Overview

Dataset statistics

Number of variables48
Number of observations3567
Missing cells37128
Missing cells (%)21.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory413.0 B

Variable types

Numeric11
Categorical21
Text6
Unsupported8
DateTime1
Boolean1

Dataset

Description2021-02-01
Author지방행정인허가공개데이터
URLhttps://bigdata.busan.go.kr/data/bigDataDetailView.do?menuCode=M00000000007&hdfs_file_sn=20230901050101123101

Alerts

개방서비스명 has constant value ""Constant
개방서비스id has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (98.7%)Imbalance
위생업태명 is highly imbalanced (98.7%)Imbalance
남성종사자수 is highly imbalanced (65.3%)Imbalance
여성종사자수 is highly imbalanced (65.4%)Imbalance
영업장주변구분명 is highly imbalanced (70.8%)Imbalance
등급구분명 is highly imbalanced (52.5%)Imbalance
급수시설구분명 is highly imbalanced (68.7%)Imbalance
공장사무직종업원수 is highly imbalanced (55.9%)Imbalance
공장판매직종업원수 is highly imbalanced (55.3%)Imbalance
보증액 is highly imbalanced (72.8%)Imbalance
월세액 is highly imbalanced (72.8%)Imbalance
홈페이지 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 3567 (100.0%) missing valuesMissing
폐업일자 has 1011 (28.3%) missing valuesMissing
휴업시작일자 has 3567 (100.0%) missing valuesMissing
휴업종료일자 has 3567 (100.0%) missing valuesMissing
재개업일자 has 3567 (100.0%) missing valuesMissing
소재지전화 has 971 (27.2%) missing valuesMissing
소재지면적 has 1037 (29.1%) missing valuesMissing
도로명전체주소 has 1719 (48.2%) missing valuesMissing
도로명우편번호 has 1750 (49.1%) missing valuesMissing
좌표정보(x) has 166 (4.7%) missing valuesMissing
좌표정보(y) has 166 (4.7%) missing valuesMissing
총종업원수 has 3567 (100.0%) missing valuesMissing
공장생산직종업원수 has 1738 (48.7%) missing valuesMissing
전통업소지정번호 has 3567 (100.0%) missing valuesMissing
전통업소주된음식 has 3567 (100.0%) missing valuesMissing
Unnamed: 47 has 3567 (100.0%) missing valuesMissing
공장생산직종업원수 is highly skewed (γ1 = 42.76205472)Skewed
시설총규모 is highly skewed (γ1 = 30.5302491)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
총종업원수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 47 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공장생산직종업원수 has 1790 (50.2%) zerosZeros
시설총규모 has 3329 (93.3%) zerosZeros

Reproduction

Analysis started2024-04-18 02:31:23.666958
Analysis finished2024-04-18 02:31:24.868504
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct3567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1784
Minimum1
Maximum3567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:24.930972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile179.3
Q1892.5
median1784
Q32675.5
95-th percentile3388.7
Maximum3567
Range3566
Interquartile range (IQR)1783

Descriptive statistics

Standard deviation1029.8485
Coefficient of variation (CV)0.57726936
Kurtosis-1.2
Mean1784
Median Absolute Deviation (MAD)892
Skewness0
Sum6363528
Variance1060588
MonotonicityStrictly increasing
2024-04-18T11:31:25.052892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2384 1
 
< 0.1%
2373 1
 
< 0.1%
2374 1
 
< 0.1%
2375 1
 
< 0.1%
2376 1
 
< 0.1%
2377 1
 
< 0.1%
2378 1
 
< 0.1%
2379 1
 
< 0.1%
2380 1
 
< 0.1%
Other values (3557) 3557
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3567 1
< 0.1%
3566 1
< 0.1%
3565 1
< 0.1%
3564 1
< 0.1%
3563 1
< 0.1%
3562 1
< 0.1%
3561 1
< 0.1%
3560 1
< 0.1%
3559 1
< 0.1%
3558 1
< 0.1%

개방서비스명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
식품소분업
3567 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 3567
100.0%

Length

2024-04-18T11:31:25.178528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:25.266190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 3567
100.0%

개방서비스id
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
07_22_08_P
3567 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row07_22_08_P
2nd row07_22_08_P
3rd row07_22_08_P
4th row07_22_08_P
5th row07_22_08_P

Common Values

ValueCountFrequency (%)
07_22_08_P 3567
100.0%

Length

2024-04-18T11:31:25.360700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:25.443813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
07_22_08_p 3567
100.0%

개방자치단체코드
Real number (ℝ)

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3327723.6
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:25.521122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13290000
median3330000
Q33350000
95-th percentile3400000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation42204.12
Coefficient of variation (CV)0.01268258
Kurtosis-0.86163082
Mean3327723.6
Median Absolute Deviation (MAD)30000
Skewness-0.026845324
Sum1.186999 × 1010
Variance1.7811877 × 109
MonotonicityNot monotonic
2024-04-18T11:31:25.627762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3340000 520
14.6%
3330000 386
10.8%
3290000 362
10.1%
3390000 282
 
7.9%
3300000 264
 
7.4%
3350000 229
 
6.4%
3400000 216
 
6.1%
3320000 201
 
5.6%
3270000 171
 
4.8%
3250000 170
 
4.8%
Other values (6) 766
21.5%
ValueCountFrequency (%)
3250000 170
 
4.8%
3260000 146
 
4.1%
3270000 171
 
4.8%
3280000 78
 
2.2%
3290000 362
10.1%
3300000 264
7.4%
3310000 152
 
4.3%
3320000 201
 
5.6%
3330000 386
10.8%
3340000 520
14.6%
ValueCountFrequency (%)
3400000 216
6.1%
3390000 282
7.9%
3380000 125
 
3.5%
3370000 129
 
3.6%
3360000 136
 
3.8%
3350000 229
6.4%
3340000 520
14.6%
3330000 386
10.8%
3320000 201
 
5.6%
3310000 152
 
4.3%

관리번호
Text

UNIQUE 

Distinct3567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2024-04-18T11:31:25.802057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters78474
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

Unique3567 ?
Unique (%)100.0%

Sample

1st row3300000-109-2011-00006
2nd row3300000-109-2014-00006
3rd row3300000-109-2014-00007
4th row3300000-109-2017-00004
5th row3300000-109-2017-00005
ValueCountFrequency (%)
3300000-109-2011-00006 1
 
< 0.1%
3340000-109-2000-01123 1
 
< 0.1%
3340000-109-2000-01036 1
 
< 0.1%
3340000-109-2000-00963 1
 
< 0.1%
3340000-109-2000-00977 1
 
< 0.1%
3340000-109-2000-01000 1
 
< 0.1%
3340000-109-2000-01003 1
 
< 0.1%
3340000-109-2000-01008 1
 
< 0.1%
3340000-109-2000-01015 1
 
< 0.1%
3340000-109-2000-01022 1
 
< 0.1%
Other values (3557) 3557
99.7%
2024-04-18T11:31:26.107041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35841
45.7%
- 10701
 
13.6%
3 7284
 
9.3%
1 6996
 
8.9%
9 5788
 
7.4%
2 5475
 
7.0%
4 1681
 
2.1%
5 1325
 
1.7%
6 1230
 
1.6%
7 1118
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67773
86.4%
Dash Punctuation 10701
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35841
52.9%
3 7284
 
10.7%
1 6996
 
10.3%
9 5788
 
8.5%
2 5475
 
8.1%
4 1681
 
2.5%
5 1325
 
2.0%
6 1230
 
1.8%
7 1118
 
1.6%
8 1035
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 10701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 78474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35841
45.7%
- 10701
 
13.6%
3 7284
 
9.3%
1 6996
 
8.9%
9 5788
 
7.4%
2 5475
 
7.0%
4 1681
 
2.1%
5 1325
 
1.7%
6 1230
 
1.6%
7 1118
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35841
45.7%
- 10701
 
13.6%
3 7284
 
9.3%
1 6996
 
8.9%
9 5788
 
7.4%
2 5475
 
7.0%
4 1681
 
2.1%
5 1325
 
1.7%
6 1230
 
1.6%
7 1118
 
1.4%

인허가일자
Real number (ℝ)

Distinct2499
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20071836
Minimum19780414
Maximum20201230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:26.242101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19780414
5-th percentile19970424
Q120020502
median20060921
Q320125664
95-th percentile20190616
Maximum20201230
Range420816
Interquartile range (IQR)105162

Descriptive statistics

Standard deviation70444.677
Coefficient of variation (CV)0.0035096279
Kurtosis-0.20794869
Mean20071836
Median Absolute Deviation (MAD)50001
Skewness-0.040190389
Sum7.159624 × 1010
Variance4.9624525 × 109
MonotonicityNot monotonic
2024-04-18T11:31:26.371563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000720 12
 
0.3%
20091016 10
 
0.3%
20051026 9
 
0.3%
20100111 7
 
0.2%
20060921 6
 
0.2%
20110426 6
 
0.2%
20030721 6
 
0.2%
20010704 6
 
0.2%
20060424 6
 
0.2%
20100113 6
 
0.2%
Other values (2489) 3493
97.9%
ValueCountFrequency (%)
19780414 1
< 0.1%
19791217 2
0.1%
19820520 1
< 0.1%
19821209 1
< 0.1%
19830706 1
< 0.1%
19831130 1
< 0.1%
19840526 1
< 0.1%
19840818 1
< 0.1%
19841111 1
< 0.1%
19851025 1
< 0.1%
ValueCountFrequency (%)
20201230 1
< 0.1%
20201229 1
< 0.1%
20201228 1
< 0.1%
20201223 1
< 0.1%
20201221 1
< 0.1%
20201217 1
< 0.1%
20201216 1
< 0.1%
20201215 1
< 0.1%
20201214 1
< 0.1%
20201211 1
< 0.1%

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
3
2556 
1
1011 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 2556
71.7%
1 1011
 
28.3%

Length

2024-04-18T11:31:26.493281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:26.584111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2556
71.7%
1 1011
 
28.3%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
폐업
2556 
영업/정상
1011 

Length

Max length5
Median length2
Mean length2.8502944
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2556
71.7%
영업/정상 1011
 
28.3%

Length

2024-04-18T11:31:26.696650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:26.800915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2556
71.7%
영업/정상 1011
 
28.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2
2556 
1
1011 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 2556
71.7%
1 1011
 
28.3%

Length

2024-04-18T11:31:26.893778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:26.982688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2556
71.7%
1 1011
 
28.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
폐업
2556 
영업
1011 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2556
71.7%
영업 1011
 
28.3%

Length

2024-04-18T11:31:27.075834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:27.161841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2556
71.7%
영업 1011
 
28.3%

폐업일자
Real number (ℝ)

MISSING 

Distinct1774
Distinct (%)69.4%
Missing1011
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean20095082
Minimum19900725
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:27.261878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19900725
5-th percentile20010524
Q120050904
median20090420
Q320141003
95-th percentile20191029
Maximum20201231
Range300506
Interquartile range (IQR)90099

Descriptive statistics

Standard deviation57691.17
Coefficient of variation (CV)0.0028709099
Kurtosis-0.75127452
Mean20095082
Median Absolute Deviation (MAD)40111
Skewness0.13475263
Sum5.136303 × 1010
Variance3.3282711 × 109
MonotonicityNot monotonic
2024-04-18T11:31:27.390189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100111 22
 
0.6%
20050614 19
 
0.5%
20100208 13
 
0.4%
20051102 10
 
0.3%
20050322 10
 
0.3%
20070820 9
 
0.3%
20060509 8
 
0.2%
20201006 8
 
0.2%
20070809 7
 
0.2%
20060131 7
 
0.2%
Other values (1764) 2443
68.5%
(Missing) 1011
28.3%
ValueCountFrequency (%)
19900725 2
0.1%
19910223 1
< 0.1%
19920114 1
< 0.1%
19921128 1
< 0.1%
19921221 1
< 0.1%
19950214 1
< 0.1%
19950313 1
< 0.1%
19950320 1
< 0.1%
19950608 1
< 0.1%
19950612 1
< 0.1%
ValueCountFrequency (%)
20201231 2
0.1%
20201230 2
0.1%
20201228 1
< 0.1%
20201222 1
< 0.1%
20201208 1
< 0.1%
20201125 2
0.1%
20201124 1
< 0.1%
20201123 1
< 0.1%
20201119 1
< 0.1%
20201106 2
0.1%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

소재지전화
Text

MISSING 

Distinct2018
Distinct (%)77.7%
Missing971
Missing (%)27.2%
Memory size28.0 KiB
2024-04-18T11:31:27.680410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.493066
Min length3

Characters and Unicode

Total characters27240
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

Unique1793 ?
Unique (%)69.1%

Sample

1st row051 531 8282
2nd row051 529 5005
3rd row0515015810
4th row051 756 9991
5th row051 751 1600
ValueCountFrequency (%)
051 2213
39.7%
070 54
 
1.0%
722 29
 
0.5%
055 18
 
0.3%
262 16
 
0.3%
724 14
 
0.3%
265 12
 
0.2%
266 11
 
0.2%
245 11
 
0.2%
727 10
 
0.2%
Other values (2217) 3184
57.1%
2024-04-18T11:31:28.129786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4739
17.4%
5 4195
15.4%
1 4024
14.8%
3014
11.1%
2 2196
8.1%
6 1754
 
6.4%
3 1717
 
6.3%
7 1604
 
5.9%
8 1545
 
5.7%
4 1456
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24226
88.9%
Space Separator 3014
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4739
19.6%
5 4195
17.3%
1 4024
16.6%
2 2196
9.1%
6 1754
 
7.2%
3 1717
 
7.1%
7 1604
 
6.6%
8 1545
 
6.4%
4 1456
 
6.0%
9 996
 
4.1%
Space Separator
ValueCountFrequency (%)
3014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4739
17.4%
5 4195
15.4%
1 4024
14.8%
3014
11.1%
2 2196
8.1%
6 1754
 
6.4%
3 1717
 
6.3%
7 1604
 
5.9%
8 1545
 
5.7%
4 1456
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4739
17.4%
5 4195
15.4%
1 4024
14.8%
3014
11.1%
2 2196
8.1%
6 1754
 
6.4%
3 1717
 
6.3%
7 1604
 
5.9%
8 1545
 
5.7%
4 1456
 
5.3%

소재지면적
Text

MISSING 

Distinct1371
Distinct (%)54.2%
Missing1037
Missing (%)29.1%
Memory size28.0 KiB
2024-04-18T11:31:28.462587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.683004
Min length3

Characters and Unicode

Total characters11848
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

Unique1060 ?
Unique (%)41.9%

Sample

1st row21.53
2nd row29.50
3rd row8.00
4th row10.00
5th row9.20
ValueCountFrequency (%)
00 113
 
4.5%
6.00 40
 
1.6%
6.60 40
 
1.6%
4.00 37
 
1.5%
3.00 36
 
1.4%
2.00 35
 
1.4%
10.00 33
 
1.3%
3.30 28
 
1.1%
5.00 24
 
0.9%
12.00 24
 
0.9%
Other values (1361) 2120
83.8%
2024-04-18T11:31:28.922715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2665
22.5%
. 2530
21.4%
1 1063
 
9.0%
2 979
 
8.3%
6 750
 
6.3%
4 737
 
6.2%
3 733
 
6.2%
5 730
 
6.2%
8 636
 
5.4%
9 553
 
4.7%
Other values (2) 472
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9308
78.6%
Other Punctuation 2540
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2665
28.6%
1 1063
 
11.4%
2 979
 
10.5%
6 750
 
8.1%
4 737
 
7.9%
3 733
 
7.9%
5 730
 
7.8%
8 636
 
6.8%
9 553
 
5.9%
7 462
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 2530
99.6%
, 10
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 11848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2665
22.5%
. 2530
21.4%
1 1063
 
9.0%
2 979
 
8.3%
6 750
 
6.3%
4 737
 
6.2%
3 733
 
6.2%
5 730
 
6.2%
8 636
 
5.4%
9 553
 
4.7%
Other values (2) 472
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2665
22.5%
. 2530
21.4%
1 1063
 
9.0%
2 979
 
8.3%
6 750
 
6.3%
4 737
 
6.2%
3 733
 
6.2%
5 730
 
6.2%
8 636
 
5.4%
9 553
 
4.7%
Other values (2) 472
 
4.0%

소재지우편번호
Real number (ℝ)

Distinct644
Distinct (%)18.2%
Missing32
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean610588.78
Minimum600011
Maximum619953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:29.068732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600011
5-th percentile601511.5
Q1604845
median611811
Q3614848
95-th percentile619901
Maximum619953
Range19942
Interquartile range (IQR)10003

Descriptive statistics

Standard deviation5836.8436
Coefficient of variation (CV)0.0095593693
Kurtosis-1.1572055
Mean610588.78
Median Absolute Deviation (MAD)5010
Skewness-0.13232793
Sum2.1584313 × 109
Variance34068743
MonotonicityNot monotonic
2024-04-18T11:31:29.198206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
604842 75
 
2.1%
602030 73
 
2.0%
614843 68
 
1.9%
617831 62
 
1.7%
601803 54
 
1.5%
619904 53
 
1.5%
607804 48
 
1.3%
604843 48
 
1.3%
604846 46
 
1.3%
612020 40
 
1.1%
Other values (634) 2968
83.2%
ValueCountFrequency (%)
600011 1
 
< 0.1%
600012 2
 
0.1%
600015 1
 
< 0.1%
600016 4
 
0.1%
600017 17
0.5%
600021 1
 
< 0.1%
600022 1
 
< 0.1%
600031 1
 
< 0.1%
600032 1
 
< 0.1%
600041 35
1.0%
ValueCountFrequency (%)
619953 1
 
< 0.1%
619952 4
 
0.1%
619951 9
 
0.3%
619913 5
 
0.1%
619912 10
 
0.3%
619911 5
 
0.1%
619906 20
 
0.6%
619905 18
 
0.5%
619904 53
1.5%
619903 30
0.8%
Distinct2736
Distinct (%)76.7%
Missing2
Missing (%)0.1%
Memory size28.0 KiB
2024-04-18T11:31:29.514652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length57
Mean length24.892847
Min length16

Characters and Unicode

Total characters88743
Distinct characters372
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2408 ?
Unique (%)67.5%

Sample

1st row부산광역시 동래구 사직동 114-46번지
2nd row부산광역시 동래구 안락동 243-57번지 1층
3rd row부산광역시 동래구 안락동 425-4번지 1층
4th row부산광역시 동래구 사직동 28-9번지
5th row부산광역시 동래구 명륜동 515-43번지
ValueCountFrequency (%)
부산광역시 3566
 
21.4%
사하구 520
 
3.1%
해운대구 385
 
2.3%
부산진구 362
 
2.2%
사상구 282
 
1.7%
동래구 264
 
1.6%
금정구 229
 
1.4%
장림동 219
 
1.3%
기장군 216
 
1.3%
북구 201
 
1.2%
Other values (3164) 10452
62.6%
2024-04-18T11:31:29.964783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13147
 
14.8%
4412
 
5.0%
4262
 
4.8%
4061
 
4.6%
3858
 
4.3%
1 3769
 
4.2%
3691
 
4.2%
3639
 
4.1%
3573
 
4.0%
3472
 
3.9%
Other values (362) 40859
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54596
61.5%
Decimal Number 17064
 
19.2%
Space Separator 13147
 
14.8%
Dash Punctuation 3012
 
3.4%
Uppercase Letter 308
 
0.3%
Close Punctuation 219
 
0.2%
Open Punctuation 219
 
0.2%
Other Punctuation 173
 
0.2%
Lowercase Letter 3
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4412
 
8.1%
4262
 
7.8%
4061
 
7.4%
3858
 
7.1%
3691
 
6.8%
3639
 
6.7%
3573
 
6.5%
3472
 
6.4%
3400
 
6.2%
871
 
1.6%
Other values (325) 19357
35.5%
Uppercase Letter
ValueCountFrequency (%)
B 129
41.9%
T 89
28.9%
G 26
 
8.4%
A 25
 
8.1%
S 21
 
6.8%
L 8
 
2.6%
E 3
 
1.0%
O 1
 
0.3%
C 1
 
0.3%
M 1
 
0.3%
Other values (4) 4
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 3769
22.1%
2 2314
13.6%
3 1856
10.9%
5 1685
9.9%
4 1467
 
8.6%
0 1404
 
8.2%
6 1397
 
8.2%
7 1238
 
7.3%
8 1001
 
5.9%
9 933
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 150
86.7%
. 12
 
6.9%
@ 9
 
5.2%
/ 2
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
s 1
33.3%
e 1
33.3%
Space Separator
ValueCountFrequency (%)
13147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3012
100.0%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Open Punctuation
ValueCountFrequency (%)
( 219
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54595
61.5%
Common 33836
38.1%
Latin 311
 
0.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4412
 
8.1%
4262
 
7.8%
4061
 
7.4%
3858
 
7.1%
3691
 
6.8%
3639
 
6.7%
3573
 
6.5%
3472
 
6.4%
3400
 
6.2%
871
 
1.6%
Other values (324) 19356
35.5%
Common
ValueCountFrequency (%)
13147
38.9%
1 3769
 
11.1%
- 3012
 
8.9%
2 2314
 
6.8%
3 1856
 
5.5%
5 1685
 
5.0%
4 1467
 
4.3%
0 1404
 
4.1%
6 1397
 
4.1%
7 1238
 
3.7%
Other values (10) 2547
 
7.5%
Latin
ValueCountFrequency (%)
B 129
41.5%
T 89
28.6%
G 26
 
8.4%
A 25
 
8.0%
S 21
 
6.8%
L 8
 
2.6%
E 3
 
1.0%
c 1
 
0.3%
s 1
 
0.3%
O 1
 
0.3%
Other values (7) 7
 
2.3%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54595
61.5%
ASCII 34147
38.5%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13147
38.5%
1 3769
 
11.0%
- 3012
 
8.8%
2 2314
 
6.8%
3 1856
 
5.4%
5 1685
 
4.9%
4 1467
 
4.3%
0 1404
 
4.1%
6 1397
 
4.1%
7 1238
 
3.6%
Other values (27) 2858
 
8.4%
Hangul
ValueCountFrequency (%)
4412
 
8.1%
4262
 
7.8%
4061
 
7.4%
3858
 
7.1%
3691
 
6.8%
3639
 
6.7%
3573
 
6.5%
3472
 
6.4%
3400
 
6.2%
871
 
1.6%
Other values (324) 19356
35.5%
CJK
ValueCountFrequency (%)
1
100.0%

도로명전체주소
Text

MISSING 

Distinct1714
Distinct (%)92.7%
Missing1719
Missing (%)48.2%
Memory size28.0 KiB
2024-04-18T11:31:30.303251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length55
Mean length31.140152
Min length19

Characters and Unicode

Total characters57547
Distinct characters394
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1622 ?
Unique (%)87.8%

Sample

1st row부산광역시 동래구 아시아드대로114번길 28-1, 1층 (사직동)
2nd row부산광역시 동래구 안남로 112, 1층 (안락동)
3rd row부산광역시 동래구 안락로 14, 1층 (안락동)
4th row부산광역시 동래구 사직북로33번길 34, 1층 (사직동)
5th row부산광역시 동래구 충렬대로182번가길 26, 1층 (명륜동)
ValueCountFrequency (%)
부산광역시 1849
 
16.7%
1층 361
 
3.3%
사하구 296
 
2.7%
해운대구 194
 
1.8%
부산진구 171
 
1.5%
기장군 162
 
1.5%
사상구 144
 
1.3%
2층 141
 
1.3%
강서구 112
 
1.0%
장림동 112
 
1.0%
Other values (2216) 7515
68.0%
2024-04-18T11:31:30.775618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9214
 
16.0%
1 2359
 
4.1%
2357
 
4.1%
2335
 
4.1%
2227
 
3.9%
2057
 
3.6%
1917
 
3.3%
1854
 
3.2%
1805
 
3.1%
1769
 
3.1%
Other values (384) 29653
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34038
59.1%
Space Separator 9214
 
16.0%
Decimal Number 8954
 
15.6%
Open Punctuation 1765
 
3.1%
Close Punctuation 1765
 
3.1%
Other Punctuation 1401
 
2.4%
Dash Punctuation 295
 
0.5%
Uppercase Letter 102
 
0.2%
Math Symbol 10
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2357
 
6.9%
2335
 
6.9%
2227
 
6.5%
2057
 
6.0%
1917
 
5.6%
1854
 
5.4%
1805
 
5.3%
1769
 
5.2%
916
 
2.7%
847
 
2.5%
Other values (347) 15954
46.9%
Uppercase Letter
ValueCountFrequency (%)
B 44
43.1%
A 34
33.3%
C 5
 
4.9%
S 4
 
3.9%
G 4
 
3.9%
E 2
 
2.0%
I 1
 
1.0%
V 1
 
1.0%
K 1
 
1.0%
W 1
 
1.0%
Other values (5) 5
 
4.9%
Decimal Number
ValueCountFrequency (%)
1 2359
26.3%
2 1361
15.2%
3 917
 
10.2%
4 780
 
8.7%
5 778
 
8.7%
0 666
 
7.4%
6 648
 
7.2%
7 592
 
6.6%
9 461
 
5.1%
8 392
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 1394
99.5%
@ 3
 
0.2%
. 3
 
0.2%
/ 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
s 1
33.3%
c 1
33.3%
b 1
33.3%
Space Separator
ValueCountFrequency (%)
9214
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1765
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 295
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34038
59.1%
Common 23404
40.7%
Latin 105
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2357
 
6.9%
2335
 
6.9%
2227
 
6.5%
2057
 
6.0%
1917
 
5.6%
1854
 
5.4%
1805
 
5.3%
1769
 
5.2%
916
 
2.7%
847
 
2.5%
Other values (347) 15954
46.9%
Common
ValueCountFrequency (%)
9214
39.4%
1 2359
 
10.1%
( 1765
 
7.5%
) 1765
 
7.5%
, 1394
 
6.0%
2 1361
 
5.8%
3 917
 
3.9%
4 780
 
3.3%
5 778
 
3.3%
0 666
 
2.8%
Other values (9) 2405
 
10.3%
Latin
ValueCountFrequency (%)
B 44
41.9%
A 34
32.4%
C 5
 
4.8%
S 4
 
3.8%
G 4
 
3.8%
E 2
 
1.9%
s 1
 
1.0%
c 1
 
1.0%
b 1
 
1.0%
I 1
 
1.0%
Other values (8) 8
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34038
59.1%
ASCII 23509
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9214
39.2%
1 2359
 
10.0%
( 1765
 
7.5%
) 1765
 
7.5%
, 1394
 
5.9%
2 1361
 
5.8%
3 917
 
3.9%
4 780
 
3.3%
5 778
 
3.3%
0 666
 
2.8%
Other values (27) 2510
 
10.7%
Hangul
ValueCountFrequency (%)
2357
 
6.9%
2335
 
6.9%
2227
 
6.5%
2057
 
6.0%
1917
 
5.6%
1854
 
5.4%
1805
 
5.3%
1769
 
5.2%
916
 
2.7%
847
 
2.5%
Other values (347) 15954
46.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct745
Distinct (%)41.0%
Missing1750
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean47861.363
Minimum46002
Maximum49526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:30.913460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46002
5-th percentile46068.8
Q146926
median48011
Q348984
95-th percentile49478
Maximum49526
Range3524
Interquartile range (IQR)2058

Descriptive statistics

Standard deviation1156.7943
Coefficient of variation (CV)0.02416969
Kurtosis-1.3319426
Mean47861.363
Median Absolute Deviation (MAD)1024
Skewness-0.033307136
Sum86964097
Variance1338173.1
MonotonicityNot monotonic
2024-04-18T11:31:31.040102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49277 47
 
1.3%
46079 40
 
1.1%
47032 35
 
1.0%
49478 33
 
0.9%
48984 31
 
0.9%
47252 28
 
0.8%
48737 28
 
0.8%
48031 24
 
0.7%
48983 22
 
0.6%
47251 17
 
0.5%
Other values (735) 1512
42.4%
(Missing) 1750
49.1%
ValueCountFrequency (%)
46002 2
0.1%
46004 2
0.1%
46008 2
0.1%
46012 2
0.1%
46014 1
 
< 0.1%
46017 3
0.1%
46019 1
 
< 0.1%
46020 3
0.1%
46022 3
0.1%
46023 1
 
< 0.1%
ValueCountFrequency (%)
49526 15
0.4%
49525 1
 
< 0.1%
49523 1
 
< 0.1%
49522 1
 
< 0.1%
49520 3
 
0.1%
49519 6
 
0.2%
49514 1
 
< 0.1%
49511 4
 
0.1%
49507 1
 
< 0.1%
49503 3
 
0.1%
Distinct2641
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2024-04-18T11:31:31.332654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length5.8993552
Min length2

Characters and Unicode

Total characters21043
Distinct characters630
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

Unique2257 ?
Unique (%)63.3%

Sample

1st row부성상사
2nd row더월마트
3rd row탑플러스마트
4th row(주)서원유통탑마트사직점
5th row찡오언니
ValueCountFrequency (%)
주식회사 81
 
2.1%
영우유통 31
 
0.8%
주)두루찬 31
 
0.8%
진경식품 29
 
0.7%
개미농특산 27
 
0.7%
우리농수산 22
 
0.6%
현식품 20
 
0.5%
민속식품 18
 
0.5%
주)서원유통 18
 
0.5%
남해식품 18
 
0.5%
Other values (2744) 3630
92.5%
2024-04-18T11:31:31.760839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
932
 
4.4%
) 859
 
4.1%
849
 
4.0%
( 835
 
4.0%
738
 
3.5%
493
 
2.3%
421
 
2.0%
401
 
1.9%
397
 
1.9%
386
 
1.8%
Other values (620) 14732
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18572
88.3%
Close Punctuation 859
 
4.1%
Open Punctuation 835
 
4.0%
Space Separator 359
 
1.7%
Uppercase Letter 257
 
1.2%
Decimal Number 68
 
0.3%
Lowercase Letter 56
 
0.3%
Other Punctuation 33
 
0.2%
Dash Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
932
 
5.0%
849
 
4.6%
738
 
4.0%
493
 
2.7%
421
 
2.3%
401
 
2.2%
397
 
2.1%
386
 
2.1%
318
 
1.7%
309
 
1.7%
Other values (558) 13328
71.8%
Uppercase Letter
ValueCountFrequency (%)
F 29
 
11.3%
O 29
 
11.3%
S 17
 
6.6%
A 16
 
6.2%
G 15
 
5.8%
D 15
 
5.8%
C 15
 
5.8%
N 15
 
5.8%
M 12
 
4.7%
E 12
 
4.7%
Other values (15) 82
31.9%
Lowercase Letter
ValueCountFrequency (%)
a 9
16.1%
n 8
14.3%
s 8
14.3%
r 5
8.9%
e 5
8.9%
m 4
7.1%
o 3
 
5.4%
i 3
 
5.4%
t 2
 
3.6%
d 2
 
3.6%
Other values (7) 7
12.5%
Decimal Number
ValueCountFrequency (%)
2 19
27.9%
3 15
22.1%
1 12
17.6%
8 6
 
8.8%
9 4
 
5.9%
5 4
 
5.9%
7 3
 
4.4%
6 2
 
2.9%
4 2
 
2.9%
0 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 14
42.4%
& 11
33.3%
, 6
18.2%
· 1
 
3.0%
' 1
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 859
100.0%
Open Punctuation
ValueCountFrequency (%)
( 835
100.0%
Space Separator
ValueCountFrequency (%)
359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18572
88.3%
Common 2158
 
10.3%
Latin 313
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
932
 
5.0%
849
 
4.6%
738
 
4.0%
493
 
2.7%
421
 
2.3%
401
 
2.2%
397
 
2.1%
386
 
2.1%
318
 
1.7%
309
 
1.7%
Other values (558) 13328
71.8%
Latin
ValueCountFrequency (%)
F 29
 
9.3%
O 29
 
9.3%
S 17
 
5.4%
A 16
 
5.1%
G 15
 
4.8%
D 15
 
4.8%
C 15
 
4.8%
N 15
 
4.8%
M 12
 
3.8%
E 12
 
3.8%
Other values (32) 138
44.1%
Common
ValueCountFrequency (%)
) 859
39.8%
( 835
38.7%
359
16.6%
2 19
 
0.9%
3 15
 
0.7%
. 14
 
0.6%
1 12
 
0.6%
& 11
 
0.5%
8 6
 
0.3%
, 6
 
0.3%
Other values (10) 22
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18572
88.3%
ASCII 2470
 
11.7%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
932
 
5.0%
849
 
4.6%
738
 
4.0%
493
 
2.7%
421
 
2.3%
401
 
2.2%
397
 
2.1%
386
 
2.1%
318
 
1.7%
309
 
1.7%
Other values (558) 13328
71.8%
ASCII
ValueCountFrequency (%)
) 859
34.8%
( 835
33.8%
359
14.5%
F 29
 
1.2%
O 29
 
1.2%
2 19
 
0.8%
S 17
 
0.7%
A 16
 
0.6%
G 15
 
0.6%
D 15
 
0.6%
Other values (51) 277
 
11.2%
None
ValueCountFrequency (%)
· 1
100.0%

최종수정시점
Real number (ℝ)

Distinct2875
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.010557 × 1013
Minimum1.9990315 × 1013
Maximum2.0201231 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:31.895822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9990315 × 1013
5-th percentile2.0011115 × 1013
Q12.0050324 × 1013
median2.0100617 × 1013
Q32.0170525 × 1013
95-th percentile2.0200614 × 1013
Maximum2.0201231 × 1013
Range2.1091615 × 1011
Interquartile range (IQR)1.2020064 × 1011

Descriptive statistics

Standard deviation6.4402081 × 1010
Coefficient of variation (CV)0.003203196
Kurtosis-1.4010735
Mean2.010557 × 1013
Median Absolute Deviation (MAD)5.9999154 × 1010
Skewness0.060696005
Sum7.1716567 × 1016
Variance4.147628 × 1021
MonotonicityNot monotonic
2024-04-18T11:31:32.025688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20010731000000 48
 
1.3%
20020612000000 32
 
0.9%
20020805000000 31
 
0.9%
20020227000000 31
 
0.9%
20020305000000 29
 
0.8%
20010728000000 24
 
0.7%
20040618000000 22
 
0.6%
20020214000000 21
 
0.6%
20020823000000 16
 
0.4%
20010510000000 14
 
0.4%
Other values (2865) 3299
92.5%
ValueCountFrequency (%)
19990315000000 4
0.1%
19990318000000 1
 
< 0.1%
19990322000000 1
 
< 0.1%
19990510000000 1
 
< 0.1%
19990903000000 1
 
< 0.1%
19990918000000 1
 
< 0.1%
19990927000000 2
0.1%
19991012000000 1
 
< 0.1%
19991125000000 1
 
< 0.1%
20000209000000 1
 
< 0.1%
ValueCountFrequency (%)
20201231153702 1
< 0.1%
20201231121608 1
< 0.1%
20201231120258 1
< 0.1%
20201231120009 1
< 0.1%
20201230165559 1
< 0.1%
20201230162201 1
< 0.1%
20201230161320 1
< 0.1%
20201230143617 1
< 0.1%
20201230104301 1
< 0.1%
20201229161439 1
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
I
3025 
U
542 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 3025
84.8%
U 542
 
15.2%

Length

2024-04-18T11:31:32.148422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:32.260054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 3025
84.8%
u 542
 
15.2%
Distinct420
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
Minimum2018-08-31 23:59:59
Maximum2021-01-02 02:40:00
2024-04-18T11:31:32.357816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T11:31:32.486455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
식품소분업
3563 
<NA>
 
4

Length

Max length5
Median length5
Mean length4.9988786
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 3563
99.9%
<NA> 4
 
0.1%

Length

2024-04-18T11:31:32.607857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:32.698719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 3563
99.9%
na 4
 
0.1%

좌표정보(x)
Real number (ℝ)

MISSING 

Distinct1944
Distinct (%)57.2%
Missing166
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean387274.3
Minimum364927.7
Maximum407820.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:32.793313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum364927.7
5-th percentile378638.3
Q1382220.74
median387537.8
Q3391321.29
95-th percentile401172.34
Maximum407820.17
Range42892.471
Interquartile range (IQR)9100.5433

Descriptive statistics

Standard deviation6521.0095
Coefficient of variation (CV)0.016838219
Kurtosis0.15582961
Mean387274.3
Median Absolute Deviation (MAD)4533.5115
Skewness0.33821723
Sum1.3171199 × 109
Variance42523565
MonotonicityNot monotonic
2024-04-18T11:31:32.913803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
387539.767677801 53
 
1.5%
387271.299492377 49
 
1.4%
389097.800933845 46
 
1.3%
389455.109101676 43
 
1.2%
390319.153766629 37
 
1.0%
387564.611330896 36
 
1.0%
393233.931123062 34
 
1.0%
382983.91775577 32
 
0.9%
383449.669961635 30
 
0.8%
379321.651090423 29
 
0.8%
Other values (1934) 3012
84.4%
(Missing) 166
 
4.7%
ValueCountFrequency (%)
364927.696730227 1
 
< 0.1%
367163.559903774 1
 
< 0.1%
367390.559894293 1
 
< 0.1%
367947.282236205 1
 
< 0.1%
368550.469102158 1
 
< 0.1%
368725.685782611 1
 
< 0.1%
368902.33317113 1
 
< 0.1%
368967.073191522 1
 
< 0.1%
369324.667538937 3
0.1%
369510.064865473 2
0.1%
ValueCountFrequency (%)
407820.16806719 1
< 0.1%
407418.648415535 1
< 0.1%
407245.567193252 2
0.1%
406761.100883744 1
< 0.1%
405571.252933322 1
< 0.1%
405546.316597851 1
< 0.1%
405482.261906849 1
< 0.1%
405236.333123992 1
< 0.1%
405188.961854622 2
0.1%
404883.27844185 1
< 0.1%

좌표정보(y)
Real number (ℝ)

MISSING 

Distinct1943
Distinct (%)57.1%
Missing166
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean186473.12
Minimum170813.58
Maximum211459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:33.036924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170813.58
5-th percentile176378.69
Q1180289.36
median186654.62
Q3191546.13
95-th percentile196546.15
Maximum211459
Range40645.417
Interquartile range (IQR)11256.771

Descriptive statistics

Standard deviation6630.3304
Coefficient of variation (CV)0.035556494
Kurtosis-0.41420489
Mean186473.12
Median Absolute Deviation (MAD)5271.4572
Skewness0.18451662
Sum6.3419508 × 108
Variance43961282
MonotonicityNot monotonic
2024-04-18T11:31:33.169467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184402.96650913 53
 
1.5%
186099.137533193 49
 
1.4%
192260.811648263 46
 
1.3%
191427.549247975 43
 
1.2%
195305.783615849 37
 
1.0%
183837.166587761 36
 
1.0%
192684.487637586 34
 
1.0%
196375.470354374 32
 
0.9%
174683.069423816 30
 
0.8%
183030.975769364 29
 
0.8%
Other values (1933) 3012
84.4%
(Missing) 166
 
4.7%
ValueCountFrequency (%)
170813.584718477 1
 
< 0.1%
173969.719902491 1
 
< 0.1%
174209.665999624 1
 
< 0.1%
174211.496764498 1
 
< 0.1%
174289.976688419 1
 
< 0.1%
174353.431844675 1
 
< 0.1%
174415.425547526 6
0.2%
174419.270504403 1
 
< 0.1%
174422.347875421 1
 
< 0.1%
174428.418993288 2
 
0.1%
ValueCountFrequency (%)
211459.001777975 1
< 0.1%
210945.104382171 1
< 0.1%
210855.07033937 1
< 0.1%
209943.892133999 1
< 0.1%
208399.251667629 1
< 0.1%
206512.517255249 1
< 0.1%
206150.925111083 1
< 0.1%
205930.084201689 1
< 0.1%
205620.650144116 1
< 0.1%
205540.482346069 2
0.1%

위생업태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
식품소분업
3563 
<NA>
 
4

Length

Max length5
Median length5
Mean length4.9988786
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품소분업 3563
99.9%
<NA> 4
 
0.1%

Length

2024-04-18T11:31:33.298503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:33.392931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품소분업 3563
99.9%
na 4
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3136 
0
422 
1
 
9

Length

Max length4
Median length4
Mean length3.6375105
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> 3136
87.9%
0 422
 
11.8%
1 9
 
0.3%

Length

2024-04-18T11:31:33.502117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:33.607455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3136
87.9%
0 422
 
11.8%
1 9
 
0.3%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3136 
0
423 
1
 
8

Length

Max length4
Median length4
Mean length3.6375105
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> 3136
87.9%
0 423
 
11.9%
1 8
 
0.2%

Length

2024-04-18T11:31:33.724830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:33.818335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3136
87.9%
0 423
 
11.9%
1 8
 
0.2%

영업장주변구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
2896 
기타
644 
주택가주변
 
15
아파트지역
 
7
학교정화(상대)
 
4

Length

Max length8
Median length4
Mean length3.6506869
Min length2

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> 2896
81.2%
기타 644
 
18.1%
주택가주변 15
 
0.4%
아파트지역 7
 
0.2%
학교정화(상대) 4
 
0.1%
유흥업소밀집지역 1
 
< 0.1%

Length

2024-04-18T11:31:33.920429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:34.018883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2896
81.2%
기타 644
 
18.1%
주택가주변 15
 
0.4%
아파트지역 7
 
0.2%
학교정화(상대 4
 
0.1%
유흥업소밀집지역 1
 
< 0.1%

등급구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
2896 
기타
636 
자율
 
35

Length

Max length4
Median length4
Mean length3.6237735
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> 2896
81.2%
기타 636
 
17.8%
자율 35
 
1.0%

Length

2024-04-18T11:31:34.142575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:34.246324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2896
81.2%
기타 636
 
17.8%
자율 35
 
1.0%

급수시설구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3025 
상수도전용
538 
간이상수도
 
3
지하수전용
 
1

Length

Max length5
Median length4
Mean length4.1519484
Min length4

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> 3025
84.8%
상수도전용 538
 
15.1%
간이상수도 3
 
0.1%
지하수전용 1
 
< 0.1%

Length

2024-04-18T11:31:34.350579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:34.444805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3025
84.8%
상수도전용 538
 
15.1%
간이상수도 3
 
0.1%
지하수전용 1
 
< 0.1%

총종업원수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
0
1820 
<NA>
1744 
1
 
2
3
 
1

Length

Max length4
Median length1
Mean length2.4667788
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1820
51.0%
<NA> 1744
48.9%
1 2
 
0.1%
3 1
 
< 0.1%

Length

2024-04-18T11:31:34.551515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:34.653664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1820
51.0%
na 1744
48.9%
1 2
 
0.1%
3 1
 
< 0.1%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
0
1815 
<NA>
1743 
1
 
7
3
 
1
2
 
1

Length

Max length4
Median length1
Mean length2.4659378
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1815
50.9%
<NA> 1743
48.9%
1 7
 
0.2%
3 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-04-18T11:31:34.759334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:34.863647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1815
50.9%
na 1743
48.9%
1 7
 
0.2%
3 1
 
< 0.1%
2 1
 
< 0.1%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
0
1811 
<NA>
1742 
1
 
10
2
 
3
3
 
1

Length

Max length4
Median length1
Mean length2.4650967
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1811
50.8%
<NA> 1742
48.8%
1 10
 
0.3%
2 3
 
0.1%
3 1
 
< 0.1%

Length

2024-04-18T11:31:34.971396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:35.075805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1811
50.8%
na 1742
48.8%
1 10
 
0.3%
2 3
 
0.1%
3 1
 
< 0.1%

공장생산직종업원수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)0.4%
Missing1738
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean0.66867141
Minimum0
Maximum1170
Zeros1790
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:35.165118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1170
Range1170
Interquartile range (IQR)0

Descriptive statistics

Standard deviation27.358
Coefficient of variation (CV)40.913968
Kurtosis1828.7283
Mean0.66867141
Median Absolute Deviation (MAD)0
Skewness42.762055
Sum1223
Variance748.46018
MonotonicityNot monotonic
2024-04-18T11:31:35.268908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1790
50.2%
1 29
 
0.8%
2 6
 
0.2%
4 1
 
< 0.1%
1170 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 1738
48.7%
ValueCountFrequency (%)
0 1790
50.2%
1 29
 
0.8%
2 6
 
0.2%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
1170 1
 
< 0.1%
ValueCountFrequency (%)
1170 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1 29
 
0.8%
0 1790
50.2%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
2723 
자가
528 
임대
316 

Length

Max length4
Median length4
Mean length3.5267732
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> 2723
76.3%
자가 528
 
14.8%
임대 316
 
8.9%

Length

2024-04-18T11:31:35.391183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:35.504643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2723
76.3%
자가 528
 
14.8%
임대 316
 
8.9%

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3401 
0
 
166

Length

Max length4
Median length4
Mean length3.8603869
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> 3401
95.3%
0 166
 
4.7%

Length

2024-04-18T11:31:36.412662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:36.506226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3401
95.3%
0 166
 
4.7%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3401 
0
 
166

Length

Max length4
Median length4
Mean length3.8603869
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> 3401
95.3%
0 166
 
4.7%

Length

2024-04-18T11:31:36.613065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:36.712531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3401
95.3%
0 166
 
4.7%

다중이용업소여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
False
3567 
ValueCountFrequency (%)
False 3567
100.0%
2024-04-18T11:31:36.785063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

SKEWED  ZEROS 

Distinct180
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3967816
Minimum0
Maximum1011.02
Zeros3329
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2024-04-18T11:31:36.886583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.532
Maximum1011.02
Range1011.02
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.836882
Coefficient of variation (CV)9.1109187
Kurtosis1303.6854
Mean2.3967816
Median Absolute Deviation (MAD)0
Skewness30.530249
Sum8549.32
Variance476.84943
MonotonicityNot monotonic
2024-04-18T11:31:37.018378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3329
93.3%
4.0 8
 
0.2%
6.6 7
 
0.2%
10.0 7
 
0.2%
9.9 6
 
0.2%
3.0 5
 
0.1%
33.0 4
 
0.1%
3.3 4
 
0.1%
16.5 4
 
0.1%
12.0 3
 
0.1%
Other values (170) 190
 
5.3%
ValueCountFrequency (%)
0.0 3329
93.3%
0.58 1
 
< 0.1%
1.0 1
 
< 0.1%
1.17 2
 
0.1%
1.44 1
 
< 0.1%
1.51 1
 
< 0.1%
1.6 1
 
< 0.1%
1.7 2
 
0.1%
1.95 1
 
< 0.1%
2.0 1
 
< 0.1%
ValueCountFrequency (%)
1011.02 1
< 0.1%
319.5 1
< 0.1%
246.24 1
< 0.1%
226.98 1
< 0.1%
194.7 1
< 0.1%
175.0 1
< 0.1%
174.89 1
< 0.1%
172.55 1
< 0.1%
157.93 1
< 0.1%
132.0 2
0.1%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

홈페이지
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
<NA>
3566 
8
 
1

Length

Max length4
Median length4
Mean length3.999159
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> 3566
> 99.9%
8 1
 
< 0.1%

Length

2024-04-18T11:31:37.142605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T11:31:37.247149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3566
> 99.9%
8 1
 
< 0.1%

Unnamed: 47
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3567
Missing (%)100.0%
Memory size31.5 KiB

Sample

번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지Unnamed: 47
01식품소분업07_22_08_P33000003300000-109-2011-0000620110223<NA>1영업/정상1영업<NA><NA><NA><NA><NA>21.53607821부산광역시 동래구 사직동 114-46번지부산광역시 동래구 아시아드대로114번길 28-1, 1층 (사직동)47845부성상사20180920095551U2018-09-20 23:59:59.0식품소분업392206.237892190039.60345식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
12식품소분업07_22_08_P33000003300000-109-2014-0000620140811<NA>1영업/정상1영업<NA><NA><NA><NA>051 531 828229.50607825부산광역시 동래구 안락동 243-57번지 1층부산광역시 동래구 안남로 112, 1층 (안락동)47900더월마트20140811114122I2018-08-31 23:59:59.0식품소분업391562.200354190165.951521식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
23식품소분업07_22_08_P33000003300000-109-2014-0000720141007<NA>1영업/정상1영업<NA><NA><NA><NA>051 529 50058.00607827부산광역시 동래구 안락동 425-4번지 1층부산광역시 동래구 안락로 14, 1층 (안락동)47786탑플러스마트20141007153821I2018-08-31 23:59:59.0식품소분업391050.74744191065.280568식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
34식품소분업07_22_08_P33000003300000-109-2017-0000420170830<NA>1영업/정상1영업<NA><NA><NA><NA>051501581010.00607815부산광역시 동래구 사직동 28-9번지부산광역시 동래구 사직북로33번길 34, 1층 (사직동)47860(주)서원유통탑마트사직점20170901112355I2018-08-31 23:59:59.0식품소분업387382.806313190792.095628식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
45식품소분업07_22_08_P33000003300000-109-2017-0000520171206<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.20607804부산광역시 동래구 명륜동 515-43번지부산광역시 동래구 충렬대로182번가길 26, 1층 (명륜동)47815찡오언니20180102101523I2018-08-31 23:59:59.0식품소분업389358.8991191229.69749식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
56식품소분업07_22_08_P33800003380000-109-2015-0000420150518<NA>1영업/정상1영업<NA><NA><NA><NA>051 756 999155.50613804부산광역시 수영구 광안동 151-23번지부산광역시 수영구 광안로 37, 1,2층 (광안동)48296농축산마트20151203160903I2018-08-31 23:59:59.0식품소분업392776.170928186106.557382식품소분업<NA><NA><NA><NA>상수도전용<NA>0000자가<NA><NA>N0.0<NA><NA><NA><NA>
67식품소분업07_22_08_P33800003380000-109-2010-0000120100223<NA>1영업/정상1영업<NA><NA><NA><NA>051 751 160016.00613828부산광역시 수영구 민락동 35-8번지부산광역시 수영구 광안해변로277번길 18 (민락동)48287(주)자이마트20110930170550I2018-08-31 23:59:59.0식품소분업393508.751358186240.494914식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
78식품소분업07_22_08_P33000003300000-109-2017-0000620171218<NA>1영업/정상1영업<NA><NA><NA><NA>051521825316.00607830부산광역시 동래구 안락동 603-1번지 안락시장상가아파트부산광역시 동래구 충렬대로410번길 21, 19호 (안락동, 안락시장상가아파트)47890창대식품20180102103939I2018-08-31 23:59:59.0식품소분업391486.787091190515.879939식품소분업<NA><NA><NA><NA><NA><NA>0000<NA><NA><NA>N0.0<NA><NA><NA><NA>
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번호개방서비스명개방서비스id개방자치단체코드관리번호인허가일자인허가취소일자영업상태구분코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자소재지전화소재지면적소재지우편번호소재지전체주소도로명전체주소도로명우편번호사업장명최종수정시점데이터갱신구분데이터갱신일자업태구분명좌표정보(x)좌표정보(y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총종업원수본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지Unnamed: 47
35573558식품소분업07_22_08_P33300003330000-109-2016-0000320160318<NA>3폐업2폐업20191113<NA><NA><NA>051 724 7781100.00612040부산광역시 해운대구 송정동 189-20번지부산광역시 해운대구 송정중앙로21번길 67, 1층 (송정동)48070우진유통20191113153406U2019-11-15 02:40:00.0식품소분업400753.655089189624.712393식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
35583559식품소분업07_22_08_P33300003330000-109-2016-0000420160323<NA>3폐업2폐업20160810<NA><NA><NA><NA>78.00612810부산광역시 해운대구 반여동 763-11번지부산광역시 해운대구 선수촌로207번가길 21, 1층 (반여동)48032채미원20160323152229I2018-08-31 23:59:59.0식품소분업393332.617596192155.203339식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>
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35603561식품소분업07_22_08_P33300003330000-109-2014-0000320140331<NA>3폐업2폐업20141219<NA><NA><NA>051 723 02359.86612809부산광역시 해운대구 반여동 910-1번지부산광역시 해운대구 선수촌로 164-10 (반여동)48034(주)풍년방20140415172558I2018-08-31 23:59:59.0식품소분업393256.407763191848.772596식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N4.55<NA><NA><NA><NA>
35613562식품소분업07_22_08_P33300003330000-109-2014-0000420140503<NA>3폐업2폐업20150722<NA><NA><NA><NA>31.00612020부산광역시 해운대구 우동 1495번지 신세계백화점 지하1층 일부부산광역시 해운대구 센텀남대로 35 (우동, 신세계백화점 지하1층 일부)48058샘골잣집20140624092103I2018-08-31 23:59:59.0식품소분업393952.264486187602.933161식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
35623563식품소분업07_22_08_P33300003330000-109-2014-0000520141223<NA>3폐업2폐업20171018<NA><NA><NA><NA>9.87612809부산광역시 해운대구 반여동 910-1번지부산광역시 해운대구 선수촌로 164-10 (반여동)48034(주)풍년방20171018110811I2018-08-31 23:59:59.0식품소분업393256.407763191848.772596식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>
35633564식품소분업07_22_08_P34000003400000-109-2018-0001220181228<NA>3폐업2폐업20200716<NA><NA><NA><NA>19.20619873부산광역시 기장군 철마면 송정리 363-12부산광역시 기장군 철마면 철마삼동로 58, 1층46002동우유통20200716161337U2020-09-16 02:40:00.0식품소분업392275.996065203203.409705식품소분업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>
35643565식품소분업07_22_08_P33600003360000-109-2019-0000120190104<NA>3폐업2폐업20190620<NA><NA><NA>051 337 257816.12618800부산광역시 강서구 강동동 107-8번지부산광역시 강서구 낙동북로43번길 38-16, 일부 (강동동)46705(주)현백20190620140333U2019-06-22 02:40:00.0식품소분업376268.26048192826.372166식품소분업<NA><NA><NA><NA>상수도전용<NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>
35653566식품소분업07_22_08_P33500003350000-109-2018-0000720181120<NA>3폐업2폐업20200320<NA><NA><NA><NA>6.60609801부산광역시 금정구 구서동 167-9번지부산광역시 금정구 중앙대로1945번길 21, 1층 (구서동)46230우영이네20200320143835U2020-03-22 02:40:00.0식품소분업390137.078464197397.134893식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>자가<NA><NA>N0.0<NA><NA><NA><NA>
35663567식품소분업07_22_08_P33000003300000-109-2020-0000420201214<NA>3폐업2폐업20201228<NA><NA><NA><NA><NA>607802부산광역시 동래구 명륜동 98-17부산광역시 동래구 시실로 54, 2층 (명륜동)47744죽로재20201228160040U2020-12-30 02:40:00.0식품소분업390095.642233192619.170961식품소분업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N0.0<NA><NA><NA><NA>