-08-31 23:59:59.0일반미용업389842.46312194480.027607미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N22<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
2092420925미용업05_18_01_P33200003320000-211-2001-0001520010731<NA>3폐업2폐업20170828<NA><NA><NA><NA>26.40616821부산광역시 북구 덕천동 387-9번지부산광역시 북구 의성로109번길 59 (덕천동)46567나드리헤어샵20170828162349I2018-08-31 23:59:59.0일반미용업383318.229155192140.817821미용업(일반)001100000N3<NA><NA><NA><NA>0<NA><NA>00N<NA>
49484949미용업05_18_01_P32800003280000-212-2011-0000520111110<NA>1영업/정상1영업<NA><NA><NA><NA><NA>31.64606041부산광역시 영도구 영선동1가 21-1번지 영도오션트라움부산광역시 영도구 태종로 107, 영도오션트라움 1동 4층 403호 (영선동1가)49036진에스테틱20190725092248U2019-07-27 02:40:00.0피부미용업386293.328839179065.007268미용업(피부)202200000N0<NA><NA><NA><NA>0<NA><NA>02N<NA>
53935394미용업05_18_01_P33100003310000-211-2012-0000620120726<NA>1영업/정상1영업<NA><NA><NA><NA>051 909 844362.00608020부산광역시 남구 대연동 1536-27 ,31호부산광역시 남구 못골로41번길 35-1 (대연동)48452정원헤어20210317105943U2021-03-19 02:40:00.0일반미용업390116.804112184013.556101일반미용업301100000N6<NA><NA><NA><NA>0<NA><NA>00N<NA>
1378213783미용업05_18_01_P33300003330000-204-2003-0005020030709<NA>3폐업2폐업20080917<NA><NA><NA>051 703224438.50612843부산광역시 해운대구 좌동 1438번지 대우2차상가107호 지상1층<NA><NA>이민탑헤어20071102114831I2018-08-31 23:59:59.0일반미용업398866.445015188038.176406미용업<NA><NA><NA>1<NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>
1368913690미용업05_18_01_P33300003330000-212-2015-0000620150306<NA>3폐업2폐업20170410<NA><NA><NA><NA>99.00612824부산광역시 해운대구 우동 1407번지 해운대 두산위브더제니스 530~531호 104동부산광역시 해운대구 마린시티2로 33, 104동 530~531호 (우동, 해운대 두산위브더제니스)48119미다움에스테틱20170410144502I2018-08-31 23:59:59.0피부미용업395388.71507186268.853283미용업(피부)00<NA><NA><NA><NA>000N5<NA><NA><NA><NA>00005N<NA>
43414342미용업05_18_01_P33800003380000-211-1987-0000719870818<NA>1영업/정상1영업<NA><NA><NA><NA>051 752648212.24613809부산광역시 수영구 광안동 559-9번지부산광역시 수영구 수영로611번길 19-8, 1층 (광안동)48254준 미용원20130225160523I2018-08-31 23:59:59.0일반미용업392398.302308186616.724665미용업(일반)2<NA>11<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA>