Overview

Dataset statistics

Number of variables20
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory184.0 B

Variable types

Numeric13
Categorical7

Dataset

Description년도,신규등록 수,변경등록 수,휴업신고 수,폐업신고 수,양도양수 수,상속신고 수,합병신고 수,타도시 이관 수,업소 상태 수,재발급 등 기타,변경등록 총 수,사업정지 30일 수,사업정지 40일 수,사업정지 60일 수,벌금 수,과태료 수,과징금 수,등록취소 수,행정처분 총 수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13286/S/1/datasetView.do

Alerts

과태료 수 is highly overall correlated with 변경등록 수 and 4 other fieldsHigh correlation
사업정지 40일 수 is highly overall correlated with 변경등록 수 and 4 other fieldsHigh correlation
년도 is highly overall correlated with 변경등록 수 and 7 other fieldsHigh correlation
신규등록 수 is highly overall correlated with 업소 상태 수High correlation
변경등록 수 is highly overall correlated with 년도 and 10 other fieldsHigh correlation
휴업신고 수 is highly overall correlated with 년도 and 8 other fieldsHigh correlation
폐업신고 수 is highly overall correlated with 년도 and 8 other fieldsHigh correlation
타도시 이관 수 is highly overall correlated with 년도 and 10 other fieldsHigh correlation
업소 상태 수 is highly overall correlated with 신규등록 수 and 1 other fieldsHigh correlation
재발급 등 기타 is highly overall correlated with 년도 and 7 other fieldsHigh correlation
변경등록 총 수 is highly overall correlated with 년도 and 9 other fieldsHigh correlation
사업정지 30일 수 is highly overall correlated with 년도 and 11 other fieldsHigh correlation
사업정지 60일 수 is highly overall correlated with 변경등록 수 and 9 other fieldsHigh correlation
등록취소 수 is highly overall correlated with 업소 상태 수 and 3 other fieldsHigh correlation
행정처분 총 수 is highly overall correlated with 년도 and 9 other fieldsHigh correlation
양도양수 수 is highly overall correlated with 사업정지 30일 수 and 1 other fieldsHigh correlation
합병신고 수 is highly overall correlated with 등록취소 수High correlation
벌금 수 is highly overall correlated with 사업정지 30일 수 and 4 other fieldsHigh correlation
과징금 수 is highly overall correlated with 사업정지 60일 수 and 2 other fieldsHigh correlation
상속신고 수 is highly imbalanced (80.4%)Imbalance
사업정지 40일 수 is highly imbalanced (80.4%)Imbalance
벌금 수 is highly imbalanced (60.2%)Imbalance
과태료 수 is highly imbalanced (66.6%)Imbalance
과징금 수 is highly imbalanced (70.8%)Imbalance
년도 has unique valuesUnique
변경등록 수 has 3 (9.1%) zerosZeros
휴업신고 수 has 16 (48.5%) zerosZeros
폐업신고 수 has 8 (24.2%) zerosZeros
타도시 이관 수 has 9 (27.3%) zerosZeros
업소 상태 수 has 11 (33.3%) zerosZeros
재발급 등 기타 has 12 (36.4%) zerosZeros
변경등록 총 수 has 2 (6.1%) zerosZeros
사업정지 30일 수 has 17 (51.5%) zerosZeros
사업정지 60일 수 has 17 (51.5%) zerosZeros
등록취소 수 has 12 (36.4%) zerosZeros
행정처분 총 수 has 8 (24.2%) zerosZeros

Reproduction

Analysis started2024-04-27 10:43:34.040184
Analysis finished2024-04-27 10:44:20.777243
Duration46.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008
Minimum1992
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:20.947046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993.6
Q12000
median2008
Q32016
95-th percentile2022.4
Maximum2024
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.0048155079
Kurtosis-1.2
Mean2008
Median Absolute Deviation (MAD)8
Skewness0
Sum66264
Variance93.5
MonotonicityStrictly decreasing
2024-04-27T10:44:21.351713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2024 1
 
3.0%
1999 1
 
3.0%
2005 1
 
3.0%
2004 1
 
3.0%
2003 1
 
3.0%
2002 1
 
3.0%
2001 1
 
3.0%
2000 1
 
3.0%
1998 1
 
3.0%
2023 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1992 1
3.0%
1993 1
3.0%
1994 1
3.0%
1995 1
3.0%
1996 1
3.0%
1997 1
3.0%
1998 1
3.0%
1999 1
3.0%
2000 1
3.0%
2001 1
3.0%
ValueCountFrequency (%)
2024 1
3.0%
2023 1
3.0%
2022 1
3.0%
2021 1
3.0%
2020 1
3.0%
2019 1
3.0%
2018 1
3.0%
2017 1
3.0%
2016 1
3.0%
2015 1
3.0%

신규등록 수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.30303
Minimum26
Maximum401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:21.743336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33.2
Q1136
median162
Q3192
95-th percentile247.8
Maximum401
Range375
Interquartile range (IQR)56

Descriptive statistics

Standard deviation73.517636
Coefficient of variation (CV)0.45577343
Kurtosis2.8300531
Mean161.30303
Median Absolute Deviation (MAD)27
Skewness0.4948845
Sum5323
Variance5404.8428
MonotonicityNot monotonic
2024-04-27T10:44:22.155613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
153 2
 
6.1%
166 2
 
6.1%
136 2
 
6.1%
37 1
 
3.0%
245 1
 
3.0%
192 1
 
3.0%
189 1
 
3.0%
225 1
 
3.0%
236 1
 
3.0%
252 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
26 1
3.0%
29 1
3.0%
36 1
3.0%
37 1
3.0%
51 1
3.0%
129 1
3.0%
133 1
3.0%
136 2
6.1%
142 1
3.0%
148 1
3.0%
ValueCountFrequency (%)
401 1
3.0%
252 1
3.0%
245 1
3.0%
236 1
3.0%
225 1
3.0%
204 1
3.0%
203 1
3.0%
194 1
3.0%
192 1
3.0%
189 1
3.0%

변경등록 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.45455
Minimum0
Maximum618
Zeros3
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:22.458022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median356
Q3443
95-th percentile512.8
Maximum618
Range618
Interquartile range (IQR)439

Descriptive statistics

Standard deviation202.11477
Coefficient of variation (CV)0.69346927
Kurtosis-1.2827881
Mean291.45455
Median Absolute Deviation (MAD)122
Skewness-0.46554892
Sum9618
Variance40850.381
MonotonicityNot monotonic
2024-04-27T10:44:22.848113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 4
 
12.1%
0 3
 
9.1%
200 1
 
3.0%
394 1
 
3.0%
3 1
 
3.0%
4 1
 
3.0%
152 1
 
3.0%
252 1
 
3.0%
284 1
 
3.0%
334 1
 
3.0%
Other values (18) 18
54.5%
ValueCountFrequency (%)
0 3
9.1%
1 4
12.1%
3 1
 
3.0%
4 1
 
3.0%
152 1
 
3.0%
200 1
 
3.0%
252 1
 
3.0%
284 1
 
3.0%
321 1
 
3.0%
334 1
 
3.0%
ValueCountFrequency (%)
618 1
3.0%
514 1
3.0%
512 1
3.0%
509 1
3.0%
490 1
3.0%
478 1
3.0%
467 1
3.0%
450 1
3.0%
443 1
3.0%
442 1
3.0%

휴업신고 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6969697
Minimum0
Maximum8
Zeros16
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:23.195793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4556675
Coefficient of variation (CV)1.4470898
Kurtosis1.9323934
Mean1.6969697
Median Absolute Deviation (MAD)1
Skewness1.6541507
Sum56
Variance6.030303
MonotonicityNot monotonic
2024-04-27T10:44:23.553863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
48.5%
1 5
 
15.2%
2 4
 
12.1%
8 3
 
9.1%
4 2
 
6.1%
3 2
 
6.1%
5 1
 
3.0%
ValueCountFrequency (%)
0 16
48.5%
1 5
 
15.2%
2 4
 
12.1%
3 2
 
6.1%
4 2
 
6.1%
5 1
 
3.0%
8 3
 
9.1%
ValueCountFrequency (%)
8 3
 
9.1%
5 1
 
3.0%
4 2
 
6.1%
3 2
 
6.1%
2 4
 
12.1%
1 5
 
15.2%
0 16
48.5%

폐업신고 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.121212
Minimum0
Maximum89
Zeros8
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:23.926874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median26
Q351
95-th percentile71.2
Maximum89
Range89
Interquartile range (IQR)49

Descriptive statistics

Standard deviation26.893026
Coefficient of variation (CV)0.89282682
Kurtosis-0.93824573
Mean30.121212
Median Absolute Deviation (MAD)25
Skewness0.49480589
Sum994
Variance723.23485
MonotonicityNot monotonic
2024-04-27T10:44:24.255710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 8
24.2%
37 2
 
6.1%
15 2
 
6.1%
11 1
 
3.0%
58 1
 
3.0%
2 1
 
3.0%
9 1
 
3.0%
18 1
 
3.0%
22 1
 
3.0%
20 1
 
3.0%
Other values (14) 14
42.4%
ValueCountFrequency (%)
0 8
24.2%
2 1
 
3.0%
9 1
 
3.0%
11 1
 
3.0%
15 2
 
6.1%
18 1
 
3.0%
20 1
 
3.0%
22 1
 
3.0%
26 1
 
3.0%
27 1
 
3.0%
ValueCountFrequency (%)
89 1
3.0%
73 1
3.0%
70 1
3.0%
69 1
3.0%
68 1
3.0%
66 1
3.0%
58 1
3.0%
53 1
3.0%
51 1
3.0%
48 1
3.0%

양도양수 수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
19 
1
3
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 19
57.6%
1 9
27.3%
3 4
 
12.1%
2 1
 
3.0%

Length

2024-04-27T10:44:24.642361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:24.966821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
57.6%
1 9
27.3%
3 4
 
12.1%
2 1
 
3.0%

상속신고 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
32 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 32
97.0%
1 1
 
3.0%

Length

2024-04-27T10:44:25.337157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:25.602279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
97.0%
1 1
 
3.0%

합병신고 수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
22 
2
1
5
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.1%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
0 22
66.7%
2 5
 
15.2%
1 4
 
12.1%
5 1
 
3.0%
3 1
 
3.0%

Length

2024-04-27T10:44:25.795028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:26.125241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
66.7%
2 5
 
15.2%
1 4
 
12.1%
5 1
 
3.0%
3 1
 
3.0%

타도시 이관 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.575758
Minimum0
Maximum41
Zeros9
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:26.475619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q319
95-th percentile34.4
Maximum41
Range41
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.779299
Coefficient of variation (CV)0.86767161
Kurtosis-0.40939858
Mean13.575758
Median Absolute Deviation (MAD)12
Skewness0.52059751
Sum448
Variance138.75189
MonotonicityNot monotonic
2024-04-27T10:44:26.841569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
27.3%
17 4
12.1%
13 3
 
9.1%
25 3
 
9.1%
3 2
 
6.1%
11 2
 
6.1%
8 1
 
3.0%
15 1
 
3.0%
14 1
 
3.0%
26 1
 
3.0%
Other values (6) 6
18.2%
ValueCountFrequency (%)
0 9
27.3%
3 2
 
6.1%
8 1
 
3.0%
11 2
 
6.1%
13 3
 
9.1%
14 1
 
3.0%
15 1
 
3.0%
17 4
12.1%
18 1
 
3.0%
19 1
 
3.0%
ValueCountFrequency (%)
41 1
 
3.0%
38 1
 
3.0%
32 1
 
3.0%
27 1
 
3.0%
26 1
 
3.0%
25 3
9.1%
19 1
 
3.0%
18 1
 
3.0%
17 4
12.1%
15 1
 
3.0%

업소 상태 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.30303
Minimum0
Maximum214
Zeros11
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:27.132393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q354
95-th percentile128.2
Maximum214
Range214
Interquartile range (IQR)54

Descriptive statistics

Standard deviation52.342314
Coefficient of variation (CV)1.5258802
Kurtosis3.3348061
Mean34.30303
Median Absolute Deviation (MAD)7
Skewness1.8610082
Sum1132
Variance2739.7178
MonotonicityNot monotonic
2024-04-27T10:44:27.429277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 11
33.3%
54 2
 
6.1%
3 2
 
6.1%
12 2
 
6.1%
7 2
 
6.1%
99 1
 
3.0%
18 1
 
3.0%
60 1
 
3.0%
101 1
 
3.0%
119 1
 
3.0%
Other values (9) 9
27.3%
ValueCountFrequency (%)
0 11
33.3%
1 1
 
3.0%
2 1
 
3.0%
3 2
 
6.1%
4 1
 
3.0%
7 2
 
6.1%
12 2
 
6.1%
18 1
 
3.0%
32 1
 
3.0%
35 1
 
3.0%
ValueCountFrequency (%)
214 1
3.0%
142 1
3.0%
119 1
3.0%
110 1
3.0%
101 1
3.0%
99 1
3.0%
60 1
3.0%
54 2
6.1%
43 1
3.0%
35 1
3.0%

재발급 등 기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612.24242
Minimum0
Maximum2249
Zeros12
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:27.726690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q32008
95-th percentile2157.4
Maximum2249
Range2249
Interquartile range (IQR)2008

Descriptive statistics

Standard deviation942.62784
Coefficient of variation (CV)1.5396317
Kurtosis-0.96863616
Mean612.24242
Median Absolute Deviation (MAD)5
Skewness1.011469
Sum20204
Variance888547.25
MonotonicityNot monotonic
2024-04-27T10:44:28.090826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 12
36.4%
1 3
 
9.1%
750 1
 
3.0%
8 1
 
3.0%
5 1
 
3.0%
11 1
 
3.0%
9 1
 
3.0%
4 1
 
3.0%
18 1
 
3.0%
36 1
 
3.0%
Other values (10) 10
30.3%
ValueCountFrequency (%)
0 12
36.4%
1 3
 
9.1%
4 1
 
3.0%
5 1
 
3.0%
8 1
 
3.0%
9 1
 
3.0%
11 1
 
3.0%
18 1
 
3.0%
36 1
 
3.0%
374 1
 
3.0%
ValueCountFrequency (%)
2249 1
3.0%
2164 1
3.0%
2153 1
3.0%
2144 1
3.0%
2121 1
3.0%
2050 1
3.0%
2049 1
3.0%
2048 1
3.0%
2008 1
3.0%
750 1
3.0%

변경등록 총 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean984.78788
Minimum0
Maximum2964
Zeros2
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:28.433579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q118
median495
Q32560
95-th percentile2767.8
Maximum2964
Range2964
Interquartile range (IQR)2542

Descriptive statistics

Standard deviation1102.763
Coefficient of variation (CV)1.1197975
Kurtosis-0.98632981
Mean984.78788
Median Absolute Deviation (MAD)477
Skewness0.90123008
Sum32498
Variance1216086.2
MonotonicityNot monotonic
2024-04-27T10:44:28.778945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 4
 
12.1%
0 2
 
6.1%
18 2
 
6.1%
970 1
 
3.0%
483 1
 
3.0%
3 1
 
3.0%
224 1
 
3.0%
384 1
 
3.0%
429 1
 
3.0%
517 1
 
3.0%
Other values (18) 18
54.5%
ValueCountFrequency (%)
0 2
6.1%
1 4
12.1%
3 1
 
3.0%
18 2
6.1%
224 1
 
3.0%
384 1
 
3.0%
429 1
 
3.0%
470 1
 
3.0%
483 1
 
3.0%
484 1
 
3.0%
ValueCountFrequency (%)
2964 1
3.0%
2784 1
3.0%
2757 1
3.0%
2734 1
3.0%
2710 1
3.0%
2632 1
3.0%
2631 1
3.0%
2597 1
3.0%
2560 1
3.0%
970 1
3.0%

사업정지 30일 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.060606
Minimum0
Maximum136
Zeros17
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:29.197682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q341
95-th percentile86
Maximum136
Range136
Interquartile range (IQR)41

Descriptive statistics

Standard deviation34.890847
Coefficient of variation (CV)1.5130065
Kurtosis2.1349366
Mean23.060606
Median Absolute Deviation (MAD)0
Skewness1.6011254
Sum761
Variance1217.3712
MonotonicityNot monotonic
2024-04-27T10:44:29.528270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 17
51.5%
27 1
 
3.0%
5 1
 
3.0%
17 1
 
3.0%
2 1
 
3.0%
136 1
 
3.0%
89 1
 
3.0%
84 1
 
3.0%
66 1
 
3.0%
61 1
 
3.0%
Other values (7) 7
21.2%
ValueCountFrequency (%)
0 17
51.5%
2 1
 
3.0%
5 1
 
3.0%
10 1
 
3.0%
12 1
 
3.0%
17 1
 
3.0%
27 1
 
3.0%
33 1
 
3.0%
41 1
 
3.0%
45 1
 
3.0%
ValueCountFrequency (%)
136 1
3.0%
89 1
3.0%
84 1
3.0%
70 1
3.0%
66 1
3.0%
63 1
3.0%
61 1
3.0%
45 1
3.0%
41 1
3.0%
33 1
3.0%

사업정지 40일 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
32 
60
 
1

Length

Max length2
Median length1
Mean length1.030303
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row60
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
97.0%
60 1
 
3.0%

Length

2024-04-27T10:44:29.999402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:30.276708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
97.0%
60 1
 
3.0%

사업정지 60일 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.090909
Minimum0
Maximum226
Zeros17
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:30.530945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q343
95-th percentile84
Maximum226
Range226
Interquartile range (IQR)43

Descriptive statistics

Standard deviation45.120923
Coefficient of variation (CV)1.7982977
Kurtosis11.868807
Mean25.090909
Median Absolute Deviation (MAD)0
Skewness3.0344578
Sum828
Variance2035.8977
MonotonicityNot monotonic
2024-04-27T10:44:30.728139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 17
51.5%
1 2
 
6.1%
70 1
 
3.0%
87 1
 
3.0%
49 1
 
3.0%
82 1
 
3.0%
43 1
 
3.0%
226 1
 
3.0%
56 1
 
3.0%
35 1
 
3.0%
Other values (6) 6
 
18.2%
ValueCountFrequency (%)
0 17
51.5%
1 2
 
6.1%
2 1
 
3.0%
10 1
 
3.0%
35 1
 
3.0%
36 1
 
3.0%
41 1
 
3.0%
43 1
 
3.0%
44 1
 
3.0%
45 1
 
3.0%
ValueCountFrequency (%)
226 1
3.0%
87 1
3.0%
82 1
3.0%
70 1
3.0%
56 1
3.0%
49 1
3.0%
45 1
3.0%
44 1
3.0%
43 1
3.0%
41 1
3.0%

벌금 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
29 
1
15
 
1

Length

Max length2
Median length1
Mean length1.030303
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
87.9%
1 3
 
9.1%
15 1
 
3.0%

Length

2024-04-27T10:44:31.089920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:31.425677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
87.9%
1 3
 
9.1%
15 1
 
3.0%

과태료 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
29 
4
 
1
13
 
1
19
 
1
1
 
1

Length

Max length2
Median length1
Mean length1.0606061
Min length1

Unique

Unique4 ?
Unique (%)12.1%

Sample

1st row4
2nd row13
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
87.9%
4 1
 
3.0%
13 1
 
3.0%
19 1
 
3.0%
1 1
 
3.0%

Length

2024-04-27T10:44:31.774263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:32.046822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
87.9%
4 1
 
3.0%
13 1
 
3.0%
19 1
 
3.0%
1 1
 
3.0%

과징금 수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
30 
2
 
1
10
 
1
33
 
1

Length

Max length2
Median length1
Mean length1.0606061
Min length1

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
90.9%
2 1
 
3.0%
10 1
 
3.0%
33 1
 
3.0%

Length

2024-04-27T10:44:32.366580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-27T10:44:32.704033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
90.9%
2 1
 
3.0%
10 1
 
3.0%
33 1
 
3.0%

등록취소 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.151515
Minimum0
Maximum140
Zeros12
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:33.019331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18
Q356
95-th percentile133.6
Maximum140
Range140
Interquartile range (IQR)56

Descriptive statistics

Standard deviation43.300636
Coefficient of variation (CV)1.2318284
Kurtosis0.76461359
Mean35.151515
Median Absolute Deviation (MAD)18
Skewness1.2928024
Sum1160
Variance1874.9451
MonotonicityNot monotonic
2024-04-27T10:44:33.405761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 12
36.4%
39 2
 
6.1%
47 1
 
3.0%
18 1
 
3.0%
12 1
 
3.0%
58 1
 
3.0%
73 1
 
3.0%
75 1
 
3.0%
140 1
 
3.0%
56 1
 
3.0%
Other values (11) 11
33.3%
ValueCountFrequency (%)
0 12
36.4%
2 1
 
3.0%
8 1
 
3.0%
12 1
 
3.0%
15 1
 
3.0%
18 1
 
3.0%
23 1
 
3.0%
31 1
 
3.0%
36 1
 
3.0%
39 2
 
6.1%
ValueCountFrequency (%)
140 1
3.0%
136 1
3.0%
132 1
3.0%
115 1
3.0%
75 1
3.0%
73 1
3.0%
64 1
3.0%
58 1
3.0%
56 1
3.0%
47 1
3.0%

행정처분 총 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.151515
Minimum0
Maximum468
Zeros8
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-04-27T10:44:33.797506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median71
Q3113
95-th percentile259.4
Maximum468
Range468
Interquartile range (IQR)109

Descriptive statistics

Standard deviation103.63913
Coefficient of variation (CV)1.1756932
Kurtosis4.5132143
Mean88.151515
Median Absolute Deviation (MAD)66
Skewness1.8737701
Sum2909
Variance10741.07
MonotonicityNot monotonic
2024-04-27T10:44:34.133131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 8
24.2%
137 3
 
9.1%
90 2
 
6.1%
113 2
 
6.1%
12 2
 
6.1%
266 1
 
3.0%
18 1
 
3.0%
39 1
 
3.0%
58 1
 
3.0%
73 1
 
3.0%
Other values (11) 11
33.3%
ValueCountFrequency (%)
0 8
24.2%
4 1
 
3.0%
12 2
 
6.1%
18 1
 
3.0%
37 1
 
3.0%
39 1
 
3.0%
43 1
 
3.0%
58 1
 
3.0%
71 1
 
3.0%
73 1
 
3.0%
ValueCountFrequency (%)
468 1
 
3.0%
266 1
 
3.0%
255 1
 
3.0%
227 1
 
3.0%
219 1
 
3.0%
137 3
9.1%
113 2
6.1%
111 1
 
3.0%
103 1
 
3.0%
90 2
6.1%

Interactions

2024-04-27T10:44:16.586609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:39.985834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:43.399388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:46.777111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:49.758504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:52.333520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:55.444461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:57.916558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:00.787779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:03.624727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:06.839696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:10.017655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:13.024449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:16.841919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:40.287045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:43.662951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:47.024376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:49.925107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:52.561633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:55.677437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:58.066733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:00.940419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:03.878269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:07.093313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:10.269109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:13.291416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:17.066058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:40.546884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:43.925399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:47.287176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:50.095757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:52.814067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:55.953351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:58.224728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:01.135755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:04.134845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:07.384223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:10.521842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:13.558414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:17.316025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:40.796881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:44.176188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:47.530468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:50.260496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:53.053094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.193544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:58.372761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:01.336506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:04.381776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:07.583110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:10.767544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:13.802427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:17.583834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:41.061249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:44.453012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:47.718485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:50.508120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:53.304612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.445790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:58.716059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:01.591755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:04.647439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:07.789243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:11.070053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:14.051454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:17.824208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:41.305506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:44.862772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:47.858901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:50.702892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:53.681644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.585649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:58.959378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:01.820038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:04.894076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:07.986089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:11.292965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:14.443262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:18.028831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:41.604204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:45.113613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:48.003263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:50.943366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:53.814137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.720695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:59.251421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:02.045962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:05.131163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:08.227155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:11.506466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:14.679829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:18.179991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:41.849251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:45.353648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:48.156546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:51.184471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:54.036816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.856995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:59.472284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:02.260639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:05.364528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:08.400796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:11.717793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:14.918035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:18.415488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:42.093036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:45.592434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:48.435032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:51.423572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:54.259345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:56.995271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:59.697376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:02.484094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:05.599529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:08.580369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:11.954515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:15.204334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:18.682187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:42.365749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:45.783238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:48.708404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:51.684734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:54.506047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:57.143299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:59.947375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:02.725432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:05.851267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:08.893274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:12.199098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:15.495827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:18.937975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:42.651108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:46.048776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:49.078738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:51.863157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:54.751819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:57.335301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:00.198186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:02.917371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:06.114277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:09.152031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:12.412263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:15.765866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:19.186562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:42.885159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:46.283146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:49.314407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:52.015160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:54.976100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:57.566350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:00.422706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:03.053890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:06.351244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:09.469568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:12.550483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:16.031688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:19.342114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:43.167711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:46.539608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:49.580326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:52.183382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:55.220731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:43:57.782708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:00.638283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:03.234553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:06.606193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:09.740572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:12.781694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:44:16.317478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T10:44:34.381081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도신규등록 수변경등록 수휴업신고 수폐업신고 수양도양수 수상속신고 수합병신고 수타도시 이관 수업소 상태 수재발급 등 기타변경등록 총 수사업정지 30일 수사업정지 40일 수사업정지 60일 수벌금 수과태료 수과징금 수등록취소 수행정처분 총 수
년도1.0000.8130.7830.6600.7260.5020.2690.5070.7150.5450.7820.8540.5320.0000.7400.4500.0000.0000.7420.620
신규등록 수0.8131.0000.4580.0000.0000.0000.2190.0000.3940.0000.1580.5800.0000.0000.0000.0000.0000.0000.3790.000
변경등록 수0.7830.4581.0000.5610.8110.5070.0000.0000.7890.4390.7210.8550.3441.0000.0000.0000.8280.0000.4860.347
휴업신고 수0.6600.0000.5611.0000.6670.3320.2190.5520.5430.5270.6530.6000.5350.5660.5210.0000.4660.1840.6430.296
폐업신고 수0.7260.0000.8110.6671.0000.6670.0000.6350.8330.5330.8940.7140.7930.3050.4430.5390.0000.4760.5510.415
양도양수 수0.5020.0000.5070.3320.6671.0000.0000.2530.6120.4670.0000.5890.8930.0000.2010.5500.0000.3480.0000.416
상속신고 수0.2690.2190.0000.2190.0000.0001.0000.2020.0000.0000.0000.2310.4740.0000.0000.0000.0000.0000.0000.000
합병신고 수0.5070.0000.0000.5520.6350.2530.2021.0000.4210.4360.0000.0000.4030.0000.8570.4820.0000.3470.6620.472
타도시 이관 수0.7150.3940.7890.5430.8330.6120.0000.4211.0000.0000.7840.8580.9170.7630.4510.0000.7250.0000.3740.304
업소 상태 수0.5450.0000.4390.5270.5330.4670.0000.4360.0001.0000.0000.3060.0000.0000.1560.0000.0000.3800.7310.418
재발급 등 기타0.7820.1580.7210.6530.8940.0000.0000.0000.7840.0001.0000.8580.3680.0000.6310.0000.9480.0000.0000.421
변경등록 총 수0.8540.5800.8550.6000.7140.5890.2310.0000.8580.3060.8581.0000.3170.2310.5820.0000.7060.0000.6260.884
사업정지 30일 수0.5320.0000.3440.5350.7930.8930.4740.4030.9170.0000.3680.3171.0000.7630.6700.6920.5620.7090.1910.648
사업정지 40일 수0.0000.0001.0000.5660.3050.0000.0000.0000.7630.0000.0000.2310.7631.0000.0000.0001.0000.0000.0000.000
사업정지 60일 수0.7400.0000.0000.5210.4430.2010.0000.8570.4510.1560.6310.5820.6700.0001.0000.7460.5520.6720.7460.843
벌금 수0.4500.0000.0000.0000.5390.5500.0000.4820.0000.0000.0000.0000.6920.0000.7461.0000.2700.7420.3260.962
과태료 수0.0000.0000.8280.4660.0000.0000.0000.0000.7250.0000.9480.7060.5621.0000.5520.2701.0000.5670.0000.521
과징금 수0.0000.0000.0000.1840.4760.3480.0000.3470.0000.3800.0000.0000.7090.0000.6720.7420.5671.0000.2960.798
등록취소 수0.7420.3790.4860.6430.5510.0000.0000.6620.3740.7310.0000.6260.1910.0000.7460.3260.0000.2961.0000.704
행정처분 총 수0.6200.0000.3470.2960.4150.4160.0000.4720.3040.4180.4210.8840.6480.0000.8430.9620.5210.7980.7041.000
2024-04-27T10:44:34.773359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상속신고 수벌금 수합병신고 수과태료 수과징금 수양도양수 수사업정지 40일 수
상속신고 수1.0000.0000.2270.0000.0000.0000.000
벌금 수0.0001.0000.3970.1950.7750.5450.000
합병신고 수0.2270.3971.0000.0000.2780.1960.000
과태료 수0.0000.1950.0001.0000.4820.0000.950
과징금 수0.0000.7750.2780.4821.0000.1300.000
양도양수 수0.0000.5450.1960.0000.1301.0000.000
사업정지 40일 수0.0000.0000.0000.9500.0000.0001.000
2024-04-27T10:44:35.020532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도신규등록 수변경등록 수휴업신고 수폐업신고 수타도시 이관 수업소 상태 수재발급 등 기타변경등록 총 수사업정지 30일 수사업정지 60일 수등록취소 수행정처분 총 수양도양수 수상속신고 수합병신고 수사업정지 40일 수벌금 수과태료 수과징금 수
년도1.000-0.1550.8730.7220.8450.8270.0870.9270.9490.6850.4820.1970.5440.2940.1800.2160.0000.2680.0000.000
신규등록 수-0.1551.000-0.083-0.200-0.067-0.0940.641-0.255-0.132-0.1880.0370.4210.1830.0000.2010.0000.0000.0000.0000.000
변경등록 수0.873-0.0831.0000.7600.9150.8920.1300.8860.9280.7620.5920.3320.6350.3060.0000.0000.8800.0000.6140.000
휴업신고 수0.722-0.2000.7601.0000.6200.7050.0670.7160.8040.5740.5100.3030.4940.2070.2010.3740.5540.0000.3000.091
폐업신고 수0.845-0.0670.9150.6201.0000.8340.1680.8560.8520.8170.5390.3550.6730.4070.0000.2700.1800.3230.0000.254
타도시 이관 수0.827-0.0940.8920.7050.8341.0000.1620.8720.8980.7580.7320.4110.7400.2770.0000.2450.5240.0000.5220.000
업소 상태 수0.0870.6410.1300.0670.1680.1621.000-0.0300.131-0.0710.2310.5350.3800.3120.0000.2760.0000.0000.0000.244
재발급 등 기타0.927-0.2550.8860.7160.8560.872-0.0301.0000.9330.7200.4490.1060.4980.0000.0000.0000.0000.0000.6780.000
변경등록 총 수0.949-0.1320.9280.8040.8520.8980.1310.9331.0000.6900.5560.2930.6060.4010.1390.0000.1390.0000.5580.000
사업정지 30일 수0.685-0.1880.7620.5740.8170.758-0.0710.7200.6901.0000.5880.2620.7370.5420.3110.2320.5240.5220.3590.346
사업정지 60일 수0.4820.0370.5920.5100.5390.7320.2310.4490.5560.5881.0000.6900.8310.1490.0000.4930.0000.7190.2250.592
등록취소 수0.1970.4210.3320.3030.3550.4110.5350.1060.2930.2620.6901.0000.7680.0000.0000.5050.0000.2570.0000.093
행정처분 총 수0.5440.1830.6350.4940.6730.7400.3800.4980.6060.7370.8310.7681.0000.2610.0000.3300.0000.7220.3740.621
양도양수 수0.2940.0000.3060.2070.4070.2770.3120.0000.4010.5420.1490.0000.2611.0000.0000.1960.0000.5450.0000.130
상속신고 수0.1800.2010.0000.2010.0000.0000.0000.0000.1390.3110.0000.0000.0000.0001.0000.2270.0000.0000.0000.000
합병신고 수0.2160.0000.0000.3740.2700.2450.2760.0000.0000.2320.4930.5050.3300.1960.2271.0000.0000.3970.0000.278
사업정지 40일 수0.0000.0000.8800.5540.1800.5240.0000.0000.1390.5240.0000.0000.0000.0000.0000.0001.0000.0000.9500.000
벌금 수0.2680.0000.0000.0000.3230.0000.0000.0000.0000.5220.7190.2570.7220.5450.0000.3970.0001.0000.1950.775
과태료 수0.0000.0000.6140.3000.0000.5220.0000.6780.5580.3590.2250.0000.3740.0000.0000.0000.9500.1951.0000.482
과징금 수0.0000.0000.0000.0910.2540.0000.2440.0000.0000.3460.5920.0930.6210.1300.0000.2780.0000.7750.4821.000

Missing values

2024-04-27T10:44:19.712542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T10:44:20.480205image/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

년도신규등록 수변경등록 수휴업신고 수폐업신고 수양도양수 수상속신고 수합병신고 수타도시 이관 수업소 상태 수재발급 등 기타변경등록 총 수사업정지 30일 수사업정지 40일 수사업정지 60일 수벌금 수과태료 수과징금 수등록취소 수행정처분 총 수
02024372001110008075097000004004
12023153618448000387224929642760101302103
2202215547847300113542008263110020003143
3202114245083710141432153273445045000090
42020129490270112252216427576300000871
5201913651255100219021212710120100001537
62018133514289101321214427847004400023137
72017160410069000274205025604103600036113
82016148442868200253204925973304100039113
92015151509842000250204826326103500041137
년도신규등록 수변경등록 수휴업신고 수폐업신고 수양도양수 수상속신고 수합병신고 수타도시 이관 수업소 상태 수재발급 등 기타변경등록 총 수사업정지 30일 수사업정지 40일 수사업정지 60일 수벌금 수과태료 수과징금 수등록취소 수행정처분 총 수
2320012361520900036002240000003939
2420002454020000120180000001212
2519992520000000180180000001818
261998153300000000300000000
271997401100000000100000000
281996136100000000100000000
29199526000000000000000000
30199429100000000100000000
31199351100000000100000000
32199236000000000000000000