Overview

Dataset statistics

Number of variables16
Number of observations469
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.3 KiB
Average record size in memory138.3 B

Variable types

Numeric5
Categorical10
Text1

Dataset

Description경상남도 김해시 행정동별 위기업종 정보데이터로 기준(년, 월), 시도명, 시군구명, 행정동명, 업종대분류, 업종중분류, 업종명, 위기등급, 폐업율(퍼센트) 등에 대한 항목으로 구성되어 있습니다. 2022년 7월 인허가 데이터를 기준으로 구축하였으며, 참고용으로 사용하셔야 합니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15109782/fileData.do

Alerts

기준_년 has constant value ""Constant
기준_월 has constant value ""Constant
시도_명 has constant value ""Constant
시군구_명 has constant value ""Constant
시도_코드 has constant value ""Constant
시군구_코드 has constant value ""Constant
업종중분류 is highly overall correlated with 업종대분류High correlation
업종대분류 is highly overall correlated with 업종중분류High correlation
관리번호 is highly overall correlated with 행정동_명High correlation
행정동_코드 is highly overall correlated with 행정동_명High correlation
폐업율(퍼센트) is highly overall correlated with 소상공인_영업수High correlation
소상공인_폐업수 is highly overall correlated with 소상공인_영업수High correlation
소상공인_영업수 is highly overall correlated with 폐업율(퍼센트) and 1 other fieldsHigh correlation
행정동_명 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:32:14.417279
Analysis finished2023-12-12 12:32:18.624825
Duration4.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct469
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235
Minimum1
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T21:32:18.728235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.4
Q1118
median235
Q3352
95-th percentile445.6
Maximum469
Range468
Interquartile range (IQR)234

Descriptive statistics

Standard deviation135.5329
Coefficient of variation (CV)0.57673574
Kurtosis-1.2
Mean235
Median Absolute Deviation (MAD)117
Skewness0
Sum110215
Variance18369.167
MonotonicityStrictly increasing
2023-12-12T21:32:18.891555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
310 1
 
0.2%
322 1
 
0.2%
321 1
 
0.2%
320 1
 
0.2%
319 1
 
0.2%
318 1
 
0.2%
317 1
 
0.2%
316 1
 
0.2%
315 1
 
0.2%
Other values (459) 459
97.9%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
469 1
0.2%
468 1
0.2%
467 1
0.2%
466 1
0.2%
465 1
0.2%
464 1
0.2%
463 1
0.2%
462 1
0.2%
461 1
0.2%
460 1
0.2%

기준_년
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2022
469 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 469
100.0%

Length

2023-12-12T21:32:19.299468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:19.385937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 469
100.0%

기준_월
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
7
469 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 469
100.0%

Length

2023-12-12T21:32:19.475338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:19.560252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 469
100.0%

시도_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
경상남도
469 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 469
100.0%

Length

2023-12-12T21:32:19.644760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:19.726198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 469
100.0%

시군구_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
김해시
469 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김해시 469
100.0%

Length

2023-12-12T21:32:19.808029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:19.887697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김해시 469
100.0%

행정동_명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
진영읍
40 
북부동
39 
내외동
36 
장유2동
33 
삼안동
32 
Other values (14)
289 

Length

Max length5
Median length3
Mean length3.2835821
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진영읍 40
 
8.5%
북부동 39
 
8.3%
내외동 36
 
7.7%
장유2동 33
 
7.0%
삼안동 32
 
6.8%
활천동 31
 
6.6%
장유1동 29
 
6.2%
주촌면 26
 
5.5%
부원동 25
 
5.3%
장유3동 25
 
5.3%
Other values (9) 153
32.6%

Length

2023-12-12T21:32:20.000598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진영읍 40
 
8.5%
북부동 39
 
8.3%
내외동 36
 
7.7%
장유2동 33
 
7.0%
삼안동 32
 
6.8%
활천동 31
 
6.6%
장유1동 29
 
6.2%
주촌면 26
 
5.5%
장유3동 25
 
5.3%
부원동 25
 
5.3%
Other values (9) 153
32.6%

시도_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
48
469 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48
2nd row48
3rd row48
4th row48
5th row48

Common Values

ValueCountFrequency (%)
48 469
100.0%

Length

2023-12-12T21:32:20.153361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:20.250451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48 469
100.0%

시군구_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
48250
469 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48250
2nd row48250
3rd row48250
4th row48250
5th row48250

Common Values

ValueCountFrequency (%)
48250 469
100.0%

Length

2023-12-12T21:32:20.345956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:20.433329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48250 469
100.0%

행정동_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8250494 × 109
Minimum4.825025 × 109
Maximum4.825063 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T21:32:20.520745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.825025 × 109
5-th percentile4.825025 × 109
Q14.825036 × 109
median4.825054 × 109
Q34.825059 × 109
95-th percentile4.825063 × 109
Maximum4.825063 × 109
Range38000
Interquartile range (IQR)23000

Descriptive statistics

Standard deviation12503.973
Coefficient of variation (CV)2.5914705 × 10-6
Kurtosis-0.96570699
Mean4.8250494 × 109
Median Absolute Deviation (MAD)6000
Skewness-0.76605826
Sum2.2629482 × 1012
Variance1.5634934 × 108
MonotonicityNot monotonic
2023-12-12T21:32:20.649975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4825025000 40
 
8.5%
4825055000 39
 
8.3%
4825054000 36
 
7.7%
4825062000 33
 
7.0%
4825059000 32
 
6.8%
4825058000 31
 
6.6%
4825061000 29
 
6.2%
4825032000 26
 
5.5%
4825053000 25
 
5.3%
4825063000 25
 
5.3%
Other values (9) 153
32.6%
ValueCountFrequency (%)
4825025000 40
8.5%
4825032000 26
5.5%
4825033000 18
3.8%
4825034000 20
4.3%
4825035000 11
 
2.3%
4825036000 17
3.6%
4825037000 13
 
2.8%
4825051000 11
 
2.3%
4825052000 23
4.9%
4825053000 25
5.3%
ValueCountFrequency (%)
4825063000 25
5.3%
4825062000 33
7.0%
4825061000 29
6.2%
4825060000 17
3.6%
4825059000 32
6.8%
4825058000 31
6.6%
4825056500 23
4.9%
4825055000 39
8.3%
4825054000 36
7.7%
4825053000 25
5.3%

업종대분류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
식품
189 
생활
90 
자원환경
43 
문화
39 
건강
38 
Other values (2)
70 

Length

Max length4
Median length2
Mean length2.1833689
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row식품
2nd row생활
3rd row문화
4th row동물
5th row동물

Common Values

ValueCountFrequency (%)
식품 189
40.3%
생활 90
19.2%
자원환경 43
 
9.2%
문화 39
 
8.3%
건강 38
 
8.1%
동물 35
 
7.5%
기타 35
 
7.5%

Length

2023-12-12T21:32:20.828087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:20.946709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품 189
40.3%
생활 90
19.2%
자원환경 43
 
9.2%
문화 39
 
8.3%
건강 38
 
8.1%
동물 35
 
7.5%
기타 35
 
7.5%

업종중분류
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
식품제조/가공/판매
129 
유통
43 
음식점
40 
동물
25 
환경관리
24 
Other values (21)
208 

Length

Max length11
Median length10
Mean length4.8592751
Min length2

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row식품제조/가공/판매
2nd row유통
3rd row게임
4th row동물
5th row동물

Common Values

ValueCountFrequency (%)
식품제조/가공/판매 129
27.5%
유통 43
 
9.2%
음식점 40
 
8.5%
동물 25
 
5.3%
환경관리 24
 
5.1%
의료기관 24
 
5.1%
에너지 19
 
4.1%
체육 18
 
3.8%
담배 18
 
3.8%
게임 16
 
3.4%
Other values (16) 113
24.1%

Length

2023-12-12T21:32:21.080718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식품제조/가공/판매 129
27.5%
유통 43
 
9.2%
음식점 40
 
8.5%
동물 25
 
5.3%
환경관리 24
 
5.1%
의료기관 24
 
5.1%
에너지 19
 
4.1%
체육 18
 
3.8%
담배 18
 
3.8%
게임 16
 
3.4%
Other values (16) 113
24.1%

업종명
Text

Distinct81
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2023-12-12T21:32:21.322244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.6375267
Min length2

Characters and Unicode

Total characters3113
Distinct characters138
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)3.6%

Sample

1st row즉석판매제조가공업
2nd row후원방문판매업체
3rd row청소년게임제공업
4th row동물미용업
5th row동물위탁관리업
ValueCountFrequency (%)
일반음식점 19
 
4.0%
통신판매업 19
 
4.0%
휴게음식점 19
 
4.0%
건강기능식품일반판매업 18
 
3.7%
담배소매업 18
 
3.7%
즉석판매제조가공업 16
 
3.3%
방문판매업 14
 
2.9%
식품자동판매기업 13
 
2.7%
축산판매업 13
 
2.7%
미용업 13
 
2.7%
Other values (72) 319
66.3%
2023-12-12T21:32:21.740076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 394
 
12.7%
매 176
 
5.7%
판 159
 
5.1%
식 136
 
4.4%
기 92
 
3.0%
소 83
 
2.7%
품 81
 
2.6%
제 59
 
1.9%
점 52
 
1.7%
공 51
 
1.6%
Other values (128) 1830
58.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3043
97.8%
Close Punctuation 27
 
0.9%
Open Punctuation 27
 
0.9%
Space Separator 12
 
0.4%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 394
 
12.9%
매 176
 
5.8%
판 159
 
5.2%
식 136
 
4.5%
기 92
 
3.0%
소 83
 
2.7%
품 81
 
2.7%
제 59
 
1.9%
점 52
 
1.7%
공 51
 
1.7%
Other values (124) 1760
57.8%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3043
97.8%
Common 70
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 394
 
12.9%
매 176
 
5.8%
판 159
 
5.2%
식 136
 
4.5%
기 92
 
3.0%
소 83
 
2.7%
품 81
 
2.7%
제 59
 
1.9%
점 52
 
1.7%
공 51
 
1.7%
Other values (124) 1760
57.8%
Common
ValueCountFrequency (%)
) 27
38.6%
( 27
38.6%
12
17.1%
· 4
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3043
97.8%
ASCII 66
 
2.1%
None 4
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 394
 
12.9%
매 176
 
5.8%
판 159
 
5.2%
식 136
 
4.5%
기 92
 
3.0%
소 83
 
2.7%
품 81
 
2.7%
제 59
 
1.9%
점 52
 
1.7%
공 51
 
1.7%
Other values (124) 1760
57.8%
ASCII
ValueCountFrequency (%)
) 27
40.9%
( 27
40.9%
12
18.2%
None
ValueCountFrequency (%)
· 4
100.0%

위기등급
Categorical

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
5
198 
3
87 
4
78 
1
53 
2
53 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
5 198
42.2%
3 87
18.6%
4 78
 
16.6%
1 53
 
11.3%
2 53
 
11.3%

Length

2023-12-12T21:32:21.881338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:32:22.002851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 198
42.2%
3 87
18.6%
4 78
 
16.6%
1 53
 
11.3%
2 53
 
11.3%

폐업율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.998934
Minimum1.03
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T21:32:22.162055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.03
5-th percentile2.78
Q16.25
median11.54
Q320
95-th percentile50
Maximum100
Range98.97
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation15.243769
Coefficient of variation (CV)0.95279905
Kurtosis9.5732591
Mean15.998934
Median Absolute Deviation (MAD)6.28
Skewness2.6186645
Sum7503.5
Variance232.37249
MonotonicityNot monotonic
2023-12-12T21:32:22.339964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.33 26
 
5.5%
20.0 25
 
5.3%
14.29 24
 
5.1%
25.0 21
 
4.5%
50.0 18
 
3.8%
10.0 15
 
3.2%
16.67 14
 
3.0%
9.09 14
 
3.0%
12.5 14
 
3.0%
11.11 10
 
2.1%
Other values (179) 288
61.4%
ValueCountFrequency (%)
1.03 1
0.2%
1.12 1
0.2%
1.2 2
0.4%
1.27 1
0.2%
1.47 1
0.2%
1.5 1
0.2%
1.96 1
0.2%
2.0 2
0.4%
2.07 1
0.2%
2.08 1
0.2%
ValueCountFrequency (%)
100.0 5
 
1.1%
71.43 1
 
0.2%
66.67 3
 
0.6%
60.0 1
 
0.2%
50.0 18
3.8%
42.86 1
 
0.2%
42.06 1
 
0.2%
41.88 1
 
0.2%
40.0 2
 
0.4%
37.21 1
 
0.2%

소상공인_폐업수
Real number (ℝ)

HIGH CORRELATION 

Distinct156
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.573561
Minimum1
Maximum1765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T21:32:22.538996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median22
Q374
95-th percentile366.8
Maximum1765
Range1764
Interquartile range (IQR)67

Descriptive statistics

Standard deviation170.30205
Coefficient of variation (CV)2.1401838
Kurtosis34.587734
Mean79.573561
Median Absolute Deviation (MAD)18
Skewness5.0559503
Sum37320
Variance29002.788
MonotonicityNot monotonic
2023-12-12T21:32:22.722103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
 
5.8%
3 22
 
4.7%
12 17
 
3.6%
7 17
 
3.6%
6 16
 
3.4%
8 13
 
2.8%
5 13
 
2.8%
2 13
 
2.8%
4 12
 
2.6%
13 12
 
2.6%
Other values (146) 307
65.5%
ValueCountFrequency (%)
1 27
5.8%
2 13
2.8%
3 22
4.7%
4 12
2.6%
5 13
2.8%
6 16
3.4%
7 17
3.6%
8 13
2.8%
9 9
 
1.9%
10 8
 
1.7%
ValueCountFrequency (%)
1765 1
0.2%
1432 1
0.2%
1067 1
0.2%
935 1
0.2%
886 1
0.2%
844 1
0.2%
759 1
0.2%
713 1
0.2%
693 1
0.2%
601 1
0.2%

소상공인_영업수
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.880597
Minimum1
Maximum1102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2023-12-12T21:32:22.908940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median18
Q354
95-th percentile292.4
Maximum1102
Range1101
Interquartile range (IQR)47

Descriptive statistics

Standard deviation139.59827
Coefficient of variation (CV)2.1189588
Kurtosis19.097747
Mean65.880597
Median Absolute Deviation (MAD)14
Skewness4.0810794
Sum30898
Variance19487.678
MonotonicityNot monotonic
2023-12-12T21:32:23.086293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
 
5.3%
3 25
 
5.3%
2 21
 
4.5%
7 18
 
3.8%
8 16
 
3.4%
5 15
 
3.2%
4 15
 
3.2%
14 12
 
2.6%
9 12
 
2.6%
10 12
 
2.6%
Other values (133) 298
63.5%
ValueCountFrequency (%)
1 25
5.3%
2 21
4.5%
3 25
5.3%
4 15
3.2%
5 15
3.2%
6 12
2.6%
7 18
3.8%
8 16
3.4%
9 12
2.6%
10 12
2.6%
ValueCountFrequency (%)
1102 1
0.2%
914 1
0.2%
853 1
0.2%
806 1
0.2%
794 1
0.2%
774 1
0.2%
770 1
0.2%
740 1
0.2%
647 1
0.2%
608 1
0.2%

Interactions

2023-12-12T21:32:17.546121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.164413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.741775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.375807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.929070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.662221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.264062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.867708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.473030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.047904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.775045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.382069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.992373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.622671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.178695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.898668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.498095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.101262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.723607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.289083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:18.054388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:15.615682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.241840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:16.835380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:17.422425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:32:23.203835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정동_명행정동_코드업종대분류업종중분류업종명위기등급폐업율(퍼센트)소상공인_폐업수소상공인_영업수
관리번호1.0000.9870.8270.0000.0000.0000.3380.1160.0800.000
행정동_명0.9871.0001.0000.0000.0000.0000.1570.1700.0000.000
행정동_코드0.8271.0001.0000.2310.0000.0000.1290.1670.0000.000
업종대분류0.0000.0000.2311.0001.0001.0000.3330.0820.0000.116
업종중분류0.0000.0000.0001.0001.0001.0000.6400.3220.0000.232
업종명0.0000.0000.0001.0001.0001.0000.8320.8590.0000.000
위기등급0.3380.1570.1290.3330.6400.8321.0000.4760.0000.271
폐업율(퍼센트)0.1160.1700.1670.0820.3220.8590.4761.0000.0000.000
소상공인_폐업수0.0800.0000.0000.0000.0000.0000.0000.0001.0000.841
소상공인_영업수0.0000.0000.0000.1160.2320.0000.2710.0000.8411.000
2023-12-12T21:32:23.348813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종중분류행정동_명업종대분류위기등급
업종중분류1.0000.0000.9790.361
행정동_명0.0001.0000.0000.076
업종대분류0.9790.0001.0000.220
위기등급0.3610.0760.2201.000
2023-12-12T21:32:23.498227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정동_코드폐업율(퍼센트)소상공인_폐업수소상공인_영업수행정동_명업종대분류업종중분류위기등급
관리번호1.000-0.152-0.011-0.0370.0250.9120.0000.0000.146
행정동_코드-0.1521.0000.0010.087-0.0010.9880.0650.0000.081
폐업율(퍼센트)-0.0110.0011.000-0.427-0.6270.0660.0420.1240.298
소상공인_폐업수-0.0370.087-0.4271.0000.8140.0000.0000.0000.000
소상공인_영업수0.025-0.001-0.6270.8141.0000.0000.0580.0840.115
행정동_명0.9120.9880.0660.0000.0001.0000.0000.0000.076
업종대분류0.0000.0650.0420.0000.0580.0001.0000.9790.220
업종중분류0.0000.0000.1240.0000.0840.0000.9791.0000.361
위기등급0.1460.0810.2980.0000.1150.0760.2200.3611.000

Missing values

2023-12-12T21:32:18.250934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:32:18.515474image/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.

Sample

관리번호기준_년기준_월시도_명시군구_명행정동_명시도_코드시군구_코드행정동_코드업종대분류업종중분류업종명위기등급폐업율(퍼센트)소상공인_폐업수소상공인_영업수
0120227경상남도김해시내외동48482504825054000식품식품제조/가공/판매즉석판매제조가공업142.061432180
1220227경상남도김해시내외동48482504825054000생활유통후원방문판매업체133.33168
2320227경상남도김해시내외동48482504825054000문화게임청소년게임제공업19.09457
3420227경상남도김해시내외동48482504825054000동물동물동물미용업14.76722
4520227경상남도김해시내외동48482504825054000동물동물동물위탁관리업218.1876
5620227경상남도김해시내외동48482504825054000생활유통통신판매업217.03759914
6720227경상남도김해시내외동48482504825054000식품식품제조/가공/판매식품운반업350.022
7820227경상남도김해시내외동48482504825054000식품식품제조/가공/판매건강기능식품일반판매업319.46394158
8920227경상남도김해시내외동48482504825054000식품음식점휴게음식점315.41387300
91020227경상남도김해시내외동48482504825054000식품식품제조/가공/판매집단급식소식품판매업314.2995
관리번호기준_년기준_월시도_명시군구_명행정동_명시도_코드시군구_코드행정동_코드업종대분류업종중분류업종명위기등급폐업율(퍼센트)소상공인_폐업수소상공인_영업수
45946020227경상남도김해시회현동48482504825052000동물동물동물병원525.033
46046120227경상남도김해시회현동48482504825052000식품식품제조/가공/판매식품판매업(기타)525.023
46146220227경상남도김해시회현동48482504825052000생활목욕탕/찜질방/사우나목욕장업520.064
46246320227경상남도김해시회현동48482504825052000식품식품제조/가공/판매식품제조가공업520.0193
46346420227경상남도김해시회현동48482504825052000생활이용이용업520.0218
46446520227경상남도김해시회현동48482504825052000식품식품제조/가공/판매식품자동판매기업516.678116
46546620227경상남도김해시회현동48482504825052000식품음식점휴게음식점516.48102100
46646720227경상남도김해시회현동48482504825052000식품음식점일반음식점56.14489226
46746820227경상남도김해시회현동48482504825052000생활미용미용업55.267664
46846920227경상남도김해시회현동48482504825052000문화숙박숙박업54.351422