Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells39
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory162.4 B

Variable types

Categorical7
Numeric6
Text6

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/d2bd5a7e-dae6-44f0-a1cd-4168d044e86f

Alerts

기준년월 has constant value ""Constant
등록일 has constant value ""Constant
작업자명 has constant value ""Constant
지점명 is highly overall correlated with 회사코드 and 1 other fieldsHigh correlation
기업상태 is highly overall correlated with 기업번호 and 2 other fieldsHigh correlation
기업번호 is highly overall correlated with 기업상태High correlation
상위번호 is highly overall correlated with 기업상태High correlation
회사코드 is highly overall correlated with 지점명 and 2 other fieldsHigh correlation
시도명 is highly overall correlated with 회사코드High correlation
상가업종대분류 is highly overall correlated with 회사코드High correlation
지점명 is highly imbalanced (78.9%)Imbalance
빌딩명 has 18 (60.0%) missing valuesMissing
회사코드 has 21 (70.0%) missing valuesMissing
기업번호 has unique valuesUnique
기업명 has unique valuesUnique
행정동명 has unique valuesUnique
지번주소 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique
하위번호 has 8 (26.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:02:43.962270
Analysis finished2023-12-10 14:02:50.845172
Duration6.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01
2nd row2021-01
3rd row2021-01
4th row2021-01
5th row2021-01

Common Values

ValueCountFrequency (%)
2021-01 30
100.0%

Length

2023-12-10T23:02:50.937767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:51.058978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01 30
100.0%

기업번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19469657
Minimum8929317
Maximum25256744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:51.206986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8929317
5-th percentile12154702
Q116415372
median20427253
Q323191554
95-th percentile24416887
Maximum25256744
Range16327427
Interquartile range (IQR)6776182.8

Descriptive statistics

Standard deviation4419579.2
Coefficient of variation (CV)0.22699831
Kurtosis-0.6161177
Mean19469657
Median Absolute Deviation (MAD)3628824.5
Skewness-0.63527667
Sum5.8408971 × 108
Variance1.9532681 × 1013
MonotonicityNot monotonic
2023-12-10T23:02:51.364206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20348235 1
 
3.3%
15534155 1
 
3.3%
16476371 1
 
3.3%
23977980 1
 
3.3%
16720331 1
 
3.3%
20490101 1
 
3.3%
23147044 1
 
3.3%
22952532 1
 
3.3%
24571494 1
 
3.3%
14526864 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
8929317 1
3.3%
11900970 1
3.3%
12464818 1
3.3%
14247140 1
3.3%
14526864 1
3.3%
15534155 1
3.3%
15860686 1
3.3%
16408206 1
3.3%
16436868 1
3.3%
16476371 1
3.3%
ValueCountFrequency (%)
25256744 1
3.3%
24571494 1
3.3%
24227923 1
3.3%
23977980 1
3.3%
23460477 1
3.3%
23298644 1
3.3%
23214234 1
3.3%
23206391 1
3.3%
23147044 1
3.3%
23101608 1
3.3%

기업명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:51.647326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length5.9333333
Min length2

Characters and Unicode

Total characters178
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row완도식당
2nd row똑소리매운탕
3rd row영동감자탕
4th row경천건강원
5th row내경고겐건강원
ValueCountFrequency (%)
완도식당 1
 
3.3%
똑소리매운탕 1
 
3.3%
남양슈퍼 1
 
3.3%
대일코퍼레이션 1
 
3.3%
가든경양식 1
 
3.3%
화목이용소 1
 
3.3%
초원미용실 1
 
3.3%
무릉도원 1
 
3.3%
그린분식 1
 
3.3%
비정규직노동자복지센터 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:02:52.053928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6
 
3.4%
소 5
 
2.8%
원 5
 
2.8%
이 5
 
2.8%
식 4
 
2.2%
대 3
 
1.7%
가 3
 
1.7%
에 3
 
1.7%
경 3
 
1.7%
도 3
 
1.7%
Other values (105) 138
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 178
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6
 
3.4%
소 5
 
2.8%
원 5
 
2.8%
이 5
 
2.8%
식 4
 
2.2%
대 3
 
1.7%
가 3
 
1.7%
에 3
 
1.7%
경 3
 
1.7%
도 3
 
1.7%
Other values (105) 138
77.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 178
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6
 
3.4%
소 5
 
2.8%
원 5
 
2.8%
이 5
 
2.8%
식 4
 
2.2%
대 3
 
1.7%
가 3
 
1.7%
에 3
 
1.7%
경 3
 
1.7%
도 3
 
1.7%
Other values (105) 138
77.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 178
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6
 
3.4%
소 5
 
2.8%
원 5
 
2.8%
이 5
 
2.8%
식 4
 
2.2%
대 3
 
1.7%
가 3
 
1.7%
에 3
 
1.7%
경 3
 
1.7%
도 3
 
1.7%
Other values (105) 138
77.5%

지점명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
29 
전북대리점
 
1

Length

Max length5
Median length1
Mean length1.1333333
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
29
96.7%
전북대리점 1
 
3.3%

Length

2023-12-10T23:02:52.231657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:52.367987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북대리점 1
100.0%

시도명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
충청남도
7 
경기도
6 
경상북도
5 
전라북도
3 
서울특별시
3 
Other values (4)
6 

Length

Max length5
Median length4
Mean length3.9
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row전라북도
2nd row충청남도
3rd row경기도
4th row전라북도
5th row충청남도

Common Values

ValueCountFrequency (%)
충청남도 7
23.3%
경기도 6
20.0%
경상북도 5
16.7%
전라북도 3
10.0%
서울특별시 3
10.0%
전라남도 2
 
6.7%
경상남도 2
 
6.7%
부산광역시 1
 
3.3%
강원도 1
 
3.3%

Length

2023-12-10T23:02:52.518290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:52.684638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 7
23.3%
경기도 6
20.0%
경상북도 5
16.7%
전라북도 3
10.0%
서울특별시 3
10.0%
전라남도 2
 
6.7%
경상남도 2
 
6.7%
부산광역시 1
 
3.3%
강원도 1
 
3.3%

시군구명
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:52.990605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.6333333
Min length2

Characters and Unicode

Total characters109
Distinct characters45
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)76.7%

Sample

1st row순창군
2nd row논산시
3rd row광명시
4th row완주군
5th row당진시
ValueCountFrequency (%)
논산시 3
 
8.6%
진해구 2
 
5.7%
수원시 2
 
5.7%
장안구 2
 
5.7%
창원시 2
 
5.7%
보령시 1
 
2.9%
순창군 1
 
2.9%
봉화군 1
 
2.9%
수지구 1
 
2.9%
용인시 1
 
2.9%
Other values (19) 19
54.3%
2023-12-10T23:02:53.436680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 15
 
13.8%
군 11
 
10.1%
구 9
 
8.3%
5
 
4.6%
원 4
 
3.7%
진 4
 
3.7%
산 4
 
3.7%
창 3
 
2.8%
주 3
 
2.8%
수 3
 
2.8%
Other values (35) 48
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
95.4%
Space Separator 5
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 15
 
14.4%
군 11
 
10.6%
구 9
 
8.7%
원 4
 
3.8%
진 4
 
3.8%
산 4
 
3.8%
창 3
 
2.9%
주 3
 
2.9%
수 3
 
2.9%
장 3
 
2.9%
Other values (34) 45
43.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
95.4%
Common 5
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 15
 
14.4%
군 11
 
10.6%
구 9
 
8.7%
원 4
 
3.8%
진 4
 
3.8%
산 4
 
3.8%
창 3
 
2.9%
주 3
 
2.9%
수 3
 
2.9%
장 3
 
2.9%
Other values (34) 45
43.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
95.4%
ASCII 5
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 15
 
14.4%
군 11
 
10.6%
구 9
 
8.7%
원 4
 
3.8%
진 4
 
3.8%
산 4
 
3.8%
창 3
 
2.9%
주 3
 
2.9%
수 3
 
2.9%
장 3
 
2.9%
Other values (34) 45
43.3%
ASCII
ValueCountFrequency (%)
5
100.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:53.787940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2
Min length3

Characters and Unicode

Total characters96
Distinct characters56
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row팔덕면
2nd row가야곡면
3rd row광명6동
4th row경천면
5th row우강면
ValueCountFrequency (%)
팔덕면 1
 
3.3%
가야곡면 1
 
3.3%
임계면 1
 
3.3%
상현1동 1
 
3.3%
여좌동 1
 
3.3%
현서면 1
 
3.3%
자양1동 1
 
3.3%
서삼면 1
 
3.3%
소원면 1
 
3.3%
정자3동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:02:54.729518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 20
20.8%
동 12
 
12.5%
곡 4
 
4.2%
천 2
 
2.1%
서 2
 
2.1%
1 2
 
2.1%
현 2
 
2.1%
주 2
 
2.1%
명 2
 
2.1%
자 2
 
2.1%
Other values (46) 46
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
93.8%
Decimal Number 6
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 20
22.2%
동 12
 
13.3%
곡 4
 
4.4%
천 2
 
2.2%
서 2
 
2.2%
현 2
 
2.2%
주 2
 
2.2%
명 2
 
2.2%
자 2
 
2.2%
영 1
 
1.1%
Other values (41) 41
45.6%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
3 1
16.7%
2 1
16.7%
6 1
16.7%
5 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
93.8%
Common 6
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 20
22.2%
동 12
 
13.3%
곡 4
 
4.4%
천 2
 
2.2%
서 2
 
2.2%
현 2
 
2.2%
주 2
 
2.2%
명 2
 
2.2%
자 2
 
2.2%
영 1
 
1.1%
Other values (41) 41
45.6%
Common
ValueCountFrequency (%)
1 2
33.3%
3 1
16.7%
2 1
16.7%
6 1
16.7%
5 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
93.8%
ASCII 6
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 20
22.2%
동 12
 
13.3%
곡 4
 
4.4%
천 2
 
2.2%
서 2
 
2.2%
현 2
 
2.2%
주 2
 
2.2%
명 2
 
2.2%
자 2
 
2.2%
영 1
 
1.1%
Other values (41) 41
45.6%
ASCII
ValueCountFrequency (%)
1 2
33.3%
3 1
16.7%
2 1
16.7%
6 1
16.7%
5 1
16.7%

상위번호
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.5
Minimum20
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:54.954240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20.9
Q1108.5
median225.5
Q3542.25
95-th percentile912.35
Maximum961
Range941
Interquartile range (IQR)433.75

Descriptive statistics

Standard deviation304.00247
Coefficient of variation (CV)0.877352
Kurtosis-0.70396838
Mean346.5
Median Absolute Deviation (MAD)184
Skewness0.77049321
Sum10395
Variance92417.5
MonotonicityNot monotonic
2023-12-10T23:02:55.165401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20 2
 
6.7%
961 1
 
3.3%
101 1
 
3.3%
176 1
 
3.3%
721 1
 
3.3%
867 1
 
3.3%
105 1
 
3.3%
44 1
 
3.3%
634 1
 
3.3%
153 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
20 2
6.7%
22 1
3.3%
39 1
3.3%
44 1
3.3%
98 1
3.3%
101 1
3.3%
105 1
3.3%
119 1
3.3%
120 1
3.3%
131 1
3.3%
ValueCountFrequency (%)
961 1
3.3%
920 1
3.3%
903 1
3.3%
867 1
3.3%
721 1
3.3%
634 1
3.3%
621 1
3.3%
549 1
3.3%
522 1
3.3%
505 1
3.3%

하위번호
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.933333
Minimum0
Maximum238
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:55.355299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median2
Q39.5
95-th percentile43.5
Maximum238
Range238
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation43.808308
Coefficient of variation (CV)3.1441369
Kurtosis25.739049
Mean13.933333
Median Absolute Deviation (MAD)2
Skewness4.9604781
Sum418
Variance1919.1678
MonotonicityNot monotonic
2023-12-10T23:02:55.520365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 8
26.7%
1 5
16.7%
10 3
 
10.0%
3 3
 
10.0%
2 3
 
10.0%
5 2
 
6.7%
57 1
 
3.3%
238 1
 
3.3%
8 1
 
3.3%
16 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
0 8
26.7%
1 5
16.7%
2 3
 
10.0%
3 3
 
10.0%
5 2
 
6.7%
8 1
 
3.3%
10 3
 
10.0%
12 1
 
3.3%
16 1
 
3.3%
27 1
 
3.3%
ValueCountFrequency (%)
238 1
 
3.3%
57 1
 
3.3%
27 1
 
3.3%
16 1
 
3.3%
12 1
 
3.3%
10 3
10.0%
8 1
 
3.3%
5 2
6.7%
3 3
10.0%
2 3
10.0%

상가업종대분류
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
음식
10 
생활서비스
9 
소매
7 
학문/교육
2 
부동산
 
1

Length

Max length5
Median length2
Mean length3.1666667
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row음식
2nd row음식
3rd row음식
4th row소매
5th row소매

Common Values

ValueCountFrequency (%)
음식 10
33.3%
생활서비스 9
30.0%
소매 7
23.3%
학문/교육 2
 
6.7%
부동산 1
 
3.3%
스포츠 1
 
3.3%

Length

2023-12-10T23:02:55.723851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:55.918059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
음식 10
33.3%
생활서비스 9
30.0%
소매 7
23.3%
학문/교육 2
 
6.7%
부동산 1
 
3.3%
스포츠 1
 
3.3%

상가업종중분류
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:56.166326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.6333333
Min length2

Characters and Unicode

Total characters169
Distinct characters70
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row한식
2nd row일식/수산물
3rd row한식
4th row건강/미용식품
5th row건강/미용식품
ValueCountFrequency (%)
이/미용/건강 6
20.0%
한식 4
13.3%
건강/미용식품 2
 
6.7%
커피점/카페 2
 
6.7%
분식 2
 
6.7%
기타판매업 2
 
6.7%
부동산중개 1
 
3.3%
세탁/가사서비스 1
 
3.3%
운영관리시설 1
 
3.3%
양식 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T23:02:56.639956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 24
 
14.2%
식 11
 
6.5%
용 10
 
5.9%
미 8
 
4.7%
건 8
 
4.7%
강 8
 
4.7%
이 6
 
3.6%
소 4
 
2.4%
매 4
 
2.4%
한 4
 
2.4%
Other values (60) 82
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
85.2%
Other Punctuation 24
 
14.2%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 11
 
7.6%
용 10
 
6.9%
미 8
 
5.6%
건 8
 
5.6%
강 8
 
5.6%
이 6
 
4.2%
소 4
 
2.8%
매 4
 
2.8%
한 4
 
2.8%
품 3
 
2.1%
Other values (58) 78
54.2%
Other Punctuation
ValueCountFrequency (%)
/ 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
85.2%
Common 25
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 11
 
7.6%
용 10
 
6.9%
미 8
 
5.6%
건 8
 
5.6%
강 8
 
5.6%
이 6
 
4.2%
소 4
 
2.8%
매 4
 
2.8%
한 4
 
2.8%
품 3
 
2.1%
Other values (58) 78
54.2%
Common
ValueCountFrequency (%)
/ 24
96.0%
- 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
85.2%
ASCII 25
 
14.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 24
96.0%
- 1
 
4.0%
Hangul
ValueCountFrequency (%)
식 11
 
7.6%
용 10
 
6.9%
미 8
 
5.6%
건 8
 
5.6%
강 8
 
5.6%
이 6
 
4.2%
소 4
 
2.8%
매 4
 
2.8%
한 4
 
2.8%
품 3
 
2.1%
Other values (58) 78
54.2%

지번주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:57.134695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21.5
Mean length20.766667
Min length16

Characters and Unicode

Total characters623
Distinct characters107
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row전라북도 순창군 팔덕면 청계리 961-10
2nd row충청남도 논산시 가야곡면 병암리 98-5
3rd row경기도 광명시 광명동 337-57
4th row전라북도 완주군 경천면 경천리 621-1
5th row충청남도 당진시 우강면 내경리 20-238
ValueCountFrequency (%)
충청남도 7
 
4.8%
경기도 6
 
4.1%
경상북도 5
 
3.4%
전라북도 3
 
2.1%
논산시 3
 
2.1%
서울특별시 3
 
2.1%
수원시 2
 
1.4%
장안구 2
 
1.4%
경상남도 2
 
1.4%
전라남도 2
 
1.4%
Other values (108) 110
75.9%
2023-12-10T23:02:57.861850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
18.5%
도 29
 
4.7%
1 26
 
4.2%
- 22
 
3.5%
리 21
 
3.4%
시 20
 
3.2%
면 20
 
3.2%
2 18
 
2.9%
경 16
 
2.6%
3 13
 
2.1%
Other values (97) 323
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 371
59.6%
Space Separator 115
 
18.5%
Decimal Number 115
 
18.5%
Dash Punctuation 22
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 29
 
7.8%
리 21
 
5.7%
시 20
 
5.4%
면 20
 
5.4%
경 16
 
4.3%
동 13
 
3.5%
남 12
 
3.2%
군 11
 
3.0%
구 10
 
2.7%
청 10
 
2.7%
Other values (85) 209
56.3%
Decimal Number
ValueCountFrequency (%)
1 26
22.6%
2 18
15.7%
3 13
11.3%
5 12
10.4%
0 12
10.4%
9 8
 
7.0%
7 8
 
7.0%
4 7
 
6.1%
6 7
 
6.1%
8 4
 
3.5%
Space Separator
ValueCountFrequency (%)
115
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 371
59.6%
Common 252
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 29
 
7.8%
리 21
 
5.7%
시 20
 
5.4%
면 20
 
5.4%
경 16
 
4.3%
동 13
 
3.5%
남 12
 
3.2%
군 11
 
3.0%
구 10
 
2.7%
청 10
 
2.7%
Other values (85) 209
56.3%
Common
ValueCountFrequency (%)
115
45.6%
1 26
 
10.3%
- 22
 
8.7%
2 18
 
7.1%
3 13
 
5.2%
5 12
 
4.8%
0 12
 
4.8%
9 8
 
3.2%
7 8
 
3.2%
4 7
 
2.8%
Other values (2) 11
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 371
59.6%
ASCII 252
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
45.6%
1 26
 
10.3%
- 22
 
8.7%
2 18
 
7.1%
3 13
 
5.2%
5 12
 
4.8%
0 12
 
4.8%
9 8
 
3.2%
7 8
 
3.2%
4 7
 
2.8%
Other values (2) 11
 
4.4%
Hangul
ValueCountFrequency (%)
도 29
 
7.8%
리 21
 
5.7%
시 20
 
5.4%
면 20
 
5.4%
경 16
 
4.3%
동 13
 
3.5%
남 12
 
3.2%
군 11
 
3.0%
구 10
 
2.7%
청 10
 
2.7%
Other values (85) 209
56.3%

빌딩명
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing18
Missing (%)60.0%
Memory size372.0 B
2023-12-10T23:02:58.206232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9.5
Mean length7.25
Min length3

Characters and Unicode

Total characters87
Distinct characters65
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st row문영시티빌라
2nd row벌곡휴게소
3rd row주공1단지아파트
4th row흥부가기가막혀
5th row서초더샵포레
ValueCountFrequency (%)
문영시티빌라 1
8.3%
벌곡휴게소 1
8.3%
주공1단지아파트 1
8.3%
흥부가기가막혀 1
8.3%
서초더샵포레 1
8.3%
포천윤중후레쉬빌아파트 1
8.3%
금호타운 1
8.3%
진해장천대동다:숲아파트 1
8.3%
근로자종합복지관 1
8.3%
음식점 1
8.3%
Other values (2) 2
16.7%
2023-12-10T23:02:58.682357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 4
 
4.6%
빌 4
 
4.6%
가 3
 
3.4%
파 3
 
3.4%
아 3
 
3.4%
지 3
 
3.4%
레 2
 
2.3%
천 2
 
2.3%
단 2
 
2.3%
포 2
 
2.3%
Other values (55) 59
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
96.6%
Decimal Number 2
 
2.3%
Other Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 4
 
4.8%
빌 4
 
4.8%
가 3
 
3.6%
파 3
 
3.6%
아 3
 
3.6%
지 3
 
3.6%
레 2
 
2.4%
천 2
 
2.4%
단 2
 
2.4%
포 2
 
2.4%
Other values (52) 56
66.7%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
1 1
50.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
96.6%
Common 3
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 4
 
4.8%
빌 4
 
4.8%
가 3
 
3.6%
파 3
 
3.6%
아 3
 
3.6%
지 3
 
3.6%
레 2
 
2.4%
천 2
 
2.4%
단 2
 
2.4%
포 2
 
2.4%
Other values (52) 56
66.7%
Common
ValueCountFrequency (%)
: 1
33.3%
5 1
33.3%
1 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
96.6%
ASCII 3
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 4
 
4.8%
빌 4
 
4.8%
가 3
 
3.6%
파 3
 
3.6%
아 3
 
3.6%
지 3
 
3.6%
레 2
 
2.4%
천 2
 
2.4%
단 2
 
2.4%
포 2
 
2.4%
Other values (52) 56
66.7%
ASCII
ValueCountFrequency (%)
: 1
33.3%
5 1
33.3%
1 1
33.3%

회사코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing21
Missing (%)70.0%
Infinite0
Infinite (%)0.0%
Mean7.0235221 × 109
Minimum523142
Maximum9.0531218 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:58.904964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum523142
5-th percentile1353549.2
Q19.0106209 × 109
median9.020216 × 109
Q39.0401066 × 109
95-th percentile9.0530821 × 109
Maximum9.0531218 × 109
Range9.0525986 × 109
Interquartile range (IQR)29485713

Descriptive statistics

Standard deviation3.9811105 × 109
Coefficient of variation (CV)0.56682537
Kurtosis0.73457343
Mean7.0235221 × 109
Median Absolute Deviation (MAD)19890651
Skewness-1.6197757
Sum6.3211699 × 1010
Variance1.5849241 × 1019
MonotonicityNot monotonic
2023-12-10T23:02:59.091570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9020469170 1
 
3.3%
9040106621 1
 
3.3%
9020215970 1
 
3.3%
2599160 1
 
3.3%
523142 1
 
3.3%
9011019815 1
 
3.3%
9010620908 1
 
3.3%
9053022706 1
 
3.3%
9053121777 1
 
3.3%
(Missing) 21
70.0%
ValueCountFrequency (%)
523142 1
3.3%
2599160 1
3.3%
9010620908 1
3.3%
9011019815 1
3.3%
9020215970 1
3.3%
9020469170 1
3.3%
9040106621 1
3.3%
9053022706 1
3.3%
9053121777 1
3.3%
ValueCountFrequency (%)
9053121777 1
3.3%
9053022706 1
3.3%
9040106621 1
3.3%
9020469170 1
3.3%
9020215970 1
3.3%
9011019815 1
3.3%
9010620908 1
3.3%
2599160 1
3.3%
523142 1
3.3%

기업상태
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
21 
정상
8 
폐업
 
1

Length

Max length4
Median length4
Mean length3.4
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
70.0%
정상 8
 
26.7%
폐업 1
 
3.3%

Length

2023-12-10T23:02:59.365276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:59.548026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
70.0%
정상 8
 
26.7%
폐업 1
 
3.3%

경도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.50518
Minimum126.19581
Maximum129.03017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:59.728522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.19581
5-th percentile126.59235
Q1126.95043
median127.07945
Q3128.63042
95-th percentile128.92592
Maximum129.03017
Range2.8343588
Interquartile range (IQR)1.679991

Descriptive statistics

Standard deviation0.88592988
Coefficient of variation (CV)0.0069481871
Kurtosis-1.1291907
Mean127.50518
Median Absolute Deviation (MAD)0.25013361
Skewness0.70704678
Sum3825.1555
Variance0.78487174
MonotonicityNot monotonic
2023-12-10T23:02:59.959397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
127.0695757125 1
 
3.3%
127.2307664984 1
 
3.3%
127.0323140265 1
 
3.3%
128.8512129286 1
 
3.3%
127.0781860122 1
 
3.3%
128.6619306224 1
 
3.3%
128.8963239596 1
 
3.3%
127.0807095383 1
 
3.3%
126.740636139 1
 
3.3%
126.195814155 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.195814155 1
3.3%
126.5566330304 1
3.3%
126.6360141005 1
3.3%
126.7294480026 1
3.3%
126.740636139 1
3.3%
126.8333663958 1
3.3%
126.8528458465 1
3.3%
126.9444631224 1
3.3%
126.9683440651 1
3.3%
126.9821416776 1
3.3%
ValueCountFrequency (%)
129.0301729751 1
3.3%
128.9501356517 1
3.3%
128.8963239596 1
3.3%
128.8512129286 1
3.3%
128.7665268088 1
3.3%
128.7063514554 1
3.3%
128.6619306224 1
3.3%
128.6585011118 1
3.3%
128.5461941619 1
3.3%
127.3336336072 1
3.3%

위도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.476753
Minimum35.01522
Maximum37.94099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:00.218762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.01522
5-th percentile35.119025
Q136.005793
median36.38009
Q337.306387
95-th percentile37.599272
Maximum37.94099
Range2.9257692
Interquartile range (IQR)1.3005942

Descriptive statistics

Standard deviation0.88417142
Coefficient of variation (CV)0.024239313
Kurtosis-1.1767389
Mean36.476753
Median Absolute Deviation (MAD)0.91982566
Skewness-0.12353079
Sum1094.3026
Variance0.7817591
MonotonicityNot monotonic
2023-12-10T23:03:00.422650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.4084346021 1
 
3.3%
37.940989705 1
 
3.3%
36.2003070203 1
 
3.3%
37.4955230374 1
 
3.3%
37.3068736037 1
 
3.3%
35.1579069163 1
 
3.3%
36.27622163 1
 
3.3%
37.5330963715 1
 
3.3%
35.3428501686 1
 
3.3%
36.7592326505 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
35.0152204667 1
3.3%
35.111363796 1
3.3%
35.1283883958 1
3.3%
35.1579069163 1
3.3%
35.3428501686 1
3.3%
35.4084346021 1
3.3%
35.6420718386 1
3.3%
36.0051131154 1
3.3%
36.0078330555 1
3.3%
36.0229238563 1
3.3%
ValueCountFrequency (%)
37.940989705 1
3.3%
37.6534150791 1
3.3%
37.5330963715 1
3.3%
37.4955230374 1
3.3%
37.4728764764 1
3.3%
37.4700491652 1
3.3%
37.4543864441 1
3.3%
37.3068736037 1
3.3%
37.3049282829 1
3.3%
37.2949031101 1
3.3%

등록일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-12-03
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-03
2nd row2021-12-03
3rd row2021-12-03
4th row2021-12-03
5th row2021-12-03

Common Values

ValueCountFrequency (%)
2021-12-03 30
100.0%

Length

2023-12-10T23:03:00.628536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:03:00.766001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-03 30
100.0%

작업자명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
KEDSYSTEM
30 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYSTEM 30
100.0%

Length

2023-12-10T23:03:01.033870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:03:01.175890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsystem 30
100.0%

Interactions

2023-12-10T23:02:49.436709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:45.183940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.387061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.230276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.059991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.720715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.565695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:45.672425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.527191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.387925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.179497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.867957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.687510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:45.809259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.650826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.505597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.293918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.984959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.786650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:45.958612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.771812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.609636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.391743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.097001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.908517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.096030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.897071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.765784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.494537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.207173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:50.029001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:46.248052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.056430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:47.911188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:48.603711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:49.318285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:03:01.300599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업번호기업명지점명시도명시군구명행정동명상위번호하위번호상가업종대분류상가업종중분류지번주소빌딩명회사코드기업상태경도위도
기업번호1.0001.0000.3990.0000.8661.0000.0000.9410.0000.0001.0001.0000.0001.0000.1510.614
기업명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지점명0.3991.0001.0000.2441.0001.0000.0000.0000.0000.0001.0001.000NaNNaN0.0000.000
시도명0.0001.0000.2441.0001.0001.0000.6620.0000.2740.6161.0001.0001.0000.0000.6280.632
시군구명0.8661.0001.0001.0001.0001.0000.8420.0001.0000.8351.0001.0001.0001.0000.9810.985
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
상위번호0.0001.0000.0000.6620.8421.0001.0000.0000.0000.5681.0001.0000.0001.0000.5940.000
하위번호0.9411.0000.0000.0000.0001.0000.0001.0000.0000.0971.0001.0000.0000.4550.0000.000
상가업종대분류0.0001.0000.0000.2741.0001.0000.0000.0001.0001.0001.0001.0001.0000.0000.0000.312
상가업종중분류0.0001.0000.0000.6160.8351.0000.5680.0971.0001.0001.0001.0001.0000.0000.0000.565
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
빌딩명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.000
회사코드0.0001.000NaN1.0001.0001.0000.0000.0001.0001.0001.000NaN1.0000.0001.0001.000
기업상태1.0001.000NaN0.0001.0001.0001.0000.4550.0000.0001.0001.0000.0001.0000.0001.000
경도0.1511.0000.0000.6280.9811.0000.5940.0000.0000.0001.0001.0001.0000.0001.0000.573
위도0.6141.0000.0000.6320.9851.0000.0000.0000.3120.5651.0001.0001.0001.0000.5731.000
2023-12-10T23:03:01.578950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명지점명기업상태상가업종대분류
시도명1.0000.1890.0000.078
지점명0.1891.0001.0000.000
기업상태0.0001.0001.0000.000
상가업종대분류0.0780.0000.0001.000
2023-12-10T23:03:01.734749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업번호상위번호하위번호회사코드경도위도지점명시도명상가업종대분류기업상태
기업번호1.000-0.034-0.1250.3000.051-0.1130.1890.0000.0000.756
상위번호-0.0341.000-0.286-0.1670.1420.2510.0000.2430.0000.756
하위번호-0.125-0.2861.0000.059-0.029-0.0550.0000.0000.0000.275
회사코드0.300-0.1670.0591.000-0.150-0.0331.0000.6550.8450.000
경도0.0510.142-0.029-0.1501.000-0.0880.0000.3660.0000.000
위도-0.1130.251-0.055-0.033-0.0881.0000.0000.3240.1090.378
지점명0.1890.0000.0001.0000.0000.0001.0000.1890.0001.000
시도명0.0000.2430.0000.6550.3660.3240.1891.0000.0780.000
상가업종대분류0.0000.0000.0000.8450.0000.1090.0000.0781.0000.000
기업상태0.7560.7560.2750.0000.0000.3781.0000.0000.0001.000

Missing values

2023-12-10T23:02:50.228960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:02:50.584733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T23:02:50.757651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준년월기업번호기업명지점명시도명시군구명행정동명상위번호하위번호상가업종대분류상가업종중분류지번주소빌딩명회사코드기업상태경도위도등록일작업자명
02021-0120348235완도식당전라북도순창군팔덕면96110음식한식전라북도 순창군 팔덕면 청계리 961-10<NA>9020469170정상127.06957635.4084352021-12-03KEDSYSTEM
12021-0120364405똑소리매운탕충청남도논산시가야곡면985음식일식/수산물충청남도 논산시 가야곡면 병암리 98-5<NA><NA><NA>127.20074336.1734542021-12-03KEDSYSTEM
22021-0112464818영동감자탕경기도광명시광명6동33757음식한식경기도 광명시 광명동 337-57문영시티빌라9040106621폐업126.85284637.4728762021-12-03KEDSYSTEM
32021-018929317경천건강원전라북도완주군경천면6211소매건강/미용식품전라북도 완주군 경천면 경천리 621-1<NA><NA><NA>127.24576336.0229242021-12-03KEDSYSTEM
42021-0120254667내경고겐건강원충청남도당진시우강면20238소매건강/미용식품충청남도 당진시 우강면 내경리 20-238<NA><NA><NA>126.83336636.8025972021-12-03KEDSYSTEM
52021-0123214234예인헤어경기도수원시 장안구율천동5490생활서비스이/미용/건강경기도 수원시 장안구 율전동 549<NA>9020215970정상126.96834437.3049282021-12-03KEDSYSTEM
62021-0123460477수정휴게실경상북도의성군점곡면1318음식커피점/카페경상북도 의성군 점곡면 서변리 131-8<NA><NA><NA>128.76652736.4243472021-12-03KEDSYSTEM
72021-0123206391에렉스에프앤비벌곡휴게소분식점충청남도논산시벌곡면4750음식분식충청남도 논산시 벌곡면 신양리 475벌곡휴게소<NA><NA>127.27362436.2181992021-12-03KEDSYSTEM
82021-0122407829부림씽크경상북도청도군각남면5053소매가정/주방/인테리어경상북도 청도군 각남면 예리리 505-3<NA>2599160정상128.65850135.6420722021-12-03KEDSYSTEM
92021-0116607928카페마노경기도광주시퇴촌면2755음식커피점/카페경기도 광주시 퇴촌면 도수리 275-5<NA><NA><NA>127.33363437.4700492021-12-03KEDSYSTEM
기준년월기업번호기업명지점명시도명시군구명행정동명상위번호하위번호상가업종대분류상가업종중분류지번주소빌딩명회사코드기업상태경도위도등록일작업자명
202021-0114247140할매곰탕경상남도창원시 진해구풍호동11912음식한식경상남도 창원시 진해구 장천동 119-12진해장천대동다:숲아파트<NA><NA>128.70635135.1283882021-12-03KEDSYSTEM
212021-0123298644비정규직노동자복지센터경기도수원시 장안구정자3동5220생활서비스인력/고용/용역알선경기도 수원시 장안구 천천동 522근로자종합복지관<NA><NA>126.98214237.2949032021-12-03KEDSYSTEM
222021-0114526864그린분식충청남도태안군소원면1532음식분식충청남도 태안군 소원면 신덕리 153-2<NA><NA><NA>126.19581436.7592332021-12-03KEDSYSTEM
232021-0124571494무릉도원전라남도장성군서삼면200학문/교육학원-보습교습입시전라남도 장성군 서삼면 추암리 20음식점<NA><NA>126.74063635.342852021-12-03KEDSYSTEM
242021-0122952532초원미용실서울특별시광진구자양1동63410생활서비스이/미용/건강서울특별시 광진구 자양동 634-10<NA><NA><NA>127.0807137.5330962021-12-03KEDSYSTEM
252021-0123147044화목이용소경상북도청송군현서면443생활서비스이/미용/건강경상북도 청송군 현서면 구산리 44-3<NA><NA><NA>128.89632436.2762222021-12-03KEDSYSTEM
262021-0120490101가든경양식경상남도창원시 진해구여좌동10527음식양식경상남도 창원시 진해구 여좌동 105-27가든빌라<NA><NA>128.66193135.1579072021-12-03KEDSYSTEM
272021-0116720331대일코퍼레이션경기도용인시 수지구상현1동8670스포츠운영관리시설경기도 용인시 수지구 상현동 867서원마을5단지금호베스트빌<NA><NA>127.07818637.3068742021-12-03KEDSYSTEM
282021-0123977980남양슈퍼강원도정선군임계면7211소매종합소매점강원도 정선군 임계면 송계리 721-1<NA><NA><NA>128.85121337.4955232021-12-03KEDSYSTEM
292021-0116476371임가네가든충청남도논산시성동면1762음식한식충청남도 논산시 성동면 삼산리 176-2<NA>9053121777정상127.03231436.2003072021-12-03KEDSYSTEM